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Use of combinatorial chemistry to speed drug discovery.
Rádl, S
IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.
Academic Drug Discovery Centres
DEFF Research Database (Denmark)
Kirkegaard, Henriette Schultz; Valentin, Finn
Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic...
Harvey, Alan L
Components from venoms have stimulated many drug discovery projects, with some notable successes. These are briefly reviewed, from captopril to ziconotide. However, there have been many more disappointments on the road from toxin discovery to approval of a new medicine. Drug discovery and development is an inherently risky business, and the main causes of failure during development programmes are outlined in order to highlight steps that might be taken to increase the chances of success with toxin-based drug discovery. These include having a clear focus on unmet therapeutic needs, concentrating on targets that are well-validated in terms of their relevance to the disease in question, making use of phenotypic screening rather than molecular-based assays, and working with development partners with the resources required for the long and expensive development process. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.
Computational methods in drug discovery
Sumudu P. Leelananda; Steffen Lindert
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery project...
RAS - Screens & Assays - Drug Discovery
The RAS Drug Discovery group aims to develop assays that will reveal aspects of RAS biology upon which cancer cells depend. Successful assay formats are made available for high-throughput screening programs to yield potentially effective drug compounds.
Antibody informatics for drug discovery
DEFF Research Database (Denmark)
Shirai, Hiroki; Prades, Catherine; Vita, Randi
to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic...... infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies...... for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii...
Computational methods in drug discovery
Directory of Open Access Journals (Sweden)
Sumudu P. Leelananda
Full Text Available The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
Applied metabolomics in drug discovery.
Cuperlovic-Culf, M; Culf, A S
The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.
Bioinformatics in translational drug discovery.
Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G
Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).
The discovery of drug-induced illness.
Jick, H
The increased use of drugs (and the concurrent increased risks of drug-induced illness) require definition of relevant research areas and strategy. For established marketed drugs, research needs depend on the magnitudes of risk of an illness from a drug and the base-line risk. With the drug risk high and the base-line risk low, the problem surfaces in premarketing studies or through the epidemic that develops after marketing. If the drug adds slightly to a high base-line risk, the effect is undetectable. When both risks are low, adverse effects can be discovered by chance, but systematic case-referent studies can speed discovery. If both risks are high, clinical trials and nonexperimental studies may be used. With both risks intermediate, systematic evaluations, especially case-referent studies are needed. Newly marketed drugs should be routinely evaluated through compulsory registration and follow-up study of the earliest users.
Deep Learning in Drug Discovery.
Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
[Artificial Intelligence in Drug Discovery].
Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi
According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.
Maximum Entropy in Drug Discovery
Directory of Open Access Journals (Sweden)
Chih-Yuan Tseng
Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes' pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.
Translational medicine and drug discovery
National Research Council Canada - National Science Library
Littman, Bruce H; Krishna, Rajesh
..., and examples of their application to real-life drug discovery and development. The latest thinking is presented by researchers from many of the world's leading pharmaceutical companies, including Pfizer, Merck, Eli Lilly, Abbott, and Novartis, as well as from academic institutions and public- private partnerships that support translational research...
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery
Directory of Open Access Journals (Sweden)
Nicholas Ekow Thomford
Full Text Available The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.
Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin
The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review
Arthritis Genetics Analysis Aids Drug Discovery
... NIH Research Matters January 13, 2014 Arthritis Genetics Analysis Aids Drug Discovery An international research team identified 42 new ... Edition Distracted Driving Raises Crash Risk Arthritis Genetics Analysis Aids Drug Discovery Oxytocin Affects Facial Recognition Connect with Us ...
Drug-Target Kinetics in Drug Discovery.
Tonge, Peter J
The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.
Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W
Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.
West Nile Virus Drug Discovery
Directory of Open Access Journals (Sweden)
Siew Pheng Lim
Full Text Available The outbreak of West Nile virus (WNV in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.
Applying genetics in inflammatory disease drug discovery
DEFF Research Database (Denmark)
Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard
, with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....
Drug discovery and developments in developing countries ...
African Journals Online (AJOL)
the major burden being in developing countries. Many of ... The driving force for drug discovery and development by pharmaceutical firms ... world and particularly in the third world countries ..... GFHR (2000) Global Forum for Health Research:.
Introduction to fragment-based drug discovery.
Erlanson, Daniel A
Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.
Computational neuropharmacology: dynamical approaches in drug discovery.
Aradi, Ildiko; Erdi, Péter
Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.
Virtual drug discovery: beyond computational chemistry?
Gilardoni, Francois; Arvanites, Anthony C
This editorial looks at how a fully integrated structure that performs all aspects in the drug discovery process, under one company, is slowly disappearing. The steps in the drug discovery paradigm have been slowly increasing toward virtuality or outsourcing at various phases of product development in a company's candidate pipeline. Each step in the process, such as target identification and validation and medicinal chemistry, can be managed by scientific teams within a 'virtual' company. Pharmaceutical companies to biotechnology start-ups have been quick in adopting this new research and development business strategy in order to gain flexibility, access the best technologies and technical expertise, and decrease product developmental costs. In today's financial climate, the term virtual drug discovery has an organizational meaning. It represents the next evolutionary step in outsourcing drug development.
Financing drug discovery for orphan diseases
Fagnan, David Erik; Gromatzky, Austin A.; Stein, Roger Mark; Fernandez, Jose-Maria; Lo, Andrew W.
Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures...
An invertebrate model for CNS drug discovery
DEFF Research Database (Denmark)
Al-Qadi, Sonia; Schiøtt, Morten; Hansen, Steen Honoré
BACKGROUND: ABC efflux transporters at the blood brain barrier (BBB), namely the P-glycoprotein (P-gp), restrain the development of central nervous system (CNS) drugs. Consequently, early screening of CNS drug candidates is pivotal to identify those affected by efflux activity. Therefore, simple,...... barriers. CONCLUSION: Findings suggest a conserved mechanism of brain efflux activity between insects and vertebrates, confirming that this model holds promise for inexpensive and high-throughput screening relative to in vivo models, for CNS drug discovery....
Systems Pharmacology in Small Molecular Drug Discovery
Directory of Open Access Journals (Sweden)
Wei Zhou
Full Text Available Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity, target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.
Net present value approaches for drug discovery.
Svennebring, Andreas M; Wikberg, Jarl Es
Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.
Oncology drug discovery: planning a turnaround.
Toniatti, Carlo; Jones, Philip; Graham, Hilary; Pagliara, Bruno; Draetta, Giulio
We have made remarkable progress in our understanding of the pathophysiology of cancer. This improved understanding has resulted in increasingly effective targeted therapies that are better tolerated than conventional cytotoxic agents and even curative in some patients. Unfortunately, the success rate of drug approval has been limited, and therapeutic improvements have been marginal, with too few exceptions. In this article, we review the current approach to oncology drug discovery and development, identify areas in need of improvement, and propose strategies to improve patient outcomes. We also suggest future directions that may improve the quality of preclinical and early clinical drug evaluation, which could lead to higher approval rates of anticancer drugs.
Perspectives on bioanalytical mass spectrometry and automation in drug discovery.
Janiszewski, John S; Liston, Theodore E; Cole, Mark J
The use of high speed synthesis technologies has resulted in a steady increase in the number of new chemical entities active in the drug discovery research stream. Large organizations can have thousands of chemical entities in various stages of testing and evaluation across numerous projects on a weekly basis. Qualitative and quantitative measurements made using LC/MS are integrated throughout this process from early stage lead generation through candidate nomination. Nearly all analytical processes and procedures in modern research organizations are automated to some degree. This includes both hardware and software automation. In this review we discuss bioanalytical mass spectrometry and automation as components of the analytical chemistry infrastructure in pharma. Analytical chemists are presented as members of distinct groups with similar skillsets that build automated systems, manage test compounds, assays and reagents, and deliver data to project teams. The ADME-screening process in drug discovery is used as a model to highlight the relationships between analytical tasks in drug discovery. Emerging software and process automation tools are described that can potentially address gaps and link analytical chemistry related tasks. The role of analytical chemists and groups in modern 'industrialized' drug discovery is also discussed.
Comment on "drug discovery: turning the titanic".
Lesterhuis, W Joost; Bosco, Anthony; Lake, Richard A
The pathobiology-based approach to research and development has been the dominant paradigm for successful drug discovery over the last decades. We propose that the molecular and cellular events that govern a resolving, rather than an evolving, disease may reveal new druggable pathways.
Conference Abstracts: Translational Science and Drug Discovery ...
African Journals Online (AJOL)
... and Drug Discovery: Impact on Health, Wellness, Environment and Economics" conference, July 27-29th, 2015, at the Hennessy Park Hotel, Ebène Cybercity, Mauritius. The conference was hosted by the Society for Free radical Research Africa and the International Association of Medical and Biomedical Researchers.
Biomimicry as a basis for drug discovery.
Kolb, V M
Selected works are discussed which clearly demonstrate that mimicking various aspects of the process by which natural products evolved is becoming a powerful tool in contemporary drug discovery. Natural products are an established and rich source of drugs. The term "natural product" is often used synonymously with "secondary metabolite." Knowledge of genetics and molecular evolution helps us understand how biosynthesis of many classes of secondary metabolites evolved. One proposed hypothesis is termed "inventive evolution." It invokes duplication of genes, and mutation of the gene copies, among other genetic events. The modified duplicate genes, per se or in conjunction with other genetic events, may give rise to new enzymes, which, in turn, may generate new products, some of which may be selected for. Steps of the inventive evolution can be mimicked in several ways for purpose of drug discovery. For example, libraries of chemical compounds of any imaginable structure may be produced by combinatorial synthesis. Out of these libraries new active compounds can be selected. In another example, genetic system can be manipulated to produce modified natural products ("unnatural natural products"), from which new drugs can be selected. In some instances, similar natural products turn up in species that are not direct descendants of each other. This is presumably due to a horizontal gene transfer. The mechanism of this inter-species gene transfer can be mimicked in therapeutic gene delivery. Mimicking specifics or principles of chemical evolution including experimental and test-tube evolution also provides leads for new drug discovery.
Pathways to new drug discovery in neuropsychiatry
Directory of Open Access Journals (Sweden)
Berk Michael
Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150
Cloud computing approaches to accelerate drug discovery value chain.
Garg, Vibhav; Arora, Suchir; Gupta, Chitra
Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.
Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.
Wójcikowski, Maciej; Zielenkiewicz, Piotr; Siedlecki, Pawel
There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software. The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery. Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).
Experiences in fragment-based drug discovery.
Murray, Christopher W; Verdonk, Marcel L; Rees, David C
Fragment-based drug discovery (FBDD) has become established in both industry and academia as an alternative approach to high-throughput screening for the generation of chemical leads for drug targets. In FBDD, specialised detection methods are used to identify small chemical compounds (fragments) that bind to the drug target, and structural biology is usually employed to establish their binding mode and to facilitate their optimisation. In this article, we present three recent and successful case histories in FBDD. We then re-examine the key concepts and challenges of FBDD with particular emphasis on recent literature and our own experience from a substantial number of FBDD applications. Our opinion is that careful application of FBDD is living up to its promise of delivering high quality leads with good physical properties and that in future many drug molecules will be derived from fragment-based approaches. Copyright © 2012 Elsevier Ltd. All rights reserved.
Synthetic biology for pharmaceutical drug discovery
Directory of Open Access Journals (Sweden)
Trosset JY
Full Text Available Jean-Yves Trosset,1 Pablo Carbonell2,3 1Bioinformation Research Laboratory, Sup'Biotech, Villejuif, France; 2Faculty of Life Sciences, SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3Department of Experimental and Health Sciences (DCEXS, Research Programme on Biomedical Informatics (GRIB, Hospital del Mar Medical Research Institute (IMIM, Universitat Pompeu Fabra (UPF, Barcelona, Spain Abstract: Synthetic biology (SB is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity. Keywords: metabolic engineering, plant synthetic biology, natural products, synthetic quorum sensing, drug resistance
Financing drug discovery for orphan diseases.
Fagnan, David E; Gromatzky, Austin A; Stein, Roger M; Fernandez, Jose-Maria; Lo, Andrew W
Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10-20 projects in the portfolio. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.
Financing drug discovery via dynamic leverage.
Montazerhodjat, Vahid; Frishkopf, John J; Lo, Andrew W
We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Drug target ontology to classify and integrate drug discovery data.
Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C
One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic
Plant natural products research in tuberculosis drug discovery and ...
African Journals Online (AJOL)
Plant natural products research in tuberculosis drug discovery and development: A situation report ... African Journal of Biotechnology ... tuberculosis (XDR-TB), call for the development of new anti-tuberculosis drugs to combat this disease.
Orphan diseases: state of the drug discovery art.
Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P
Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.
Fungal Anticancer Metabolites: Synthesis Towards Drug Discovery.
Barbero, Margherita; Artuso, Emma; Prandi, Cristina
Fungi are a well-known and valuable source of compounds of therapeutic relevance, in particular of novel anticancer compounds. Although seldom obtainable through isolation from the natural source, the total organic synthesis still remains one of the most efficient alternatives to resupply them. Furthermore, natural product total synthesis is a valuable tool not only for discovery of new complex biologically active compounds but also for the development of innovative methodologies in enantioselective organic synthesis. We undertook an in-depth literature searching by using chemical bibliographic databases (SciFinder, Reaxys) in order to have a comprehensive insight into the wide research field. The literature has been then screened, refining the obtained results by subject terms focused on both biological activity and innovative synthetic procedures. The literature on fungal metabolites has been recently reviewed and these publications have been used as a base from which we consider the synthetic feasibility of the most promising compounds, in terms of anticancer properties and drug development. In this paper, compounds are classified according to their chemical structure. This review summarizes the anticancer potential of fungal metabolites, highlighting the role of total synthesis outlining the feasibility of innovative synthetic procedures that facilitate the development of fungal metabolites into drugs that may become a real future perspective. To our knowledge, this review is the first effort to deal with the total synthesis of these active fungi metabolites and demonstrates that total chemical synthesis is a fruitful means of yielding fungal derivatives as aided by recent technological and innovative advancements. Copyright© Bentham Science Publishers; For any queries, please email at email@example.com.
Brivaracetam: a rational drug discovery success story
Rogawski, M A
Levetiracetam, the α-ethyl analogue of the nootropic piracetam, is a widely used antiepileptic drug (AED) that provides protection against partial seizures and is also effective in the treatment of primary generalized seizure syndromes including juvenile myoclonic epilepsy. Levetiracetam was discovered in 1992 through screening in audiogenic seizure susceptible mice and, 3 years later, was reported to exhibit saturable, stereospecific binding in brain to a ∼90 kDa protein, later identified as the ubiquitous synaptic vesicle glycoprotein SV2A. A large-scale screening effort to optimize binding affinity identified the 4-n-propyl analogue, brivaracetam, as having greater potency and a broadened spectrum of activity in animal seizure models. Recent phase II clinical trials demonstrating that brivaracetam is efficacious and well tolerated in the treatment of partial onset seizures have validated the strategy of the discovery programme. Brivaracetam is among the first clinically effective AEDs to be discovered by optimization of pharmacodynamic activity at a molecular target. PMID:18552880
Solution NMR Spectroscopy in Target-Based Drug Discovery.
Li, Yan; Kang, Congbao
Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.
Science of the science, drug discovery and artificial neural networks.
Patel, Jigneshkumar
Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.
Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.
Bhardwaj, Anshu; Scaria, Vinod; Raghava, Gajendra Pal Singh; Lynn, Andrew Michael; Chandra, Nagasuma; Banerjee, Sulagna; Raghunandanan, Muthukurussi V; Pandey, Vikas; Taneja, Bhupesh; Yadav, Jyoti; Dash, Debasis; Bhattacharya, Jaijit; Misra, Amit; Kumar, Anil; Ramachandran, Srinivasan; Thomas, Zakir; Brahmachari, Samir K
It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. Copyright © 2011 Elsevier Ltd. All rights reserved.
Novel opportunities for computational biology and sociology in drug discovery
Yao, Lixia
Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801
Drug Discovery in an Academic Setting: Playing to the Strengths
Huryn, Donna M.
Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these an...
High throughput electrophysiology: new perspectives for ion channel drug discovery
DEFF Research Database (Denmark)
Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter
. A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct....... The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery....
The Critical Role of Organic Chemistry in Drug Discovery.
Rotella, David P
Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.
Mathematical modeling for novel cancer drug discovery and development.
Zhang, Ping; Brusic, Vladimir
Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.
Protein Complex Production from the Drug Discovery Standpoint.
Moarefi, Ismail
Small molecule drug discovery critically depends on the availability of meaningful in vitro assays to guide medicinal chemistry programs that are aimed at optimizing drug potency and selectivity. As it becomes increasingly evident, most disease relevant drug targets do not act as a single protein. In the body, they are instead generally found in complex with protein cofactors that are highly relevant for their correct function and regulation. This review highlights selected examples of the increasing trend to use biologically relevant protein complexes for rational drug discovery to reduce costly late phase attritions due to lack of efficacy or toxicity.
Use of allosteric targets in the discovery of safer drugs.
Grover, Ashok Kumar
The need for drugs with fewer side effects cannot be overemphasized. Today, most drugs modify the actions of enzymes, receptors, transporters and other molecules by directly binding to their active (orthosteric) sites. However, orthosteric site configuration is similar in several proteins performing related functions and this leads to a lower specificity of a drug for the desired protein. Consequently, such drugs may have adverse side effects. A new basis of drug discovery is emerging based on the binding of the drug molecules to sites away (allosteric) from the orthosteric sites. It is possible to find allosteric sites which are unique and hence more specific as targets for drug discovery. Of many available examples, two are highlighted here. The first is caloxins - a new class of highly specific inhibitors of plasma membrane Ca²⁺ pumps. The second concerns the modulation of receptors for the neurotransmitter acetylcholine, which binds to 12 types of receptors. Exploitation of allosteric sites has led to the discovery of drugs which can selectively modulate the activation of only 1 (M1 muscarinic) out of the 12 different types of acetylcholine receptors. These drugs are being tested for schizophrenia treatment. It is anticipated that the drug discovery exploiting allosteric sites will lead to more effective therapeutic agents with fewer side effects. Copyright © 2013 S. Karger AG, Basel.
The Elements of Antifungal Drug Discovery
DEFF Research Database (Denmark)
Kjellerup, Lasse
In this PhD thesis I will explore the development of antifungal drugs. Fungal infections are estimated to cause the death of 1.5 million patients each year. There is currently a need for new antifungal drugs as the existing drugs are hampered by lack of broad-spectrum antifungal activity, resista...
Four disruptive strategies for removing drug discovery bottlenecks.
Ekins, Sean; Waller, Chris L; Bradley, Mary P; Clark, Alex M; Williams, Antony J
Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation. Copyright © 2012 Elsevier Ltd. All rights reserved.
Integration of distributed computing into the drug discovery process.
von Korff, Modest; Rufener, Christian; Stritt, Manuel; Freyss, Joel; Bär, Roman; Sander, Thomas
Grid computing offers an opportunity to gain massive computing power at low costs. We give a short introduction into the drug discovery process and exemplify the use of grid computing for image processing, docking and 3D pharmacophore descriptor calculations. The principle of a grid and its architecture are briefly explained. More emphasis is laid on the issues related to a company-wide grid installation and embedding the grid into the research process. The future of grid computing in drug discovery is discussed in the expert opinion section. Most needed, besides reliable algorithms to predict compound properties, is embedding the grid seamlessly into the discovery process. User friendly access to powerful algorithms without any restrictions, that is, by a limited number of licenses, has to be the goal of grid computing in drug discovery.
Recent advances in inkjet dispensing technologies: applications in drug discovery.
Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin
Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.
Revisiting lab-on-a-chip technology for drug discovery.
Neuži, Pavel; Giselbrecht, Stefan; Länge, Kerstin; Huang, Tony Jun; Manz, Andreas
The field of microfluidics or lab-on-a-chip technology aims to improve and extend the possibilities of bioassays, cell biology and biomedical research based on the idea of miniaturization. Microfluidic systems allow more accurate modelling of physiological situations for both fundamental research and drug development, and enable systematic high-volume testing for various aspects of drug discovery. Microfluidic systems are in development that not only model biological environments but also physically mimic biological tissues and organs; such 'organs on a chip' could have an important role in expediting early stages of drug discovery and help reduce reliance on animal testing. This Review highlights the latest lab-on-a-chip technologies for drug discovery and discusses the potential for future developments in this field.
The in silico drug discovery toolbox: applications in lead discovery and optimization.
Bruno, Agostino; Costantino, Gabriele; Sartori, Luca; Radi, Marco
Discovery and development of a new drug is a long lasting and expensive journey that takes around 15 years from starting idea to approval and marketing of new medication. Despite the R&D expenditures have been constantly increasing in the last few years, number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. From this point of view, it is clear that if we want to increase drug-discovery success rate and reduce costs associated with development of a new drug, a comprehensive evaluation/prediction of potential safety issues should be conducted as soon as possible during early drug discovery phase. In the present review, we will analyse the early steps of drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Copyright© Bentham Science Publishers; For any queries, please email at firstname.lastname@example.com.
Integration of Antibody Array Technology into Drug Discovery and Development.
Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan
Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.
Cell and small animal models for phenotypic drug discovery
Directory of Open Access Journals (Sweden)
Szabo M
Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug
Drug discovery: Fighting evolution with chemical synthesis
Yan, Ming; Baran, Phil S.
A synthetic strategy has been developed that provides easy access to structurally diverse analogues of naturally occurring antibiotics, providing a fresh means of attack in the war against drug-resistant bacteria. See Article p.338
Discovery and development of new antibacterial drugs: learning from experience?
Jackson, Nicole; Czaplewski, Lloyd; Piddock, Laura J V
Antibiotic (antibacterial) resistance is a serious global problem and the need for new treatments is urgent. The current antibiotic discovery model is not delivering new agents at a rate that is sufficient to combat present levels of antibiotic resistance. This has led to fears of the arrival of a 'post-antibiotic era'. Scientific difficulties, an unfavourable regulatory climate, multiple company mergers and the low financial returns associated with antibiotic drug development have led to the withdrawal of many pharmaceutical companies from the field. The regulatory climate has now begun to improve, but major scientific hurdles still impede the discovery and development of novel antibacterial agents. To facilitate discovery activities there must be increased understanding of the scientific problems experienced by pharmaceutical companies. This must be coupled with addressing the current antibiotic resistance crisis so that compounds and ultimately drugs are delivered to treat the most urgent clinical challenges. By understanding the causes of the failures and successes of the pharmaceutical industry's research history, duplication of discovery programmes will be reduced, increasing the productivity of the antibiotic drug discovery pipeline by academia and small companies. The most important scientific issues to address are getting molecules into the Gram-negative bacterial cell and avoiding their efflux. Hence screening programmes should focus their efforts on whole bacterial cells rather than cell-free systems. Despite falling out of favour with pharmaceutical companies, natural product research still holds promise for providing new molecules as a basis for discovery.
Bead-based screening in chemical biology and drug discovery
DEFF Research Database (Denmark)
Komnatnyy, Vitaly V.; Nielsen, Thomas Eiland; Qvortrup, Katrine
libraries for early drug discovery. Among the various library forms, the one-bead-one-compound (OBOC) library, where each bead carries many copies of a single compound, holds the greatest potential for the rapid identification of novel hits against emerging drug targets. However, this potential has not yet...... been fully realized due to a number of technical obstacles. In this feature article, we review the progress that has been made towards bead-based library screening and applications to the discovery of bioactive compounds. We identify the key challenges of this approach and highlight key steps needed......High-throughput screening is an important component of the drug discovery process. The screening of libraries containing hundreds of thousands of compounds requires assays amanable to miniaturisation and automization. Combinatorial chemistry holds a unique promise to deliver structural diverse...
"Drug" Discovery with the Help of Organic Chemistry.
Itoh, Yukihiro; Suzuki, Takayoshi
The first step in "drug" discovery is to find compounds binding to a potential drug target. In modern medicinal chemistry, the screening of a chemical library, structure-based drug design, and ligand-based drug design, or a combination of these methods, are generally used for identifying the desired compounds. However, they do not necessarily lead to success and there is no infallible method for drug discovery. Therefore, it is important to explore medicinal chemistry based on not only the conventional methods but also new ideas. So far, we have found various compounds as drug candidates. In these studies, some strategies based on organic chemistry have allowed us to find drug candidates, through 1) construction of a focused library using organic reactions and 2) rational design of enzyme inhibitors based on chemical reactions catalyzed by the target enzyme. Medicinal chemistry based on organic chemical reactions could be expected to supplement the conventional methods. In this review, we present drug discovery with the help of organic chemistry showing examples of our explorative studies on histone deacetylase inhibitors and lysine-specific demethylase 1 inhibitors.
Drug discovery in an academic setting: playing to the strengths.
Huryn, Donna M
Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these and other distinguishing characteristics of an academic setting can lead to novel and innovative therapies that might not be discovered otherwise.
Phenotypic and genomic comparison of Mycobacterium aurum and surrogate model species to Mycobacterium tuberculosis: implications for drug discovery.
Namouchi, Amine; Cimino, Mena; Favre-Rochex, Sandrine; Charles, Patricia; Gicquel, Brigitte
Tuberculosis (TB) is caused by Mycobacterium tuberculosis and represents one of the major challenges facing drug discovery initiatives worldwide. The considerable rise in bacterial drug resistance in recent years has led to the need of new drugs and drug regimens. Model systems are regularly used to speed-up the drug discovery process and circumvent biosafety issues associated with manipulating M. tuberculosis. These include the use of strains such as Mycobacterium smegmatis and Mycobacterium marinum that can be handled in biosafety level 2 facilities, making high-throughput screening feasible. However, each of these model species have their own limitations. We report and describe the first complete genome sequence of Mycobacterium aurum ATCC23366, an environmental mycobacterium that can also grow in the gut of humans and animals as part of the microbiota. This species shows a comparable resistance profile to that of M. tuberculosis for several anti-TB drugs. The aims of this study were to (i) determine the drug resistance profile of a recently proposed model species, Mycobacterium aurum, strain ATCC23366, for anti-TB drug discovery as well as Mycobacterium smegmatis and Mycobacterium marinum (ii) sequence and annotate the complete genome sequence of this species obtained using Pacific Bioscience technology (iii) perform comparative genomics analyses of the various surrogate strains with M. tuberculosis (iv) discuss how the choice of the surrogate model used for drug screening can affect the drug discovery process. We describe the complete genome sequence of M. aurum, a surrogate model for anti-tuberculosis drug discovery. Most of the genes already reported to be associated with drug resistance are shared between all the surrogate strains and M. tuberculosis. We consider that M. aurum might be used in high-throughput screening for tuberculosis drug discovery. We also highly recommend the use of different model species during the drug discovery screening process.
Can biochemistry drive drug discovery beyond simple potency measurements?
Chène, Patrick
Among the fields of expertise required to develop drugs successfully, biochemistry holds a key position in drug discovery at the interface between chemistry, structural biology and cell biology. However, taking the example of protein kinases, it appears that biochemical assays are mostly used in the pharmaceutical industry to measure compound potency and/or selectivity. This limited use of biochemistry is surprising, given that detailed biochemical analyses are commonly used in academia to unravel molecular recognition processes. In this article, I show that biochemistry can provide invaluable information on the dynamics and energetics of compound-target interactions that cannot be obtained on the basis of potency measurements and structural data. Therefore, an extensive use of biochemistry in drug discovery could facilitate the identification and/or development of new drugs. Copyright © 2012 Elsevier Ltd. All rights reserved.
Zebrafish models in neuropsychopharmacology and CNS drug discovery.
Khan, Kanza M; Collier, Adam D; Meshalkina, Darya A; Kysil, Elana V; Khatsko, Sergey L; Kolesnikova, Tatyana; Morzherin, Yury Yu; Warnick, Jason E; Kalueff, Allan V; Echevarria, David J
Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets. © 2017 The British Pharmacological Society.
From crystal to compound: structure-based antimalarial drug discovery.
Drinkwater, Nyssa; McGowan, Sheena
Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.
Scientific workflows as productivity tools for drug discovery.
Shon, John; Ohkawa, Hitomi; Hammer, Juergen
Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.
Advances in fragment-based drug discovery platforms.
Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya
Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.
FLIPR assays of intracellular calcium in GPCR drug discovery
DEFF Research Database (Denmark)
Hansen, Kasper Bø; Bräuner-Osborne, Hans
Fluorescent dyes sensitive to changes in intracellular calcium have become increasingly popular in G protein-coupled receptor (GPCR) drug discovery for several reasons. First of all, the assays using the dyes are easy to perform and are of low cost compared to other assays. Second, most non...
BPS Pharmacology 2014 - Drug Discovery Pathways symposium Report
Marsh, Andrew
Report on BPS Pharmacology 2014, BPS Industry Committe and Learned Societies Drug Discovery Pathways Group symposium: "Realizing the potential of new approaches to target identification and validation" by Dr Andrew Marsh Associate Professor Department of Chemistry University of Warwick go.warwick.ac.uk/marshgroup Twitter @marshgroup
Competitive intelligence and patent analysis in drug discovery.
Grandjean, Nicolas; Charpiot, Brigitte; Pena, Carlos Andres; Peitsch, Manuel C
Patents are a major source of information in drug discovery and, when properly processed and analyzed, can yield a wealth of information on competitors activities, R&D trends, emerging fields, collaborations, among others. This review discusses the current state-of-the-art in textual data analysis and exploration methods as applied to patent analysis.: © 2005 Elsevier Ltd . All rights reserved.
Genetics of rheumatoid arthritis conributes to biology and drug discovery
Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, A.; Yoshida, S.; Graham, R.R.; Manoharan, A.; Ortmann, W.; Bhangale, T.; Denny, J.C.; Carroll, R.J.; Eyler, A.E.; Greenberg, J.D.; Kremer, J.M.; Pappas, D.A.; Jiang, L.; Yin, L.; Ye, L.; Su, D.F.; Yang, J.; Xie, G.; Keystone, E.; Westra, H.J.; Esko, T.; Metspalu, A.; Zhou, X.; Gupta, N.; Mirel, D.; Stahl, Eli A.; Diogo, D.; Cui, J.; Liao, K.; Guo, M.H.; Myouzen, K.; Kawaguchi, T.; Coenen, M.J.; van Riel, P.L.; van de Laar, Mart A.F.J.; Guchelaar, H.J.; Huizinga, T.W.; Dieudé, P.; Mariette, X.; Louis Bridges Jr, S.; Zhernakova, A.; Toes, R.E.; Tak, P.P.; Miceli-Richard, C.; Bang, S.Y.; Lee, H.S.; Martin, J.; Gonzales-Gay, M.A.; Rodriguez-Rodriguez, L.; Rantapää-Dhlqvist, S.; Arlestig, L.; Choi, H.K.; Kamatani, Y.; Galan, P.; Lathrop, M.; Eyre, S.; Bowes, J.; Barton, A.; de Vries, N.; Moreland, L.W.; Criswell, L.A.; Karlson, E.W.; Taniguchi, A.; Yamada, R; Kubo, M.; Bae, S.C.; Worthington, J.; Padyukov, L.; Klareskog, L.; Gregersen, Peter K.; Raychaudhuri, S.; Stranger, B.E.; de Jager, P.L.; Franke, L.; Visscher, P.M.; Brown, M.A.; Yamanaka, H.; Mimori, T.; Takahashi, A.; Xu, H.; Behrens, T.W.; Siminovitch, K.A.; Momohara, S.; Matsuda, F.; Yamamoto, K.; Plenge, Robert M.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed
Breakthroughs in neuroactive steroid drug discovery.
Blanco, Maria-Jesus; La, Daniel; Coughlin, Quinn; Newman, Caitlin A; Griffin, Andrew M; Harrison, Boyd L; Salituro, Francesco G
Endogenous and synthetic neuroactive steroids (NASs) or neurosteroids are effective modulators of multiple signaling pathways including receptors for the γ-aminobutyric acid A (GABA A ) and glutamate, in particular N-methyl-d-aspartate (NMDA). These receptors are the major inhibitory and excitatory neurotransmitters in the central nervous system (CNS), and there is growing evidence suggesting that dysregulation of neurosteroid production plays a role in numerous neurological disorders. The significant unmet medical need for treatment of CNS disorders has increased the interest for these types of compounds. In this review, we highlight recent progress in the clinical development of NAS drug candidates, in addition to preclinical breakthroughs in the identification of novel NASs, mainly for GABA A and NMDA receptor modulation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Challenges in drug discovery for thiazolidinedione substitute
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Jian-ping Ye
Full Text Available Thiazolidinedione (TZD is a powerful insulin sensitizer in the treatment of type 2 diabetes. It acts as a ligand to the nuclear receptor PPARγ (peroxisome proliferator-activated receptor-gamma and induces transcription of PPARγ-responsive genes. TZD controls lipid synthesis and storage in adipose tissue, liver and many other tissues through PPARγ. Derivatives of TZD, such as rosiglitazone (Avandia and pioglitazone (Actos, are more powerful than metformin or berberine in insulin sensitization. Although they have common side effects such as weight gain and edema, these did not influence their clinical application in general. However, recent findings of risk for congestive heart failure and bladder cancer have significantly impaired their future in many countries. European countries have prohibited those drugs, and US will terminate application of rosiglitazone in clinics and hospitals. The multiple country actions may mark the end of TZD era. As a result, there is a strong demand for identification of TZD substitute in the treatment of type 2 diabetes. In this regard, literature about PPARγ ligands and potential TZD substitute are reviewed in this article. Histone deacetylase (HDAC inhibitor is emphasized as a new class of insulin sensitizer here. Regulators of SIRT1, CREB, NO, p38, ERK and Cdk5 are discussed in the activation of PPARγ.
[Application of Imaging Mass Spectrometry for Drug Discovery].
Hayasaka, Takahiro
Imaging mass spectrometry (IMS) can reveal the distribution of biomolecules on tissue sections. In this process, the biomolecules are directly ionized within tissue sections using matrix-assisted laser desorption/ionization, and then their distribution is visualized by pseudo-color based on the relative signal intensity. The biomolecules, such as fatty acids, phospholipids, glycolipids, peptides, proteins, and neurotransmitters, have been analyzed at a spatial resolution of 5 μm. A special instrument for IMS analysis was developed by Shimadzu. The IMS analysis does not require the labeling of biomolecules and is capable of analyzing all the ionized biomolecules. Interest in this method has expanded to many research fields, including biology, agriculture, medicine, and pharmacology. The technique is especially relevant to the drug discovery process. As practiced currently, drug discovery is expensive and time consuming, requiring the preparation of probes for each drug and its metabolites, followed by systematic probe tracking in animal models. The IMS technique is expected to overcome these drawbacks by revealing the distribution of drugs and their metabolites using only a single analysis. In this symposium, I introduced the methodology and applications of IMS and discussed the feasibility of its application to drug discovery in the near future.
Accessing external innovation in drug discovery and development.
Tufféry, Pierre
A decline in the productivity of the pharmaceutical industry research and development (R&D) pipeline has highlighted the need to reconsider the classical strategies of drug discovery and development, which are based on internal resources, and to identify new means to improve the drug discovery process. Accepting that the combination of internal and external ideas can improve innovation, ways to access external innovation, that is, opening projects to external contributions, have recently been sought. In this review, the authors look at a number of external innovation opportunities. These include increased interactions with academia via academic centers of excellence/innovation centers, better communication on projects using crowdsourcing or social media and new models centered on external providers such as built-to-buy startups or virtual pharmaceutical companies. The buzz for accessing external innovation relies on the pharmaceutical industry's major challenge to improve R&D productivity, a conjuncture favorable to increase interactions with academia and new business models supporting access to external innovation. So far, access to external innovation has mostly been considered during early stages of drug development, and there is room for enhancement. First outcomes suggest that external innovation should become part of drug development in the long term. However, the balance between internal and external developments in drug discovery can vary largely depending on the company strategies.
In silico pharmacology for a multidisciplinary drug discovery process.
Ortega, Santiago Schiaffino; Cara, Luisa Carlota López; Salvador, María Kimatrai
The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.
Medicinal chemistry inspired fragment-based drug discovery.
Lanter, James; Zhang, Xuqing; Sui, Zhihua
Lead generation can be a very challenging phase of the drug discovery process. The two principal methods for this stage of research are blind screening and rational design. Among the rational or semirational design approaches, fragment-based drug discovery (FBDD) has emerged as a useful tool for the generation of lead structures. It is particularly powerful as a complement to high-throughput screening approaches when the latter failed to yield viable hits for further development. Engagement of medicinal chemists early in the process can accelerate the progression of FBDD efforts by incorporating drug-friendly properties in the earliest stages of the design process. Medium-chain acyl-CoA synthetase 2b and ketohexokinase are chosen as examples to illustrate the importance of close collaboration of medicinal chemists, crystallography, and modeling. Copyright © 2011 Elsevier Inc. All rights reserved.
Twenty years on: the impact of fragments on drug discovery.
Erlanson, Daniel A; Fesik, Stephen W; Hubbard, Roderick E; Jahnke, Wolfgang; Jhoti, Harren
After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has become mainstream. More than 30 drug candidates derived from fragments have entered the clinic, with two approved and several more in advanced trials. FBDD has been widely applied in both academia and industry, as evidenced by the large number of papers from universities, non-profit research institutions, biotechnology companies and pharmaceutical companies. Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly realized, now is an opportune time to draw lessons and point the way to the future. This Review briefly discusses how to design fragment libraries, how to select screening techniques and how to make the most of information gleaned from them. It also shows how concepts from FBDD have permeated and enhanced drug discovery efforts.
Drug design and discovery: translational biomedical science varies among countries.
Weaver, Ian N; Weaver, Donald F
Drug design and discovery is an innovation process that translates the outcomes of fundamental biomedical research into therapeutics that are ultimately made available to people with medical disorders in many countries throughout the world. To identify which nations succeed, exceed, or fail at the drug design/discovery endeavor--more specifically, which countries, within the context of their national size and wealth, are "pulling their weight" when it comes to developing medications targeting the myriad of diseases that afflict humankind--we compiled and analyzed a comprehensive survey of all new drugs (small molecular entities and biologics) approved annually throughout the world over the 20-year period from 1991 to 2010. Based upon this analysis, we have devised prediction algorithms to ascertain which countries are successful (or not) in contributing to the worldwide need for effective new therapeutics. © 2013 Wiley Periodicals, Inc.
Computer-Aided Drug Discovery in Plant Pathology.
Shanmugam, Gnanendra; Jeon, Junhyun
Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides .
Drug discovery for alopecia: gone today, hair tomorrow.
Santos, Zenildo; Avci, Pinar; Hamblin, Michael R
Hair loss or alopecia affects the majority of the population at some time in their life, and increasingly, sufferers are demanding treatment. Three main types of alopecia (androgenic [AGA], areata [AA] and chemotherapy-induced [CIA]) are very different, and have their own laboratory models and separate drug-discovery efforts. In this article, the authors review the biology of hair, hair follicle (HF) cycling, stem cells and signaling pathways. AGA, due to dihydrotesterone, is treated by 5-α reductase inhibitors, androgen receptor blockers and ATP-sensitive potassium channel-openers. AA, which involves attack by CD8(+)NK group 2D-positive (NKG2D(+)) T cells, is treated with immunosuppressives, biologics and JAK inhibitors. Meanwhile, CIA is treated by apoptosis inhibitors, cytokines and topical immunotherapy. The desire to treat alopecia with an easy topical preparation is expected to grow with time, particularly with an increasing aging population. The discovery of epidermal stem cells in the HF has given new life to the search for a cure for baldness. Drug discovery efforts are being increasingly centered on these stem cells, boosting the hair cycle and reversing miniaturization of HF. Better understanding of the molecular mechanisms underlying the immune attack in AA will yield new drugs. New discoveries in HF neogenesis and low-level light therapy will undoubtedly have a role to play.
Hierarchical virtual screening approaches in small molecule drug discovery.
Kumar, Ashutosh; Zhang, Kam Y J
Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.
The impact of the Orphan Drug Act on drug discovery.
Haffner, Marlene E; Maher, Paul D
For nearly a quarter of a century the FDA Office of Orphan Products Development has administered the US Orphan Drug Act, which assists in bringing a wide variety of drug and biological (drug) products to treat rare diseases to market. Enthusiasm for rare disease product development has been sustained, seen throughout a wide spectrum of product types and disease conditions, and has resulted in clinically meaningful medical advances. Development of programmes for rare disease treatment worldwide, coupled with the development of drugs for diseases affecting developing countries, attests to the strength of this legislation. The marketing of almost 300 products in the US for rare diseases also testifies to the depth and intensity of scientific endeavour in this area.
Preclinical experimental models of drug metabolism and disposition in drug discovery and development
Directory of Open Access Journals (Sweden)
Donglu Zhang
Full Text Available Drug discovery and development involve the utilization of in vitro and in vivo experimental models. Different models, ranging from test tube experiments to cell cultures, animals, healthy human subjects, and even small numbers of patients that are involved in clinical trials, are used at different stages of drug discovery and development for determination of efficacy and safety. The proper selection and applications of correct models, as well as appropriate data interpretation, are critically important in decision making and successful advancement of drug candidates. In this review, we discuss strategies in the applications of both in vitro and in vivo experimental models of drug metabolism and disposition.
Natural Products as Leads in Schistosome Drug Discovery
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Bruno J. Neves
Full Text Available Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ, the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.
A role for physicians in ethnopharmacology and drug discovery.
Raza, Mohsin
Ethnopharmacology investigations classically involved traditional healers, botanists, anthropologists, chemists and pharmacologists. The role of some groups of researchers but not of physician has been highlighted and well defined in ethnopharmacological investigations. Historical data shows that discovery of several important modern drugs of herbal origin owe to the medical knowledge and clinical expertise of physicians. Current trends indicate negligible role of physicians in ethnopharmacological studies. Rising cost of modern drug development is attributed to the lack of classical ethnopharmacological approach. Physicians can play multiple roles in the ethnopharmacological studies to facilitate drug discovery as well as to rescue authentic traditional knowledge of use of medicinal plants. These include: (1) Ethnopharmacological field work which involves interviewing healers, interpreting traditional terminologies into their modern counterparts, examining patients consuming herbal remedies and identifying the disease for which an herbal remedy is used. (2) Interpretation of signs and symptoms mentioned in ancient texts and suggesting proper use of old traditional remedies in the light of modern medicine. (3) Clinical studies on herbs and their interaction with modern medicines. (4) Advising pharmacologists to carryout laboratory studies on herbs observed during field studies. (5) Work in collaboration with local healers to strengthen traditional system of medicine in a community. In conclusion, physician's involvement in ethnopharmacological studies will lead to more reliable information on traditional use of medicinal plants both from field and ancient texts, more focused and cheaper natural product based drug discovery, as well as bridge the gap between traditional and modern medicine.
Targeting cysteine proteases in trypanosomatid disease drug discovery.
Ferreira, Leonardo G; Andricopulo, Adriano D
Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.
Advancing Drug Discovery through Enhanced Free Energy Calculations.
Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A
A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates
Use of machine learning approaches for novel drug discovery.
Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria
The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.
Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases.
Mello, Juliana da Fonseca Rezende E; Gomes, Renan Augusto; Vital-Fujii, Drielli Gomes; Ferreira, Glaucio Monteiro; Trossini, Gustavo Henrique Goulart
Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs. © 2017 John Wiley & Sons A/S.
Scientific Prediction and Prophetic Patenting in Drug Discovery.
Curry, Stephen H; Schneiderman, Anne M
Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.
New perspectives on innovative drug discovery: an overview.
Pan, Si Yuan; Pan, Shan; Yu, Zhi-Ling; Ma, Dik-Lung; Chen, Si-Bao; Fong, Wang-Fun; Han, Yi-Fan; Ko, Kam-Ming
Despite advances in technology, drug discovery is still a lengthy, expensive, difficult, and inefficient process, with a low rate of success. Today, advances in biomedical science have brought about great strides in therapeutic interventions for a wide spectrum of diseases. The advent of biochemical techniques and cutting-edge bio/chemical technologies has made available a plethora of practical approaches to drug screening and design. In 2010, the total sales of the global pharmaceutical market will reach 600 billion US dollars and expand to over 975 billion dollars by 2013. The aim of this review is to summarize available information on contemporary approaches and strategies in the discovery of novel therapeutic agents, especially from the complementary and alternative medicines, including natural products and traditional remedies such as Chinese herbal medicine.
Fluorescence lifetime assays: current advances and applications in drug discovery.
Pritz, Stephan; Doering, Klaus; Woelcke, Julian; Hassiepen, Ulrich
Fluorescence lifetime assays complement the portfolio of established assay formats available in drug discovery, particularly with the recent advances in microplate readers and the commercial availability of novel fluorescent labels. Fluorescence lifetime assists in lowering complexity of compound screening assays, affording a modular, toolbox-like approach to assay development and yielding robust homogeneous assays. To date, materials and procedures have been reported for biochemical assays on proteases, as well as on protein kinases and phosphatases. This article gives an overview of two assay families, distinguished by the origin of the fluorescence signal modulation. The pharmaceutical industry demands techniques with a robust, integrated compound profiling process and short turnaround times. Fluorescence lifetime assays have already helped the drug discovery field, in this sense, by enhancing productivity during the hit-to-lead and lead optimization phases. Future work will focus on covering other biochemical molecular modifications by investigating the detailed photo-physical mechanisms underlying the fluorescence signal.
Stem cell technology for drug discovery and development.
Hook, Lilian A
Stem cells have enormous potential to revolutionise the drug discovery process at all stages, from target identification through to toxicology studies. Their ability to generate physiologically relevant cells in limitless supply makes them an attractive alternative to currently used recombinant cell lines or primary cells. However, realisation of the full potential of stem cells is currently hampered by the difficulty in routinely directing stem cell differentiation to reproducibly and cost effectively generate pure populations of specific cell types. In this article we discuss how stem cells have already been used in the drug discovery process and how novel technologies, particularly in relation to stem cell differentiation, can be applied to attain widespread adoption of stem cell technology by the pharmaceutical industry. Copyright © 2011 Elsevier Ltd. All rights reserved.
An Ontology for Description of Drug Discovery Investigations
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Qi Da
Full Text Available The paper presents an ontology for the description of Drug Discovery Investigation (DDI. This has been developed through the use of a Robot Scientist "Eve", and in consultation with industry. DDI aims to define the principle entities and the relations in the research and development phase of the drug discovery pipeline. DDI is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. This enables DDI to be integrated with such related ontologies as the Vaccine Ontology, the Advancing Clinico-Genomic Trials on Cancer Master Ontology, etc. DDI is available at http://purl.org/ddi/wikipedia or http://purl.org/ddi/home
Alkaloids as Cyclooxygenase Inhibitors in Anticancer Drug Discovery.
Hashmi, Muhammad Ali; Khan, Afsar; Farooq, Umar; Khan, Sehroon
Cancer is the leading cause of death worldwide and anticancer drug discovery is a very hot area of research at present. There are various factors which control and affect cancer, out of which enzymes like cyclooxygenase-2 (COX-2) play a vital role in the growth of tumor cells. Inhibition of this enzyme is a very useful target for the prevention of various types of cancers. Alkaloids are a diverse group of naturally occurring compounds which have shown great COX-2 inhibitory activity both in vitro and in vivo. In this mini-review, we have discussed different alkaloids with COX-2 inhibitory activities and anticancer potential which may act as leads in modern anticancer drug discovery. Different classes of alkaloids including isoquinoline alkaloids, indole alkaloids, piperidine alkaloids, quinazoline alkaloids, and various miscellaneous alkaloids obtained from natural sources have been discussed in detail in this review. Copyright© Bentham Science Publishers; For any queries, please email at firstname.lastname@example.com.
Bioactive secondary metabolites from marine microbes for drug discovery.
Nikapitiya, Chamilani
The isolation and extraction of novel bioactive secondary metabolites from marine microorganisms have a biomedical potential for future drug discovery as the oceans cover 70% of the planet's surface and life on earth originates from sea. Wide range of novel bioactive secondary metabolites exhibiting pharmacodynamic properties has been isolated from marine microorganisms and many to be discovered. The compounds isolated from marine organisms (macro and micro) are important in their natural form and also as templates for synthetic modifications for the treatments for variety of deadly to minor diseases. Many technical issues are yet to overcome before wide-scale bioprospecting of marine microorganisms becomes a reality. This chapter focuses on some novel secondary metabolites having antitumor, antivirus, enzyme inhibitor, and other bioactive properties identified and isolated from marine microorganisms including bacteria, actinomycetes, fungi, and cyanobacteria, which could serve as potentials for drug discovery after their clinical trials. Copyright © 2012 Elsevier Inc. All rights reserved.
Trends in GPCR drug discovery: new agents, targets and indications.
Hauser, Alexander S; Attwood, Misty M; Rask-Andersen, Mathias; Schiöth, Helgi B; Gloriam, David E
G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially novel GPCR targets without an approved drug, and the number of biological drugs, allosteric modulators and biased agonists has increased. The major disease indications for GPCR modulators show a shift towards diabetes, obesity and Alzheimer disease, although several central nervous system disorders are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug discovery.
Key drivers of biomedical innovation in cancer drug discovery
Huber, Margit A; Kraut, Norbert
Discovery and translational research has led to the identification of a series of ?cancer drivers??genes that, when mutated or otherwise misregulated, can drive malignancy. An increasing number of drugs that directly target such drivers have demonstrated activity in clinical trials and are shaping a new landscape for molecularly targeted cancer therapies. Such therapies rely on molecular and genetic diagnostic tests to detect the presence of a biomarker that predicts response. Here, we highli...
Drug Discovery Gets a Boost from Data Science.
Amaro, Rommie E
In this issue of Structure, Schiebel et al. (2016) describe a workflow-driven approach to high-throughput X-ray crystallographic fragment screening and refinement. In doing so, they extend the applicability of X-ray crystallography as a primary fragment-screening tool and show how data science techniques can favorably impact drug discovery efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.
Recent Advances in Drug Discovery from South African Marine Invertebrates
Directory of Open Access Journals (Sweden)
Michael T. Davies-Coleman
Full Text Available Recent developments in marine drug discovery from three South African marine invertebrates, the tube worm Cephalodiscus gilchristi, the ascidian Lissoclinum sp. and the sponge Topsentia pachastrelloides, are presented. Recent reports of the bioactivity and synthesis of the anti-cancer secondary metabolites cephalostatin and mandelalides (from C. gilchristi and Lissoclinum sp., respectively and various analogues are presented. The threat of drug-resistant pathogens, e.g., methicillin-resistant Staphylococcus aureus (MRSA, is assuming greater global significance, and medicinal chemistry strategies to exploit the potent MRSA PK inhibition, first revealed by two marine secondary metabolites, cis-3,4-dihydrohamacanthin B and bromodeoxytopsentin from T. pachastrelloides, are compared.
Simulation with quantum mechanics/molecular mechanics for drug discovery.
Barbault, Florent; Maurel, François
Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.
Organic synthesis provides opportunities to transform drug discovery
Blakemore, David C.; Castro, Luis; Churcher, Ian; Rees, David C.; Thomas, Andrew W.; Wilson, David M.; Wood, Anthony
Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.
ACFIS: a web server for fragment-based drug discovery.
Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu
In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
ACFIS: a web server for fragment-based drug discovery
Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu
In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808
The impact of genetics on future drug discovery in schizophrenia.
Matsumoto, Mitsuyuki; Walton, Noah M; Yamada, Hiroshi; Kondo, Yuji; Marek, Gerard J; Tajinda, Katsunori
Failures of investigational new drugs (INDs) for schizophrenia have left huge unmet medical needs for patients. Given the recent lackluster results, it is imperative that new drug discovery approaches (and resultant drug candidates) target pathophysiological alterations that are shared in specific, stratified patient populations that are selected based on pre-identified biological signatures. One path to implementing this paradigm is achievable by leveraging recent advances in genetic information and technologies. Genome-wide exome sequencing and meta-analysis of single nucleotide polymorphism (SNP)-based association studies have already revealed rare deleterious variants and SNPs in patient populations. Areas covered: Herein, the authors review the impact that genetics have on the future of schizophrenia drug discovery. The high polygenicity of schizophrenia strongly indicates that this disease is biologically heterogeneous so the identification of unique subgroups (by patient stratification) is becoming increasingly necessary for future investigational new drugs. Expert opinion: The authors propose a pathophysiology-based stratification of genetically-defined subgroups that share deficits in particular biological pathways. Existing tools, including lower-cost genomic sequencing and advanced gene-editing technology render this strategy ever more feasible. Genetically complex psychiatric disorders such as schizophrenia may also benefit from synergistic research with simpler monogenic disorders that share perturbations in similar biological pathways.
An integrated dataset for in silico drug discovery
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Cockell Simon J
Full Text Available Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.
A kernel for open source drug discovery in tropical diseases.
Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A
Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.
Fragment-based drug discovery and protein–protein interactions
Directory of Open Access Journals (Sweden)
Turnbull AP
Full Text Available Andrew P Turnbull,1 Susan M Boyd,2 Björn Walse31CRT Discovery Laboratories, Department of Biological Sciences, Birkbeck, University of London, London, UK; 2IOTA Pharmaceuticals Ltd, Cambridge, UK; 3SARomics Biostructures AB, Lund, SwedenAbstract: Protein–protein interactions (PPIs are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in "hot spots", has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of "standard" fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three
Drug target ontology to classify and integrate drug discovery data
DEFF Research Database (Denmark)
Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande
using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem...... of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target...... characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein...
Web-based services for drug design and discovery.
Frey, Jeremy G; Bird, Colin L
Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.
Structural Genomics and Drug Discovery for Infectious Diseases
International Nuclear Information System (INIS)
Anderson, W.F.
The application of structural genomics methods and approaches to proteins from organisms causing infectious diseases is making available the three dimensional structures of many proteins that are potential drug targets and laying the groundwork for structure aided drug discovery efforts. There are a number of structural genomics projects with a focus on pathogens that have been initiated worldwide. The Center for Structural Genomics of Infectious Diseases (CSGID) was recently established to apply state-of-the-art high throughput structural biology technologies to the characterization of proteins from the National Institute for Allergy and Infectious Diseases (NIAID) category A-C pathogens and organisms causing emerging, or re-emerging infectious diseases. The target selection process emphasizes potential biomedical benefits. Selected proteins include known drug targets and their homologs, essential enzymes, virulence factors and vaccine candidates. The Center also provides a structure determination service for the infectious disease scientific community. The ultimate goal is to generate a library of structures that are available to the scientific community and can serve as a starting point for further research and structure aided drug discovery for infectious diseases. To achieve this goal, the CSGID will determine protein crystal structures of 400 proteins and protein-ligand complexes using proven, rapid, highly integrated, and cost-effective methods for such determination, primarily by X-ray crystallography. High throughput crystallographic structure determination is greatly aided by frequent, convenient access to high-performance beamlines at third-generation synchrotron X-ray sources.
[Fragment-based drug discovery: concept and aim].
Tanaka, Daisuke
Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.
Fragment approaches in structure-based drug discovery
International Nuclear Information System (INIS)
Hubbard, Roderick E.
Fragment-based methods are successfully generating novel and selective drug-like inhibitors of protein targets, with a number of groups reporting compounds entering clinical trials. This paper summarizes the key features of the approach as one of the tools in structure-guided drug discovery. There has been considerable interest recently in what is known as 'fragment-based lead discovery'. The novel feature of the approach is to begin with small low-affinity compounds. The main advantage is that a larger potential chemical diversity can be sampled with fewer compounds, which is particularly important for new target classes. The approach relies on careful design of the fragment library, a method that can detect binding of the fragment to the protein target, determination of the structure of the fragment bound to the target, and the conventional use of structural information to guide compound optimization. In this article the methods are reviewed, and experiences in fragment-based discovery of lead series of compounds against kinases such as PDK1 and ATPases such as Hsp90 are discussed. The examples illustrate some of the key benefits and issues of the approach and also provide anecdotal examples of the patterns seen in selectivity and the binding mode of fragments across different protein targets
In vivo brain microdialysis: advances in neuropsychopharmacology and drug discovery.
Darvesh, Altaf S; Carroll, Richard T; Geldenhuys, Werner J; Gudelsky, Gary A; Klein, Jochen; Meshul, Charles K; Van der Schyf, Cornelis J
INTRODUCTION: Microdialysis is an important in vivo sampling technique, useful in the assay of extracellular tissue fluid. The technique has both pre-clinical and clinical applications but is most widely used in neuroscience. The in vivo microdialysis technique allows measurement of neurotransmitters such as acetycholine (ACh), the biogenic amines including dopamine (DA), norepinephrine (NE) and serotonin (5-HT), amino acids such as glutamate (Glu) and gamma aminobutyric acid (GABA), as well as the metabolites of the aforementioned neurotransmitters, and neuropeptides in neuronal extracellular fluid in discrete brain regions of laboratory animals such as rodents and non-human primates. AREAS COVERED: In this review we present a brief overview of the principles and procedures related to in vivo microdialysis and detail the use of this technique in the pre-clinical measurement of drugs designed to be used in the treatment of chemical addiction, neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD) and as well as psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and schizophrenia. This review offers insight into the tremendous utility and versatility of this technique in pursuing neuropharmacological investigations as well its significant potential in rational drug discovery. EXPERT OPINION: In vivo microdialysis is an extremely versatile technique, routinely used in the neuropharmacological investigation of drugs used for the treatment of neurological disorders. This technique has been a boon in the elucidation of the neurochemical profile and mechanism of action of several classes of drugs especially their effects on neurotransmitter systems. The exploitation and development of this technique for drug discovery in the near future will enable investigational new drug candidates to be rapidly moved into the clinical trial stages and to market thus providing new successful therapies for neurological diseases
Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.
Zhang, Hongkang; Cohen, Adam E
Recent advances in optogenetics have opened new routes to drug discovery, particularly in neuroscience. Physiological cellular assays probe functional phenotypes that connect genomic data to patient health. Optogenetic tools, in particular tools for all-optical electrophysiology, now provide a means to probe cellular disease models with unprecedented throughput and information content. These techniques promise to identify functional phenotypes associated with disease states and to identify compounds that improve cellular function regardless of whether the compound acts directly on a target or through a bypass mechanism. This review discusses opportunities and unresolved challenges in applying optogenetic techniques throughout the discovery pipeline - from target identification and validation, to target-based and phenotypic screens, to clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.
Fragment-based drug discovery and molecular docking in drug design.
Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong
Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.
Drug Discovery of Host CLK1 Inhibitors for Influenza Treatment
Directory of Open Access Journals (Sweden)
Mian Zu
Full Text Available The rapid evolution of influenza virus makes antiviral drugs less effective, which is considered to be a major bottleneck in antiviral therapy. The key proteins in the host cells, which are related with the replication cycle of influenza virus, are regarded as potential drug targets due to their distinct advantage of lack of evolution and drug resistance. Cdc2-like kinase 1 (CLK1 in the host cells is responsible for alternative splicing of the M2 gene of influenza virus during influenza infection and replication. In this study, we carried out baculovirus-mediated expression and purification of CLK1 and established a reliable screening assay for CLK1 inhibitors. After a virtual screening of CLK1 inhibitors was performed, the activities of the selected compounds were evaluated. Finally, several compounds with strong inhibitory activity against CLK1 were discovered and their in vitro anti-influenza virus activities were validated using a cytopathic effect (CPE reduction assay. The assay results showed that clypearin, corilagin, and pinosylvine were the most potential anti-influenza virus compounds as CLK1 inhibitors among the compounds tested. These findings will provide important information for new drug design and development in influenza treatment, and CLK1 may be a potent drug target for anti-influenza drug screening and discovery.
Pharmacokinetic properties and in silico ADME modeling in drug discovery.
Honório, Kathia M; Moda, Tiago L; Andricopulo, Adriano D
The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME--absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.
Dynamic Docking: A Paradigm Shift in Computational Drug Discovery
Directory of Open Access Journals (Sweden)
Dario Gioia
Full Text Available Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking. Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.
Fragment-based drug discovery using rational design.
Jhoti, H
Fragment-based drug discovery (FBDD) is established as an alternative approach to high-throughput screening for generating novel small molecule drug candidates. In FBDD, relatively small libraries of low molecular weight compounds (or fragments) are screened using sensitive biophysical techniques to detect their binding to the target protein. A lower absolute affinity of binding is expected from fragments, compared to much higher molecular weight hits detected by high-throughput screening, due to their reduced size and complexity. Through the use of iterative cycles of medicinal chemistry, ideally guided by three-dimensional structural data, it is often then relatively straightforward to optimize these weak binding fragment hits into potent and selective lead compounds. As with most other lead discovery methods there are two key components of FBDD; the detection technology and the compound library. In this review I outline the two main approaches used for detecting the binding of low affinity fragments and also some of the key principles that are used to generate a fragment library. In addition, I describe an example of how FBDD has led to the generation of a drug candidate that is now being tested in clinical trials for the treatment of cancer.
The case for open-source software in drug discovery.
DeLano, Warren L
Widespread adoption of open-source software for network infrastructure, web servers, code development, and operating systems leads one to ask how far it can go. Will "open source" spread broadly, or will it be restricted to niches frequented by hopeful hobbyists and midnight hackers? Here we identify reasons for the success of open-source software and predict how consumers in drug discovery will benefit from new open-source products that address their needs with increased flexibility and in ways complementary to proprietary options.
Anticancer drug discovery and pharmaceutical chemistry: a history.
Braña, Miguel F; Sánchez-Migallón, Ana
There are several procedures for the chemical discovery and design of new drugs from the point of view of the pharmaceutical or medicinal chemistry. They range from classical methods to the very new ones, such as molecular modeling or high throughput screening. In this review, we will consider some historical approaches based on the screening of natural products, the chances for luck, the systematic screening of new chemical entities and serendipity. Another group comprises rational design, as in the case of metabolic pathways, conformation versus configuration and, finally, a brief description on available new targets to be carried out. In each approach, the structure of some examples of clinical interest will be shown.
Native Mass Spectrometry in Fragment-Based Drug Discovery.
Pedro, Liliana; Quinn, Ronald J
The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.
Design of diversity and focused combinatorial libraries in drug discovery.
Young, S Stanley; Ge, Nanxiang
Using well-characterized chemical reactions and readily available monomers, chemists are able to create sets of compounds, termed libraries, which are useful in drug discovery processes. The design of combinatorial chemical libraries can be complex and there has been much information recently published offering suggestions on how the design process can be carried out. This review focuses on literature with the goal of organizing current thinking. At this point in time, it is clear that benchmarking of current suggested methods is required as opposed to further new methods.
Designing an intuitive web application for drug discovery scientists.
Karamanis, Nikiforos; Pignatelli, Miguel; Carvalho-Silva, Denise; Rowland, Francis; Cham, Jennifer A; Dunham, Ian
We discuss how we designed the Open Targets Platform (www.targetvalidation.org), an intuitive application for bench scientists working in early drug discovery. To meet the needs of our users, we applied lean user experience (UX) design methods: we started engaging with users very early and carried out research, design and evaluation activities within an iterative development process. We also emphasize the collaborative nature of applying lean UX design, which we believe is a foundation for success in this and many other scientific projects. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.
Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru
Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.
International Drug Discovery Science and Technology--BIT's Seventh Annual Congress.
Bodovitz, Steven
BIT's Seventh Annual International Drug Discovery Science and Technology Congress, held in Shanghai, included topics covering new therapeutic and technological developments in the field of drug discovery. This conference report highlights selected presentations on open-access approaches to R&D, novel and multifactorial targets, and technologies that assist drug discovery. Investigational drugs discussed include the anticancer agents astuprotimut-r (GlaxoSmithKline plc) and AS-1411 (Antisoma plc).
Fragment-based approaches to anti-HIV drug discovery: state of the art and future opportunities.
Huang, Boshi; Kang, Dongwei; Zhan, Peng; Liu, Xinyong
The search for additional drugs to treat HIV infection is a continuing effort due to the emergence and spread of HIV strains resistant to nearly all current drugs. The recent literature reveals that fragment-based drug design/discovery (FBDD) has become an effective alternative to conventional high-throughput screening strategies for drug discovery. In this critical review, the authors describe the state of the art in FBDD strategies for the discovery of anti-HIV drug-like compounds. The article focuses on fragment screening techniques, direct fragment-based design and early hit-to-lead progress. Rapid progress in biophysical detection and in silico techniques has greatly aided the application of FBDD to discover candidate agents directed at a variety of anti-HIV targets. Growing evidence suggests that structural insights on key proteins in the HIV life cycle can be applied in the early phase of drug discovery campaigns, providing valuable information on the binding modes and efficiently prompting fragment hit-to-lead progression. The combination of structural insights with improved methodologies for FBDD, including the privileged fragment-based reconstruction approach, fragment hybridization based on crystallographic overlays, fragment growth exploiting dynamic combinatorial chemistry, and high-speed fragment assembly via diversity-oriented synthesis followed by in situ screening, offers the possibility of more efficient and rapid discovery of novel drugs for HIV-1 prevention or treatment. Though the use of FBDD in anti-HIV drug discovery is still in its infancy, it is anticipated that anti-HIV agents developed via fragment-based strategies will be introduced into the clinic in the future.
Applications of fiber-optics-based nanosensors to drug discovery.
Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria
Fiber-optic nanosensors are fabricated by heating and pulling optical fibers to yield sub-micron diameter tips and have been used for in vitro analysis of individual living mammalian cells. Immobilization of bioreceptors (e.g., antibodies, peptides, DNA) selective to targeting analyte molecules of interest provides molecular specificity. Excitation light can be launched into the fiber, and the resulting evanescent field at the tip of the nanofiber can be used to excite target molecules bound to the bioreceptor molecules. The fluorescence or surface-enhanced Raman scattering produced by the analyte molecules is detected using an ultra-sensitive photodetector. This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. The nanosensors provide minimally invasive tools to probe subcellular compartments inside single living cells for health effect studies (e.g., detection of benzopyrene adducts) and medical applications (e.g., monitoring of apoptosis in cells treated with anticancer drugs).
High throughput electrophysiology: new perspectives for ion channel drug discovery
DEFF Research Database (Denmark)
Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter
Proper function of ion channels is crucial for all living cells. Ion channel dysfunction may lead to a number of diseases, so-called channelopathies, and a number of common diseases, including epilepsy, arrhythmia, and type II diabetes, are primarily treated by drugs that modulate ion channels....... A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct...... characterization of ion channel properties. However, patch clamp is a slow, labor-intensive, and thus expensive, technique. New techniques combining the reliability and high information content of patch clamping with the virtues of high throughput philosophy are emerging and predicted to make a number of ion...
Machine-learning techniques applied to antibacterial drug discovery.
Durrant, Jacob D; Amaro, Rommie E
The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.
RNA Editing and Drug Discovery for Cancer Therapy
Directory of Open Access Journals (Sweden)
Wei-Hsuan Huang
Full Text Available RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.
Recent advances in combinatorial biosynthesis for drug discovery
Directory of Open Access Journals (Sweden)
Sun H
Full Text Available Huihua Sun,1,* Zihe Liu,1,* Huimin Zhao,1,2 Ee Lui Ang1 1Metabolic Engineering Research Laboratory, Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore; 2Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA *These authors contributed equally to this work Abstract: Because of extraordinary structural diversity and broad biological activities, natural products have played a significant role in drug discovery. These therapeutically important secondary metabolites are assembled and modified by dedicated biosynthetic pathways in their host living organisms. Traditionally, chemists have attempted to synthesize natural product analogs that are important sources of new drugs. However, the extraordinary structural complexity of natural products sometimes makes it challenging for traditional chemical synthesis, which usually involves multiple steps, harsh conditions, toxic organic solvents, and byproduct wastes. In contrast, combinatorial biosynthesis exploits substrate promiscuity and employs engineered enzymes and pathways to produce novel "unnatural" natural products, substantially expanding the structural diversity of natural products with potential pharmaceutical value. Thus, combinatorial biosynthesis provides an environmentally friendly way to produce natural product analogs. Efficient expression of the combinatorial biosynthetic pathway in genetically tractable heterologous hosts can increase the titer of the compound, eventually resulting in less expensive drugs. In this review, we will discuss three major strategies for combinatorial biosynthesis: 1 precursor-directed biosynthesis; 2 enzyme-level modification, which includes swapping of the entire domains, modules and subunits, site-specific mutagenesis, and directed evolution; 3 pathway-level recombination. Recent examples of combinatorial biosynthesis employing these
Current approaches for the discovery of drugs that deter substance and drug abuse.
Yasgar, Adam; Simeonov, Anton
Much has been presented and debated on the topic of drug abuse and its multidimensional nature, including the role of society and its customs and laws, economical factors, and the magnitude and nature of the burden. Given the complex nature of the receptors and pathways implicated in regulation of the cognitive and behavioral processes associated with addiction, a large number of molecular targets have been interrogated during recent years to discover starting points for development of small-molecule interventions. This review describes recent developments in the field of early drug discovery for drug abuse interventions with an emphasis on the advances published during the 2012 - 2014 period. Technologically, the processes/platforms utilized in drug abuse drug discovery are nearly identical to those used in the other disease areas. A key complicating factor in drug abuse research is the enormous biological complexity surrounding the brain processes involved and the associated difficulty in finding 'good' targets and achieving exquisite selectivity of treatment agents. While tremendous progress has been made during recent years to use the power of high-throughput technologies to discover proof-of-principle molecules for many new targets, next-generation models will be especially important in this field. Examples include: seeking advantageous drug-drug combinations, the use of automated whole-animal behavioral screening systems, advancing our understanding of the role of epigenetics in drug addiction and the employment of organoid-level 3D test platforms (also referred to as tissue-chip or organs-on-chip).
Fragment-based drug discovery and its application to challenging drug targets.
Price, Amanda J; Howard, Steven; Cons, Benjamin D
Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.
Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.
Wagner, Jeffrey R; Lee, Christopher T; Durrant, Jacob D; Malmstrom, Robert D; Feher, Victoria A; Amaro, Rommie E
Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.
Inositol Polyphosphate Kinases, Fungal Virulence and Drug Discovery
Directory of Open Access Journals (Sweden)
Cecilia Li
Full Text Available Opportunistic fungi are a major cause of morbidity and mortality world-wide, particularly in immunocompromised individuals. Developing new treatments to combat invasive fungal disease is challenging given that fungal and mammalian host cells are eukaryotic, with similar organization and physiology. Even therapies targeting unique fungal cell features have limitations and drug resistance is emerging. New approaches to the development of antifungal drugs are therefore needed urgently. Cryptococcus neoformans, the commonest cause of fungal meningitis worldwide, is an accepted model for studying fungal pathogenicity and driving drug discovery. We recently characterized a phospholipase C (Plc1-dependent pathway in C. neoformans comprising of sequentially-acting inositol polyphosphate kinases (IPK, which are involved in synthesizing inositol polyphosphates (IP. We also showed that the pathway is essential for fungal cellular function and pathogenicity. The IP products of the pathway are structurally diverse, each consisting of an inositol ring, with phosphate (P and pyrophosphate (PP groups covalently attached at different positions. This review focuses on (1 the characterization of the Plc1/IPK pathway in C. neoformans; (2 the identification of PP-IP5 (IP7 as the most crucial IP species for fungal fitness and virulence in a mouse model of fungal infection; and (3 why IPK enzymes represent suitable candidates for drug development.
Structure and organization of drug-target networks: insights from genomic approaches for drug discovery.
Janga, Sarath Chandra; Tzakos, Andreas
Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.
3D in vitro technology for drug discovery.
Hosseinkhani, Hossein
Three-dimensional (3D) in vitro systems that can mimic organ and tissue structure and function in vivo, will be of great benefit for a variety of biological applications from basic biology to toxicity testing and drug discovery. There have been several attempts to generate 3D tissue models but most of these models require costly equipment, and the most serious disadvantage in them is that they are too far from the mature human organs in vivo. Because of these problems, research and development in drug discovery, toxicity testing and biotech industries are highly expensive, and involve sacrifice of countless animals and it takes several years to bring a single drug/product to the market or to find the toxicity or otherwise of chemical entities. Our group has been actively working on several alternative models by merging biomaterials science, nanotechnology and biological principles to generate 3D in vitro living organs, to be called "Human Organs-on-Chip", to mimic natural organ/tissues, in order to reduce animal testing and clinical trials. We have fabricated a novel type of mechanically and biologically bio-mimicking collagen-based hydrogel that would provide for interconnected mini-wells in which 3D cell/organ culture of human samples in a manner similar to human organs with extracellular matrix (ECM) molecules would be possible. These products mimic the physical, chemical, and biological properties of natural organs and tissues at different scales. This paper will review the outcome of our several experiments so far in this direction and the future perspectives.
Open source drug discovery in practice: a case study.
Årdal, Christine; Røttingen, John-Arne
Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly
Open Source Drug Discovery in Practice: A Case Study
Årdal, Christine; Røttingen, John-Arne
Background Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. Methodology/Principal Findings A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Conclusions/Significance Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality
Biomarkers: in medicine, drug discovery, and environmental health
National Research Council Canada - National Science Library
Vaidya, Vishal S; Bonventre, Joseph V
... Identification Using Mass Spectrometry Sample Preparation Protein Quantitation Examples of Biomarker Discovery and Evaluation Challenges in Proteomic Biomarker Discovery The Road Forward: Targeted ...
The rise of fragment-based drug discovery.
Murray, Christopher W; Rees, David C
The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (∼150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug-like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns. Here, we review the rise of FBDD, including its application to discovering clinical candidates against targets for which other chemistry approaches have struggled.
SemaTyP: a knowledge graph based literature mining method for drug discovery.
Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian
Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.
The application of molecular topology for ulcerative colitis drug discovery.
Bellera, Carolina L; Di Ianni, Mauricio E; Talevi, Alan
Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.
A practical drug discovery project at the undergraduate level.
Fray, M Jonathan; Macdonald, Simon J F; Baldwin, Ian R; Barton, Nick; Brown, Jack; Campbell, Ian B; Churcher, Ian; Coe, Diane M; Cooper, Anthony W J; Craven, Andrew P; Fisher, Gail; Inglis, Graham G A; Kelly, Henry A; Liddle, John; Maxwell, Aoife C; Patel, Vipulkumar K; Swanson, Stephen; Wellaway, Natalie
In this article, we describe a practical drug discovery project for third-year undergraduates. No previous knowledge of medicinal chemistry is assumed. Initial lecture workshops cover the basic principles; then students, in teams, seek to improve the profile of a weakly potent, insoluble phosphatidylinositide 3-kinase delta (PI3Kδ) inhibitor (1) through compound array design, molecular modelling, screening data analysis and the synthesis of target compounds in the laboratory. The project benefits from significant industrial support, including lectures, student mentoring and consumables. The aim is to make the learning experience as close as possible to real-life industrial situations. In total, 48 target compounds were prepared, the best of which (5b, 5j, 6b and 6ap) improved the potency and aqueous solubility of the lead compound (1) by 100-1000 fold and ≥tenfold, respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.
Native Mass Spectrometry in Fragment-Based Drug Discovery
Directory of Open Access Journals (Sweden)
Liliana Pedro
Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.
A high content screening assay to predict human drug-induced liver injury during drug discovery.
Persson, Mikael; Løye, Anni F; Mow, Tomas; Hornberg, Jorrit J
Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe
Human embryonic stem cell technologies and drug discovery.
Jensen, Janne; Hyllner, Johan; Björquist, Petter
Development of new drugs is costly and takes huge resources into consideration. The big pharmaceutical companies are currently facing increasing developmental costs and a lower success-rate of bringing new compounds to the market. Therefore, it is now of outmost importance that the drug-hunting companies minimize late attritions due to sub-optimal pharmacokinetic properties or unexpected toxicity when entering the clinical programs. To achieve this, a strong need to test new candidate drugs in assays of high human relevance in vitro as early as possible has been identified. The traditionally used cell systems are however remarkably limited in this sense, and new improved technologies are of greatest importance. The human embryonic stem cells (hESC) is one of the most powerful cell types known. They have not only the possibility to divide indefinitely; these cells can also differentiate into all mature cell types of the human body. This makes them potentially very valuable for pharmaceutical development, spanning from use as tools in early target studies, DMPK or safety assessment, as screening models to find new chemical entities modulating adult stem cell fate, or as the direct use in cell therapies. This review illustrates the use of hESC in the drug discovery process, today, as well as in a future perspective. This will specifically be exemplified with the most important cell type for pharmaceutical development-the hepatocyte. We discuss how hESC-derived hepatocyte-like cells could improve this process, and how these cells should be cultured if optimized functionality and usefulness should be achieved. J. Cell. Physiol. 219: 513-519, 2009. (c) 2009 Wiley-Liss, Inc.
Drugs from the Oceans: Marine Natural Products as Leads for Drug Discovery.
Altmann, Karl-Heinz
The marine environment harbors a vast number of species that are the source of a wide array of structurally diverse bioactive secondary metabolites. At this point in time, roughly 27'000 marine natural products are known, of which eight are (were) at the origin of seven marketed drugs, mostly for the treatment of cancer. The majority of these drugs and also of drug candidates currently undergoing clinical evaluation (excluding antibody-drug conjugates) are unmodified natural products, but synthetic chemistry has played a central role in the discovery and/or development of all but one of the approved marine-derived drugs. More than 1000 new marine natural products have been isolated per year over the last decade, but the pool of new and unique structures is far from exhausted. To fully leverage the potential offered by the structural diversity of marine-produced secondary metabolites for drug discovery will require their broad assessment for different bioactivities and the productive interplay between new fermentation technologies, synthetic organic chemistry, and medicinal chemistry, in order to secure compound supply and enable lead optimization.
AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery
International Nuclear Information System (INIS)
Tsai, Yingssu; McPhillips, Scott E.; González, Ana; McPhillips, Timothy M.; Zinn, Daniel; Cohen, Aina E.; Feese, Michael D.; Bushnell, David; Tiefenbrunn, Theresa; Stout, C. David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O.; Soltis, S. Michael
New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully
Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.
Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C
Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.
Epigenetics and cancer: implications for drug discovery and safety assessment
International Nuclear Information System (INIS)
Moggs, Jonathan G.; Goodman, Jay I.; Trosko, James E.; Roberts, Ruth A.
It is necessary to determine whether chemicals or drugs have the potential to pose a threat to human health. Research conducted over the last two decades has led to the paradigm that chemicals can cause cancer either by damaging DNA or by altering cellular growth, probably via receptor-mediated changes in gene expression. However, recent evidence suggests that gene expression can be altered markedly via several diverse epigenetic mechanisms that can lead to permanent or reversible changes in cellular behavior. Key molecular events underlying these mechanisms include the alteration of DNA methylation and chromatin, and changes in the function of cell surface molecules. Thus, for example, DNA methyltransferase enzymes together with chromatin-associated proteins such as histone modifying enzymes and remodelling factors can modify the genetic code and contribute to the establishment and maintenance of altered epigenetic states. This is relevant to many types of toxicity including but not limited to cancer. In this paper, we describe the potential for interplay between genetic alteration and epigenetic changes in cell growth regulation and discuss the implications for drug discovery and safety assessment
Drug discovery and development tomorrow -- changing the mindset.
Coleman, Robert A
Today's drug discovery and development paradigm is not working, and something needs to be done about it. There is good reason to believe that a move away from reliance on animal surrogates for human subjects in the Pharma Industry's R&D programmes could provide an important step forward. However, no serious move will be made in that direction until there is some hard evidence that it will be rewarded with improved productivity outcomes. The Safer Medicines Trust are proposing that a study be undertaken, involving a range of drugs that have been approved for human use, but have subsequently proved to have limitations in terms of safety and/or efficacy. The aim is to determine the efficiency of a battery of human-based test methods to identify a compound's safety and efficacy profiles, and to compare this with that of the more traditional, largely animal-based methods that were employed in their original development. Should such an approach prove more reliable, the authorities will be faced with important decisions relating to the role of human biological test data in regulatory submissions, while the Pharma Industry will be faced with the key logistical issue of how to acquire the human biomaterials necessary to make possible the routine application of such test methods. 2009 FRAME.
Biomarkers as drug development tools: discovery, validation, qualification and use.
Kraus, Virginia B
The 21st Century Cures Act, approved in the USA in December 2016, has encouraged the establishment of the national Precision Medicine Initiative and the augmentation of efforts to address disease prevention, diagnosis and treatment on the basis of a molecular understanding of disease. The Act adopts into law the formal process, developed by the FDA, of qualification of drug development tools, including biomarkers and clinical outcome assessments, to increase the efficiency of clinical trials and encourage an era of molecular medicine. The FDA and European Medicines Agency (EMA) have developed similar processes for the qualification of biomarkers intended for use as companion diagnostics or for development and regulatory approval of a drug or therapeutic. Biomarkers that are used exclusively for the diagnosis, monitoring or stratification of patients in clinical trials are not subject to regulatory approval, although their qualification can facilitate the conduct of a trial. In this Review, the salient features of biomarker discovery, analytical validation, clinical qualification and utilization are described in order to provide an understanding of the process of biomarker development and, through this understanding, convey an appreciation of their potential advantages and limitations.
DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.
Wang, QuanQiu; Xu, Rong
Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.
Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents.
Llorach-Pares, Laura; Nonell-Canals, Alfons; Sanchez-Martinez, Melchor; Avila, Conxita
Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium , against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.
Computational medicinal chemistry in fragment-based drug discovery: what, how and when.
Rabal, Obdulia; Urbano-Cuadrado, Manuel; Oyarzabal, Julen
The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.
Scientometrics of drug discovery efforts: pain-related molecular targets
Directory of Open Access Journals (Sweden)
Kissin I
Full Text Available Igor KissinDepartment of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USAAbstract: The aim of this study was to make a scientometric assessment of drug discovery efforts centered on pain-related molecular targets. The following scientometric indices were used: the popularity index, representing the share of articles (or patents on a specific topic among all articles (or patents on pain over the same 5-year period; the index of change, representing the change in the number of articles (or patents on a topic from one 5-year period to the next; the index of expectations, representing the ratio of the number of all types of articles on a topic in the top 20 journals relative to the number of articles in all (>5,000 biomedical journals covered by PubMed over a 5-year period; the total number of articles representing Phase I–III trials of investigational drugs over a 5-year period; and the trial balance index, a ratio of Phase I–II publications to Phase III publications. Articles (PubMed database and patents (US Patent and Trademark Office database on 17 topics related to pain mechanisms were assessed during six 5-year periods from 1984 to 2013. During the most recent 5-year period (2009–2013, seven of 17 topics have demonstrated high research activity (purinergic receptors, serotonin, transient receptor potential channels, cytokines, gamma aminobutyric acid, glutamate, and protein kinases. However, even with these seven topics, the index of expectations decreased or did not change compared with the 2004–2008 period. In addition, publications representing Phase I–III trials of investigational drugs (2009–2013 did not indicate great enthusiasm on the part of the pharmaceutical industry regarding drugs specifically designed for treatment of pain. A promising development related to the new tool of molecular targeting, ie, monoclonal antibodies, for pain treatment has not
Three-dimensional compound comparison methods and their application in drug discovery.
Shin, Woong-Hee; Zhu, Xiaolei; Bures, Mark Gregory; Kihara, Daisuke
Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D). Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed.
Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery
Directory of Open Access Journals (Sweden)
Woong-Hee Shin
Full Text Available Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D, two-dimensional (2D, and three-dimensional (3D. Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed.
Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.
Benson, Neil
Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.
Cunninghamella Biotransformation--Similarities to Human Drug Metabolism and Its Relevance for the Drug Discovery Process.
Piska, Kamil; Żelaszczyk, Dorota; Jamrozik, Marek; Kubowicz-Kwaśny, Paulina; Pękala, Elżbieta
Studies of drug metabolism are one of the most significant issues in the process of drug development, its introduction to the market and also in treatment. Even the most promising molecule may show undesirable metabolic properties that would disqualify it as a potential drug. Therefore, such studies are conducted in the early phases of drug discovery and development process. Cunninghamella is a filamentous fungus known for its catalytic properties, which mimics mammalian drug metabolism. It has been proven that C. elegans carries at least one gene coding for a CYP enzyme closely related to the CYP51 family. The transformation profile of xenobiotics in Cunninghamella spp. spans a number of reactions catalyzed by different mammalian CYP isoforms. This paper presents detailed data on similar biotransformation drug products in humans and Cunninghamella spp. and covers the most important aspects of preparative biosynthesis of metabolites, since this model allows to obtain metabolites in sufficient quantities to conduct the further detailed investigations, as quantification, structure analysis and pharmacological activity and toxicity testing. The metabolic activity of three mostly used Cunninghamella species in obtaining hydroxylated, dealkylated and oxidated metabolites of different drugs confirmed its convergence with human biotransformation. Though it cannot replace the standard methods, it can provide support in the field of biotransformation and identifying metabolic soft spots of new chemicals and in predicting possible metabolic pathways. Another aspect is the biosynthesis of metabolites. In this respect, techniques using Cunninghamella spp. seem to be competitive to the chemical methods currently used.
DrugQuest - a text mining workflow for drug association discovery.
Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis
Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .
Drug discovery for Chagas disease should consider Trypanosoma cruzi strain diversity
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Bianca Zingales
Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.
Advances in phage display technology for drug discovery.
Omidfar, Kobra; Daneshpour, Maryam
Over the past decade, several library-based methods have been developed to discover ligands with strong binding affinities for their targets. These methods mimic the natural evolution for screening and identifying ligand-target interactions with specific functional properties. Phage display technology is a well-established method that has been applied to many technological challenges including novel drug discovery. This review describes the recent advances in the use of phage display technology for discovering novel bioactive compounds. Furthermore, it discusses the application of this technology to produce proteins and peptides as well as minimize the use of antibodies, such as antigen-binding fragment, single-chain fragment variable or single-domain antibody fragments like VHHs. Advances in screening, manufacturing and humanization technologies demonstrate that phage display derived products can play a significant role in the diagnosis and treatment of disease. The effects of this technology are inevitable in the development pipeline for bringing therapeutics into the market, and this number is expected to rise significantly in the future as new advances continue to take place in display methods. Furthermore, a widespread application of this methodology is predicted in different medical technological areas, including biosensing, monitoring, molecular imaging, gene therapy, vaccine development and nanotechnology.
Mass spectrometry-driven drug discovery for development of herbal medicine.
Zhang, Aihua; Sun, Hui; Wang, Xijun
Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. © 2016 Wiley Periodicals, Inc.
Current status and future prospects for enabling chemistry technology in the drug discovery process.
Djuric, Stevan W; Hutchins, Charles W; Talaty, Nari N
This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.
Current status and future prospects for enabling chemistry technology in the drug discovery process
Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.
This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094
Establishing MALDI-TOF as Versatile Drug Discovery Readout to Dissect the PTP1B Enzymatic Reaction.
Winter, Martin; Bretschneider, Tom; Kleiner, Carola; Ries, Robert; Hehn, Jörg P; Redemann, Norbert; Luippold, Andreas H; Bischoff, Daniel; Büttner, Frank H
Label-free, mass spectrometric (MS) detection is an emerging technology in the field of drug discovery. Unbiased deciphering of enzymatic reactions is a proficient advantage over conventional label-based readouts suffering from compound interference and intricate generation of tailored signal mediators. Significant evolvements of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, as well as associated liquid handling instrumentation, triggered extensive efforts in the drug discovery community to integrate the comprehensive MS readout into the high-throughput screening (HTS) portfolio. Providing speed, sensitivity, and accuracy comparable to those of conventional, label-based readouts, combined with merits of MS-based technologies, such as label-free parallelized measurement of multiple physiological components, emphasizes the advantages of MALDI-TOF for HTS approaches. Here we describe the assay development for the identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors. In the context of this precious drug target, MALDI-TOF was integrated into the HTS environment and cross-compared with the well-established AlphaScreen technology. We demonstrate robust and accurate IC 50 determination with high accordance to data generated by AlphaScreen. Additionally, a tailored MALDI-TOF assay was developed to monitor compound-dependent, irreversible modification of the active cysteine of PTP1B. Overall, the presented data proves the promising perspective for the integration of MALDI-TOF into drug discovery campaigns.
Understanding mechanisms of toxicity: Insights from drug discovery research
International Nuclear Information System (INIS)
Houck, Keith A.; Kavlock, Robert J.
Toxicology continues to rely heavily on use of animal testing for prediction of potential for toxicity in humans. Where mechanisms of toxicity have been elucidated, for example endocrine disruption by xenoestrogens binding to the estrogen receptor, in vitro assays have been developed as surrogate assays for toxicity prediction. This mechanistic information can be combined with other data such as exposure levels to inform a risk assessment for the chemical. However, there remains a paucity of such mechanistic assays due at least in part to lack of methods to determine specific mechanisms of toxicity for many toxicants. A means to address this deficiency lies in utilization of a vast repertoire of tools developed by the drug discovery industry for interrogating the bioactivity of chemicals. This review describes the application of high-throughput screening assays as experimental tools for profiling chemicals for potential for toxicity and understanding underlying mechanisms. The accessibility of broad panels of assays covering an array of protein families permits evaluation of chemicals for their ability to directly modulate many potential targets of toxicity. In addition, advances in cell-based screening have yielded tools capable of reporting the effects of chemicals on numerous critical cell signaling pathways and cell health parameters. Novel, more complex cellular systems are being used to model mammalian tissues and the consequences of compound treatment. Finally, high-throughput technology is being applied to model organism screens to understand mechanisms of toxicity. However, a number of formidable challenges to these methods remain to be overcome before they are widely applicable. Integration of successful approaches will contribute towards building a systems approach to toxicology that will provide mechanistic understanding of the effects of chemicals on biological systems and aid in rationale risk assessments
A new in vivo screening paradigm to accelerate antimalarial drug discovery.
Directory of Open Access Journals (Sweden)
María Belén Jiménez-Díaz
Full Text Available The emergence of resistance to available antimalarials requires the urgent development of new medicines. The recent disclosure of several thousand compounds active in vitro against the erythrocyte stage of Plasmodium falciparum has been a major breakthrough, though converting these hits into new medicines challenges current strategies. A new in vivo screening concept was evaluated as a strategy to increase the speed and efficiency of drug discovery projects in malaria. The new in vivo screening concept was developed based on human disease parameters, i.e. parasitemia in the peripheral blood of patients on hospital admission and parasite reduction ratio (PRR, which were allometrically down-scaled into P. berghei-infected mice. Mice with an initial parasitemia (P0 of 1.5% were treated orally for two consecutive days and parasitemia measured 24 h after the second dose. The assay was optimized for detection of compounds able to stop parasite replication (PRR = 1 or induce parasite clearance (PRR >1 with statistical power >99% using only two mice per experimental group. In the P. berghei in vivo screening assay, the PRR of a set of eleven antimalarials with different mechanisms of action correlated with human-equivalent data. Subsequently, 590 compounds from the Tres Cantos Antimalarial Set with activity in vitro against P. falciparum were tested at 50 mg/kg (orally in an assay format that allowed the evaluation of hundreds of compounds per month. The rate of compounds with detectable efficacy was 11.2% and about one third of active compounds showed in vivo efficacy comparable with the most potent antimalarials used clinically. High-throughput, high-content in vivo screening could rapidly select new compounds, dramatically speeding up the discovery of new antimalarial medicines. A global multilateral collaborative project aimed at screening the significant chemical diversity within the antimalarial in vitro hits described in the literature is a
A New In Vivo Screening Paradigm to Accelerate Antimalarial Drug Discovery
Jiménez-Díaz, María Belén; Viera, Sara; Ibáñez, Javier; Mulet, Teresa; Magán-Marchal, Noemí; Garuti, Helen; Gómez, Vanessa; Cortés-Gil, Lorena; Martínez, Antonio; Ferrer, Santiago; Fraile, María Teresa; Calderón, Félix; Fernández, Esther; Shultz, Leonard D.; Leroy, Didier; Wilson, David M.; García-Bustos, José Francisco; Gamo, Francisco Javier; Angulo-Barturen, Iñigo
The emergence of resistance to available antimalarials requires the urgent development of new medicines. The recent disclosure of several thousand compounds active in vitro against the erythrocyte stage of Plasmodium falciparum has been a major breakthrough, though converting these hits into new medicines challenges current strategies. A new in vivo screening concept was evaluated as a strategy to increase the speed and efficiency of drug discovery projects in malaria. The new in vivo screening concept was developed based on human disease parameters, i.e. parasitemia in the peripheral blood of patients on hospital admission and parasite reduction ratio (PRR), which were allometrically down-scaled into P. berghei-infected mice. Mice with an initial parasitemia (P0) of 1.5% were treated orally for two consecutive days and parasitemia measured 24 h after the second dose. The assay was optimized for detection of compounds able to stop parasite replication (PRR = 1) or induce parasite clearance (PRR >1) with statistical power >99% using only two mice per experimental group. In the P. berghei in vivo screening assay, the PRR of a set of eleven antimalarials with different mechanisms of action correlated with human-equivalent data. Subsequently, 590 compounds from the Tres Cantos Antimalarial Set with activity in vitro against P. falciparum were tested at 50 mg/kg (orally) in an assay format that allowed the evaluation of hundreds of compounds per month. The rate of compounds with detectable efficacy was 11.2% and about one third of active compounds showed in vivo efficacy comparable with the most potent antimalarials used clinically. High-throughput, high-content in vivo screening could rapidly select new compounds, dramatically speeding up the discovery of new antimalarial medicines. A global multilateral collaborative project aimed at screening the significant chemical diversity within the antimalarial in vitro hits described in the literature is a feasible task
Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery
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Jian Li
Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally "one gene, one drug, one disease." However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.
Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development
Greef, J. van der; Adourian, A.; Muntendam, P.; McBurney, R.N.
Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain.
Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.
Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas
The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.
The heat is on: thermodynamic analysis in fragment-based drug discovery
Edink, E.S.; Jansen, C.J.W.; Leurs, R.; De Esch, I.J.
Thermodynamic analysis provides access to the determinants of binding affinity, enthalpy and entropy. In fragment-based drug discovery (FBDD), thermodynamic analysis provides a powerful tool to discriminate fragments based on their potential for successful optimization. The thermodynamic data
Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery.
Brothers, John F; Ung, Matthew; Escalante-Chong, Renan; Ross, Jermaine; Zhang, Jenny; Cha, Yoonjeong; Lysaght, Andrew; Funt, Jason; Kusko, Rebecca
The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data. Copyright © 2018 Elsevier Inc. All rights reserved.
[Frontiers in Live Bone Imaging Researches. Novel drug discovery by means of intravital bone imaging technology].
Ishii, Masaru
Recent advances in intravital bone imaging technology has enabled us to grasp the real cellular behaviors and functions in vivo , revolutionizing the field of drug discovery for novel therapeutics against intractable bone diseases. In this chapter, I introduce various updated information on pharmacological actions of several antibone resorptive agents, which could only be derived from advanced imaging techniques, and also discuss the future perspectives of this new trend in drug discovery.
The development of high-content screening (HCS) technology and its importance to drug discovery.
Fraietta, Ivan; Gasparri, Fabio
High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development. Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology. Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.
Common characteristics of open source software development and applicability for drug discovery: a systematic review.
Ardal, Christine; Alstadsæter, Annette; Røttingen, John-Arne
Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery. A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project. Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined. We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.
Semiconductor technology in protein kinase research and drug discovery: sensing a revolution.
Bhalla, Nikhil; Di Lorenzo, Mirella; Estrela, Pedro; Pula, Giordano
Since the discovery of protein kinase activity in 1954, close to 600 kinases have been discovered that have crucial roles in cell physiology. In several pathological conditions, aberrant protein kinase activity leads to abnormal cell and tissue physiology. Therefore, protein kinase inhibitors are investigated as potential treatments for several diseases, including dementia, diabetes, cancer and autoimmune and cardiovascular disease. Modern semiconductor technology has recently been applied to accelerate the discovery of novel protein kinase inhibitors that could become the standard-of-care drugs of tomorrow. Here, we describe current techniques and novel applications of semiconductor technologies in protein kinase inhibitor drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.
Trends in GPCR drug discovery: new agents, targets and indications
DEFF Research Database (Denmark)
Hauser, Alexander Sebastian; Gloriam, David E.; Attwood, Misty M.
current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially...... are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug......G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals...
How can attrition rates be reduced in cancer drug discovery?
Moreno, Lucas; Pearson, Andrew D J
Attrition is a major issue in anticancer drug development with up to 95% of drugs tested in Phase I trials not reaching a marketing authorisation making the drug development process enormously costly and inefficient. It is essential that this problem is addressed throughout the whole drug development process to improve efficiency which will ultimately result in increased patient benefit with more profitable drugs. The approach to reduce cancer drug attrition rates must be based on three pillars. The first of these is that there is a need for new pre-clinical models which can act as better predictors of success in clinical trials. Furthermore, clinical trials driven by tumour biology with the incorporation of predictive and pharmacodynamic biomarkers would be beneficial in drug development. Finally, there is a need for increased collaboration to combine the unique strengths between industry, academia and regulators to ensure that the needs of all stakeholders are met.
Preparative Scale Resolution of Enantiomers Enables Accelerated Drug Discovery and Development
Directory of Open Access Journals (Sweden)
Hanna Leek
Full Text Available The provision of pure enantiomers is of increasing importance not only for the pharmaceutical industry but also for agro-chemistry and biotechnology. In drug discovery and development, the enantiomers of a chiral drug depict unique chemical and pharmacological behaviors in a chiral environment, such as the human body, in which the stereochemistry of the chiral drugs determines their pharmacokinetic, pharmacodynamic and toxicological properties. We present a number of challenging case studies of up-to-kilogram separations of racemic or enriched isomer mixtures using preparative liquid chromatography and super critical fluid chromatography to generate individual enantiomers that have enabled the development of new candidate drugs within AstraZeneca. The combination of chromatography and racemization as well as strategies on when to apply preparative chiral chromatography of enantiomers in a multi-step synthesis of a drug compound can further facilitate accelerated drug discovery and the early clinical evaluation of the drug candidates.
Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?
Zuberi, Aamir; Lutz, Cathleen
Abstract The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling
The use of web ontology languages and other semantic web tools in drug discovery.
Chen, Huajun; Xie, Guotong
To optimize drug development processes, pharmaceutical companies require principled approaches to integrate disparate data on a unified infrastructure, such as the web. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. Particularly, the survey covers three major application categories including: i) semantic integration and open data linking; ii) semantic web service and scientific collaboration and iii) semantic data mining and integrative network analysis. The reader will gain: i) basic knowledge of the semantic web technologies; ii) an overview of the web ontology landscape for drug discovery and iii) a basic understanding of the values and benefits of utilizing the web ontologies in drug discovery. i) The semantic web enables a network effect for linking open data for integrated drug discovery; ii) The semantic web service technology can support instant ad hoc collaboration to improve pipeline productivity and iii) The semantic web encourages publishing data in a semantic way such as resource description framework attributes and thus helps move away from a reliance on pure textual content analysis toward more efficient semantic data mining.
Open Access Could Transform Drug Discovery: A Case Study of JQ1.
Arshad, Zeeshaan; Smith, James; Roberts, Mackenna; Lee, Wen Hwa; Davies, Ben; Bure, Kim; Hollander, Georg A; Dopson, Sue; Bountra, Chas; Brindley, David
The cost to develop a new drug from target discovery to market is a staggering $1.8 billion, largely due to the very high attrition rate of drug candidates and the lengthy transition times during development. Open access is an emerging model of open innovation that places no restriction on the use of information and has the potential to accelerate the development of new drugs. To date, no quantitative assessment has yet taken place to determine the effects and viability of open access on the process of drug translation. This need is addressed within this study. The literature and intellectual property landscapes of the drug candidate JQ1, which was made available on an open access basis when discovered, and conventionally developed equivalents that were not are compared using the Web of Science and Thomson Innovation software, respectively. Results demonstrate that openly sharing the JQ1 molecule led to a greater uptake by a wider and more multi-disciplinary research community. A comparative analysis of the patent landscapes for each candidate also found that the broader scientific diaspora of the publically released JQ1 data enhanced innovation, evidenced by a greater number of downstream patents filed in relation to JQ1. The authors' findings counter the notion that open access drug discovery would leak commercial intellectual property. On the contrary, JQ1 serves as a test case to evidence that open access drug discovery can be an economic model that potentially improves efficiency and cost of drug discovery and its subsequent commercialization.
Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.
Rodrigues, Tiago
Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.
Discovery of drugs that possess activity against feline leukemia virus.
Greggs, Willie M; Clouser, Christine L; Patterson, Steven E; Mansky, Louis M
Feline leukemia virus (FeLV) is a gammaretrovirus that is a significant cause of neoplastic-related disorders affecting cats worldwide. Treatment options for FeLV are limited, associated with serious side effects, and can be cost-prohibitive. The development of drugs used to treat a related retrovirus, human immunodeficiency virus type 1 (HIV-1), has been rapid, leading to the approval of five drug classes. Although structural differences affect the susceptibility of gammaretroviruses to anti-HIV drugs, the similarities in mechanism of replication suggest that some anti-HIV-1 drugs may also inhibit FeLV. This study demonstrates the anti-FeLV activity of four drugs approved by the US FDA (Food and Drug Administration) at non-toxic concentrations. Of these, tenofovir and raltegravir are anti-HIV-1 drugs, while decitabine and gemcitabine are approved to treat myelodysplastic syndromes and pancreatic cancer, respectively, but also have anti-HIV-1 activity in cell culture. Our results indicate that these drugs may be useful for FeLV treatment and should be investigated for mechanism of action and suitability for veterinary use.
Rescuing drug discovery: In vivo systems pathology and systems pharmacology
Greef, J. van der; McBurney, R.N.
The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been
Systems biology-embedded target validation: improving efficacy in drug discovery.
Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter
The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.
An overview of aldehyde oxidase: an enzyme of emerging importance in novel drug discovery.
Rashidi, Mohammad-Reza; Soltani, Somaieh
Given the rising trend in medicinal chemistry strategy to reduce cytochrome P450-dependent metabolism, aldehyde oxidase (AOX) has recently gained increased attention in drug discovery programs and the number of drug candidates that are metabolized by AOX is steadily growing. Areas covered: Despite the emerging importance of AOX in drug discovery, there are certain major recognized problems associated with AOX-mediated metabolism of drugs. Intra- and inter-species variations in AOX activity, the lack of reliable and predictive animal models using the common experimental animals, and failure in the predictions of in vivo metabolic activity of AOX using traditional in vitro methods are among these issues that are covered in this article. A comprehensive review of computational human AOX (hAOX) related studies are also provided. Expert opinion: Following the recent progress in the stem cell field, the authors recommend the application of organoids technology as an effective tool to solve the fundamental problems associated with the evaluation of AOX in drug discovery. The recent success in resolving the hAOX crystal structure can too be another valuable data source for the study of AOX-catalyzed metabolism of new drug candidates, using computer-aided drug discovery methods.
Providing data science support for systems pharmacology and its implications to drug discovery.
Hart, Thomas; Xie, Lei
The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.
From machine learning to deep learning: progress in machine intelligence for rational drug discovery.
Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.
Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.
San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul
Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA. ©2014 American Association for Cancer Research.
The successes and failures of HIV drug discovery.
Hashimoto, Chie; Tanaka, Tomohiro; Narumi, Tetsuo; Nomura, Wataru; Tamamura, Hirokazu
To date, several anti-human immunodeficiency virus (HIV) drugs, including reverse transcriptase inhibitors and protease inhibitors, have been developed and used clinically for the treatment of patients infected with HIV. Recently, novel drugs have been discovered which have different mechanisms of action from those of the above inhibitors, including entry inhibitors and integrase (IN) inhibitors; the clinical use of three of these inhibitors has been approved. Other inhibitors are still in development. This review article summarizes the history of the development of anti-HIV drugs and also focuses on successes in the development of these entry and IN inhibitors, along with looking at exploratory approaches for the development of other inhibitors. Currently used highly active antiretroviral therapy can be subject to a loss of efficacy, due to the emergence of multi-drug resistant (MDR) strains; a change of regimens of the drug combination is required to combat this, along with careful monitoring of the virus and CD4 in the blood, by methods such as cellular tropism testing. In such a situation, entry inhibitors such as CCR5/CXCR4 antagonists, CD4 mimics, fusion inhibitors and IN inhibitors might be optional agents for an expansion of the drug repertoire available to patients at all stages of HIV infection.
Application of PBPK modelling in drug discovery and development at Pfizer.
Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B
Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.
G-protein-coupled receptors: new approaches to maximise the impact of GPCRS in drug discovery.
Davey, John
IBC's Drug Discovery Technology Series is a group of conferences highlighting technological advances and applications in niche areas of the drug discovery pipeline. This 2-day meeting focused on G-protein-coupled receptors (GPCRs), probably the most important and certainly the most valuable class of targets for drug discovery. The meeting was chaired by J Beesley (Vice President, European Business Development for LifeSpan Biosciences, Seattle, USA) and included 17 presentations on various aspects of GPCR activity, drug screens and therapeutic analyses. Keynote Addresses covered two of the emerging areas in GPCR regulation; receptor dimerisation (G Milligan, Professor of Molecular Pharmacology and Biochemistry, University of Glasgow, UK) and proteins that interact with GPCRs (J Bockaert, Laboratory of Functional Genomics, CNRS Montpellier, France). A third Keynote Address from W Thomsen (Director of GPCR Drug Screening, Arena Pharmaceuticals, USA) discussed Arena's general approach to drug discovery and illustrated this with reference to the development of an agonist with potential efficacy in Type II diabetes.
Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.
Verbist, Bie; Klambauer, Günter; Vervoort, Liesbet; Talloen, Willem; Shkedy, Ziv; Thas, Olivier; Bender, Andreas; Göhlmann, Hinrich W H; Hochreiter, Sepp
The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
[Chapter 2. Transitions in drug-discovery technology and drug-development in Japan (1980-2010)].
Sakakibara, Noriko; Yoshioka, Ryuzo; Matsumoto, Kazuo
In 1970s, the material patent system was introduced in Japan. Since then, many Japanese pharmaceutical companies have endeavored to create original in-house products. From 1980s, many of the innovative products were small molecular drugs and were developed using powerful medicinal-chemical technologies. Among them were antibiotics and effective remedies for the digestive organs and circulatory organs. During this period, Japanese companies were able to launch some blockbuster drugs. At the same time, the pharmaceutical market, which had grown rapidly for two decades, was beginning to level off. From the late 1990s, drug development was slowing down due to the lack of expertise in biotechnology such as genetic engineering. In response to the circumstances, the research and development on biotechnology-based drugs such as antibody drugs have become more dynamic and popular at companies than small molecule drugs. In this paper, the writers reviewed in detail the transitions in drug discovery and development between 1980 and 2010.
Discovery and Development of Therapeutic Drugs against Lethal Human RNA Viruses: a Multidisciplinary Assault.
AD-A239 742 AD GRANT NO: DAMD17-89-Z-9021 TITLE: DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A MULTIDISCIPLINARY...62787A871 AB WrJDA317987 11. TITLE (Include Securty Classification) DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A...G. R. Pettit, III, D.-S. Huang, and G. R. Pettit, 23rd Int'l. Horticulture Congress, Italy, 8/27 - 9/1/90. "Bryostatins Define the Role of Protein
Laboratory informatics tools integration strategies for drug discovery: integration of LIMS, ELN, CDS, and SDMS.
Machina, Hari K; Wild, David J
There are technologies on the horizon that could dramatically change how informatics organizations design, develop, deliver, and support applications and data infrastructures to deliver maximum value to drug discovery organizations. Effective integration of data and laboratory informatics tools promises the ability of organizations to make better informed decisions about resource allocation during the drug discovery and development process and for more informed decisions to be made with respect to the market opportunity for compounds. We propose in this article a new integration model called ELN-centric laboratory informatics tools integration.
Open Access Target Validation Is a More Efficient Way to Accelerate Drug Discovery
Lee, Wen Hwa
There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public–private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations. PMID:26042736
Molecularly Imprinted Polymers: Novel Discovery for Drug Delivery.
Dhanashree, Surve; Priyanka, Mohite; Manisha, Karpe; Vilasrao, Kadam
Molecularly imprinted polymers (MIP) are novel carriers synthesized by imprinting of a template over a polymer. This paper presents the recent application of MIP for diagnostic and therapeutic drug delivery. MIP owing to their 3D polymeric structures and due to bond formation with the template serves as a reservoir of active causing stimuli sensitive, enantioselective, targetted and/or controlled release. The review elaborates about key factors for optimization of MIP, controlled release by MIP for various administration routes various forms like patches, contact lenses, nanowires along with illustrations. To overcome the limitation of organic solvent usage causing increased cost, water compatible MIP and use of supercritical fluid technology for molecular imprinting were developed. Novel methods for developing water compatible MIP like pickering emulsion polymerization, co-precipitation method, cyclodextrin imprinting, surface grafting, controlled/living radical chain polymerization methods are described with illustration in this review. Various protein imprinting methods like bulk, epitope and surface imprinting are described along with illustrations. Further, application of MIP in microdevices as biomimetic sensing element for personalized therapy is elaborated. Although development and application of MIP in drug delivery is still at its infancy, constant efforts of researchers will lead to a novel intelligent drug delivery with commercial value. Efforts should be directed in developing solid oral dosage forms consisting of MIP for therapeutic protein and peptide delivery and targeted release of potent drugs addressing life threatening disease like cancer. Amalgamation of bio-engineering and pharmaceutical techniques can make these future prospects into reality.
A Drug Discovery Partnership for Personalized Breast Cancer Therapy
antagonists) and then virtually screen the USDA Phytochemical, Chinese Herbal Medicine , and the FDA Marketed Drug Databases for new estrogens. Task 1...and antagonists that are in the registered pharmaceuticals and herbal medicine databases. The 29 analogs obtained have been characterized for...Marleesa Bastian, Technician at Xavier University (Sridhar lab and is now pursuing graduation at Meharry Medical College school of Medicine , Tennessee
Animal models of pain and migraine in drug discovery
DEFF Research Database (Denmark)
Munro, Gordon; Jansen-Olesen, Inger; Olesen, Jes
of the most commonly used models and methods employed within 'pain and migraine' drug development will be presented. Recent advances within these disciplines suggest that, with the addition of a few extra carefully chosen ancillary models and/or endpoints, the relative value in terms of resources used...
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. Speed. The rate of information transferred per second is the speed of the information. Measured in bits per second. Need for speed on the net: You-Tube phenomenon; IPTV; 3D Video telephony. Online gaming; HDTV.
Perspectives of biomolecular NMR in drug discovery: the blessing and curse of versatility
International Nuclear Information System (INIS)
Jahnke, Wolfgang
The versatility of NMR and its broad applicability to several stages in the drug discovery process is well known and generally considered one of the major strengths of NMR (Pellecchia et al., Nature Rev Drug Discov 1:211-219, 2002; Stockman and Dalvit, Prog Nucl Magn Reson Spectrosc 41:187-231, 2002; Lepre et al., Comb Chem High throughput screen 5:583-590, 2002; Wyss et al., Curr Opin Drug Discov Devel 5:630-647, 2002; Jahnke and Widmer, Cell Mol Life Sci 61:580-599, 2004; Huth et al., Methods Enzymol 394:549-571, 2005b; Klages et al., Mol Biosyst 2:318-332, 2006; Takeuchi and Wagner, Curr Opin Struct Biol 16:109-117, 2006; Zartler and Shapiro, Curr Pharm Des 12:3963-3972, 2006). Indeed, NMR is the only biophysical technique which can detect and quantify molecular interactions, and at the same time provide detailed structural information with atomic level resolution. NMR should therefore be ideally suited and widely requested as a tool for drug discovery research, and numerous examples of drug discovery projects which have substantially benefited from NMR contributions or were even driven by NMR have been described in the literature. However, not all pharmaceutical companies have rigorously implemented NMR as integral tool of their research processes. Some companies invest with limited resources, and others do not use biomolecular NMR at all. This discrepancy in assessing the value of a technology is striking, and calls for clarification-under which circumstances can NMR provide added value to the drug discovery process? What kind of contributions can NMR make, and how is it implemented and integrated for maximum impact? This perspectives article suggests key areas of impact for NMR, and a model of integrating NMR with other technologies to realize synergies and maximize their value for drug discovery
The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery
Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa
In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042
Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?
Dobchev, Dimitar; Karelson, Mati
Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.
Trends in discovery of new drugs for tuberculosis therapy.
Riccardi, Giovanna; Pasca, Maria Rosalia
After the introduction of isoniazid and rifampicin, the second one discovered in the Lepetit Research Laboratories (Milan, Italy), under the supervision of Professor Piero Sensi, tuberculosis (TB) was considered an illness of the past. Unfortunately, this infectious disease is still a global health fear, due to the multidrug-resistant Mycobacterium tuberculosis and extensively circulating drug-resistant strains, as well as the unrecognized TB transmission, especially in regions with high HIV incidence. In the last few years, new antitubercular molecules appeared on the horizon both in preclinical and clinical stage of evaluation. In this review, we focus on a few of them and on their mechanism of action. Two new promising drug targets, DprE1 and MmpL3, are also discussed.
Mechanistic systems modeling to guide drug discovery and development.
Schmidt, Brian J; Papin, Jason A; Musante, Cynthia J
A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. Copyright © 2012 Elsevier Ltd. All rights reserved.
3D Miniaturization of Human Organs for Drug Discovery.
Park, Joseph; Wetzel, Isaac; Dréau, Didier; Cho, Hansang
"Engineered human organs" hold promises for predicting the effectiveness and accuracy of drug responses while reducing cost, time, and failure rates in clinical trials. Multiorgan human models utilize many aspects of currently available technologies including self-organized spherical 3D human organoids, microfabricated 3D human organ chips, and 3D bioprinted human organ constructs to mimic key structural and functional properties of human organs. They enable precise control of multicellular activities, extracellular matrix (ECM) compositions, spatial distributions of cells, architectural organizations of ECM, and environmental cues. Thus, engineered human organs can provide the microstructures and biological functions of target organs and advantageously substitute multiscaled drug-testing platforms including the current in vitro molecular assays, cell platforms, and in vivo models. This review provides an overview of advanced innovative designs based on the three main technologies used for organ construction leading to single and multiorgan systems useable for drug development. Current technological challenges and future perspectives are also discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Novel strategies for anti-aging drug discovery.
Saraswat, Komal; Rizvi, Syed Ibrahim
Scientific achievements in the last few decades, leading to effective therapeutic interventions, have dramatically improved human life expectancy. Consequently, aging has become a significant problem and represents the major risk factor for most human pathologies including diabetes, cardiovascular diseases, neurological disorders, and cancer. Scientific discoveries over the past two decades have been instrumental in dissecting molecular mechanism(s) which play important roles in determining longevity. The same understanding has also led to the acknowledgement of the plurality of 'causes' which act either alone or in combination to create the condition which can be defined as 'aging'. Areas covered: Over the years, several concepts have been put forward for the development of a viable anti-aging regimen. In this review, the authors extensively review anti aging interventions based on caloric restriction, activation of telomerase, autophagy inducers, senolytic therapeutics, plasma membrane redox system (PMRS) activators, epigenetic modulators, and stem cell therapies. Expert opinion: Based upon our current understanding, one of the most promising approaches for a successful anti-aging strategy includes the activation of adenosine monophosphate dependent protein kinase (AMPK). Another strategy may involve activation of PMRS. Future research efforts are likely to focus on nutrient and energy sensing molecular pathways which include mTOR, IGF-1, AMPK and the sirtuins.
Synthetic biology approaches in drug discovery and pharmaceutical biotechnology.
Neumann, Heinz; Neumann-Staubitz, Petra
Synthetic biology is the attempt to apply the concepts of engineering to biological systems with the aim to create organisms with new emergent properties. These organisms might have desirable novel biosynthetic capabilities, act as biosensors or help us to understand the intricacies of living systems. This approach has the potential to assist the discovery and production of pharmaceutical compounds at various stages. New sources of bioactive compounds can be created in the form of genetically encoded small molecule libraries. The recombination of individual parts has been employed to design proteins that act as biosensors, which could be used to identify and quantify molecules of interest. New biosynthetic pathways may be designed by stitching together enzymes with desired activities, and genetic code expansion can be used to introduce new functionalities into peptides and proteins to increase their chemical scope and biological stability. This review aims to give an insight into recently developed individual components and modules that might serve as parts in a synthetic biology approach to pharmaceutical biotechnology.
Stem cells: a model for screening, discovery and development of drugs.
Kitambi, Satish Srinivas; Chandrasekar, Gayathri
The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.
Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.
Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M
Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.
The evolution of the matrix metalloproteinase inhibitor drug discovery program at abbott laboratories.
Wada, Carol K
Matrix metalloproteinases (MMPs) have been implicated in several pathologies. At Abbott Laboratories, the matrix metalloproteinases inhibitor drug discovery program has focused on the discovery of a potent, selective, orally bioavailable MMP inhibitor for the treatment of cancer. The program evolved from early succinate-based inhibitors to utilizing in-house technology such as SAR by NMR to develop a novel class of biaryl hydroxamate MMP inhibitors. The metabolic instability of the biaryl hydroxamates led to the discovery of a new class of N-formylhydroxylamine (retrohydroxamate) biaryl ethers, exemplified by ABT-770 (16). Toxicity issues with this pre-clinical candidate led to the discovery of another novel class of retrohydroxamate MMP inhibitors, the phenoxyphenyl sulfones such as ABT-518 (19j). ABT-518 is a potent, orally bioavailable, selective inhibitor of MMP-2 and 9 over MMP-1 that has been evaluated in Phase I clinical trials in cancer patients.
Discovery of the target for immunomodulatory drugs (IMiDs).
Ito, Takumi; Ando, Hideki; Handa, Hiroshi
Half a century ago, the sedative thalidomide caused a serious drug disaster because of its teratogenicity and was withdrawn from the market. However, thalidomide, which has returned to the market, is now used for the treatment of leprosy and multiple myeloma (MM) under strict control. The mechanism of thalidomide action had been a long-standing question. We developed a new affinity bead technology and identified cereblon (CRBN) as a thalidomide-binding protein. We found that CRBN functions as a substrate receptor of an E3 cullin-Ring ligase complex 4 (CRL4) and is a primary target of thalidomide teratogenicity. Recently, new thalidomide derivatives, called immunomodulatory drugs (IMiDs), have been developed by Celgene. Among them, lenalidomide (Len) and pomalidomide (Pom) were shown to exert strong therapeutic effects against MM. It was found that Len and Pom both bind CRBN-CRL4 and recruit neomorphic substrates (Ikaros and Aiolos). More recently it was reported that casein kinase 1a (Ck1a) was identified as a substrate for CRBN-CRL4 in the presence of Len, but not Pom. Ck1a breakdown explains why Len is specifically effective for myelodysplastic syndrome with 5q deletion. It is now proposed that binding of IMiDs to CRBN appears to alter the substrate specificity of CRBN-CRL4. In this review, we introduce recent findings on IMiDs.
Microfluidic-Based Multi-Organ Platforms for Drug Discovery
Directory of Open Access Journals (Sweden)
Ahmad Rezaei Kolahchi
Full Text Available Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.
Screening applications in drug discovery based on microfluidic technology.
Eribol, P; Uguz, A K; Ulgen, K O
Microfluidics has been the focus of interest for the last two decades for all the advantages such as low chemical consumption, reduced analysis time, high throughput, better control of mass and heat transfer, downsizing a bench-top laboratory to a chip, i.e., lab-on-a-chip, and many others it has offered. Microfluidic technology quickly found applications in the pharmaceutical industry, which demands working with leading edge scientific and technological breakthroughs, as drug screening and commercialization are very long and expensive processes and require many tests due to unpredictable results. This review paper is on drug candidate screening methods with microfluidic technology and focuses specifically on fabrication techniques and materials for the microchip, types of flow such as continuous or discrete and their advantages, determination of kinetic parameters and their comparison with conventional systems, assessment of toxicities and cytotoxicities, concentration generations for high throughput, and the computational methods that were employed. An important conclusion of this review is that even though microfluidic technology has been in this field for around 20 years there is still room for research and development, as this cutting edge technology requires ingenuity to design and find solutions for each individual case. Recent extensions of these microsystems are microengineered organs-on-chips and organ arrays.
Screening applications in drug discovery based on microfluidic technology
Eribol, P.; Uguz, A. K.; Ulgen, K. O.
Microfluidics has been the focus of interest for the last two decades for all the advantages such as low chemical consumption, reduced analysis time, high throughput, better control of mass and heat transfer, downsizing a bench-top laboratory to a chip, i.e., lab-on-a-chip, and many others it has offered. Microfluidic technology quickly found applications in the pharmaceutical industry, which demands working with leading edge scientific and technological breakthroughs, as drug screening and commercialization are very long and expensive processes and require many tests due to unpredictable results. This review paper is on drug candidate screening methods with microfluidic technology and focuses specifically on fabrication techniques and materials for the microchip, types of flow such as continuous or discrete and their advantages, determination of kinetic parameters and their comparison with conventional systems, assessment of toxicities and cytotoxicities, concentration generations for high throughput, and the computational methods that were employed. An important conclusion of this review is that even though microfluidic technology has been in this field for around 20 years there is still room for research and development, as this cutting edge technology requires ingenuity to design and find solutions for each individual case. Recent extensions of these microsystems are microengineered organs-on-chips and organ arrays. PMID:26865904
Recent Advances in Prostate Cancer Treatment and Drug Discovery
Directory of Open Access Journals (Sweden)
Ekaterina Nevedomskaya
Full Text Available Novel drugs, drug sequences and combinations have improved the outcome of prostate cancer in recent years. The latest approvals include abiraterone acetate, enzalutamide and apalutamide which target androgen receptor (AR signaling, radium-223 dichloride for reduction of bone metastases, sipuleucel-T immunotherapy and taxane-based chemotherapy. Adding abiraterone acetate to androgen deprivation therapy (ADT in order to achieve complete androgen blockade has proven highly beneficial for treatment of locally advanced prostate cancer and metastatic hormone-sensitive prostate cancer (mHSPC. Also, ADT together with docetaxel treatment showed significant benefit in mHSPC. Ongoing clinical trials for different subgroups of prostate cancer patients include the evaluation of the second-generation AR antagonists enzalutamide, apalutamide and darolutamide, of inhibitors of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K pathway, of inhibitors of DNA damage response, of targeted alpha therapy and of prostate-specific membrane antigen (PSMA targeting approaches. Advanced clinical studies with immune checkpoint inhibitors have shown limited benefits in prostate cancer and more trials are needed to demonstrate efficacy. The identification of improved, personalized treatments will be much supported by the major progress recently made in the molecular characterization of early- and late-stage prostate cancer using "omics" technologies. This has already led to novel classifications of prostate tumors based on gene expression profiles and mutation status, and should greatly help in the choice of novel targeted therapies best tailored to the needs of patients.
A novel compact mass detection platform for the open access (OA) environment in drug discovery and early development.
Gao, Junling; Ceglia, Scott S; Jones, Michael D; Simeone, Jennifer; Antwerp, John Van; Zhang, Li-Kang; Ross, Charles W; Helmy, Roy
A new 'compact mass detector' co-developed with an instrument manufacturer (Waters Corporation) as an interface for liquid chromatography (LC), specifically Ultra-high performance LC(®) (UPLC(®) or UHPLC) analysis was evaluated as a potential new Open Access (OA) LC-MS platform in the Drug Discovery and Early Development space. This new compact mass detector based platform was envisioned to provide increased reliability and speed while exhibiting significant cost, noise, and footprint reductions. The new detector was evaluated in batch mode (typically 1-3 samples per run) to monitor reactions and check purity, as well as in High Throughput Screening (HTS) mode to run 24, 48, and 96 well plates. The latter workflows focused on screening catalysis conditions, process optimization, and library work. The objective of this investigation was to assess the performance, reliability, and flexibility of the compact mass detector in the OA setting for a variety of applications. The compact mass detector results were compared to those obtained by current OA LC-MS systems, and the capabilities and benefits of the compact mass detector in the open access setting for chemists in the drug discovery and development space are demonstrated. Copyright © 2016 Elsevier B.V. All rights reserved.
Mouse Models for Drug Discovery. Can New Tools and Technology Improve Translational Power?
Zuberi, Aamir; Lutz, Cathleen
The use of mouse models in biomedical research and preclinical drug evaluation is on the rise. The advent of new molecular genome-altering technologies such as CRISPR/Cas9 allows for genetic mutations to be introduced into the germ line of a mouse faster and less expensively than previous methods. In addition, the rapid progress in the development and use of somatic transgenesis using viral vectors, as well as manipulations of gene expression with siRNAs and antisense oligonucleotides, allow for even greater exploration into genomics and systems biology. These technological advances come at a time when cost reductions in genome sequencing have led to the identification of pathogenic mutations in patient populations, providing unprecedented opportunities in the use of mice to model human disease. The ease of genetic engineering in mice also offers a potential paradigm shift in resource sharing and the speed by which models are made available in the public domain. Predictively, the knowledge alone that a model can be quickly remade will provide relief to resources encumbered by licensing and Material Transfer Agreements. For decades, mouse strains have provided an exquisite experimental tool to study the pathophysiology of the disease and assess therapeutic options in a genetically defined system. However, a major limitation of the mouse has been the limited genetic diversity associated with common laboratory mice. This has been overcome with the recent development of the Collaborative Cross and Diversity Outbred mice. These strains provide new tools capable of replicating genetic diversity to that approaching the diversity found in human populations. The Collaborative Cross and Diversity Outbred strains thus provide a means to observe and characterize toxicity or efficacy of new therapeutic drugs for a given population. The combination of traditional and contemporary mouse genome editing tools, along with the addition of genetic diversity in new modeling systems
In silico tools used for compound selection during target-based drug discovery and development.
Caldwell, Gary W
The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies. This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach. There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.
The future of drug discovery: enabling technologies for enhancing lead characterization and profiling therapeutic potential.
Janero, David R
Technology often serves as a handmaiden and catalyst of invention. The discovery of safe, effective medications depends critically upon experimental approaches capable of providing high-impact information on the biological effects of drug candidates early in the discovery pipeline. This information can enable reliable lead identification, pharmacological compound differentiation and successful translation of research output into clinically useful therapeutics. The shallow preclinical profiling of candidate compounds promulgates a minimalistic understanding of their biological effects and undermines the level of value creation necessary for finding quality leads worth moving forward within the development pipeline with efficiency and prognostic reliability sufficient to help remediate the current pharma-industry productivity drought. Three specific technologies discussed herein, in addition to experimental areas intimately associated with contemporary drug discovery, appear to hold particular promise for strengthening the preclinical valuation of drug candidates by deepening lead characterization. These are: i) hydrogen-deuterium exchange mass spectrometry for characterizing structural and ligand-interaction dynamics of disease-relevant proteins; ii) activity-based chemoproteomics for profiling the functional diversity of mammalian proteomes; and iii) nuclease-mediated precision gene editing for developing more translatable cellular and in vivo models of human diseases. When applied in an informed manner congruent with the clinical understanding of disease processes, technologies such as these that span levels of biological organization can serve as valuable enablers of drug discovery and potentially contribute to reducing the current, unacceptably high rates of compound clinical failure.
When fragments link : a bibliometric perspective on the development of fragment-based drug discovery
Romasanta, A.K.S.; van der Sijde, P.C.; Hellsten, I.; Hubbard, Roderick E.; Keseru, Gyorgy M.; van Muijlwijk-Koezen, Jacqueline E.; de Esch, I.J.P.
Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in
[From the discovery of antibiotics to emerging highly drug-resistant bacteria].
Meunier, Olivier
The discovery of antibiotics has enabled serious infections to be treated. However, bacteria resistant to several families of antibiotics and the emergence of new highly drug-resistant bacteria constitute a public health issue in France and across the world. Actions to prevent their transmission are being put in place. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
Feature Issue Introduction: Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery
Nordstrom, Robert J.; Almutairi, Adah; Hillman, Elizabeth M.C.
The editors introduce the Biomedical Optics Express feature issue, "Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery," which combines three technical areas from the 2010 Optical Society of America (OSA), Biomedical Optics (BIOMED) Topical Meeting held on 11–14 April in Miami, FL and includes contributions from conference attendees.
Bioenergetics of Mycobacterium: An Emerging Landscape for Drug Discovery
Directory of Open Access Journals (Sweden)
Iram Khan Iqbal
Full Text Available Mycobacterium tuberculosis (Mtb exhibits remarkable metabolic flexibility that enables it to survive a plethora of host environments during its life cycle. With the advent of bedaquiline for treatment of multidrug-resistant tuberculosis, oxidative phosphorylation has been validated as an important target and a vulnerable component of mycobacterial metabolism. Exploiting the dependence of Mtb on oxidative phosphorylation for energy production, several components of this pathway have been targeted for the development of new antimycobacterial agents. This includes targeting NADH dehydrogenase by phenothiazine derivatives, menaquinone biosynthesis by DG70 and other compounds, terminal oxidase by imidazopyridine amides and ATP synthase by diarylquinolines. Importantly, oxidative phosphorylation also plays a critical role in the survival of persisters. Thus, inhibitors of oxidative phosphorylation can synergize with frontline TB drugs to shorten the course of treatment. In this review, we discuss the oxidative phosphorylation pathway and development of its inhibitors in detail.
Bioenergetics of Mycobacterium: An Emerging Landscape for Drug Discovery
Iqbal, Iram Khan; Bajeli, Sapna; Akela, Ajit Kumar
Mycobacterium tuberculosis (Mtb) exhibits remarkable metabolic flexibility that enables it to survive a plethora of host environments during its life cycle. With the advent of bedaquiline for treatment of multidrug-resistant tuberculosis, oxidative phosphorylation has been validated as an important target and a vulnerable component of mycobacterial metabolism. Exploiting the dependence of Mtb on oxidative phosphorylation for energy production, several components of this pathway have been targeted for the development of new antimycobacterial agents. This includes targeting NADH dehydrogenase by phenothiazine derivatives, menaquinone biosynthesis by DG70 and other compounds, terminal oxidase by imidazopyridine amides and ATP synthase by diarylquinolines. Importantly, oxidative phosphorylation also plays a critical role in the survival of persisters. Thus, inhibitors of oxidative phosphorylation can synergize with frontline TB drugs to shorten the course of treatment. In this review, we discuss the oxidative phosphorylation pathway and development of its inhibitors in detail. PMID:29473841
Homeopathic drug discovery: theory update and methodological aspect.
Khuda-Bukhsh, Anisur Rahman; Pathak, Surajit
Homeopathy treats patient on the basis of totality of symptoms and is based on the principle of 'like cures like'. It uses ultra-low doses of highly diluted natural substances as remedies that originate from plants, minerals or animals. The objectives of this review are to discuss concepts, controversies and research related to understanding homeopathy in the light of modern science. Attempts have been made to focus on current views of homeopathy and to delineate its most plausible mechanism(s) of action. Although some areas of concern remain, research carried out so far both in vitro and in vivo validates the effects of highly diluted homeopathic medicines in a wide variety of organisms. The precise mechanism(s) and pathway(s) of action of highly diluted homeopathic drugs are still unknown.
Malaria in South America: a drug discovery perspective.
Cruz, Luiza R; Spangenberg, Thomas; Lacerda, Marcus V G; Wells, Timothy N C
The challenge of controlling and eventually eradicating malaria means that new tools are urgently needed. South America's role in this fight spans both ends of the research and development spectrum: both as a continent capable of discovering and developing new medicines, and also as a continent with significant numbers of malaria patients. This article reviews the contribution of groups in the South American continent to the research and development of new medicines over the last decade. Therefore, the current situation of research targeting malaria control and eradication is discussed, including endemicity, geographical distribution, treatment, drug-resistance and diagnosis. This sets the scene for a review of efforts within South America to discover and optimize compounds with anti-malarial activity.
Latest animal models for anti-HIV drug discovery.
Sliva, Katja
HIV research is limited by the fact that lentiviruses are highly species specific. The need for appropriate models to promote research has led to the development of many elaborate surrogate animal models. This review looks at the history of animal models for HIV research. Although natural animal lentivirus infections and chimeric viruses such as chimera between HIV and simian immunodeficiency virus and simian-tropic HIV are briefly discussed, the main focus is on small animal models, including the complex design of the 'humanized' mouse. The review also traces the historic evolution and milestones as well as depicting current models and future prospects for HIV research. HIV research is a complex and challenging task that is highly manpower-, money- and time-consuming. Besides factors such as hypervariability and latency, the lack of appropriate animal models that exhibit and recapitulate the entire infectious process of HIV, is one of the reasons behind the failure to eliminate the lentivirus from the human population. This obstacle has led to the exploitation and further development of many sophisticated surrogate animal models for HIV research. While there is no animal model that perfectly mirrors and mimics HIV infections in humans, there are a variety of host species and viruses that complement each other. Combining the insights from each model, and critically comparing the results obtained with data from human clinical trials should help expand our understanding of HIV pathogenesis and drive future drug development.
SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.
Directory of Open Access Journals (Sweden)
Paul A Novick
Full Text Available In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.
Optimizing Oral Bioavailability in Drug Discovery: An Overview of Design and Testing Strategies and Formulation Options.
Aungst, Bruce J
For discovery teams working toward new, orally administered therapeutic agents, one requirement is to attain adequate systemic exposure after oral dosing, which is best accomplished when oral bioavailability is optimized. This report summarizes the bioavailability challenges currently faced in drug discovery, and the design and testing methods and strategies currently utilized to address the challenges. Profiling of discovery compounds usually includes separate assessments of solubility, permeability, and susceptibility to first-pass metabolism, which are the 3 most likely contributors to incomplete oral bioavailability. An initial assessment of absorption potential may be made computationally, and high throughput in vitro assays are typically performed to prioritize compounds for in vivo studies. The initial pharmacokinetic study is a critical decision point in compound evaluation, and the importance of the effect the dosing vehicle or formulation can have on oral bioavailability, especially for poorly water soluble compounds, is emphasized. Dosing vehicles and bioavailability-enabling formulations that can be used for discovery and preclinical studies are described. Optimizing oral bioavailability within a chemical series or for a lead compound requires identification of the barrier limiting bioavailability, and methods used for this purpose are outlined. Finally, a few key guidelines are offered for consideration when facing the challenges of optimizing oral bioavailability in drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry
Directory of Open Access Journals (Sweden)
Tom L. Blundell
Full Text Available The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called `linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.
Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry.
Blundell, Tom L
The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein-protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called 'linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.
Residual Complexity Does Impact Organic Chemistry and Drug Discovery: The Case of Rufomyazine and Rufomycin.
Choules, Mary P; Klein, Larry L; Lankin, David C; McAlpine, James B; Cho, Sang-Hyun; Cheng, Jinhua; Lee, Hanki; Suh, Joo-Won; Jaki, Birgit U; Franzblau, Scott G; Pauli, Guido F
Residual complexity (RC) involves the impact of subtle but critical structural and biological features on drug lead validation, including unexplained effects related to unidentified impurities. RC commonly plagues drug discovery efforts due to the inherent imperfections of chromatographic separation methods. The new diketopiperazine, rufomyazine (6), and the previously known antibiotic, rufomycin (7), represent a prototypical case of RC that (almost) resulted in the misassignment of biological activity. The case exemplifies that impurities well below the natural abundance of 13 C (1.1%) can be highly relevant and calls for advanced analytical characterization of drug leads with extended molar dynamic ranges of >1:1,000 using qNMR and LC-MS. Isolated from an actinomycete strain, 6 was originally found to be active against Mycobacterium tuberculosis with a minimum inhibitory concentration (MIC) of 2 μg/mL and high selectivity. As a part of lead validation, the dipeptide was synthesized and surprisingly found to be inactive. The initially observed activity was eventually attributed to a very minor contamination (0.24% [m/m]) with a highly active cyclic peptide (MIC ∼ 0.02 μM), subsequently identified as an analogue of 7. This study illustrates the serious implications RC can exert on organic chemistry and drug discovery, and what efforts are vital to improve lead validation and efficiency, especially in NP-related drug discovery programs.
Medicinal chemistry in drug discovery in big pharma: past, present and future.
Campbell, Ian B; Macdonald, Simon J F; Procopiou, Panayiotis A
The changes in synthetic and medicinal chemistry and related drug discovery science as practiced in big pharma over the past few decades are described. These have been predominantly driven by wider changes in society namely the computer, internet and globalisation. Thoughts about the future of medicinal chemistry are also discussed including sharing the risks and costs of drug discovery and the future of outsourcing. The continuing impact of access to substantial computing power and big data, the use of algorithms in data analysis and drug design are also presented. The next generation of medicinal chemists will communicate in ways that reflect social media and the results of constantly being connected to each other and data. Copyright © 2017. Published by Elsevier Ltd.
The current state of GPCR-based drug discovery to treat metabolic disease.
Sloop, Kyle W; Emmerson, Paul J; Statnick, Michael A; Willard, Francis S
One approach of modern drug discovery is to identify agents that enhance or diminish signal transduction cascades in various cell types and tissues by modulating the activity of GPCRs. This strategy has resulted in the development of new medicines to treat many conditions, including cardiovascular disease, psychiatric disorders, HIV/AIDS, certain forms of cancer and Type 2 diabetes mellitus (T2DM). These successes justify further pursuit of GPCRs as disease targets and provide key learning that should help guide identifying future therapeutic agents. This report reviews the current landscape of GPCR drug discovery with emphasis on efforts aimed at developing new molecules for treating T2DM and obesity. We analyse historical efforts to generate GPCR-based drugs to treat metabolic disease in terms of causal factors leading to success and failure in this endeavour. © 2018 The British Pharmacological Society.
Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.
Hall, David R; Kozakov, Dima; Whitty, Adrian; Vajda, Sandor
Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.
Drug discovery and the impact of the safe harbor provision of the Hatch- Waxman Act.
Goodson, Susanne H
Many facets of drug discovery involve the use of patented materials and methods, subjecting the researcher to potential liability from infringement of the underlying patents. Enacted in 1984, the Hatch-Waxman Act established a "safe harbor" for activities that would otherwise constitute infringement of a patented invention, if those activities were "solely for uses reasonably related to the development and submission of information under a Federal law which regulates the manufacture, use, or sale of drugs or veterinary biological products". This article examines the major court decisions interpreting the scope of the safe harbor and their application to various activities in drug development.
Platelet-activating factor podoplanin: from discovery to drug development.
Takemoto, Ai; Miyata, Kenichi; Fujita, Naoya
Tumor cell-induced platelet aggregation facilitates hematogenous metastasis by promoting tumor embolization, preventing immunological assaults and shear stress, and the platelet-releasing growth factors support tumor growth and invasion. Podoplanin, also known as Aggrus, is a type I transmembrane mucin-like glycoprotein and is expressed on wide range of tumor cells. Podoplanin has a role in platelet aggregation and metastasis formation through the binding to its platelet receptor, C-type lectin-like receptor 2 (CLEC-2). The podoplanin research was originally started from the cloning of highly metastatic NL-17 subclone from mouse colon 26 cancer cell line and from the establishment of 8F11 monoclonal antibody (mAb) that could neutralize NL-17-induced platelet aggregation and hematogenous metastasis. Later on, podoplanin was identified as the antigen of 8F11 mAb, and its ectopic expression brought to cells the platelet-aggregating abilities and hematogenous metastasis phenotypes. From the 8F11 mAb recognition epitopes, podoplanin is found to contain tandemly repeated, highly conserved motifs, designated platelet aggregation-stimulating (PLAG) domains. Series of analyses using the cells expressing the mutants and the established neutralizing anti-podoplanin mAbs uncovered that both PLAG3 and PLAG4 domains are associated with the CLEC-2 binding. The neutralizing mAbs targeting PLAG3 or PLAG4 could suppress podoplanin-induced platelet aggregation and hematogenous metastasis through inhibiting the podoplanin-CLEC-2 binding. Therefore, these domains are certainly functional in podoplanin-mediated metastasis through its platelet-aggregating activity. This review summarizes the platelet functions in metastasis formation, the role of platelet aggregation-inducing factor podoplanin in pathological and physiological situations, and the possibility to develop podoplanin-targeting drugs in the future.
Polyphony: superposition independent methods for ensemble-based drug discovery.
Pitt, William R; Montalvão, Rinaldo W; Blundell, Tom L
Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered. New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].
Cardiovascular drug discovery in the academic setting: building infrastructure, harnessing strengths, and seeking synergies.
Gardell, Stephen J; Roth, Gregory P; Kelly, Daniel P
The flow of innovative, effective, and safe new drugs from pharmaceutical laboratories for the treatment and prevention of cardiovascular disease has slowed to a trickle. While the need for breakthrough cardiovascular disease drugs is still paramount, the incentive to develop these agents has been blunted by burgeoning clinical development costs coupled with a heightened risk of failure due to the unprecedented nature of the emerging drug targets and increasingly challenging regulatory environment. A fuller understanding of the drug targets and employing novel biomarker strategies in clinical trials should serve to mitigate the risk. In any event, these current challenges have evoked changing trends in the pharmaceutical industry, which have created an opportunity for non-profit biomedical research institutions to play a pivotal partnering role in early stage drug discovery. The obvious strengths of academic research institutions is the breadth of their scientific programs and the ability and motivation to "go deep" to identify and characterize new target pathways that unlock the specific mysteries of cardiovascular diseases--leading to a bounty of novel therapeutic targets and prescient biomarkers. However, success in the drug discovery arena within the academic environment is contingent upon assembling the requisite infrastructure, annexing the talent to interrogate and validate the drug targets, and building translational bridges with pharmaceutical organizations and patient-oriented researchers.
TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.
Ru, Jinlong; Li, Peng; Wang, Jinan; Zhou, Wei; Li, Bohui; Huang, Chao; Li, Pidong; Guo, Zihu; Tao, Weiyang; Yang, Yinfeng; Xu, Xue; Li, Yan; Wang, Yonghua; Yang, Ling
Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.
Computer-aided drug discovery [v1; ref status: indexed, http://f1000r.es/5ij
Directory of Open Access Journals (Sweden)
Jürgen Bajorath
Full Text Available Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.
Exploiting pluripotent stem cell technology for drug discovery, screening, safety, and toxicology assessments.
McGivern, Jered V; Ebert, Allison D
In order for the pharmaceutical industry to maintain a constant flow of novel drugs and therapeutics into the clinic, compounds must be thoroughly validated for safety and efficacy in multiple biological and biochemical systems. Pluripotent stem cells, because of their ability to develop into any cell type in the body and recapitulate human disease, may be an important cellular system to add to the drug development repertoire. This review will discuss some of the benefits of using pluripotent stem cells for drug discovery and safety studies as well as some of the recent applications of stem cells in drug screening studies. We will also address some of the hurdles that need to be overcome in order to make stem cell-based approaches an efficient and effective tool in the quest to produce clinically successful drug compounds. Copyright © 2013 Elsevier B.V. All rights reserved.
Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.
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Albert H Gough
Full Text Available One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.
Freely Accessible Chemical Database Resources of Compounds for in Silico Drug Discovery.
Yang, JingFang; Wang, Di; Jia, Chenyang; Wang, Mengyao; Hao, GeFei; Yang, GuangFu
In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases are crucial. To the best of our knowledge, this is the first systematic review on this issue. The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure. Copyright© Bentham Science Publishers; For any queries, please email at email@example.com.
A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery.
Joslin, John; Gilligan, James; Anderson, Paul; Garcia, Catherine; Sharif, Orzala; Hampton, Janice; Cohen, Steven; King, Miranda; Zhou, Bin; Jiang, Shumei; Trussell, Christopher; Dunn, Robert; Fathman, John W; Snead, Jennifer L; Boitano, Anthony E; Nguyen, Tommy; Conner, Michael; Cooke, Mike; Harris, Jennifer; Ainscow, Ed; Zhou, Yingyao; Shaw, Chris; Sipes, Dan; Mainquist, James; Lesley, Scott
The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.
Current status and future prospects for enabling chemistry technology in the drug discovery process [version 1; referees: 2 approved
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Stevan W. Djuric
Full Text Available This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.
Public and private sector contributions to the discovery and development of "impact" drugs.
Reichert, Janice M; Milne, Christopher-Paul
Recently, well-publicized reports by Public Citizen and the Joint Economic Committee (JEC) of the US Congress questioned the role of the drug industry in the discovery and development of therapeutically important drugs. To gain a better understanding of the relative roles of the public and private sectors in pharmaceutic innovation, the Tufts Center for the Study of Drug Development evaluated the underlying National Institutes of Health (NIH) and academic research cited in the Public Citizen and JEC reports and performed its own assessment of the relationship between the private and public sectors in drug discovery and development of 21 "impact" drugs. We found that, ultimately, any attempt to measure the relative contribution of the public and private sectors to the research and development (R&D) of therapeutically important drugs by output alone, such as counting publications or even product approvals, is flawed. Several key factors (eg, degree of uncertainty, expected market value, potential social benefit) affect investment decisions and determine whether public or private sector funds, or both, are most appropriate. Because of the competitiveness and complexity of today's R&D environment, both sectors are increasingly challenged to show returns on their investment and the traditional boundaries separating the roles of the private and public research spheres have become increasingly blurred. What remains clear, however, is that the process still starts with good science and ends with good medicine.
Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders.
Han, Chanshuai; Chaineau, Mathilde; Chen, Carol X-Q; Beitel, Lenore K; Durcan, Thomas M
Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs) from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop "first-of-their-kind" disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI) and its partners are piloting an "Open Science" model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.
Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders
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Chanshuai Han
Full Text Available Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop "first-of-their-kind" disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI and its partners are piloting an "Open Science" model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.
Lessons from hot spot analysis for fragment-based drug discovery
Hall, David R.; Vajda, Sandor
Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high affinity, druglike ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from protein three-dimensional structure, and how their strength, number and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. PMID:26538314
Protein-Templated Fragment Ligations-From Molecular Recognition to Drug Discovery.
Jaegle, Mike; Wong, Ee Lin; Tauber, Carolin; Nawrotzky, Eric; Arkona, Christoph; Rademann, Jörg
Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules ("fragments") utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.
Moretti, Loris; Sartori, Luca
In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
OSIRIS, an entirely in-house developed drug discovery informatics system.
Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian
We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.
An In Vivo Platform for Rapid High-Throughput Antitubercular Drug Discovery
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Kevin Takaki
Full Text Available Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model's utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.
Stem cells: a model for screening, discovery and development of drugs
Kitambi, Satish Srinivas; Chandrasekar, Gayathri
Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficac...
Incorporation of rapid thermodynamic data in fragment-based drug discovery.
Kobe, Akihiro; Caaveiro, Jose M M; Tashiro, Shinya; Kajihara, Daisuke; Kikkawa, Masato; Mitani, Tomoya; Tsumoto, Kouhei
Fragment-based drug discovery (FBDD) has enjoyed increasing popularity in recent years. We introduce SITE (single-injection thermal extinction), a novel thermodynamic methodology that selects high-quality hits early in FBDD. SITE is a fast calorimetric competitive assay suitable for automation that captures the essence of isothermal titration calorimetry but using significantly fewer resources. We describe the principles of SITE and identify a novel family of fragment inhibitors of the enzyme ketosteroid isomerase displaying high values of enthalpic efficiency.
Seeking Ligand Bias: Assessing GPCR Coupling to Beta-Arrestins for Drug Discovery
Bohn, Laura M.; McDonald, Patricia H.
G protein-coupled receptors (GPCR) are the major site of action for endogenous hormones and neurotransmitters. Early drug discovery efforts focused on determining whether ligands could engage G protein coupling and subsequently activate or inhibit cognate "second messengers." Gone are those simple days as we now realize that receptors can also couple βarrestins. As we delve into the complexity of ligand-directed signaling and receptosome scaffolds, we are faced with what may seem like endless...
Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury.
Weaver, Richard J; Betts, Catherine; Blomme, Eric A G; Gerets, Helga H J; Gjervig Jensen, Klaus; Hewitt, Philip G; Juhila, Satu; Labbe, Gilles; Liguori, Michael J; Mesens, Natalie; Ogese, Monday O; Persson, Mikael; Snoeys, Jan; Stevens, James L; Walker, Tracy; Park, B Kevin
The liver is an important target for drug-induced toxicities. Early detection of hepatotoxic drugs requires use of well-characterized test systems, yet current knowledge, gaps and limitations of tests employed remains an important issue for drug development. Areas Covered: The current state of the science, understanding and application of test systems in use for the detection of drug-induced cytotoxicity, mitochondrial toxicity, cholestasis and inflammation is summarized. The test systems highlighted herein cover mostly in vitro and some in vivo models and endpoint measurements used in the assessment of small molecule toxic liabilities. Opportunities for research efforts in areas necessitating the development of specific tests and improved mechanistic understanding are highlighted. Expert Opinion: Use of in vitro test systems for safety optimization will remain a core activity in drug discovery. Substantial inroads have been made with a number of assays established for human Drug-induced Liver Injury. There nevertheless remain significant gaps with a need for improved in vitro tools and novel tests to address specific mechanisms of human Drug-Induced Liver Injury. Progress in these areas will necessitate not only models fit for application, but also mechanistic understanding of how chemical insult on the liver occurs in order to identify translational and quantifiable readouts for decision-making.
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.
Cournia, Zoe; Allen, Bryce; Sherman, Woody
Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review
Csermely, Peter; Korcsmáros, Tamás; Kiss, Huba J.M.; London, Gábor; Nussinov, Ruth
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach. PMID:23384594
Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database
Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch. PMID:23019491
Weak affinity chromatography for evaluation of stereoisomers in early drug discovery.
Duong-Thi, Minh-Dao; Bergström, Maria; Fex, Tomas; Svensson, Susanne; Ohlson, Sten; Isaksson, Roland
In early drug discovery (e.g., in fragment screening), recognition of stereoisomeric structures is valuable and guides medicinal chemists to focus only on useful configurations. In this work, we concurrently screened mixtures of stereoisomers and estimated their affinities to a protein target (thrombin) using weak affinity chromatography-mass spectrometry (WAC-MS). Affinity determinations by WAC showed that minor changes in stereoisomeric configuration could have a major impact on affinity. The ability of WAC-MS to provide instant information about stereoselectivity and binding affinities directly from analyte mixtures is a great advantage in fragment library screening and drug lead development.
Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery.
Pérot, Stéphanie; Sperandio, Olivier; Miteva, Maria A; Camproux, Anne-Claude; Villoutreix, Bruno O
Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.
Outsourcing drug discovery to India and China: from surviving to thriving.
Subramaniam, Swaminathan; Dugar, Sundeep
Global pharmaceutical companies face an increasingly harsh environment for their primary business of selling medicines. They have to contend with a spiraling decline in the productivity of their R&D programs that is guaranteed to severely diminish their growth prospects. Outsourcing of drug discovery activities to low-cost locations is a growing response to this crisis. However, the upsides to outsourcing are capped by the failure of global pharmaceutical companies to take advantage of the full range of possibilities that this model provides. Companies that radically rethink and transform the way they conduct R&D, such as seeking the benefits of low-cost locations in India and China will be the ones that thrive in this environment. In this article we present our views on how the outsourcing model in drug discovery should go beyond increasing the efficiency of existing drug discovery processes to a fundamental rethink and re-engineering of these processes. Copyright © 2012. Published by Elsevier Ltd.
Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.
Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio
In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.
A high-speed drug interaction search system for ease of use in the clinical environment.
Takada, Masahiro; Inada, Hiroshi; Nakazawa, Kazuo; Tani, Shoko; Iwata, Michiaki; Sugimoto, Yoshihisa; Nagata, Satoru
With the advancement of pharmaceutical development, drug interactions have become increasingly complex. As a result, a computer-based drug interaction search system is required to organize the whole of drug interaction data. To overcome problems faced with the existing systems, we developed a drug interaction search system using a hash table, which offers higher processing speeds and easier maintenance operations compared with relational databases (RDB). In order to compare the performance of our system and MySQL RDB in terms of search speed, drug interaction searches were repeated for all 45 possible combinations of two out of a group of 10 drugs for two cases: 5,604 and 56,040 drug interaction data. As the principal result, our system was able to process the search approximately 19 times faster than the system using the MySQL RDB. Our system also has several other merits such as that drug interaction data can be created in comma-separated value (CSV) format, thereby facilitating data maintenance. Although our system uses the well-known method of a hash table, it is expected to resolve problems common to existing systems and to be an effective system that enables the safe management of drugs.
Advances in immobilized artificial membrane (IAM) chromatography for novel drug discovery.
Tsopelas, Fotios; Vallianatou, Theodosia; Tsantili-Kakoulidou, Anna
The development of immobilized artificial membrane (IAM) chromatography has unfolded new perspectives for the use of chromatographic techniques in drug discovery, combining simulation of the environment of cell membranes with rapid measurements. The present review describes the characteristics of phosphatidylcholine-based stationary phases and analyses the molecular factors governing IAM retention in comparison to n-octanol-water and liposomes partitioning systems as well as to reversed phase chromatography. Other biomimetic stationary phases are also briefly discussed. The potential of IAM chromatography to model permeability through the main physiological barriers and drug membrane interactions is outlined. Further applications to calculate complex pharmacokinetic properties, related to tissue binding, and to screen drug candidates for phospholipidosis, as well as to estimate cell accumulation/retention are surveyed. The ambivalent nature of IAM chromatography, as a border case between passive diffusion and binding, defines its multiple potential applications. However, despite its successful performance in many permeability and drug-membrane interactions studies, IAM chromatography is still used as a supportive and not a stand-alone technique. Further studies looking at IAM chromatography in different biological processes are still required if this technique is to have a more focused and consistent application in drug discovery.
Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.
Alvim-Gaston, Maria; Grese, Timothy; Mahoui, Abdelaziz; Palkowitz, Alan D; Pineiro-Nunez, Marta; Watson, Ian
The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.
Stem cells: a model for screening, discovery and development of drugs
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Kitambi SS
Full Text Available Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.Keywords: therapeutics, stem cells, cancer stem cells, screening models, drug development, high throughput screening
Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.
El-Kattan, Ayman F; Varma, Manthena V; Steyn, Stefan J; Scott, Dennis O; Maurer, Tristan S; Bergman, Arthur
To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions. A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability. Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters. ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.
DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue
Wang, QuanQiu; Xu, Rong
Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...
Ebola virus: A gap in drug design and discovery - experimental and computational perspective.
Balmith, Marissa; Faya, Mbuso; Soliman, Mahmoud E S
The Ebola virus, formally known as the Ebola hemorrhagic fever, is an acute viral syndrome causing sporadic outbreaks that have ravaged West Africa. Due to its extreme virulence and highly transmissible nature, Ebola has been classified as a category A bioweapon organism. Only recently have vaccine or drug regimens for the Ebola virus been developed, including Zmapp and peptides. In addition, existing drugs which have been repurposed toward anti-Ebola virus activity have been re-examined and are seen to be promising candidates toward combating Ebola. Drug development involving computational tools has been widely employed toward target-based drug design. Screening large libraries have greatly stimulated research toward effective anti-Ebola virus drug regimens. Current emphasis has been placed on the investigation of host proteins and druggable viral targets. There is a huge gap in the literature regarding guidelines in the discovery of Ebola virus inhibitors, which may be due to the lack of information on the Ebola drug targets, binding sites, and mechanism of action of the virus. This review focuses on Ebola virus inhibitors, drugs which could be repurposed to combat the Ebola virus, computational methods which study drug-target interactions as well as providing further insight into the mode of action of the Ebola virus. © 2016 John Wiley & Sons A/S.
Drug Repositioning Discovery for Early- and Late-Stage Non-Small-Cell Lung Cancer
Directory of Open Access Journals (Sweden)
Chien-Hung Huang
Full Text Available Drug repositioning is a popular approach in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development time. Non-small-cell lung cancer (NSCLC is one of the leading causes of death worldwide. To reduce the biological heterogeneity effects among different individuals, both normal and cancer tissues were taken from the same patient, hence allowing pairwise testing. By comparing early- and late-stage cancer patients, we can identify stage-specific NSCLC genes. Differentially expressed genes are clustered separately to form up- and downregulated communities that are used as queries to perform enrichment analysis. The results suggest that pathways for early- and late-stage cancers are different. Sets of up- and downregulated genes were submitted to the cMap web resource to identify potential drugs. To achieve high confidence drug prediction, multiple microarray experimental results were merged by performing meta-analysis. The results of a few drug findings are supported by MTT assay or clonogenic assay data. In conclusion, we have been able to assess the potential existing drugs to identify novel anticancer drugs, which may be helpful in drug repositioning discovery for NSCLC.
Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy
Directory of Open Access Journals (Sweden)
Michele Patrizii
Full Text Available Despite substantial effort and resources dedicated to drug discovery and development, new anticancer agents often fail in clinical trials. Among many reasons, the lack of reliable predictive preclinical cancer models is a fundamental one. For decades, immortalized cancer cell cultures have been used to lay the groundwork for cancer biology and the quest for therapeutic responses. However, cell lines do not usually recapitulate cancer heterogeneity or reveal therapeutic resistance cues. With the rapidly evolving exploration of cancer "omics," the scientific community is increasingly investigating whether the employment of short-term patient-derived tumor cell cultures (two- and three-dimensional and/or patient-derived xenograft models might provide a more representative delineation of the cancer core and its therapeutic response. Patient-derived cancer models allow the integration of genomic with drug sensitivity data on a personalized basis and currently represent the ultimate approach for preclinical drug development and biomarker discovery. The proper use of these patient-derived cancer models might soon influence clinical outcomes and allow the implementation of tailored personalized therapy. When assessing drug efficacy for the treatment of glioblastoma multiforme (GBM, currently, the most reliable models are generated through direct injection of patient-derived cells or more frequently the isolation of glioblastoma cells endowed with stem-like features and orthotopically injecting these cells into the cerebrum of immunodeficient mice. Herein, we present the key strengths, weaknesses, and potential applications of cell- and animal-based models of GBM, highlighting our experience with the glioblastoma stem-like patient cell-derived xenograft model and its utility in drug discovery.
Discovery of potent, reversible MetAP2 inhibitors via fragment based drug discovery and structure based drug design-Part 2.
McBride, Christopher; Cheruvallath, Zacharia; Komandla, Mallareddy; Tang, Mingnam; Farrell, Pamela; Lawson, J David; Vanderpool, Darin; Wu, Yiqin; Dougan, Douglas R; Plonowski, Artur; Holub, Corine; Larson, Chris
Methionine aminopeptidase-2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. This step is required before they will fold or function correctly. Pre-clinical and clinical studies with a MetAP2 inhibitor suggest that they could be used as a novel treatment for obesity. Herein we describe the discovery of a series of pyrazolo[4,3-b]indoles as reversible MetAP2 inhibitors. A fragment-based drug discovery (FBDD) approach was used, beginning with the screening of fragment libraries to generate hits with high ligand-efficiency (LE). An indazole core was selected for further elaboration, guided by structural information. SAR from the indazole series led to the design of a pyrazolo[4,3-b]indole core and accelerated knowledge-based fragment growth resulted in potent and efficient MetAP2 inhibitors, which have shown robust and sustainable body weight loss in DIO mice when dosed orally. Copyright © 2016 Elsevier Ltd. All rights reserved.
Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond
Van Voorhis, Wesley C.; Adams, John H.; Adelfio, Roberto; Ahyong, Vida; Akabas, Myles H.; Alano, Pietro; Alday, Aintzane; Alemán Resto, Yesmalie; Alsibaee, Aishah; Alzualde, Ainhoa; Andrews, Katherine T.; Avery, Simon V.; Avery, Vicky M.; Ayong, Lawrence; Baker, Mark; Baker, Stephen; Ben Mamoun, Choukri; Bhatia, Sangeeta; Bickle, Quentin; Bounaadja, Lotfi; Bowling, Tana; Bosch, Jürgen; Boucher, Lauren E.; Boyom, Fabrice F.; Brea, Jose; Brennan, Marian; Burton, Audrey; Caffrey, Conor R.; Camarda, Grazia; Carrasquilla, Manuela; Carter, Dee; Belen Cassera, Maria; Chih-Chien Cheng, Ken; Chindaudomsate, Worathad; Chubb, Anthony; Colon, Beatrice L.; Colón-López, Daisy D.; Corbett, Yolanda; Crowther, Gregory J.; Cowan, Noemi; D'Alessandro, Sarah; Le Dang, Na; Delves, Michael; Du, Alan Y.; Duffy, Sandra; Abd El-Salam El-Sayed, Shimaa; Ferdig, Michael T.; Fernández Robledo, José A.; Fidock, David A.; Florent, Isabelle; Fokou, Patrick V. T.; Galstian, Ani; Gamo, Francisco Javier; Gold, Ben; Golub, Todd; Goldgof, Gregory M.; Guha, Rajarshi; Guiguemde, W. Armand; Gural, Nil; Guy, R. Kiplin; Hansen, Michael A. E.; Hanson, Kirsten K.; Hemphill, Andrew; Hooft van Huijsduijnen, Rob; Horii, Takaaki; Horrocks, Paul; Hughes, Tyler B.; Huston, Christopher; Igarashi, Ikuo; Ingram-Sieber, Katrin; Itoe, Maurice A.; Jadhav, Ajit; Naranuntarat Jensen, Amornrat; Jensen, Laran T.; Jiang, Rays H. Y.; Kaiser, Annette; Keiser, Jennifer; Ketas, Thomas; Kicka, Sebastien; Kim, Sunyoung; Kirk, Kiaran; Kumar, Vidya P.; Kyle, Dennis E.; Lafuente, Maria Jose; Landfear, Scott; Lee, Nathan; Lee, Sukjun; Lehane, Adele M.; Li, Fengwu; Little, David; Liu, Liqiong; Llinás, Manuel; Loza, Maria I.; Lubar, Aristea; Lucantoni, Leonardo; Lucet, Isabelle; Maes, Louis; Mancama, Dalu; Mansour, Nuha R.; March, Sandra; McGowan, Sheena; Medina Vera, Iset; Meister, Stephan; Mercer, Luke; Mestres, Jordi; Mfopa, Alvine N.; Misra, Raj N.; Moon, Seunghyun; Moore, John P.; Morais Rodrigues da Costa, Francielly; Müller, Joachim; Muriana, Arantza; Nakazawa Hewitt, Stephen; Nare, Bakela; Nathan, Carl; Narraidoo, Nathalie; Nawaratna, Sujeevi; Ojo, Kayode K.; Ortiz, Diana; Panic, Gordana; Papadatos, George; Parapini, Silvia; Patra, Kailash; Pham, Ngoc; Prats, Sarah; Plouffe, David M.; Poulsen, Sally-Ann; Pradhan, Anupam; Quevedo, Celia; Quinn, Ronald J.; Rice, Christopher A.; Abdo Rizk, Mohamed; Ruecker, Andrea; St. Onge, Robert; Salgado Ferreira, Rafaela; Samra, Jasmeet; Robinett, Natalie G.; Schlecht, Ulrich; Schmitt, Marjorie; Silva Villela, Filipe; Silvestrini, Francesco; Sinden, Robert; Smith, Dennis A.; Soldati, Thierry; Spitzmüller, Andreas; Stamm, Serge Maximilian; Sullivan, David J.; Sullivan, William; Suresh, Sundari; Suzuki, Brian M.; Suzuki, Yo; Swamidass, S. Joshua; Taramelli, Donatella; Tchokouaha, Lauve R. Y.; Theron, Anjo; Thomas, David; Tonissen, Kathryn F.; Townson, Simon; Tripathi, Abhai K.; Trofimov, Valentin; Udenze, Kenneth O.; Ullah, Imran; Vallieres, Cindy; Vigil, Edgar; Vinetz, Joseph M.; Voong Vinh, Phat; Vu, Hoan; Watanabe, Nao-aki; Weatherby, Kate; White, Pamela M.; Wilks, Andrew F.; Winzeler, Elizabeth A.; Wojcik, Edward; Wree, Melanie; Wu, Wesley; Yokoyama, Naoaki; Zollo, Paul H. A.; Abla, Nada; Blasco, Benjamin; Burrows, Jeremy; Laleu, Benoît; Leroy, Didier; Spangenberg, Thomas; Wells, Timothy; Willis, Paul A.
A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal
Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.
Directory of Open Access Journals (Sweden)
Wesley C Van Voorhis
Full Text Available A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34% of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments
FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects.
Lagorce, David; Sperandio, Olivier; Galons, Hervé; Miteva, Maria A; Villoutreix, Bruno O
Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.
A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA).
Alagappan, Muthuraman; Jiang, Dadi; Denko, Nicholas; Koong, Albert C
In silico drug discovery refers to a combination of computational techniques that augment our ability to discover drug compounds from compound libraries. Many such techniques exist, including virtual high-throughput screening (vHTS), high-throughput screening (HTS), and mechanisms for data storage and querying. However, presently these tools are often used independent of one another. In this chapter, we describe a new multimodal in silico technique for the hit identification and lead generation phases of traditional drug discovery. Our technique leverages the benefits of three independent methods-virtual high-throughput screening, high-throughput screening, and structural fingerprint analysis-by using a fourth technique called topological data analysis (TDA). We describe how a compound library can be independently tested with vHTS, HTS, and fingerprint analysis, and how the results can be transformed into a topological data analysis network to identify compounds from a diverse group of structural families. This process of using TDA or similar clustering methods to identify drug leads is advantageous because it provides a mechanism for choosing structurally diverse compounds while maintaining the unique advantages of already established techniques such as vHTS and HTS.
Simulated drug discovery process to conduct a synoptic assessment of pharmacy students.
Richardson, Alan; Curtis, Anthony D M; Moss, Gary P; Pearson, Russell J; White, Simon; Rutten, Frank J M; Perumal, Dhaya; Maddock, Katie
OBJECTIVE. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. DESIGN. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. ASSESSMENT. Students' ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. CONCLUSION. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy.
Insights into Integrated Lead Generation and Target Identification in Malaria and Tuberculosis Drug Discovery.
Okombo, John; Chibale, Kelly
New, safe and effective drugs are urgently needed to treat and control malaria and tuberculosis, which affect millions of people annually. However, financial return on investment in the poor settings where these diseases are mostly prevalent is very minimal to support market-driven drug discovery and development. Moreover, the imminent loss of therapeutic lifespan of existing therapies due to evolution and spread of drug resistance further compounds the urgency to identify novel effective drugs. However, the advent of new public-private partnerships focused on tropical diseases and the recent release of large data sets by pharmaceutical companies on antimalarial and antituberculosis compounds derived from phenotypic whole cell high throughput screening have spurred renewed interest and opened new frontiers in malaria and tuberculosis drug discovery. This Account recaps the existing challenges facing antimalarial and antituberculosis drug discovery, including limitations associated with experimental animal models as well as biological complexities intrinsic to the causative pathogens. We enlist various highlights from a body of work within our research group aimed at identifying and characterizing new chemical leads, and navigating these challenges to contribute toward the global drug discovery and development pipeline in malaria and tuberculosis. We describe a catalogue of in-house efforts toward deriving safe and efficacious preclinical drug development candidates via cell-based medicinal chemistry optimization of phenotypic whole-cell medium and high throughput screening hits sourced from various small molecule chemical libraries. We also provide an appraisal of target-based screening, as invoked in our laboratory for mechanistic evaluation of the hits generated, with particular focus on the enzymes within the de novo pyrimidine biosynthetic and hemoglobin degradation pathways, the latter constituting a heme detoxification process and an associated cysteine
Small Molecule Drug Discovery at the Glucagon-Like Peptide-1 Receptor
Directory of Open Access Journals (Sweden)
Francis S. Willard
Full Text Available The therapeutic success of peptide glucagon-like peptide-1 (GLP-1 receptor agonists for the treatment of type 2 diabetes mellitus has inspired discovery efforts aimed at developing orally available small molecule GLP-1 receptor agonists. Although the GLP-1 receptor is a member of the structurally complex class B1 family of GPCRs, in recent years, a diverse array of orthosteric and allosteric nonpeptide ligands has been reported. These compounds include antagonists, agonists, and positive allosteric modulators with intrinsic efficacy. In this paper, a comprehensive review of currently disclosed small molecule GLP-1 receptor ligands is presented. In addition, examples of "ligand bias" and "probe dependency" for the GLP-1 receptor are discussed; these emerging concepts may influence further optimization of known molecules or persuade designs of expanded screening strategies to identify novel chemical starting points for GLP-1 receptor drug discovery.
Current Landscape of Antiviral Drug Discovery [version 1; referees: 2 approved
Directory of Open Access Journals (Sweden)
Wade Blair
Full Text Available Continued discovery and development of new antiviral medications are paramount for global human health, particularly as new pathogens emerge and old ones evolve to evade current therapeutic agents. Great success has been achieved in developing effective therapies to suppress human immunodeficiency virus (HIV and hepatitis B virus (HBV; however, the therapies are not curative and therefore current efforts in HIV and HBV drug discovery are directed toward longer-acting therapies and/or developing new mechanisms of action that could potentially lead to cure, or eradication, of the virus. Recently, exciting early clinical data have been reported for novel antivirals targeting respiratory syncytial virus (RSV and influenza (flu. Preclinical data suggest that these new approaches may be effective in treating high-risk patients afflicted with serious RSV or flu infections. In this review, we highlight new directions in antiviral approaches for HIV, HBV, and acute respiratory virus infections.
Fragment-Based Drug Discovery in the Bromodomain and Extra-Terminal Domain Family.
Radwan, Mostafa; Serya, Rabah
Bromodomain and extra-terminal domain (BET) inhibition has emerged recently as a potential therapeutic target for the treatment of many human disorders such as atherosclerosis, inflammatory disorders, chronic obstructive pulmonary disease (COPD), some viral infections, and cancer. Since the discovery of the two potent inhibitors, I-BET762 and JQ1, different research groups have used different techniques to develop novel potent and selective inhibitors. In this review, we will be concerned with the trials that used fragment-based drug discovery (FBDD) approaches to discover or optimize BET inhibitors, also showing fragments that can be further optimized in future projects to reach novel potent BET inhibitors. © 2017 Deutsche Pharmazeutische Gesellschaft.
Recreational drug discovery: natural products as lead structures for the synthesis of smart drugs.
Appendino, Giovanni; Minassi, Alberto; Taglialatela-Scafati, Orazio
Covering: up to December 2013. Over the past decade, there has been a growing transition in recreational drugs from natural materials (marijuana, hashish, opium), natural products (morphine, cocaine), or their simple derivatives (heroin), to synthetic agents more potent than their natural prototypes, which are sometimes less harmful in the short term, or that combine properties from different classes of recreational prototypes. These agents have been named smart drugs, and have become popular both for personal consumption and for collective intoxication at rave parties. The reasons for this transition are varied, but are mainly regulatory and commercial. New analogues of known illegal intoxicants are invisible to most forensic detection techniques, while the alleged natural status and the lack of avert acute toxicity make them appealing to a wide range of users. On the other hand, the advent of the internet has made possible the quick dispersal of information among users and the on-line purchase of these agents and/or the precursors for their synthesis. Unlike their natural products chemotypes (ephedrine, mescaline, cathinone, psilocybin, THC), most new drugs of abuse are largely unfamiliar to the organic chemistry community as well as to health care providers. To raise awareness of the growing plague of smart drugs we have surveyed, in a medicinal chemistry fashion, their development from natural products leads, their current methods of production, and the role that clandestine home laboratories and underground chemists have played in the surge of popularity of these drugs.
Robin Ganellin gives his views on medicinal chemistry and drug discovery. Interview by Stephen L. Carney.
Ganellin, C Robin
Robin Ganellin was born in East London and studied chemistry at Queen Mary College, London, receiving a PhD in 1958 under Professor Michael Dewar for his research on tropylium chemistry. He joined Smith Kline & French Laboratories (SK&F) in the UK in 1958 and was one of the co-inventors of the revolutionary drug cimetidine (Tagamet(R)) He subsequently became Vice-President for Research at the company's Welwyn facility. In 1986 he was awarded a DSc from London University for his work on the medicinal chemistry of drugs acting at histamine receptors and was also made a Fellow of the Royal Society and appointed to the SK&F Chair of Medicinal Chemistry at University College London, where he is now Emeritus Professor of Medicinal Chemistry. Professor Ganellin has been honoured extensively, including such awards as the Royal Society of Chemistry Award for Medicinal Chemistry, their Tilden Medal and Lectureship and their Adrien Albert Medal and Lectureship, Le Prix Charles Mentzer de France, the ACS Division of Medicinal Chemistry Award, the Society of Chemical Industry Messel Medal and the Society for Drug Research Award for Drug Discovery. He is a past Chairman of the Society for Drug Research, was President of the Medicinal Chemistry Section of IUPAC, and is currently Chairman of the IUPAC Subcommittee on Medicinal Chemistry and Drug Development.
Phenotypic Screening Approaches to Develop Aurora Kinase Inhibitors: Drug Discovery Perspectives.
Marugán, Carlos; Torres, Raquel; Lallena, María José
Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins, such as Aurora-A and -B. Current drugs, which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules), have several side effects (neutropenia, alopecia, and emesis). Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype. We will briefly describe two multiplexing technologies [high-content imaging (HCI) and flow cytometry] and two key processes for drug discovery research (assay development and validation) following our own published industry quality standards. We will further focus on HCI as a useful tool for phenotypic screening and will provide a concrete example of HCI assay to detect Aurora-A or -B selective inhibitors discriminating the off-target effects related to the inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.
Have there been improvements in Alzheimer's disease drug discovery over the past 5 years?
Cacabelos, Ramón
Alzheimer's disease (AD) is the most important neurodegenerative disorder with a global cost worldwide of over $700 billion. Pharmacological treatment accounts for 10-20% of direct costs; no new drugs have been approved during the past 15 years; and the available medications are not cost-effective. Areas covered: A massive scrutiny of AD-related PubMed publications (ps)(2013-2017) identified 42,053ps of which 8,380 (19.60%) were associated with AD treatments. The most prevalent pharmacological categories included neurotransmitter enhancers (11.38%), multi-target drugs (2.45%), anti-Amyloid agents (13.30%), anti-Tau agents (2.03%), natural products and derivatives (25.58%), novel drugs (8.13%), novel targets (5.66%), other (old) drugs (11.77%), anti-inflammatory drugs (1.20%), neuroprotective peptides (1.25%), stem cell therapy (1.85%), nanocarriers/nanotherapeutics (1.52%), and others (discovery programs, (vi) the updating of regulatory requirements, (vii) the introduction of pharmacogenomics in drug development and personalized treatments, and (viii) the implementation of preventive programs.
Phenotypic screening approaches to develop Aurora kinase inhibitors: Drug Discovery perspectives
Directory of Open Access Journals (Sweden)
Carlos eMarugán
Full Text Available Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins such as Aurora A and B. Current drugs which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules, have several side effects (neutropenia, alopecia, emesis. Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype.We will briefly describe two multiplexing technologies (high-content imaging, microarrays and flow cytometry and two key processes for drug discovery research (assay development and validation following our own published industry quality standards. We will further focus on high-content imaging as a useful tool for phenotypic screening and will provide a concrete example of high-content imaging assay to detect Aurora A or B selective inhibitors discriminating the off-target effects related to inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.
Pharmacogenetics in diverse ethnic populations--implications for drug discovery and development.
McCarthy, Linda C; Davies, Kirstie J; Campbell, David A
It is widely acknowledged that the vast quantities of data now publicly available as a result of the human genome initiative have the potential to revolutionize the pharmaceutical industry. More tangibly to the drug development business, the dawn of the pharmacogenetics era has the potential to impact not only the discovery of new medicines but also the safety and efficacy of pharmaceutical agents. Coincident with these scientific advances is the emergence of new markets for pharmaceutical agents. Japan, which represents the world's second biggest market, is a good example. With the ICH E5 agreement in 1998 and a rapid change in the drug registration process in Japan, there are increasing opportunities to improve access to more medicines in all parts of the world. However, it is increasingly clear that significant genetic variation still exists between populations, with a host of data on interethnic variation in drug metabolizing enzyme and drug transporter activity. Evidence suggesting that this genetic variation may play an important role in defining some of the interethnic variation in drug response to currently marketed compounds is reviewed here, and future possibilities of using such information to better streamline the drug development process are discussed.
Exploring open innovation with a patient focus in drug discovery: an evolving paradigm of patient engagement.
Allarakhia, Minna
It is suggested in this article that patient engagement should occur further upstream during the drug discovery stage. 'Lead patients', namely those patients who are proactive with respect to their health, possess knowledge of their disease and resulting symptoms. They are also well informed about the conventional as well as non-conventional treatments for disease management; and so can provide a nuanced perspective to drug design. Understanding how patients view the management of their diseases and how they view the use of conventional versus non-conventional interventions is of imperative importance to researchers. Indeed, this can provide insight into how conventional treatments might be designed from the outset to encourage compliance and positive health outcomes. Consequently, a continuum of lead patient engagement is employed that focuses on drug discovery processes ranging from participative, informative to collaborative engagement. This article looks at a variety of open innovation models that are currently employed across this engagement spectrum. It is no longer sufficient for industry stakeholders to consider conventional therapies as the only mechanisms being sought after by patients. Without patient engagement, the industry risks being re-prioritized in terms of its role in the patient journey towards not only recovery of health, but also sustained health and wellness before disease onset.
Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians).
Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo
Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary. In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis. As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways. This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Stem cells in drug discovery, tissue engineering, and regenerative medicine: emerging opportunities and challenges.
Nirmalanandhan, Victor Sanjit; Sittampalam, G Sitta
Stem cells, irrespective of their origin, have emerged as valuable reagents or tools in human health in the past 2 decades. Initially, a research tool to study fundamental aspects of developmental biology is now the central focus of generating transgenic animals, drug discovery, and regenerative medicine to address degenerative diseases of multiple organ systems. This is because stem cells are pluripotent or multipotent cells that can recapitulate developmental paths to repair damaged tissues. However, it is becoming clear that stem cell therapy alone may not be adequate to reverse tissue and organ damage in degenerative diseases. Existing small-molecule drugs and biologicals may be needed as "molecular adjuvants" or enhancers of stem cells administered in therapy or adult stem cells in the diseased tissues. Hence, a combination of stem cell-based, high-throughput screening and 3D tissue engineering approaches is necessary to advance the next wave of tools in preclinical drug discovery. In this review, the authors have attempted to provide a basic account of various stem cells types, as well as their biology and signaling, in the context of research in regenerative medicine. An attempt is made to link stem cells as reagents, pharmacology, and tissue engineering as converging fields of research for the next decade.
Structural biology contributions to the discovery of drugs to treat chronic myelogenous leukaemia
International Nuclear Information System (INIS)
Cowan-Jacob, Sandra W.; Fendrich, Gabriele; Floersheimer, Andreas; Furet, Pascal; Liebetanz, Janis; Rummel, Gabriele; Rheinberger, Paul; Centeleghe, Mario; Fabbro, Doriano; Manley, Paul W.
A case study showing how the determination of multiple cocrystal structures of the protein tyrosine kinase c-Abl was used to support drug discovery, resulting in a compound effective in the treatment of chronic myelogenous leukaemia. Chronic myelogenous leukaemia (CML) results from the Bcr-Abl oncoprotein, which has a constitutively activated Abl tyrosine kinase domain. Although most chronic phase CML patients treated with imatinib as first-line therapy maintain excellent durable responses, patients who have progressed to advanced-stage CML frequently fail to respond or lose their response to therapy owing to the emergence of drug-resistant mutants of the protein. More than 40 such point mutations have been observed in imatinib-resistant patients. The crystal structures of wild-type and mutant Abl kinase in complex with imatinib and other small-molecule Abl inhibitors were determined, with the aim of understanding the molecular basis of resistance and to aid in the design and optimization of inhibitors active against the resistance mutants. These results are presented in a way which illustrates the approaches used to generate multiple structures, the type of information that can be gained and the way that this information is used to support drug discovery
ChEMBL web services: streamlining access to drug discovery data and utilities.
Davies, Mark; Nowotka, Michał; Papadatos, George; Dedman, Nathan; Gaulton, Anna; Atkinson, Francis; Bellis, Louisa; Overington, John P
ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.
Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo
The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.
Hot-spot analysis for drug discovery targeting protein-protein interactions.
Rosell, Mireia; Fernández-Recio, Juan
Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.
Overcoming the challenges of drug discovery for neglected tropical diseases: the A·WOL experience.
Johnston, Kelly L; Ford, Louise; Taylor, Mark J
Neglected tropical diseases (NTDs) are a group of 17 diseases that typically affect poor people in tropical countries. Each has been neglected for decades in terms of funding, research, and policy, but the recent grouping of them into one unit, which can be targeted using integrated control measures, together with increased advocacy has helped to place them on the global health agenda. The World Health Organization has set ambitious goals to control or eliminate 10 NTDs by 2020 and launched a roadmap in January 2012 to guide this global plan. The result of the launch meeting, which brought together representatives from the pharmaceutical industry, donors, and politicians, was the London Declaration: a series of commitments to provide more drugs, research, and funds to achieve the 2020 goals. Drug discovery and development for these diseases are extremely challenging, and this article highlights these challenges in the context of the London Declaration, before focusing on an example of a drug discovery and development program for the NTDs onchocerciasis and lymphatic filariasis (the anti-Wolbachia consortium, A·WOL).
Assessment of Dengue virus helicase and methyltransferase as targets for fragment-based drug discovery.
Coutard, Bruno; Decroly, Etienne; Li, Changqing; Sharff, Andrew; Lescar, Julien; Bricogne, Gérard; Barral, Karine
Seasonal and pandemic flaviviruses continue to be leading global health concerns. With the view to help drug discovery against Dengue virus (DENV), a fragment-based experimental approach was applied to identify small molecule ligands targeting two main components of the flavivirus replication complex: the NS3 helicase (Hel) and the NS5 mRNA methyltransferase (MTase) domains. A library of 500 drug-like fragments was first screened by thermal-shift assay (TSA) leading to the identification of 36 and 32 fragment hits binding Hel and MTase from DENV, respectively. In a second stage, we set up a fragment-based X-ray crystallographic screening (FBS-X) in order to provide both validated fragment hits and structural binding information. No fragment hit was confirmed for DENV Hel. In contrast, a total of seven fragments were identified as DENV MTase binders and structures of MTase-fragment hit complexes were solved at resolution at least 2.0Å or better. All fragment hits identified contain either a five- or six-membered aromatic ring or both, and three novel binding sites were located on the MTase. To further characterize the fragment hits identified by TSA and FBS-X, we performed enzymatic assays to assess their inhibition effect on the N7- and 2'-O-MTase enzymatic activities: five of these fragment hits inhibit at least one of the two activities with IC50 ranging from 180μM to 9mM. This work validates the FBS-X strategy for identifying new anti-flaviviral hits targeting MTase, while Hel might not be an amenable target for fragment-based drug discovery (FBDD). This approach proved to be a fast and efficient screening method for FBDD target validation and discovery of starting hits for the development of higher affinity molecules that bind to novel allosteric sites. Copyright © 2014 Elsevier B.V. All rights reserved.
Patient-derived stem cells: pathways to drug discovery for brain diseases
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Alan eMackay-Sim
Full Text Available The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS. The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs. There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson's disease. Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer's patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These olfactory neurosphere-derived cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson's disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high
Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.
Macrae, Calum A
IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease
Comparative psychology and the grand challenge of drug discovery in psychiatry and neurodegeneration.
Brunner, Dani; Balcı, Fuat; Ludvig, Elliot A
Drug discovery for brain disorders is undergoing a period of upheaval. Faced with an empty drug pipeline and numerous failures of potential new drugs in clinical trials, many large pharmaceutical companies have been shrinking or even closing down their research divisions that focus on central nervous system (CNS) disorders. In this paper, we argue that many of the difficulties facing CNS drug discovery stem from a lack of robustness in pre-clinical (i.e., non-human animal) testing. There are two main sources for this lack of robustness. First, there is the lack of replicability of many results from the pre-clinical stage, which we argue is driven by a combination of publication bias and inappropriate selection of statistical and experimental designs. Second, there is the frequent failure to translate results in non-human animals to parallel results in humans in the clinic. This limitation can only be overcome by developing new behavioral tests for non-human animals that have predictive, construct, and etiological validity. Here, we present these translational difficulties as a "grand challenge" to researchers from comparative cognition, who are well positioned to provide new methods for testing behavior and cognition in non-human animals. These new experimental protocols will need to be both statistically robust and target behavioral and cognitive processes that allow for better connection with human CNS disorders. Our hope is that this downturn in industrial research may represent an opportunity to develop new protocols that will re-kindle the search for more effective and safer drugs for CNS disorders. Copyright © 2011 Elsevier B.V. All rights reserved.
Generation of Polar Semi-Saturated Bicyclic Pyrazoles for Fragment-Based Drug Discovery Campaigns.
Luise, Nicola; Wyatt, Paul
Synthesising polar semi-saturated bicyclic heterocycles can lead to better starting points for fragment-based drug discovery (FBDD) programs. This communication highlights the application of diverse chemistry to construct bicyclic systems from a common intermediate, where pyrazole, a privileged heteroaromatic able to bind effectively to biological targets, is fused to diverse saturated counterparts. The generated fragments can be further developed either after confirmation of their binding pose or early in the process, as their synthetic intermediates. Essential quality control (QC) for selection of small molecules to add to a fragment library is discussed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery
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Jung Me Hwang
Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.
FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects
Directory of Open Access Journals (Sweden)
Miteva Maria A
Full Text Available Abstract Background Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. Results This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. Conclusion We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules that can be easily tuned.
Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics
Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353
Fumigation in Ayurveda: potential strategy for drug discovery and drug delivery.
Vishnuprasad, Chethala N; Pradeep, Nediyamparambu Sukumaran; Cho, Yong Woo; Gangadharan, Geethalayam Gopinathan; Han, Sung Soo
Ayurveda has its unique perceptions and resultant methodologies for defining and treating human diseases. Fumigation therapy is one of the several treatment methods described in Ayurveda whereby fumes produced from defined drug formulations are inhaled by patients. This therapeutic procedure offers promising research opportunities from phytochemical and ethnopharmacological viewpoints, however, it remains under-noticed. Considering these facts, this review is primarily aimed at introducing said Ayurvedic fumigation therapy and discussing its scientific gaps and future challenges. A search of multiple bibliographical databases and traditional Ayurvedic text books was conducted and the articles analyzed under various key themes, e.g., Ayurvedic fumigation, fumigation therapy, medicinal fumigation, inhalation of drugs and aerosol therapy. Ayurveda recommends fumigation as a method of sterilization and therapeutic procedure for various human diseases including microbial infections and psychological disorders. However, it has not gained much attention as a prospective field with multiple research opportunities. It is necessary to have a more detailed and systematic investigation of the phytochemical and pharmacodynamic properties of Ayurvedic fumigation therapy in order to facilitate the identification of novel bioactive compounds and more effective drug administration methods. © 2013 Elsevier Ireland Ltd. All rights reserved.
Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery
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Neil O Carragher
Full Text Available Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates.
Academic-Pharma drug discovery alliances: seeking ways to eliminate the valley of death.
Hammonds, Tim
Industrial pharmaceutical companies (Pharma) share a common goal with academic scientists (Academia) in that they wish to create an environment in which patients are treated for diseases with ever more effective therapies. As disease biology has proven to be ever more complex and money and new drugs are becoming more elusive, Pharma and Academia are reaching toward each other with ever greater collaborative intent. There are a growing number of collaboration models that allow scientists to work together and profit from the creation of new drugs. Here I give a personal view of how we came to where we are, present an overview of a number of these models and look to the future in terms of running successful discovery alliances.
How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice.
Haasen, Dorothea; Schopfer, Ulrich; Antczak, Christophe; Guy, Chantale; Fuchs, Florian; Selzer, Paul
Since 2011, phenotypic screening has been a trend in the pharmaceutical industry as well as in academia. This renaissance was triggered by analyses that suggested that phenotypic screening is a superior strategy to discover first-in-class drugs. Despite these promises and considerable investments, pharmaceutical research organizations have encountered considerable challenges with the approach. Few success stories have emerged in the past 5 years and companies are questioning their investment in this area. In this contribution, we outline what we have learned about success factors and challenges of phenotypic screening. We then describe how our efforts in phenotypic screening have influenced our approach to drug discovery in general. We predict that concepts from phenotypic screening will be incorporated into target-based approaches and will thus remain influential beyond the current trend.
An evolutionary perspective on drug discovery in the plant genus Euphorbia L. (Euphorbiaceae)
DEFF Research Database (Denmark)
Ernst, Madeleine
herbivory and physical stresses or to attract pollinators. Consequently, specializedmetabolites, as well as plants used in traditional medicine, are not randomly distributed across phylogenetictrees. Evolutionary approaches to plant-based drug discovery suggest that this informationcan be used to guide...... healthcarethreats, urge for systematic and time-efficient approaches in finding new drug candidates. Manydrugs are derived from plant specialized metabolites, chemical compounds, which are synthesizedby the plants in response to evolutionary adaptation to environmental and ecological factors, for example,to combat...... evolution and diversification. Also, Euphorbia species producean often chemically highly diverse latex exhibiting an exceptional number of biological activities withpharmaceutical interest. In this PhD project, the genus Euphorbia was chosen as a model group forstudying evolutionary approaches to plant...
Hit and lead criteria in drug discovery for infectious diseases of the developing world.
Katsuno, Kei; Burrows, Jeremy N; Duncan, Ken; Hooft van Huijsduijnen, Rob; Kaneko, Takushi; Kita, Kiyoshi; Mowbray, Charles E; Schmatz, Dennis; Warner, Peter; Slingsby, B T
Reducing the burden of infectious diseases that affect people in the developing world requires sustained collaborative drug discovery efforts. The quality of the chemical starting points for such projects is a key factor in improving the likelihood of clinical success, and so it is important to set clear go/no-go criteria for the progression of hit and lead compounds. With this in mind, the Japanese Global Health Innovative Technology (GHIT) Fund convened with experts from the Medicines for Malaria Venture, the Drugs for Neglected Diseases initiative and the TB Alliance, together with representatives from the Bill &Melinda Gates Foundation, to set disease-specific criteria for hits and leads for malaria, tuberculosis, visceral leishmaniasis and Chagas disease. Here, we present the agreed criteria and discuss the underlying rationale.
Therapeutic approaches to genetic ion channelopathies and perspectives in drug discovery
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Paola eImbrici
Full Text Available In the human genome more than 400 genes encode ion channels, which are transmembrane proteins mediating ion fluxes across membranes. Being expressed in all cell types, they are involved in almost all physiological processes, including sense perception, neurotransmission, muscle contraction, secretion, immune response, cell proliferation and differentiation. Due to the widespread tissue distribution of ion channels and their physiological functions, mutations in genes encoding ion channel subunits, or their interacting proteins, are responsible for inherited ion channelopathies. These diseases can range from common to very rare disorders and their severity can be mild, disabling, or life-threatening. In spite of this, ion channels are the primary target of only about 5% of the marketed drugs suggesting their potential in drug discovery. The current review summarizes the therapeutic management of the principal ion channelopathies of central and peripheral nervous system, heart, kidney, bone, skeletal muscle and pancreas, resulting from mutations in calcium, sodium, potassium and chloride ion channels. For most channelopathies the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a significant number of patients. Other channelopathies can exploit ion channel targeted drugs, such as marketed sodium channel blockers. Developing new and more specific therapeutic approaches is therefore required. To this aim, a major advancement in the pharmacotherapy of channelopathies has been the discovery that ion channel mutations lead to change in biophysics that can in turn specifically modify the sensitivity to drugs: this opens the way to a pharmacogenetics strategy, allowing the development of a personalized therapy with increased efficacy and reduced side effects. In addition, the identification of disease modifiers in ion channelopathies appears an alternative strategy to discover novel druggable targets.
Leveraging the contribution of thermodynamics in drug discovery with the help of fluorescence-based thermal shift assays.
Hau, Jean Christophe; Fontana, Patrizia; Zimmermann, Catherine; De Pover, Alain; Erdmann, Dirk; Chène, Patrick
The development of new drugs with better pharmacological and safety properties mandates the optimization of several parameters. Today, potency is often used as the sole biochemical parameter to identify and select new molecules. Surprisingly, thermodynamics, which is at the core of any interaction, is rarely used in drug discovery, even though it has been suggested that the selection of scaffolds according to thermodynamic criteria may be a valuable strategy. This poor integration of thermodynamics in drug discovery might be due to difficulties in implementing calorimetry experiments despite recent technological progress in this area. In this report, the authors show that fluorescence-based thermal shift assays could be used as prescreening methods to identify compounds with different thermodynamic profiles. This approach allows a reduction in the number of compounds to be tested in calorimetry experiments, thus favoring greater integration of thermodynamics in drug discovery.
Open innovation for phenotypic drug discovery: The PD2 assay panel.
Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M
Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.
A polychromatic turbidity microplate assay to distinguish discovery stage drug molecules with beneficial precipitation properties.
Morrison, John; Nophsker, Michelle; Elzinga, Paul; Donoso, Maria; Park, Hyunsoo; Haskell, Roy
A material sparing microplate screening assay was developed to evaluate and compare the precipitation of discovery stage drug molecules as a function of time, concentration and media composition. Polychromatic turbidity time course profiles were collected for cinnarizine, probucol, dipyridamole as well as BMS-932481, and compared with turbidity profiles of monodisperse particle size standards. Precipitation for select sample conditions were further characterized at several time points by size, morphology, amount and form via laser diffraction, microscopy, size based particle counting and X-ray diffraction respectively. Wavelength dependent turbidity was found indicative of nanoprecipitate, while wavelength independent turbidity was consistent with larger microprecipitate formation. A transition from wavelength dependent to wavelength independent turbidity occurred for nanoparticle to microparticle growth, and a decrease in wavelength independent turbidity correlated with continued growth in size of microparticles. Other sudden changes in turbidity signal over time such as rapid fluctuation, a decrease in slope or a sharp inversion were correlated with very large or aggregated macro-precipitates exceeding 100μm in diameter, a change in the rate of precipitate formation or an amorphous to crystalline form conversion respectively. The assay provides an effective method to efficiently monitor and screen the precipitation fates of drug molecules, even during the early stages of discovery with limited amounts of available material. This capability highlights molecules with beneficial precipitation properties that are able to generate and maintain solubility enabling amorphous or nanoparticle precipitates. Copyright © 2017 Elsevier B.V. All rights reserved.
2013 Philip S. Portoghese Medicinal Chemistry Lectureship: Drug Discovery Targeting Allosteric Sites†
The identification of sites on receptors topographically distinct from the orthosteric sites, so-called allosteric sites, has heralded novel approaches and modes of pharmacology for target modulation. Over the past 20 years, our understanding of allosteric modulation has grown significantly, and numerous advantages, as well as caveats (e.g., flat structure–activity relationships, species differences, "molecular switches"), have been identified. For multiple receptors and proteins, numerous examples have been described where unprecedented levels of selectivity are achieved along with improved physiochemical properties. While not a panacea, these novel approaches represent exciting opportunities for tool compound development to probe the pharmacology and therapeutic potential of discrete molecular targets, as well as new medicines. In this Perspective, in commemoration of the 2013 Philip S. Portoghese Medicinal Chemistry Lectureship (LindsleyC. W.Adventures in allosteric drug discovery. Presented at the 246th National Meeting of the American Chemical Society, Indianapolis, IN, September 10, 2013; The 2013 Portoghese Lectureship), several vignettes of drug discovery campaigns targeting novel allosteric mechanisms will be recounted, along with lessons learned and guidelines that have emerged for successful lead optimization. PMID:25180768
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.
Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.
The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery.
Spyrakis, Francesca; Ahmed, Mostafa H; Bayden, Alexander S; Cozzini, Pietro; Mozzarelli, Andrea; Kellogg, Glen E
The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.
Renaissance in Antibiotic Discovery: Some Novel Approaches for Finding Drugs to Treat Bad Bugs.
Gadakh, Bharat; Van Aerschot, Arthur
With the alarming resistance to currently used antibiotics, there is a serious worldwide threat to public health. Therefore, there is an urgent need to search for new antibiotics or new cellular targets which are essential for survival of the pathogens. However, during the past 50 years, only two new classes of antibiotics (oxazolidinone and lipopeptides) have reached the clinic. This suggests that the success rate in discovering new/novel antibiotics using conventional approaches is limited and that we must reconsider our antibiotic discovery approaches. While many new strategies are being pursued lately, this review primarily focuses only on a few of these novel/new approaches for antibiotic discovery. These include structure-based drug design (SBDD), the genomic approach, anti-virulence strategy, targeting nonmultiplying bacteria and the use of bacteriophages. In general, recent advancements in nuclear magnetic resonance, Xcrystallography, and genomic evolution have significant impact on antibacterial drug research. This review therefore aims to discuss recent strategies in searching new antibacterial agents making use of these technical novelties, their advantages, disadvantages and limitations.
Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery
Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.
Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138
Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery.
Karthikeyan, Muthukumarasamy; Vyas, Renu
Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.
Pharmacologically directed strategies in academic anticancer drug discovery based on the European NCI compounds initiative.
Hendriks, Hans R; Govaerts, Anne-Sophie; Fichtner, Iduna; Burtles, Sally; Westwell, Andrew D; Peters, Godefridus J
The European NCI compounds programme, a joint initiative of the EORTC Research Branch, Cancer Research Campaign and the US National Cancer Institute, was initiated in 1993. The objective was to help the NCI in reducing the backlog of in vivo testing of potential anticancer compounds, synthesised in Europe that emerged from the NCI in vitro 60-cell screen. Over a period of more than twenty years the EORTC-Cancer Research Campaign panel reviewed ∼2000 compounds of which 95 were selected for further evaluation. Selected compounds were stepwise developed with clear go/no go decision points using a pharmacologically directed programme. This approach eliminated quickly compounds with unsuitable pharmacological properties. A few compounds went into Phase I clinical evaluation. The lessons learned and many of the principles outlined in the paper can easily be applied to current and future drug discovery and development programmes. Changes in the review panel, restrictions regarding numbers and types of compounds tested in the NCI in vitro screen and the appearance of targeted agents led to the discontinuation of the European NCI programme in 2017 and its transformation into an academic platform of excellence for anticancer drug discovery and development within the EORTC-PAMM group. This group remains open for advice and collaboration with interested parties in the field of cancer pharmacology.
A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing
Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.
Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. PMID:25025055
A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing
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Ginés D. Guerrero
Full Text Available Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO. This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.
Blood-brain barrier in vitro models as tools in drug discovery: assessment of the transport ranking of antihistaminic drugs.
Neuhaus, W; Mandikova, J; Pawlowitsch, R; Linz, B; Bennani-Baiti, B; Lauer, R; Lachmann, B; Noe, C R
In the course of our validation program testing blood-brain barrier (BBB) in vitro models for their usability as tools in drug discovery it was evaluated whether an established Transwell model based on porcine cell line PBMEC/C1-2 was able to differentiate between the transport properties of first and second generation antihistaminic drugs. First generation antihistamines can permeate the BBB and act in the central nervous system (CNS), whereas entry to the CNS of second generation antihistamines is restricted by efflux pumps such as P-glycoprotein (P-gP) located in brain endothelial cells. P-gP functionality of PBMEC/C1-2 cells grown on Transwell filter inserts was proven by transport studies with P-gP substrate rhodamine 123 and P-gP blocker verapamil. Subsequent drug transport studies with the first generation antihistamines promethazine, diphenhydramine and pheniramine and the second generation antihistamines astemizole, ceterizine, fexofenadine and loratadine were accomplished in single substance as well as in group studies. Results were normalised to diazepam, an internal standard for the transcellular transport route. Moreover, effects after addition of P-gP inhibitor verapamil were investigated. First generation antihistamine pheniramine permeated as fastest followed by diphenhydramine, diazepam, promethazine and second generation antihistaminic drugs ceterizine, fexofenadine, astemizole and loratadine reflecting the BBB in vivo permeability ranking well. Verapamil increased the transport rates of all second generation antihistamines, which suggested involvement of P-gP during their permeation across the BBB model. The ranking after addition of verapamil was significantly changed, only fexofenadine and ceterizine penetrated slower than internal standard diazepam in the presence of verapamil. In summary, permeability data showed that the BBB model based on porcine cell line PBMEC/C1-2 was able to reflect the BBB in vivo situation for the transport of
Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.
Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean
Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.
Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls
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Hezekiel Mathambo Kumalo
Full Text Available he present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i covalent interactions in biomolecular systems; (ii the mathematical framework of covalent molecular docking; (iii implementation of covalent docking protocol in drug design workflows; (iv applications covalent docking: case studies and (v shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.
Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations.
Singh, Pankaj Kumar; Negi, Arvind; Gupta, Pawan Kumar; Chauhan, Monika; Kumar, Raj
Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.
Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.
Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang
The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
A Bioinorganic Approach to Fragment-Based Drug Discovery Targeting Metalloenzymes.
Cohen, Seth M
Metal-dependent enzymes (i.e., metalloenzymes) make up a large fraction of all enzymes and are critically important in a wide range of biological processes, including DNA modification, protein homeostasis, antibiotic resistance, and many others. Consequently, metalloenzymes represent a vast and largely untapped space for drug development. The discovery of effective therapeutics that target metalloenzymes lies squarely at the interface of bioinorganic and medicinal chemistry and requires expertise, methods, and strategies from both fields to mount an effective campaign. In this Account, our research program that brings together the principles and methods of bioinorganic and medicinal chemistry are described, in an effort to bridge the gap between these fields and address an important class of medicinal targets. Fragment-based drug discovery (FBDD) is an important drug discovery approach that is particularly well suited for metalloenzyme inhibitor development. FBDD uses relatively small but diverse chemical structures that allow for the assembly of privileged molecular collections that focus on a specific feature of the target enzyme. For metalloenzyme inhibition, the specific feature is rather obvious, namely, a metal-dependent active site. Surprisingly, prior to our work, the exploration of diverse molecular fragments for binding the metal active sites of metalloenzymes was largely unexplored. By assembling a modest library of metal-binding pharmacophores (MBPs), we have been able to find lead hits for many metalloenzymes and, from these hits, develop inhibitors that act via novel mechanisms of action. A specific case study on the use of this strategy to identify a first-in-class inhibitor of zinc-dependent Rpn11 (a component of the proteasome) is highlighted. The application of FBDD for the development of metalloenzyme inhibitors has raised several other compelling questions, such as how the metalloenzyme active site influences the coordination chemistry of bound
Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery.
Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M
Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.
Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.
Carpenter, Kristy A; Huang, Xudong
Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at firstname.lastname@example.com.
Allosteric modulation of endogenous metabolites as an avenue for drug discovery.
Wootten, Denise; Savage, Emilia E; Valant, Celine; May, Lauren T; Sloop, Kyle W; Ficorilli, James; Showalter, Aaron D; Willard, Francis S; Christopoulos, Arthur; Sexton, Patrick M
G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and a key drug target class. Recently, allosteric drugs that can co-bind with and modulate the activity of the endogenous ligand(s) for the receptor have become a major focus of the pharmaceutical and biotechnology industry for the development of novel GPCR therapeutic agents. This class of drugs has distinct properties compared with drugs targeting the endogenous (orthosteric) ligand-binding site that include the ability to sculpt cellular signaling and to respond differently in the presence of discrete orthosteric ligands, a behavior termed "probe dependence." Here, using cell signaling assays combined with ex vivo and in vivo studies of insulin secretion, we demonstrate that allosteric ligands can cause marked potentiation of previously "inert" metabolic products of neurotransmitters and peptide hormones, a novel consequence of the phenomenon of probe dependence. Indeed, at the muscarinic M(2) receptor and glucagon-like peptide 1 (GLP-1) receptor, allosteric potentiation of the metabolites, choline and GLP-1(9-36)NH(2), respectively, was ~100-fold and up to 200-fold greater than that seen with the physiological signaling molecules acetylcholine and GLP-1(7-36)NH(2). Modulation of GLP-1(9-36)NH(2) was also demonstrated in ex vivo and in vivo assays of insulin secretion. This work opens up new avenues for allosteric drug discovery by directly targeting modulation of metabolites, but it also identifies a behavior that could contribute to unexpected clinical outcomes if interaction of allosteric drugs with metabolites is not part of their preclinical assessment.
Microbial P450 Enzymes in Bioremediation and Drug Discovery: Emerging Potentials and Challenges.
Bhattacharya, Sukanta S; Yadav, Jagjit S
Cytochrome P450 enzymes are a structurally conserved but functionally diverse group of heme-containing mixed function oxidases found across both prokaryotic and eukaryotic forms of the microbial world. Microbial P450s are known to perform diverse functions ranging from the synthesis of cell wall components to xenobiotic/drug metabolism to biodegradation of environmental chemicals. Conventionally, many microbial systems have been reported to mimic mammalian P450-like activation of drugs and were proposed as the in-vitro models of mammalian drug metabolism. Recent reports suggest that native or engineered forms of specific microbial P450s from these and other microbial systems could be employed for desired specific biotransformation reactions toward natural and synthetic (drug) compounds underscoring their emerging potential in drug improvement and discovery. On the other hand, microorganisms particularly fungi and actinomycetes have been shown to possess catabolic P450s with unusual potential to degrade toxic environmental chemicals including persistent organic pollutants (POPs). Wood-rotting basidiomycete fungi in particular have revealed the presence of exceptionally large P450 repertoire (P450ome) in their genomes, majority of which are however orphan (with no known function). Our pre- and post-genomic studies have led to functional characterization of several fungal P450s inducible in response to exposure to several environmental toxicants and demonstration of their potential in bioremediation of these chemicals. This review is an attempt to summarize the postgenomic unveiling of this versatile enzyme superfamily in microbial systems and investigation of their potential to synthesize new drugs and degrade persistent pollutants, among other biotechnological applications. Copyright© Bentham Science Publishers; For any queries, please email at firstname.lastname@example.com.
Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays
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Morton, M.J., E-mail: firstname.lastname@example.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)
Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.
Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays
International Nuclear Information System (INIS)
Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.
Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility
Open-access public-private partnerships to enable drug discovery--new approaches.
Müller, Susanne; Weigelt, Johan
The productivity of the pharmaceutical industry, as assessed by the number of NMEs produced per US dollar spent in R&D, has been in steady decline during the past 40 years. This decline in productivity not only poses a significant challenge to the pharmaceutical industry, but also to society because of the importance of developing drugs for the treatment of unmet medical needs. The major challenge in progressing a new drug to the market is the successful completion of clinical trials. However, the failure rate of drugs entering trials has not decreased, despite various technological and scientific breakthroughs in recent decades, and despite intense target validation efforts. This lack of success suggests limitations in the fundamental understanding of target biology and human pharmacology. One contributing factor may be the traditional secrecy of the pharmaceutical sector, a characteristic that does not promote scientific discovery in an optimal manner. Access to broader knowledge relating to target biology and human pharmacology is difficult to obtain because interactions between researchers in industry and academia are typically restricted to closed collaborations in which the knowledge gained is confidential.However, open-access collaborative partnerships are gaining momentum in industry, and are also favored by funding agencies. Such open-access collaborations may be a powerful alternative to closed collaborations; the sharing of early-stage research data is expected to enable scientific discovery by engaging a broader section of the scientific community in the exploration of new findings. Potentially, the sharing of data could contribute to an increased understanding of biological processes and a decrease in the attrition of clinical programs.
MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics
Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.
Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.
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Rajmohan Rajamuthiah
Full Text Available Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.
Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.
Rajamuthiah, Rajmohan; Fuchs, Beth Burgwyn; Jayamani, Elamparithi; Kim, Younghoon; Larkins-Ford, Jonah; Conery, Annie; Ausubel, Frederick M; Mylonakis, Eleftherios
Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA) and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.
Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.
Heifetz, Alexander; Southey, Michelle; Morao, Inaki; Townsend-Nicholson, Andrea; Bodkin, Mike J
GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.
Biochemistry and biomedicine of quantum dots: from biodetection to bioimaging, drug discovery, diagnostics, and therapy.
Yao, Jun; Li, Pingfan; Li, Lin; Yang, Mei
According to recent research, nanotechnology based on quantum dots (QDs) has been widely applied in the field of bioimaging, drug delivery, and drug analysis. Therefore, it has become one of the major forces driving basic and applied research. The application of nanotechnology in bioimaging has been of concern. Through in vitro labeling, it was found that luminescent QDs possess many properties such as narrow emission, broad UV excitation, bright fluorescence, and high photostability. The QDs also show great potential in whole-body imaging. The QDs can be combined with biomolecules, and hence, they can be used for targeted drug delivery and diagnosis. The characteristics of QDs make them useful for application in pharmacy and pharmacology. This review focuses on various applications of QDs, especially in imaging, drug delivery, pharmaceutical analysis, photothermal therapy, biochips, and targeted surgery. Finally, conclusions are made by providing some critical challenges and a perspective of how this field can be expected to develop in the future. Quantum dots (QDs) is an emerging field of interdisciplinary subject that involves physics, chemistry, materialogy, biology, medicine, and so on. In addition, nanotechnology based on QDs has been applied in depth in biochemistry and biomedicine. Some forward-looking fields emphatically reflected in some extremely vital areas that possess inspiring potential applicable prospects, such as immunoassay, DNA analysis, biological monitoring, drug discovery, in vitro labelling, in vivo imaging, and tumor target are closely connected to human life and health and has been the top and forefront in science and technology to date. Furthermore, this review has not only involved the traditional biochemical detection but also particularly emphasized its potential applications in life science and biomedicine. Copyright © 2018 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Trypanosomatids topoisomerase re-visited. New structural findings and role in drug discovery
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Rafael Balaña-Fouce
, their involvement both in the physiology and virulence of these parasites, as well as their use as promising targets for drug discovery.
Towards an in vitro model of Plasmodium hypnozoites suitable for drug discovery.
Dembele, Laurent; Gego, Audrey; Zeeman, Anne-Marie; Franetich, Jean-François; Silvie, Olivier; Rametti, Armelle; Le Grand, Roger; Dereuddre-Bosquet, Nathalie; Sauerwein, Robert; van Gemert, Geert-Jan; Vaillant, Jean-Christophe; Thomas, Alan W; Snounou, Georges; Kocken, Clemens H M; Mazier, Dominique
Amongst the Plasmodium species in humans, only P. vivax and P. ovale produce latent hepatic stages called hypnozoites, which are responsible for malaria episodes long after a mosquito bite. Relapses contribute to increased morbidity, and complicate malaria elimination programs. A single drug effective against hypnozoites, primaquine, is available, but its deployment is curtailed by its haemolytic potential in glucose-6-phosphate dehydrogenase deficient persons. Novel compounds are thus urgently needed to replace primaquine. Discovery of compounds active against hypnozoites is restricted to the in vivo P. cynomolgi-rhesus monkey model. Slow growing hepatic parasites reminiscent of hypnozoites had been noted in cultured P. vivax-infected hepatoma cells, but similar forms are also observed in vitro by other species including P. falciparum that do not produce hypnozoites. P. falciparum or P. cynomolgi sporozoites were used to infect human or Macaca fascicularis primary hepatocytes, respectively. The susceptibility of the slow and normally growing hepatic forms obtained in vitro to three antimalarial drugs, one active against hepatic forms including hypnozoites and two only against the growing forms, was measured. The non-dividing slow growing P. cynomolgi hepatic forms, observed in vitro in primary hepatocytes from the natural host Macaca fascicularis, can be distinguished from similar forms seen in P. falciparum-infected human primary hepatocytes by the differential action of selected anti-malarial drugs. Whereas atovaquone and pyrimethamine are active on all the dividing hepatic forms observed, the P. cynomolgi slow growing forms are highly resistant to treatment by these drugs, but remain susceptible to primaquine. Resistance of the non-dividing P. cynomolgi forms to atovaquone and pyrimethamine, which do not prevent relapses, strongly suggests that these slow growing forms are hypnozoites. This represents a first step towards the development of a practical medium
Towards an in vitro model of Plasmodium hypnozoites suitable for drug discovery.
Directory of Open Access Journals (Sweden)
Laurent Dembele
Full Text Available Amongst the Plasmodium species in humans, only P. vivax and P. ovale produce latent hepatic stages called hypnozoites, which are responsible for malaria episodes long after a mosquito bite. Relapses contribute to increased morbidity, and complicate malaria elimination programs. A single drug effective against hypnozoites, primaquine, is available, but its deployment is curtailed by its haemolytic potential in glucose-6-phosphate dehydrogenase deficient persons. Novel compounds are thus urgently needed to replace primaquine. Discovery of compounds active against hypnozoites is restricted to the in vivo P. cynomolgi-rhesus monkey model. Slow growing hepatic parasites reminiscent of hypnozoites had been noted in cultured P. vivax-infected hepatoma cells, but similar forms are also observed in vitro by other species including P. falciparum that do not produce hypnozoites.P. falciparum or P. cynomolgi sporozoites were used to infect human or Macaca fascicularis primary hepatocytes, respectively. The susceptibility of the slow and normally growing hepatic forms obtained in vitro to three antimalarial drugs, one active against hepatic forms including hypnozoites and two only against the growing forms, was measured.The non-dividing slow growing P. cynomolgi hepatic forms, observed in vitro in primary hepatocytes from the natural host Macaca fascicularis, can be distinguished from similar forms seen in P. falciparum-infected human primary hepatocytes by the differential action of selected anti-malarial drugs. Whereas atovaquone and pyrimethamine are active on all the dividing hepatic forms observed, the P. cynomolgi slow growing forms are highly resistant to treatment by these drugs, but remain susceptible to primaquine.Resistance of the non-dividing P. cynomolgi forms to atovaquone and pyrimethamine, which do not prevent relapses, strongly suggests that these slow growing forms are hypnozoites. This represents a first step towards the development of a
An update on the use of C. elegans for preclinical drug discovery: screening and identifying anti-infective drugs.
Kim, Wooseong; Hendricks, Gabriel Lambert; Lee, Kiho; Mylonakis, Eleftherios
The emergence of antibiotic-resistant and -tolerant bacteria is a major threat to human health. Although efforts for drug discovery are ongoing, conventional bacteria-centered screening strategies have thus far failed to yield new classes of effective antibiotics. Therefore, new paradigms for discovering novel antibiotics are of critical importance. Caenorhabditis elegans, a model organism used for in vivo, offers a promising solution for identification of anti-infective compounds. Areas covered: This review examines the advantages of C. elegans-based high-throughput screening over conventional, bacteria-centered in vitro screens. It discusses major anti-infective compounds identified from large-scale C. elegans-based screens and presents the first clinically-approved drugs, then known bioactive compounds, and finally novel small molecules. Expert opinion: There are clear advantages of using a C. elegans-infection based screening method. A C. elegans-based screen produces an enriched pool of non-toxic, efficacious, potential anti-infectives, covering: conventional antimicrobial agents, immunomodulators, and anti-virulence agents. Although C. elegans-based screens do not denote the mode of action of hit compounds, this can be elucidated in secondary studies by comparing the results to target-based screens, or conducting subsequent target-based screens, including the genetic knock-down of host or bacterial genes.
Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.
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Sean Ekins
Full Text Available High-throughput screening (HTS in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb for further development towards new tuberculosis (TB drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
SPME as a promising tool in translational medicine and drug discovery: From bench to bedside.
Goryński, Krzysztof; Goryńska, Paulina; Górska, Agnieszka; Harężlak, Tomasz; Jaroch, Alina; Jaroch, Karol; Lendor, Sofia; Skobowiat, Cezary; Bojko, Barbara
Solid phase microextraction (SPME) is a technology where a small amount of an extracting phase dispersed on a solid support is exposed to the sample for a well-defined period of time. The open-bed geometry and biocompatibility of the materials used for manufacturing of the devices makes it very convenient tool for direct extraction from complex biological matrices. The flexibility of the formats permits tailoring the method according the needs of the particular application. Number of studies concerning monitoring of drugs and their metabolites, analysis of metabolome of volatile as well as non-volatile compounds, determination of ligand-protein binding, permeability and compound toxicity was already reported. All these applications were performed in different matrices including biological fluids and tissues, cell cultures, and in living animals. The low invasiveness of in vivo SPME, ability of using very small sample volumes and analysis of cell cultures permits to address the rule of 3R, which is currently acknowledged ethical standard in R&D labs. In the current review systematic evaluation of the applicability of SPME to studies required to be conduct at different stages of drug discovery and development and translational medicine is presented. The advantages and challenges are discussed based on the examples directly showing given experimental design or on the studies, which could be translated to the models routinely used in drug development process. Copyright © 2016 Elsevier B.V. All rights reserved.
Symbiotic Microbes from Marine Invertebrates: Driving a New Era of Natural Product Drug Discovery
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Alix Blockley
Full Text Available Invertebrates account for more than 89% of all extant organisms in the marine environment, represented by over 174,600 species (recorded to date. Such diversity is mirrored in (or more likely increased by the microbial symbionts associated with this group and in the marine natural products (or MNPs that they produce. Since the early 1950s over 20,000 MNPs have been discovered, including compounds produced by symbiotic bacteria, and the chemical diversity of compounds produced from marine sources has led to them being referred to as "blue gold" in the search for new drugs. For example, 80% of novel antibiotics stemming from the marine environment have come from Actinomycetes, many of which can be found associated with marine sponges, and compounds with anti-tumorigenic and anti-diabetic potential have also been isolated from marine symbionts. In fact, it has been estimated that marine sources formed the basis of over 50% of FDA-approved drugs between 1981 and 2002. In this review, we explore the diversity of marine microbial symbionts by examining their use as the producers of novel pharmaceutical actives, together with a discussion of the opportunities and constraints offered by "blue gold" drug discovery.
Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab.
Arvidsson, Per I; Sandberg, Kristian; Sakariassen, Kjell S
The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.
Novel approaches to develop community-built biological network models for potential drug discovery.
Talikka, Marja; Bukharov, Natalia; Hayes, William S; Hofmann-Apitius, Martin; Alexopoulos, Leonidas; Peitsch, Manuel C; Hoeng, Julia
Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
Early phase drug discovery: cheminformatics and computational techniques in identifying lead series.
Duffy, Bryan C; Zhu, Lei; Decornez, Hélène; Kitchen, Douglas B
Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration. Copyright © 2012 Elsevier Ltd. All rights reserved.
Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects
Clematis, Andrea; Quarati, Alfonso; Cesini, Daniele; Milanesi, Luciano; Merelli, Ivan
Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements. PMID:24106693
Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery.
Barnes, Michael R; Harland, Lee; Foord, Steven M; Hall, Matthew D; Dix, Ian; Thomas, Scott; Williams-Jones, Bryn I; Brouwer, Cory R
Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public-private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery. In this article, we discuss the background to these changes and propose new areas of collaboration in computational biology and chemistry between the public domain and the pharmaceutical industry.
When fragments link: a bibliometric perspective on the development of fragment-based drug discovery.
Romasanta, Angelo K S; van der Sijde, Peter; Hellsten, Iina; Hubbard, Roderick E; Keseru, Gyorgy M; van Muijlwijk-Koezen, Jacqueline; de Esch, Iwan J P
Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in FBDD, complementing accounts by key practitioners. Mapping its core papers, we found the transfer of knowledge from academia to industry. Co-authorship analysis showed that university-industry collaboration has grown over time. Moreover, we show how ideas from other scientific disciplines have been integrated into the FBDD paradigm. Keyword analysis showed that the field is organized into four interconnected practices: library design, fragment screening, computational methods, and optimization. This study highlights the importance of interactions among various individuals and institutions from diverse disciplines in newly emerging scientific fields. Copyright © 2018. Published by Elsevier Ltd.
Cancer Chemoprevention Effects of Ginger and its Active Constituents: Potential for New Drug Discovery.
Wang, Chong-Zhi; Qi, Lian-Wen; Yuan, Chun-Su
Ginger is a commonly used spice and herbal medicine worldwide. Besides its extensive use as a condiment, ginger has been used in traditional Chinese medicine for the management of various medical conditions. In recent years, ginger has received wide attention due to its observed antiemetic and anticancer activities. This paper reviews the potential role of ginger and its active constituents in cancer chemoprevention. The phytochemistry, bioactivity, and molecular targets of ginger constituents, especially 6-shogaol, are discussed. The content of 6-shogaol is very low in fresh ginger, but significantly higher after steaming. With reported anti-cancer activities, 6-shogaol can be served as a lead compound for new drug discovery. The lead compound derivative synthesis, bioactivity evaluation, and computational docking provide a promising opportunity to identify novel anticancer compounds originating from ginger.
Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects
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Daniele D'Agostino
Full Text Available Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements.
High Throughput Screening in Duchenne Muscular Dystrophy: From Drug Discovery to Functional Genomics
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Thomas J.J. Gintjee
Full Text Available Centers for the screening of biologically active compounds and genomic libraries are becoming common in the academic setting and have enabled researchers devoted to developing strategies for the treatment of diseases or interested in studying a biological phenomenon to have unprecedented access to libraries that, until few years ago, were accessible only by pharmaceutical companies. As a result, new drugs and genetic targets have now been identified for the treatment of Duchenne muscular dystrophy (DMD, the most prominent of the neuromuscular disorders affecting children. Although the work is still at an early stage, the results obtained to date are encouraging and demonstrate the importance that these centers may have in advancing therapeutic strategies for DMD as well as other diseases. This review will provide a summary of the status and progress made toward the development of a cure for this disorder and implementing high-throughput screening (HTS technologies as the main source of discovery. As more academic institutions are gaining access to HTS as a valuable discovery tool, the identification of new biologically active molecules is likely to grow larger. In addition, the presence in the academic setting of experts in different aspects of the disease will offer the opportunity to develop novel assays capable of identifying new targets to be pursued as potential therapeutic options. These assays will represent an excellent source to be used by pharmaceutical companies for the screening of larger libraries providing the opportunity to establish strong collaborations between the private and academic sectors and maximizing the chances of bringing into the clinic new drugs for the treatment of DMD.
High throughput screening in duchenne muscular dystrophy: from drug discovery to functional genomics.
Gintjee, Thomas J J; Magh, Alvin S H; Bertoni, Carmen
Centers for the screening of biologically active compounds and genomic libraries are becoming common in the academic setting and have enabled researchers devoted to developing strategies for the treatment of diseases or interested in studying a biological phenomenon to have unprecedented access to libraries that, until few years ago, were accessible only by pharmaceutical companies. As a result, new drugs and genetic targets have now been identified for the treatment of Duchenne muscular dystrophy (DMD), the most prominent of the neuromuscular disorders affecting children. Although the work is still at an early stage, the results obtained to date are encouraging and demonstrate the importance that these centers may have in advancing therapeutic strategies for DMD as well as other diseases. This review will provide a summary of the status and progress made toward the development of a cure for this disorder and implementing high-throughput screening (HTS) technologies as the main source of discovery. As more academic institutions are gaining access to HTS as a valuable discovery tool, the identification of new biologically active molecules is likely to grow larger. In addition, the presence in the academic setting of experts in different aspects of the disease will offer the opportunity to develop novel assays capable of identifying new targets to be pursued as potential therapeutic options. These assays will represent an excellent source to be used by pharmaceutical companies for the screening of larger libraries providing the opportunity to establish strong collaborations between the private and academic sectors and maximizing the chances of bringing into the clinic new drugs for the treatment of DMD.
Regenerative Medicine, Disease Modelling, and Drug Discovery in Human Pluripotent Stem Cell-Derived Kidney Tissue
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Navin Gupta
Full Text Available The multitude of research clarifying critical factors in embryonic organ development has been instrumental in human stem cell research. Mammalian organogenesis serves as the archetype for directed differentiation protocols, subdividing the process into a series of distinct intermediate stages that can be chemically induced and monitored for the expression of stage-specific markers. Significant advances over the past few years include established directed differentiation protocols of human embryonic stem cells and human induced pluripotent stem cells (hiPSC into human kidney organoids in vitro. Human kidney tissue in vitro simulates the in vivo response when subjected to nephrotoxins, providing a novel screening platform during drug discovery to facilitate identification of lead candidates, reduce developmental expenditures, and reduce future rates of drug-induced acute kidney injury. Patient-derived hiPSC, which bear naturally occurring DNA mutations, may allow for modelling of human genetic diseases to enable determination of pathological mechanisms and screening for novel therapeutics. In addition, recent advances in genome editing with clustered regularly interspaced short palindromic repeats (CRISPR/Cas9 enable the generation of specific mutations to study genetic disease, with non-mutated lines serving as an ideal isogenic control. The growing population of patients with end-stage kidney disease is a worldwide healthcare problem, with high morbidity and mortality rates, that warrants the discovery of novel forms of renal replacement therapy. Coupling the outlined advances in hiPSC research with innovative bioengineering techniques, such as decellularised kidney and three-dimensional printed scaffolds, may contribute to the development of bioengineered transplantable human kidney tissue as a means of renal replacement therapy.
When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis.
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Jack W Scannell
Full Text Available A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a to provide quantitatively and historically plausible explanations of the contrast; and (b identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc. within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc. whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development, changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man can offset large (e.g., 10 fold, even 100 fold changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated
Organ/body-on-a-chip based on microfluidic technology for drug discovery.
Kimura, Hiroshi; Sakai, Yasuyuki; Fujii, Teruo
Although animal experiments are indispensable for preclinical screening in the drug discovery process, various issues such as ethical considerations and species differences remain. To solve these issues, cell-based assays using human-derived cells have been actively pursued. However, it remains difficult to accurately predict drug efficacy, toxicity, and organs interactions, because cultivated cells often do not retain their original organ functions and morphologies in conventional in vitro cell culture systems. In the μTAS research field, which is a part of biochemical engineering, the technologies of organ-on-a-chip, based on microfluidic devices built using microfabrication, have been widely studied recently as a novel in vitro organ model. Since it is possible to physically and chemically mimic the in vitro environment by using microfluidic device technology, maintenance of cellular function and morphology, and replication of organ interactions can be realized using organ-on-a-chip devices. So far, functions of various organs and tissues, such as the lung, liver, kidney, and gut have been reproduced as in vitro models. Furthermore, a body-on-a-chip, integrating multi organ functions on a microfluidic device, has also been proposed for prediction of organ interactions. We herein provide a background of microfluidic systems, organ-on-a-chip, Body-on-a-chip technologies, and their challenges in the future. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.
Early identification of hERG liability in drug discovery programs by automated patch clamp
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Timm eDanker
Full Text Available Blockade of the cardiac ion channel coded by hERG can lead to cardiac arrhythmia, which has become a major concern in drug discovery and development. Automated electrophysiological patch clamp allows assessment of hERG channel effects early in drug development to aid medicinal chemistry programs and has become routine in pharmaceutical companies. However, a number of potential sources of errors in setting up hERG channel assays by automated patch clamp can lead to misinterpretation of data or false effects being reported. This article describes protocols for automated electrophysiology screening of compound effects on the hERG channel current. Protocol details and the translation of criteria known from manual patch clamp experiments to automated patch clamp experiments to achieve good quality data are emphasized. Typical pitfalls and artifacts that may lead to misinterpretation of data are discussed. While this article focuses on hERG channel recordings using the QPatch (Sophion A/S, Copenhagen, Denmark technology, many of the assay and protocol details given in this article can be transferred for setting up different ion channel assays by automated patch clamp and are similar on other planar patch clamp platforms.
Innovating Chinese Herbal Medicine: From Traditional Health Practice to Scientific Drug Discovery
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Shuo Gu
Full Text Available As one of the major contemporary alternative medicines, traditional Chinese medicine (TCM continues its influence in Chinese communities and has begun to attract the academic attention in the world of western medicine. This paper aims to examine Chinese herbal medicine (CHM, the essential branch of TCM, from both narrative and scientific perspectives. CHM is a traditional health practice originated from Chinese philosophy and religion, holding the belief of holism and balance in the body. With the development of orthodox medicine and science during the last centuries, CHM also seized the opportunity to change from traditional health practice to scientific drug discovery illustrated in the famous story of the herb-derived drug artemisinin. However, hindered by its culture and founding principles, CHM faces the questions of the research paradigm posed by the convention of science. To address these questions, we discussed two essential questions concerning the relationship of CHM and science, and then upheld the paradigm of methodological reductionism in scientific research. Finally, the contemporary narrative of CHM in the 21st century was discussed in the hope to preserve this medical tradition in tandem with scientific research.
Innovating Chinese Herbal Medicine: From Traditional Health Practice to Scientific Drug Discovery.
Gu, Shuo; Pei, Jianfeng
As one of the major contemporary alternative medicines, traditional Chinese medicine (TCM) continues its influence in Chinese communities and has begun to attract the academic attention in the world of western medicine. This paper aims to examine Chinese herbal medicine (CHM), the essential branch of TCM, from both narrative and scientific perspectives. CHM is a traditional health practice originated from Chinese philosophy and religion, holding the belief of holism and balance in the body. With the development of orthodox medicine and science during the last centuries, CHM also seized the opportunity to change from traditional health practice to scientific drug discovery illustrated in the famous story of the herb-derived drug artemisinin. However, hindered by its culture and founding principles, CHM faces the questions of the research paradigm posed by the convention of science. To address these questions, we discussed two essential questions concerning the relationship of CHM and science, and then upheld the paradigm of methodological reductionism in scientific research. Finally, the contemporary narrative of CHM in the 21st century was discussed in the hope to preserve this medical tradition in tandem with scientific research.
An Innovative Cell Microincubator for Drug Discovery Based on 3D Silicon Structures
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Francesca Aredia
Full Text Available We recently employed three-dimensional (3D silicon microstructures (SMSs consisting in arrays of 3 μm-thick silicon walls separated by 50 μm-deep, 5 μm-wide gaps, as microincubators for monitoring the biomechanical properties of tumor cells. They were here applied to investigate the in vitro behavior of HT1080 human fibrosarcoma cells driven to apoptosis by the chemotherapeutic drug Bleomycin. Our results, obtained by fluorescence microscopy, demonstrated that HT1080 cells exhibited a great ability to colonize the narrow gaps. Remarkably, HT1080 cells grown on 3D-SMS, when treated with the DNA damaging agent Bleomycin under conditions leading to apoptosis, tended to shrink, reducing their volume and mimicking the normal behavior of apoptotic cells, and were prone to leave the gaps. Finally, we performed label-free detection of cells adherent to the vertical silicon wall, inside the gap of 3D-SMS, by exploiting optical low coherence reflectometry using infrared, low power radiation. This kind of approach may become a new tool for increasing automation in the drug discovery area. Our results open new perspectives in view of future applications of the 3D-SMS as the core element of a lab-on-a-chip suitable for screening the effect of new molecules potentially able to kill tumor cells.
A magnetic bead-based ligand binding assay to facilitate human kynurenine 3-monooxygenase drug discovery.
Wilson, Kris; Mole, Damian J; Homer, Natalie Z M; Iredale, John P; Auer, Manfred; Webster, Scott P
Human kynurenine 3-monooxygenase (KMO) is emerging as an important drug target enzyme in a number of inflammatory and neurodegenerative disease states. Recombinant protein production of KMO, and therefore discovery of KMO ligands, is challenging due to a large membrane targeting domain at the C-terminus of the enzyme that causes stability, solubility, and purification difficulties. The purpose of our investigation was to develop a suitable screening method for targeting human KMO and other similarly challenging drug targets. Here, we report the development of a magnetic bead-based binding assay using mass spectrometry detection for human KMO protein. The assay incorporates isolation of FLAG-tagged KMO enzyme on protein A magnetic beads. The protein-bound beads are incubated with potential binding compounds before specific cleavage of the protein-compound complexes from the beads. Mass spectrometry analysis is used to identify the compounds that demonstrate specific binding affinity for the target protein. The technique was validated using known inhibitors of KMO. This assay is a robust alternative to traditional ligand-binding assays for challenging protein targets, and it overcomes specific difficulties associated with isolating human KMO. © 2014 Society for Laboratory Automation and Screening.
Neuroproteases in peptide neurotransmission and neurodegenerative diseases: applications to drug discovery research.
Hook, Vivian Y H
The nervous system represents a key area for development of novel therapeutic agents for the treatment of neurological and neurodegenerative diseases. Recent research has demonstrated the critical importance of neuroproteases for the production of specific peptide neurotransmitters and for the production of toxic peptides in major neurodegenerative diseases that include Alzheimer, Huntington, and Parkinson diseases. This review illustrates the successful criteria that have allowed identification of proteases responsible for converting protein precursors into active peptide neurotransmitters, consisting of dual cysteine protease and subtilisin-like protease pathways in neuroendocrine cells. These peptide neurotransmitters are critical regulators of neurologic conditions, including analgesia and cognition, and numerous behaviors. Importantly, protease pathways also represent prominent mechanisms in neurodegenerative diseases, especially Alzheimer, Huntington, and Parkinson diseases. Recent studies have identified secretory vesicle cathepsin B as a novel beta-secretase for production of the neurotoxic beta-amyloid (Abeta) peptide of Alzheimer disease. Moreover, inhibition of cathepsin B reduces Abeta peptide levels in brain. These neuroproteases potentially represent new drug targets that should be explored in future pharmaceutical research endeavors for drug discovery.
Virtual target screening to rapidly identify potential protein targets of natural products in drug discovery
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Yuri Pevzner
Full Text Available Inherent biological viability and diversity of natural products make them a potentially rich source for new therapeutics. However, identification of bioactive compounds with desired therapeutic effects and identification of their protein targets is a laborious, expensive process. Extracts from organism samples may show desired activity in phenotypic assays but specific bioactive compounds must be isolated through further separation methods and protein targets must be identified by more specific phenotypic and in vitro experimental assays. Still, questions remain as to whether all relevant protein targets for a compound have been identified. The desire is to understand breadth of purposing for the compound to maximize its use and intellectual property, and to avoid further development of compounds with insurmountable adverse effects. Previously we developed a Virtual Target Screening system that computationally screens one or more compounds against a collection of virtual protein structures. By scoring each compound-protein interaction, we can compare against averaged scores of synthetic drug-like compounds to determine if a particular protein would be a potential target of a compound of interest. Here we provide examples of natural products screened through our system as we assess advantages and shortcomings of our current system in regards to natural product drug discovery.
Virtual target screening to rapidly identify potential protein targets of natural products in drug discovery
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Yuri Pevzner
Full Text Available Inherent biological viability and diversity of natural products make them a potentially rich source for new therapeutics. However, identification of bioactive compounds with desired therapeutic effects and identification of their protein targets is a laborious, expensive process. Extracts from organism samples may show desired activity in phenotypic assays but specific bioactive compounds must be isolated through further separation methods and protein targets must be identified by more specific phenotypic and in vitro experimental assays. Still, questions remain as to whether all relevant protein targets for a compound have been identified. The desire is to understand breadth of purposing for the compound to maximize its use and intellectual property, and to avoid further development of compounds with insurmountable adverse effects. Previously we developed a Virtual Target Screening system that computationally screens one or more compounds against a collection of virtual protein structures. By scoring each compound-protein interaction, we can compare against averaged scores of synthetic drug-like compounds to determine if a particular protein would be a potential target of a compound of interest. Here we provide examples of natural products screened through our system as we assess advantages and shortcomings of our current system in regards to natural product drug discovery.
Therapeutic Potential of Plants as Anti-Microbials for Drug Discovery
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Ramar Perumal Samy
Full Text Available The uses of traditional medicinal plants for primary health care have steadily increased worldwide in recent years. Scientists are in search of new phytochemicals that could be developed as useful anti-microbials for treatment of infectious diseases. Currently, out of 80% of pharmaceuticals derived from plants, very few are now being used as anti-microbials. Plants are rich in a wide variety of secondary metabolites that have found anti-microbial properties. This review highlights the current status of traditional medicine, its contribution to modern medicine, recent trends in the evaluation of anti-microbials with a special emphasis upon some tribal medicine, in vitro and in vivo experimental design for screening, and therapeutic efficacy in safety and human clinical trails for commercial outlet. Many of these commercially available compounds are crude preparations administered without performing human clinical trials. Recent methods are useful to standardize the extraction for scientific investigation of new phytochemicals and anti-microbials of traditionally used plants. It is concluded that once the local ethnomedical preparations of traditional sources are scientifically evaluated before dispensing they should replace existing drugs commonly used for the therapeutic treatment of infection. This method should be put into practice for future investigations in the field of ethnopharmacology, phytochemistry, ethnobotany and other biological fields for drug discovery.
Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the "Supply Problem" in the Initial Steps of Drug Discovery
Leal, Miguel Costa; Sheridan, Christopher; Osinga, Ronald; Dionísio, Gisela; Rocha, Rui Jorge Miranda; Silva, Bruna; Rosa, Rui; Calado, Ricardo
The chemical diversity associated with marine natural products (MNP) is unanimously acknowledged as the "blue gold" in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages) through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts. PMID:24983638
Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the "Supply Problem" in the Initial Steps of Drug Discovery
Directory of Open Access Journals (Sweden)
Miguel Costa Leal
Full Text Available The chemical diversity associated with marine natural products (MNP is unanimously acknowledged as the "blue gold" in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts.
Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.
Reichel, Andreas; Lienau, Philip
The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.
Report on the 10th anniversary of international drug discovery science and technology conference, 8 - 10 november 2012, nanjing, china.
Everett, Jeremy R
The 10th Anniversary of International Drug Discovery Science and Technology (IDDST) Conference was held in Nanjing, China from 8 to 10 November 2012. The conference ran in parallel with the 2nd Annual Symposium of Drug Delivery Systems. Over 400 delegates from both conferences came together for the Opening Ceremony and Keynote Addresses but otherwise pursued separate paths in the huge facilities of the Nanjing International Expo Centre. The IDDST was arranged into 19 separate Chapters covering drug discovery biology, target validation, chemistry, rational drug design, pharmacology and toxicology, drug screening technology, 'omics' technologies, analytical, automation and enabling technologies, informatics, stem cells and regenerative medicine, bioprocessing, generics, biosimilars and biologicals and seven disease areas: cancer, CNS, respiratory and inflammation, autoimmune, emerging infectious, bone and orphan diseases. There were also two sessions of a 'Bench to Bedside to Business' Program and a Chinese Scientist programme. In each period of the IDDST conference, up to seven sessions were running in parallel. This Meeting Highlight samples just a fraction of the content of this large meeting. The talks included have as a link, the use of new approaches to drug discovery. Many other excellent talks could have been highlighted and the author has necessarily had to be selective.
Skin too thin? The developing utility of zebrafish skin (neuro)pharmacology for CNS drug discovery research.
Nguyen, Michael; Poudel, Manoj K; Stewart, Adam Michael; Kalueff, Allan V
Skin coloration can be affected by many genetic, environmental and pharmacological factors. Zebrafish (Danio rerio) are a useful and versatile model organism in biomedical research due to their genetic tractability, physiological homology to mammals, low cost, reproducibility and high throughput. Zebrafish coloration is mediated by chromatophores - the skin color pigment cells largely controlled by endocrine and neural mechanisms. The characteristic darkening of zebrafish skin is caused by the dispersion (and paling - by aggregation) of melanosomes (pigment-containing organelles), which show high homology to mammalian structures. Various pharmacological agents potently affect zebrafish coloration - the phenotype that often accompanies behavioral effects of the drugs, and may be used for drug discovery. Although zebrafish behavior and skin responses are usually not directly related, they share common regulatory (neural, endocrine) mechanisms, and therefore may be assessed in parallel during psychotropic drug screening. For example, some psychoactive drugs can potently affect zebrafish skin coloration. Can we use this knowledge to refine phenotype-driven psychotropic drug discovery? Here, we present current models using zebrafish skin coloration assays, and discuss how these models may be applied to enhance in vivo CNS drug discovery. Copyright © 2013 Elsevier Inc. All rights reserved.
Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the "Supply Problem" in the Initial Steps of Drug Discovery
Leal, M.C.; Sheridan, C.; Osinga, R.; Dionisio, G.; Rocha, R.J.M.; Silva, B.; Rosa, R.; Calado, R.
The chemical diversity associated with marine natural products (MNP) is unanimously acknowledged as the "blue gold" in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from
Drug discovery opportunities and challenges at G protein coupled receptors for long chain free fatty acids
Directory of Open Access Journals (Sweden)
Nicholas D Holliday
Full Text Available Discovery of G protein coupled receptors for long chain free fatty acids (FFAs, FFA1 (GPR40 and GPR120, has expanded our understanding of these nutrients as signalling molecules. These receptors have emerged as important sensors for FFA levels in the circulation or the gut lumen, based on evidence from in vitro and rodent models, and an increasing number of human studies. Here we consider their promise as therapeutic targets for metabolic disease, including type 2 diabetes and obesity. FFA1 directly mediates acute FFA-induced glucose-stimulated insulin secretion in pancreatic beta-cells, while GPR120 and FFA1 trigger release of incretins from intestinal endocrine cells, and so indirectly enhance insulin secretion and promote satiety. GPR120 signalling in adipocytes and macrophages also results in insulin sensitizing and beneficial anti-inflammatory effects. Drug discovery has focussed on agonists to replicate acute benefits of FFA receptor signalling, with promising early results for FFA1 agonists in man. Controversy surrounding chronic effects of FFA1 on beta-cells illustrates that long term benefits of antagonists also need exploring. It has proved challenging to generate highly selective potent ligands for FFA1 or GPR120 subtypes, given that both receptors have hydrophobic orthosteric binding sites, which are not completely defined and have modest ligand affinity. Structure activity relationships are also reliant on functional read outs, in the absence of robust binding assays to provide direct affinity estimates. Nevertheless synthetic ligands have already helped dissect specific contributions of FFA1 and GPR120 signalling from the many possible cellular effects of FFAs. Approaches including use of fluorescent ligand binding assays, and targeting allosteric receptor sites, may improve further preclinical ligand development at these receptors, to exploit their unique potential to target multiple facets of diabetes.
Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.
Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A
In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum
Anti-amyloid aggregation activity of novel carotenoids: implications for Alzheimer's drug discovery
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Lakey-Beitia J
Full Text Available Johant Lakey-Beitia,1,2 Deborah Doens,2,3 D Jagadeesh Kumar,4 Enrique Murillo,5 Patricia L Fernandez,3 KS Rao,6 Armando A Durant-Archibold1,5 1Center for Biodiversity and Drug Discovery, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama; 2Department of Biotechnology, Acharya Nagarjuna University, Guntur, India; 3Center for Molecular and Cellular Biology of Diseases, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama; 4Department of Biotechnology, Sir M Visvesvaraya Institute of Technology, Bangalore, India; 5Department of Biochemistry, College of Natural, Exact Sciences and Technology, University of Panama, Panama, Republic of Panama; 6Center for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama Abstract: Alzheimer's disease (AD is the leading cause of dementia, affecting approximately 33.5 million people worldwide. Aging is the main risk factor associated with AD. Drug discovery based on nutraceutical molecules for prevention and treatment of AD is a growing topic. In this sense, carotenoids are phytochemicals present mainly in fruits and vegetables with reported benefits for human health. In this research, the anti-amyloidogenic activity of three carotenoids, cryptocapsin, cryptocapsin-5,6-epoxide, and zeaxanthin, was assessed. Cryptocapsin showed the highest bioactivity, while cryptocapsin-5,6-epoxide and zeaxanthin exhibited similar activity on anti-aggregation assays. Molecular modeling analysis revealed that the evaluated carotenoids might follow two mechanisms for inhibiting Aβ aggregation: by preventing the formation of the fibril and through disruption of the Aβ aggregates. Our studies provided evidence that cryptocapsin, cryptocapsin-5,6-epoxide, and zeaxanthin have anti-amyloidogenic potential and could be used for
The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery.
Thomas, Craig E; Will, Yvonne
Attrition in the drug industry due to safety findings remains high and requires a shift in the current safety testing paradigm. Many companies are now positioning safety assessment at each stage of the drug development process, including discovery, where an early perspective on potential safety issues is sought, often at chemical scaffold level, using a variety of emerging technologies. Given the lengthy development time frames of drugs in the pharmaceutical industry, the authors believe that the impact of new technologies on attrition is best measured as a function of the quality and timeliness of candidate compounds entering development. The authors provide an overview of in silico and in vitro models, as well as more complex approaches such as 'omics,' and where they are best positioned within the drug discovery process. It is important to take away that not all technologies should be applied to all projects. Technologies vary widely in their validation state, throughput and cost. A thoughtful combination of validated and emerging technologies is crucial in identifying the most promising candidates to move to proof-of-concept testing in humans. In spite of the challenges inherent in applying new technologies to drug discovery, the successes and recognition that we cannot continue to rely on safety assessment practices used for decades have led to rather dramatic strategy shifts and fostered partnerships across government agencies and industry. We are optimistic that these efforts will ultimately benefit patients by delivering effective and safe medications in a timely fashion.
The pivotal role of multimodality reporter sensors in drug discovery: from cell based assays to real time molecular imaging.
Ray, Pritha
Development and marketing of new drugs require stringent validation that are expensive and time consuming. Non-invasive multimodality molecular imaging using reporter genes holds great potential to expedite these processes at reduced cost. New generations of smarter molecular imaging strategies such as Split reporter, Bioluminescence resonance energy transfer, Multimodality fusion reporter technologies will further assist to streamline and shorten the drug discovery and developmental process. This review illustrates the importance and potential of molecular imaging using multimodality reporter genes in drug development at preclinical phases.
Drug Discovery Targeting Serotonin G Protein-Coupled Receptors in the Treatment of Neuropsychiatric Disorders
Felsing, Daniel E.
Clinical data show that activation of 5-HT2C G protein-coupled receptors (GPCRs) can treat obesity (lorcaserin/BelviqRTM) and psychotic disorders (aripiprazole/Abilify.), including schizophrenia. 5-HT2C GPCRs are members of the 5-HT2 sub-family of 5-HT GPCRs, which include 5-HT2A, 5-HT2B, and 5-HT 2C GPCRs. 5-HT2C is structurally similar to 5-HT2A and 5-HT2B GPCRs, but activation of 5-HT2A and/or 5-HT 2B causes deleterious effects, including hallucinations and cardiac valvulopathy. Thus, there is a challenge to develop drugs that selectively activate only 5-HT2C. Prolonged activation of GPCRs by agonists reduces their function via a regulatory process called desensitization. This has clinical relevance, as 45% of drugs approved by the FDA target GPCRs, and agonist drugs (e.g., morphine) typically lose efficacy over time due to desensitization, which invites tolerance. Agonists that cause less desensitization may show extended clinical efficacy as well as a more acceptable clinical dose range. We hypothesized that structurally distinct agonists of the 5-HT2C receptor may cause varying degrees of desensitization by stabilizing unique 5-HT2C receptor conformations. Discovery of 5-HT2C agonists that exhibit minimal desensitization is therapeutically relevant for the pharmacotherapeutic treatment of chronic diseases such as obesity and psychotic disorders. The 5-HT7 receptor has recently been discovered as a druggable target, and selective activation of the 5-HT7 receptor has been shown to alleviate locomotor deficits in mouse models of Rett Syndrome. Additionally, buspirone has been shown to display therapeutically relevant affinity at 5-HT 1A and is currently in phase II clinical trials to treat stereotypy in children with autism. The 5-PAT chemical scaffold shows high affinity towards the 5-HT7 and 5-HT1A receptors. Modulations around the 5-phenyl moiety were able to improve selectivity in binding towards the 5-HT 7 receptor, whereas modulations of the alkyl chains
Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.
Ekins, Sean; Madrid, Peter B; Sarker, Malabika; Li, Shao-Gang; Mittal, Nisha; Kumar, Pradeep; Wang, Xin; Stratton, Thomas P; Zimmerman, Matthew; Talcott, Carolyn; Bourbon, Pauline; Travers, Mike; Yadav, Maneesh; Freundlich, Joel S
Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.
Neurotransmitter transporters in schistosomes: structure, function and prospects for drug discovery.
Ribeiro, Paula; Patocka, Nicholas
Neurotransmitter transporters (NTTs) play a fundamental role in the control of neurotransmitter signaling and homeostasis. Sodium symporters of the plasma membrane mediate the cellular uptake of neurotransmitter from the synaptic cleft, whereas proton-driven vesicular transporters sequester the neurotransmitter into synaptic vesicles for subsequent release. Together these transporters control how much transmitter is released and how long it remains in the synaptic cleft, thereby regulating the intensity and duration of signaling. NTTs have been the subject of much research in mammals and there is growing interest in their activities among invertebrates as well. In this review we will focus our attention on NTTs of the parasitic flatworm Schistosoma mansoni. Bloodflukes of the genus Schistosoma are the causative agents of human schistosomiasis, a devastating disease that afflicts over 200 million people worldwide. Schistosomes have a well-developed nervous system and a rich diversity of neurotransmitters, including many of the small-molecule ("classical") neurotransmitters that normally employ NTTs in their mechanism of signaling. Recent advances in schistosome genomics have unveiled numerous NTTs in this parasite, some of which have now been cloned and characterized in vitro. Moreover new genetic and pharmacological evidence suggests that NTTs are required for proper control of neuromuscular signaling and movement of the worm. Among these carriers are proteins that have been successfully targeted for drug discovery in other organisms, in particular sodium symporters for biogenic amine neurotransmitters such as serotonin and dopamine. Our goal in this chapter is to review the current status of research on schistosome NTTs, with emphasis on biogenic amine sodium symporters, and to evaluate their potential for anti-schistosomal drug targeting. Through this discussion we hope to draw attention to this important superfamily of parasite proteins and to identify new
The mass-action law based algorithm for cost-effective approach for cancer drug discovery and development.
Chou, Ting-Chao
The mass-action law based system analysis via mathematical induction and deduction lead to the generalized theory and algorithm that allows computerized simulation of dose-effect dynamics with small size experiments using a small number of data points in vitro, in animals, and in humans. The median-effect equation of the mass-action law deduced from over 300 mechanism specific-equations has been shown to be the unified theory that serves as the common-link for complicated biomedical systems. After using the median-effect principle as the common denominator, its applications are mechanism-independent, drug unit-independent, and dynamic order-independent; and can be used generally for single drug analysis or for multiple drug combinations in constant-ratio or non-constant ratios. Since the "median" is the common link and universal reference point in biological systems, these general enabling lead to computerized quantitative bio-informatics for econo-green bio-research in broad disciplines. Specific applications of the theory, especially relevant to drug discovery, drug combination, and clinical trials, have been cited or illustrated in terms of algorithms, experimental design and computerized simulation for data analysis. Lessons learned from cancer research during the past fifty years provide a valuable opportunity to reflect, and to improve the conventional divergent approach and to introduce a new convergent avenue, based on the mass-action law principle, for the efficient cancer drug discovery and the low-cost drug development.
Fungal Biofilms: In Vivo Models for Discovery of Anti-Biofilm Drugs.
Nett, Jeniel E; Andes, David R
During infection, fungi frequently transition to a biofilm lifestyle, proliferating as communities of surface-adherent aggregates of cells. Phenotypically, cells in a biofilm are distinct from free-floating cells. Their high tolerance of antifungals and ability to withstand host defenses are two characteristics that foster resilience. Biofilm infections are particularly difficult to eradicate, and most available antifungals have minimal activity. Therefore, the discovery of novel compounds and innovative strategies to treat fungal biofilms is of great interest. Although many fungi have been observed to form biofilms, the most well-studied is Candida albicans. Animal models have been developed to simulate common Candida device-associated infections, including those involving vascular catheters, dentures, urinary catheters, and subcutaneous implants. Models have also reproduced the most common mucosal biofilm infections: oropharyngeal and vaginal candidiasis. These models incorporate the anatomical site, immune components, and fluid dynamics of clinical niches and have been instrumental in the study of drug resistance and investigation of novel therapies. This chapter describes the significance of fungal biofilm infections, the animal models developed for biofilm study, and how these models have contributed to the development of new strategies for the eradication of fungal biofilm infections.
Stabilized helical peptides: overview of the technologies and its impact on drug discovery.
Klein, Mark
Protein-protein interactions are predominant in the workings of all cells. Until now, there have been a few successes in targeting protein-protein interactions with small molecules. Peptides may overcome some of the challenges of small molecules in disrupting protein-protein interactions. However, peptides present a new set of challenges in drug discovery. Thus, the study of the stabilization of helical peptides has been extensive. Areas covered: Several technological approaches to helical peptide stabilization have been studied. In this review, stapled peptides, foldamers, and hydrogen bond surrogates are discussed. Issues regarding design principles are also discussed. Furthermore, this review introduces select computational techniques used to aid peptide design and discusses clinical trials of peptides in a more advanced stage of development. Expert opinion: Stabilized helical peptides hold great promise in a wide array of diseases. However, the field is still relatively new and new design principles are emerging. The possibilities of peptide modification are quite extensive and expanding, so the design of stabilized peptides requires great attention to detail in order to avoid a large number of failed lead peptides. The start of clinical trials with stapled peptides is a promising sign for the future.
The European Lead Factory: A Blueprint for Public-Private Partnerships in Early Drug Discovery.
Karawajczyk, Anna; Orrling, Kristina M; de Vlieger, Jon S B; Rijnders, Ton; Tzalis, Dimitrios
The European Lead Factory (ELF) is a public-private partnership (PPP) that provides researchers in Europe with a unique platform for translation of innovative biology and chemistry into high-quality starting points for drug discovery. It combines an exceptional collection of small molecules, high-throughput screening (HTS) infrastructure, and hit follow-up capabilities to advance research projects from both private companies and publicly funded researchers. By active interactions with the wider European life science community, ELF connects and unites bright ideas, talent, and experience from several disciplines. As a result, ELF is a unique, collaborative lead generation engine that has so far resulted in >4,500 hit compounds with a defined biological activity from 83 successfully completed HTS and hit evaluation campaigns. The PPP has also produced more than 120,000 novel innovative library compounds that complement the 327,000 compounds contributed by the participating pharmaceutical companies. Intrinsic to its setup, ELF enables breakthroughs in areas with unmet medical and societal needs, where no individual entity would be able to create a comparable impact in such a short time.
The European Lead Factory: A Blueprint for Public–Private Partnerships in Early Drug Discovery
Karawajczyk, Anna; Orrling, Kristina M.; de Vlieger, Jon S. B.; Rijnders, Ton; Tzalis, Dimitrios
The European Lead Factory (ELF) is a public–private partnership (PPP) that provides researchers in Europe with a unique platform for translation of innovative biology and chemistry into high-quality starting points for drug discovery. It combines an exceptional collection of small molecules, high-throughput screening (HTS) infrastructure, and hit follow-up capabilities to advance research projects from both private companies and publicly funded researchers. By active interactions with the wider European life science community, ELF connects and unites bright ideas, talent, and experience from several disciplines. As a result, ELF is a unique, collaborative lead generation engine that has so far resulted in >4,500 hit compounds with a defined biological activity from 83 successfully completed HTS and hit evaluation campaigns. The PPP has also produced more than 120,000 novel innovative library compounds that complement the 327,000 compounds contributed by the participating pharmaceutical companies. Intrinsic to its setup, ELF enables breakthroughs in areas with unmet medical and societal needs, where no individual entity would be able to create a comparable impact in such a short time. PMID:28154815
Opportunities and challenges provided by cloud repositories for bioinformatics-enabled drug discovery.
Dalpé, Gratien; Joly, Yann
Healthcare-related bioinformatics databases are increasingly offering the possibility to maintain, organize, and distribute DNA sequencing data. Different national and international institutions are currently hosting such databases that offer researchers website platforms where they can obtain sequencing data on which they can perform different types of analysis. Until recently, this process remained mostly one-dimensional, with most analysis concentrated on a limited amount of data. However, newer genome sequencing technology is producing a huge amount of data that current computer facilities are unable to handle. An alternative approach has been to start adopting cloud computing services for combining the information embedded in genomic and model system biology data, patient healthcare records, and clinical trials' data. In this new technological paradigm, researchers use virtual space and computing power from existing commercial or not-for-profit cloud service providers to access, store, and analyze data via different application programming interfaces. Cloud services are an alternative to the need of larger data storage; however, they raise different ethical, legal, and social issues. The purpose of this Commentary is to summarize how cloud computing can contribute to bioinformatics-based drug discovery and to highlight some of the outstanding legal, ethical, and social issues that are inherent in the use of cloud services. © 2014 Wiley Periodicals, Inc.
Production of heparanase constructs suitable for nuclear magnetic resonance and drug discovery studies.
Mosulén, Silvia; Ortí, Leticia; Bas, Esperanza; Carbajo, Rodrigo J; Pineda-Lucena, Antonio
Heparanase is an endo-β-D-glucosidase capable of specifically degrading heparan sulphate, one of the main components of the extracellular matrix. This 65 kDa polypeptide is implicated in cancer processes such as tumour formation, angiogenesis and metastasis, making it a very attractive target in antitumour treatments. Structure-based approaches to find inhibitors of heparanase have been historically hampered by the lack of success in crystallizing the protein. With the aim to undertake the NMR structural characterisation of heparanase, we have designed and produced, using recombinant methods, smaller constructs of heparanase containing the catalytically active glutamic acids and the two binding sites for heparan sulphate. An extensive range of expression and purification conditions were evaluated to alleviate the intrinsic low solubility and aggregation propensity of heparanase, allowing the obtention of the enzyme in milligram quantities, both unlabelled and ¹⁵N-labelled for NMR studies. Using the smallest of the designed constructs and applying NMR and SPR methodologies, we have demonstrated that known inhibitors of heparanase bind to this construct specifically and selectively with K(D) values in the range of those reported for human heparanase, validating it for future drug discovery projects focused on the identification of novel inhibitors of this enzyme. © 2010 Wiley Periodicals, Inc.
Very large virtual compound spaces: construction, storage and utility in drug discovery.
Peng, Zhengwei
Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.
Novel Small Molecule Inhibitors of Choline Kinase Identified by Fragment-Based Drug Discovery.
Zech, Stephan G; Kohlmann, Anna; Zhou, Tianjun; Li, Feng; Squillace, Rachel M; Parillon, Lois E; Greenfield, Matthew T; Miller, David P; Qi, Jiwei; Thomas, R Mathew; Wang, Yihan; Xu, Yongjin; Miret, Juan J; Shakespeare, William C; Zhu, Xiaotian; Dalgarno, David C
Choline kinase α (ChoKα) is an enzyme involved in the synthesis of phospholipids and thereby plays key roles in regulation of cell proliferation, oncogenic transformation, and human carcinogenesis. Since several inhibitors of ChoKα display antiproliferative activity in both cellular and animal models, this novel oncogene has recently gained interest as a promising small molecule target for cancer therapy. Here we summarize our efforts to further validate ChoKα as an oncogenic target and explore the activity of novel small molecule inhibitors of ChoKα. Starting from weakly binding fragments, we describe a structure based lead discovery approach, which resulted in novel highly potent inhibitors of ChoKα. In cancer cell lines, our lead compounds exhibit a dose-dependent decrease of phosphocholine, inhibition of cell growth, and induction of apoptosis at low micromolar concentrations. The druglike lead series presented here is optimizable for improvements in cellular potency, drug target residence time, and pharmacokinetic parameters. These inhibitors may be utilized not only to further validate ChoKα as antioncogenic target but also as novel chemical matter that may lead to antitumor agents that specifically interfere with cancer cell metabolism.
Candidiasis and the impact of flow cytometry on antifungal drug discovery.
Ku, Tsun Sheng N; Bernardo, Stella; Walraven, Carla J; Lee, Samuel A
Invasive candidiasis continues to be associated with significant morbidity and mortality as well as substantial health care costs nationally and globally. One of the contributing factors is the development of resistance to antifungal agents that are already in clinical use. Moreover, there are known treatment limitations with all of the available antifungal agents. Since traditional techniques in novel drug discovery are time consuming, high-throughput screening using flow cytometry presents as a potential tool to identify new antifungal agents that would be useful in the management of these patients. Areas covered: In this review, the authors discuss the use of automated high-throughput screening assays based upon flow cytometry to identify potential antifungals from a library comprised of a large number of bioactive compounds. They also review studies that employed the use of this research methodology that has identified compounds with antifungal activity. Expert opinion: High-throughput screening using flow cytometry has substantially decreased the processing time necessary for screening thousands of compounds, and has helped enhance our understanding of fungal pathogenesis. Indeed, the authors see this technology as a powerful tool to help scientists identify new antifungal agents that can be added to the clinician's arsenal in their fight against invasive candidiasis.
Analyzing user-generated online content for drug discovery: development and use of MedCrawler.
Helfenstein, Andreas; Tammela, Päivi
Ethnopharmacology, or the scientific validation of traditional medicine, is a respected starting point in drug discovery. Home remedies and traditional use of plants are still widespread, also in Western societies. Instead of perusing ancient pharmacopeias, we developed MedCrawler, which we used to analyze blog posts for mentions of home remedies and their applications. This method is free and accessible from the office computer. We developed MedCrawler, a data mining tool for analyzing user-generated blog posts aiming to find modern 'traditional' medicine or home remedies. It searches user-generated blog posts and analyzes them for correlations between medically relevant terms. We also present examples and show that this method is capable of delivering both scientifically validated uses as well as not so well documented applications, which might serve as a starting point for follow-up research. Source code is available on GitHub at {{ https://github.com/a-hel/medcrawler }}. firstname.lastname@example.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.com
Structured evaluation of rodent behavioral tests used in drug discovery research
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Anders eHånell
Full Text Available A large variety of rodent behavioral tests are currently being used to evaluate traits such as sensory-motor function, social interactions, anxiety-like and depressive-like behavior, substance dependence and various forms of cognitive function. Most behavioral tests have an inherent complexity, and their use requires consideration of several aspects such as the source of motivation in the test, the interaction between experimenter and animal, sources of variability, the sensory modality required by the animal to solve the task as well as costs and required work effort. Of particular importance is a test's validity because of its influence on the chance of successful translation of preclinical results to clinical settings. High validity may, however, have to be balanced against practical constraints and there are no behavioral tests with optimal characteristics. The design and development of new behavioral tests is therefore an ongoing effort and there are now well over one hundred tests described in the contemporary literature. Some of them are well established following extensive use, while others are novel and still unproven. The task of choosing a behavioral test for a particular project may therefore be daunting and the aim of the present review is to provide a structured way to evaluate rodent behavioral tests aimed at drug discovery research.
Emerging Glycolysis Targeting and Drug Discovery from Chinese Medicine in Cancer Therapy
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Zhiyu Wang
Full Text Available Molecular-targeted therapy has been developed for cancer chemoprevention and treatment. Cancer cells have different metabolic properties from normal cells. Normal cells mostly rely upon the process of mitochondrial oxidative phosphorylation to produce energy whereas cancer cells have developed an altered metabolism that allows them to sustain higher proliferation rates. Cancer cells could predominantly produce energy by glycolysis even in the presence of oxygen. This alternative metabolic characteristic is known as the "Warburg Effect." Although the exact mechanisms underlying the Warburg effect are unclear, recent progress indicates that glycolytic pathway of cancer cells could be a critical target for drug discovery. With a long history in cancer treatment, traditional Chinese medicine (TCM is recognized as a valuable source for seeking bioactive anticancer compounds. A great progress has been made to identify active compounds from herbal medicine targeting on glycolysis for cancer treatment. Herein, we provide an overall picture of the current understanding of the molecular targets in the cancer glycolytic pathway and reviewed active compounds from Chinese herbal medicine with the potentials to inhibit the metabolic targets for cancer treatment. Combination of TCM with conventional therapies will provide an attractive strategy for improving clinical outcome in cancer treatment.
Fragment based drug discovery: practical implementation based on ¹⁹F NMR spectroscopy.
Jordan, John B; Poppe, Leszek; Xia, Xiaoyang; Cheng, Alan C; Sun, Yax; Michelsen, Klaus; Eastwood, Heather; Schnier, Paul D; Nixey, Thomas; Zhong, Wenge
Fragment based drug discovery (FBDD) is a widely used tool for discovering novel therapeutics. NMR is a powerful means for implementing FBDD, and several approaches have been proposed utilizing (1)H-(15)N heteronuclear single quantum coherence (HSQC) as well as one-dimensional (1)H and (19)F NMR to screen compound mixtures against a target of interest. While proton-based NMR methods of fragment screening (FBS) have been well documented and are widely used, the use of (19)F detection in FBS has been only recently introduced (Vulpetti et al. J. Am. Chem. Soc.2009, 131 (36), 12949-12959) with the aim of targeting "fluorophilic" sites in proteins. Here, we demonstrate a more general use of (19)F NMR-based fragment screening in several areas: as a key tool for rapid and sensitive detection of fragment hits, as a method for the rapid development of structure-activity relationship (SAR) on the hit-to-lead path using in-house libraries and/or commercially available compounds, and as a quick and efficient means of assessing target druggability.
Antimycobacterial drug discovery using Mycobacteria-infected amoebae identifies anti-infectives and new molecular targets.
Trofimov, Valentin; Kicka, Sébastien; Mucaria, Sabrina; Hanna, Nabil; Ramon-Olayo, Fernando; Del Peral, Laura Vela-Gonzalez; Lelièvre, Joël; Ballell, Lluís; Scapozza, Leonardo; Besra, Gurdyal S; Cox, Jonathan A G; Soldati, Thierry
Tuberculosis remains a serious threat to human health world-wide, and improved efficiency of medical treatment requires a better understanding of the pathogenesis and the discovery of new drugs. In the present study, we performed a whole-cell based screen in order to complete the characterization of 168 compounds from the GlaxoSmithKline TB-set. We have established and utilized novel previously unexplored host-model systems to characterize the GSK compounds, i.e. the amoeboid organisms D. discoideum and A. castellanii, as well as a microglial phagocytic cell line, BV2. We infected these host cells with Mycobacterium marinum to monitor and characterize the anti-infective activity of the compounds with quantitative fluorescence measurements and high-content microscopy. In summary, 88.1% of the compounds were confirmed as antibiotics against M. marinum, 11.3% and 4.8% displayed strong anti-infective activity in, respectively, the mammalian and protozoan infection models. Additionally, in the two systems, 13-14% of the compounds displayed pro-infective activity. Our studies underline the relevance of using evolutionarily distant pathogen and host models in order to reveal conserved mechanisms of virulence and defence, respectively, which are potential "universal" targets for intervention. Subsequent mechanism of action studies based on generation of over-expresser M. bovis BCG strains, generation of spontaneous resistant mutants and whole genome sequencing revealed four new molecular targets, including FbpA, MurC, MmpL3 and GlpK.
Dystrophin-deficient cardiomyocytes derived from human urine: New biologic reagents for drug discovery
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Xuan Guan
Full Text Available The ability to extract somatic cells from a patient and reprogram them to pluripotency opens up new possibilities for personalized medicine. Induced pluripotent stem cells (iPSCs have been employed to generate beating cardiomyocytes from a patient's skin or blood cells. Here, iPSC methods were used to generate cardiomyocytes starting from the urine of a patient with Duchenne muscular dystrophy (DMD. Urine was chosen as a starting material because it contains adult stem cells called urine-derived stem cells (USCs. USCs express the canonical reprogramming factors c-myc and klf4, and possess high telomerase activity. Pluripotency of urine-derived iPSC clones was confirmed by immunocytochemistry, RT-PCR and teratoma formation. Urine-derived iPSC clones generated from healthy volunteers and a DMD patient were differentiated into beating cardiomyocytes using a series of small molecules in monolayer culture. Results indicate that cardiomyocytes retain the DMD patient's dystrophin mutation. Physiological assays suggest that dystrophin-deficient cardiomyocytes possess phenotypic differences from normal cardiomyocytes. These results demonstrate the feasibility of generating cardiomyocytes from a urine sample and that urine-derived cardiomyocytes retain characteristic features that might be further exploited for mechanistic studies and drug discovery.
Mesua beccariana (Clusiaceae, A Source of Potential Anti-cancer Lead Compounds in Drug Discovery
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Soek Sin Teh
Full Text Available An investigation on biologically active secondary metabolites from the stem bark of Mesua beccariana was carried out. A new cyclodione, mesuadione (1, along with several known constituents which are beccamarin (2, 2,5-dihydroxy-1,3,4-trimethoxy anthraquinone (3, 4-methoxy-1,3,5-trihydroxyanthraquinone (4, betulinic acid (5 and stigmasterol (6 were obtained from this ongoing research. Structures of these compounds were elucidated by extensive spectroscopic methods, including 1D and 2D-NMR, GC-MS, IR and UV techniques. Preliminary tests of the in vitro cytotoxic activities of all the isolated metabolites against a panel of human cancer cell lines Raji (lymphoma, SNU-1 (gastric carcinoma, K562 (erythroleukemia cells, LS-174T (colorectal adenocarcinoma, HeLa (cervical cells, SK-MEL-28 (malignant melanoma cells, NCI-H23 (lung adenocarcinoma, IMR-32 (neuroblastoma and Hep-G2 (hepatocellular liver carcinoma were carried out using an MTT assay. Mesuadione (1, beccamarin (2, betulinic acid (5 and stigmasterol (6 displayed strong inhibition of Raji cell proliferation, while the proliferation rate of SK-MEL-28 and HeLa were strongly inhibited by stigmasterol (6 and beccamarin (2, indicating these secondary metabolites could be anti-cancer lead compounds in drug discovery.
Pushing the accelerator - speeding up drug research with accelerator mass spectrometry
Energy Technology Data Exchange (ETDEWEB)
Garner, R.C. E-mail: email@example.com; Leong, D
Accelerator mass spectrometry (AMS) is the most sensitive analytical method yet developed for elemental isotope analysis and has a broad range of applications. The measurement of {sup 14}C is of most interest to biomedical researchers but few studies have been reported using AMS in drug discovery and development. For biomedical use, {sup 14}C is incorporated into organic molecules by either radiosynthesis or biosynthetically and the isotope is used as a surrogate for the distribution of the radiolabelled molecule either in animal or human studies. The majority of users of {sup 14}C quantitate the radioactivity using decay counting usually with a liquid scintillation counter (LSC). Our Centre over the past 12 months has been evaluating and validating the use of AMS as an alternative detection method. In vitro spiking studies of human plasma with {sup 14}C-Fluconazole, a prescription antifungal drug has demonstrated an excellent correlation between AMS and LSC (correlation coefficient 0.999). Human Phase I clinical studies have been conducted with radioactive doses ranging from 120 Bq (7000 dpm) to 11 kBq (300 nCi) to provide mass balance, plasma concentration and radioactive metabolite profiling data. Limits of detection of 0.00022 Bq {sup 14}C-labelled drug/ml plasma have been accurately quantitated in a plasma background of 0.0078 Bq/ml (0.013 dpm/ml in a plasma background of 0.47 dpm/ml or 2.72 pMC in a background of 90.19 pMC)
Combining NMR and X-ray crystallography in fragment-based drug discovery: discovery of highly potent and selective BACE-1 inhibitors.
Wyss, Daniel F; Wang, Yu-Sen; Eaton, Hugh L; Strickland, Corey; Voigt, Johannes H; Zhu, Zhaoning; Stamford, Andrew W
Fragment-based drug discovery (FBDD) has become increasingly popular over the last decade. We review here how we have used highly structure-driven fragment-based approaches to complement more traditional lead discovery to tackle high priority targets and those struggling for leads. Combining biomolecular nuclear magnetic resonance (NMR), X-ray crystallography, and molecular modeling with structure-assisted chemistry and innovative biology as an integrated approach for FBDD can solve very difficult problems, as illustrated in this chapter. Here, a successful FBDD campaign is described that has allowed the development of a clinical candidate for BACE-1, a challenging CNS drug target. Crucial to this achievement were the initial identification of a ligand-efficient isothiourea fragment through target-based NMR screening and the determination of its X-ray crystal structure in complex with BACE-1, which revealed an extensive H-bond network with the two active site aspartate residues. This detailed 3D structural information then enabled the design and validation of novel, chemically stable and accessible heterocyclic acylguanidines as aspartic acid protease inhibitor cores. Structure-assisted fragment hit-to-lead optimization yielded iminoheterocyclic BACE-1 inhibitors that possess desirable molecular properties as potential therapeutic agents to test the amyloid hypothesis of Alzheimer's disease in a clinical setting.
Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery.
Nagamani, S; Gaur, A S; Tanneeru, K; Muneeswaran, G; Madugula, S S; Consortium, Mpds; Druzhilovskiy, D; Poroikov, V V; Sastry, G N
Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.
The principle of safety evaluation in medicinal drug - how can toxicology contribute to drug discovery and development as a multidisciplinary science?
Horii, Ikuo
Pharmaceutical (drug) safety assessment covers a diverse science-field in the drug discovery and development including the post-approval and post-marketing phases in order to evaluate safety and risk management. The principle in toxicological science is to be placed on both of pure and applied sciences that are derived from past/present scientific knowledge and coming new science and technology. In general, adverse drug reactions are presented as "biological responses to foreign substances." This is the basic concept of thinking about the manifestation of adverse drug reactions. Whether or not toxic expressions are extensions of the pharmacological effect, adverse drug reactions as seen from molecular targets are captured in the category of "on-target" or "off-target", and are normally expressed as a biological defense reaction. Accordingly, reactions induced by pharmaceuticals can be broadly said to be defensive reactions. Recent molecular biological conception is in line with the new, remarkable scientific and technological developments in the medical and pharmaceutical areas, and the viewpoints in the field of toxicology have shown that they are approaching toward the same direction as well. This paper refers to the basic concept of pharmaceutical toxicology, the differences for safety assessment in each stage of drug discovery and development, regulatory submission, and the concept of scientific considerations for risk assessment and management from the viewpoint of "how can multidisciplinary toxicology contribute to innovative drug discovery and development?" And also realistic translational research from preclinical to clinical application is required to have a significant risk management in post market by utilizing whole scientific data derived from basic and applied scientific research works. In addition, the significance for employing the systems toxicology based on AOP (Adverse Outcome Pathway) analysis is introduced, and coming challenges on precision
pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery.
Varma, Manthena V; Gardner, Iain; Steyn, Stefanus J; Nkansah, Paul; Rotter, Charles J; Whitney-Pickett, Carrie; Zhang, Hui; Di, Li; Cram, Michael; Fenner, Katherine S; El-Kattan, Ayman F
The Biopharmaceutics Classification System (BCS) is a scientific framework that provides a basis for predicting the oral absorption of drugs. These concepts have been extended in the Biopharmaceutics Drug Disposition Classification System (BDDCS) to explain the potential mechanism of drug clearance and understand the effects of uptake and efflux transporters on absorption, distribution, metabolism, and elimination. The objective of present work is to establish criteria for provisional biopharmaceutics classification using pH-dependent passive permeability and aqueous solubility data generated from high throughput screening methodologies in drug discovery settings. The apparent permeability across monolayers of clonal cell line of Madin-Darby canine kidney cells, selected for low endogenous efflux transporter expression, was measured for a set of 105 drugs, with known BCS and BDDCS class. The permeability at apical pH 6.5 for acidic drugs and at pH 7.4 for nonacidic drugs showed a good correlation with the fraction absorbed in human (Fa). Receiver operating characteristic (ROC) curve analysis was utilized to define the permeability class boundary. At permeability ≥ 5 × 10(-6) cm/s, the accuracy of predicting Fa of ≥ 0.90 was 87%. Also, this cutoff showed more than 80% sensitivity and specificity in predicting the literature permeability classes (BCS), and the metabolism classes (BDDCS). The equilibrium solubility of a subset of 49 drugs was measured in pH 1.2 medium, pH 6.5 phosphate buffer, and in FaSSIF medium (pH 6.5). Although dose was not considered, good concordance of the measured solubility with BCS and BDDCS solubility class was achieved, when solubility at pH 1.2 was used for acidic compounds and FaSSIF solubility was used for basic, neutral, and zwitterionic compounds. Using a cutoff of 200 μg/mL, the data set suggested a 93% sensitivity and 86% specificity in predicting both the BCS and BDDCS solubility classes. In conclusion, this study identified
Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations.
Srinivas, Nuggehally R
The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select
The Drug Discovery and Development Industry in India-Two Decades of Proprietary Small-Molecule R&D.
Differding, Edmond
This review provides a comprehensive survey of proprietary drug discovery and development efforts performed by Indian companies between 1994 and mid-2016. It is based on the identification and detailed analysis of pharmaceutical, biotechnology, and contract research companies active in proprietary new chemical entity (NCE) research and development (R&D) in India. Information on preclinical and clinical development compounds was collected by company, therapeutic indication, mode of action, target class, and development status. The analysis focuses on the overall pipeline and its evolution over two decades, contributions by type of company, therapeutic focus, attrition rates, and contribution to Western pharmaceutical pipelines through licensing agreements. This comprehensive analysis is the first of its kind, and, in our view, represents a significant contribution to the understanding of the current state of the drug discovery and development industry in India. © 2017 The Author. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
Wnt Drug Discovery: Weaving Through the Screens, Patents and Clinical Trials
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Benjamin Lu
Full Text Available The Wnt signaling pathway is intricately involved in many aspects of development and is the root cause of an increasing number of diseases. For example, colorectal cancer is the second leading cause of death in the industrialized world and aberration of Wnt signaling within the colonic stem cell is the cause of more than 90% of these cancers. Despite our advances in successfully targeting other pathways, such as Human Epidermal Growth Factor Receptor 2 (HER2, there are no clinically relevant therapies available for Wnt-related diseases. Here, we investigated where research activities are focused with respect to Wnt signaling modulators by searching the United States Patent and Trade Office (USPTO for patents and patent applications related to Wnt modulators and compared this to clinical trials focusing on Wnt modulation. We found that while the transition of intellectual property surrounding the Wnt ligand-receptor interface to clinical trials is robust, this is not true for specific inhibitors of β-catenin, which is constitutively active in many cancers. Considering the ubiquitous use of the synthetic T-cell Factor/Lymphoid Enhancer Factor (TCF/Lef reporter system and its success in identifying novel modulators in vitro, we speculate that this model of drug discovery does not capture the complexity of in vivo Wnt signaling that may be required if we are to successfully target the Wnt pathway in the clinic. Notwithstanding, increasingly more complex models are being developed, which may not be high throughput, but more pragmatic in our pursuit to control Wnt signaling.
A modified physiological BCS for prediction of intestinal absorption in drug discovery.
Zaki, Noha M; Artursson, Per; Bergström, Christel A S
In this study, the influence of physiologically relevant media on the compound position in a biopharmaceutical classification system (BCS) which resembled the intestinal absorption was investigated. Both solubility and permeability limited compounds (n = 22) were included to analyze the importance of each of these on the final absorption. Solubility was determined in three different dissolution media, phosphate buffer pH 6.5 (PhB 6.5), fasted state simulated intestinal fluid (FaSSIF), and fed state simulated intestinal fluid (FeSSIF) at 37 °C, and permeability values were determined using the 2/4/A1 cell line. The solubility data and membrane permeability values were used for sorting the compounds into a BCS modified to reflect the fasted and fed state. Three of the seven compounds sorted as BCS II in PhB 6.5 (high permeability, low solubility) changed their position to BCS I when dissolved in FaSSIF and/or FeSSIF (high permeability, high solubility). These were low dosed (20 mg or less) lipophilic molecules displaying solvation limited solubility. In contrast, compounds having solid-state limited solubility had a minor increase in solubility when dissolved in FaSSIF and/or FeSSIF. Although further studies are needed to enable general cutoff values, our study indicates that low dosed BCS Class II compounds which have solubility normally restricted by poor solvation may behave as BCS Class I compounds in vivo. The large series of compounds investigated herein reveals the importance of investigating solubility and dissolution under physiologically relevant conditions in all stages of the drug discovery process to push suitable compounds forward, to select proper formulations, and to reduce the risk of food effects.
Traditional Medicine Collection Tracking System (TM-CTS): a database for ethnobotanically driven drug-discovery programs.
Harris, Eric S J; Erickson, Sean D; Tolopko, Andrew N; Cao, Shugeng; Craycroft, Jane A; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E; Eisenberg, David M
Ethnobotanically driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically driven natural product collection and drug-discovery programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Traditional Medicine Collection Tracking System (TM-CTS): A Database for Ethnobotanically-Driven Drug-Discovery Programs
Harris, Eric S. J.; Erickson, Sean D.; Tolopko, Andrew N.; Cao, Shugeng; Craycroft, Jane A.; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E.; Eisenberg, David M.
Aim of the study. Ethnobotanically-driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine-Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. Materials and Methods. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. Results. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. Conclusions. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically-driven natural product collection and drug-discovery programs. PMID:21420479
The AEROPATH project targeting Pseudomonas aeruginosa: crystallographic studies for assessment of potential targets in early-stage drug discovery
International Nuclear Information System (INIS)
Moynie, Lucille; Schnell, Robert; McMahon, Stephen A.; Sandalova, Tatyana; Boulkerou, Wassila Abdelli; Schmidberger, Jason W.; Alphey, Magnus; Cukier, Cyprian; Duthie, Fraser; Kopec, Jolanta; Liu, Huanting; Jacewicz, Agata; Hunter, William N.; Naismith, James H.; Schneider, Gunter
A focused strategy has been directed towards the structural characterization of selected proteins from the bacterial pathogen P. aeruginosa. The objective is to exploit the resulting structural data, in combination with ligand-binding studies, and to assess the potential of these proteins for early-stage antimicrobial drug discovery. Bacterial infections are increasingly difficult to treat owing to the spread of antibiotic resistance. A major concern is Gram-negative bacteria, for which the discovery of new antimicrobial drugs has been particularly scarce. In an effort to accelerate early steps in drug discovery, the EU-funded AEROPATH project aims to identify novel targets in the opportunistic pathogen Pseudomonas aeruginosa by applying a multidisciplinary approach encompassing target validation, structural characterization, assay development and hit identification from small-molecule libraries. Here, the strategies used for target selection are described and progress in protein production and structure analysis is reported. Of the 102 selected targets, 84 could be produced in soluble form and the de novo structures of 39 proteins have been determined. The crystal structures of eight of these targets, ranging from hypothetical unknown proteins to metabolic enzymes from different functional classes (PA1645, PA1648, PA2169, PA3770, PA4098, PA4485, PA4992 and PA5259), are reported here. The structural information is expected to provide a firm basis for the improvement of hit compounds identified from fragment-based and high-throughput screening campaigns
Small Molecules from Nature Targeting G-Protein Coupled Cannabinoid Receptors: Potential Leads for Drug Discovery and Development
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Charu Sharma
Full Text Available The cannabinoid molecules are derived from Cannabis sativa plant which acts on the cannabinoid receptors types 1 and 2 (CB1 and CB2 which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2 receptors. However, these synthetic based Cannabis derived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other than Cannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics.
From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.
Ferreira, Leonardo G; Andricopulo, Adriano D
Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at email@example.com.
Established and Emerging Trends in Computational Drug Discovery in the Structural Genomics Era
DEFF Research Database (Denmark)
Taboureau, Olivier; Baell, Jonathan B.; Fernández-Recio, Juan
Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly...
Non human primate models for Alzheimer's disease-related research and drug discovery
Van Dam, Debby; De Deyn, Peter Paul
Introduction: Pathophysiological mechanisms underlying Alzheimer's disease (AD) remain insufficiently documented for the identification of accurate diagnostic markers and purposeful target discovery and development. Nonhuman primates (NHPs) have important translational value given their close
A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery.
Hellerstein, Marc K
Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a naïve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.
Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.
Abdolmaleki, Azizeh; Ghasemi, Jahan B
Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at email@example.com.
The Current Landscape of 3D In Vitro Tumor Models: What Cancer Hallmarks Are Accessible for Drug Discovery?
Rodenhizer, Darren; Dean, Teresa; D'Arcangelo, Elisa; McGuigan, Alison P
Cancer prognosis remains a lottery dependent on cancer type, disease stage at diagnosis, and personal genetics. While investment in research is at an all-time high, new drugs are more likely to fail in clinical trials today than in the 1970s. In this review, a summary of current survival statistics in North America is provided, followed by an overview of the modern drug discovery process, classes of models used throughout different stages, and challenges associated with drug development efficiency are highlighted. Then, an overview of the cancer hallmarks that drive clinical progression is provided, and the range of available clinical therapies within the context of these hallmarks is categorized. Specifically, it is found that historically, the development of therapies is limited to a subset of possible targets. This provides evidence for the opportunities offered by novel disease-relevant in vitro models that enable identification of novel targets that facilitate interactions between the tumor cells and their surrounding microenvironment. Next, an overview of the models currently reported in literature is provided, and the cancer biology they have been used to explore is highlighted. Finally, four priority areas are suggested for the field to accelerate adoption of in vitro tumour models for cancer drug discovery. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.
Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R
Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and
Conformational transition paths harbor structures useful for aiding drug discovery and understanding enzymatic mechanisms in protein kinases.
Wong, Chung F
This short article examines the usefulness of fast simulations of conformational transition paths in elucidating enzymatic mechanisms and guiding drug discovery for protein kinases. It applies the transition path method in the MOIL software package to simulate the paths of conformational transitions between six pairs of structures from the Protein Data Bank. The structures along the transition paths were found to resemble experimental structures that mimic transient structures believed to form during enzymatic catalysis or conformational transitions, or structures that have drug candidates bound. These findings suggest that such simulations could provide quick initial insights into the enzymatic mechanisms or pathways of conformational transitions of proteins kinases, or could provide structures useful for aiding structure-based drug design. © 2015 The Protein Society.
Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery.
Therrien, Eric; Englebienne, Pablo; Arrowsmith, Andrew G; Mendoza-Sanchez, Rodrigo; Corbeil, Christopher R; Weill, Nathanael; Campagna-Slater, Valérie; Moitessier, Nicolas
As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.
Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.
Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu
Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.
Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.
Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael
Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular
Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.
Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C
Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.
Structure-based drug discovery for combating influenza virus by targeting the PA-PB1 interaction.
Watanabe, Ken; Ishikawa, Takeshi; Otaki, Hiroki; Mizuta, Satoshi; Hamada, Tsuyoshi; Nakagaki, Takehiro; Ishibashi, Daisuke; Urata, Shuzo; Yasuda, Jiro; Tanaka, Yoshimasa; Nishida, Noriyuki
Influenza virus infections are serious public health concerns throughout the world. The development of compounds with novel mechanisms of action is urgently required due to the emergence of viruses with resistance to the currently-approved anti-influenza viral drugs. We performed in silico screening using a structure-based drug discovery algorithm called Nagasaki University Docking Engine (NUDE), which is optimised for a GPU-based supercomputer (DEstination for Gpu Intensive MAchine; DEGIMA), by targeting influenza viral PA protein. The compounds selected by NUDE were tested for anti-influenza virus activity using a cell-based assay. The most potent compound, designated as PA-49, is a medium-sized quinolinone derivative bearing a tetrazole moiety, and it inhibited the replication of influenza virus A/WSN/33 at a half maximal inhibitory concentration of 0.47 μM. PA-49 has the ability to bind PA and its anti-influenza activity was promising against various influenza strains, including a clinical isolate of A(H1N1)pdm09 and type B viruses. The docking simulation suggested that PA-49 interrupts the PA-PB1 interface where important amino acids are mostly conserved in the virus strains tested, suggesting the strain independent utility. Because our NUDE/DEGIMA system is rapid and efficient, it may help effective drug discovery against the influenza virus and other emerging viruses.
Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery.
Nowotka, Michał M; Gaulton, Anna; Mendez, David; Bento, A Patricia; Hersey, Anne; Leach, Andrew
ChEMBL is a manually curated database of bioactivity data on small drug-like molecules, used by drug discovery scientists. Among many access methods, a REST API provides programmatic access, allowing the remote retrieval of ChEMBL data and its integration into other applications. This approach allows scientists to move from a world where they go to the ChEMBL web site to search for relevant data, to one where ChEMBL data can be simply integrated into their everyday tools and work environment. Areas covered: This review highlights some of the audiences who may benefit from using the ChEMBL API, and the goals they can address, through the description of several use cases. The examples cover a team communication tool (Slack), a data analytics platform (KNIME), batch job management software (Luigi) and Rich Internet Applications. Expert opinion: The advent of web technologies, cloud computing and micro services oriented architectures have made REST APIs an essential ingredient of modern software development models. The widespread availability of tools consuming RESTful resources have made them useful for many groups of users. The ChEMBL API is a valuable resource of drug discovery bioactivity data for professional chemists, chemistry students, data scientists, scientific and web developers.
Japan-China Joint Medical Workshop on Drug Discoveries and Therapeutics 2008: The need of Asian pharmaceutical researchers' cooperation.
Nakata, M; Tang, W
The Japan-China Joint Medical Workshop on Drug Discoveries and Therapeutics 2008 (JCMWDDT 2008) was held from September 29 to October 1, 2008 at The University of Tokyo, Tokyo, Japan. JCMWDDT is an international workshop that is mainly organized by Asian editorial members of Drug Discoveries & Therapeutics (http://www.ddtjournal.com/home) for the purpose of promoting research exchanges in the field of drug discovery and therapeutic. This year's JCMWDDT is the second workshop and focused particularly on novel development and technological innovation of anti-influenza agents. The workshop began with an announcement by the Japanese Co-chairperson, Dr. Sekimizu (Department of Microbiology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Japan; Editorin- Chief of Drug Discoveries & Therapeutics, DDT) followed by a speech by the Chinese Co-chairperson, Dr. Wenfang Xu (School of Pharmaceutical Sciences, Shandong University, Shandong, China; Editor in China Office of DDT), with additional speeches by Dr. Norio Matsuki (The University of Tokyo, Japan; Editor of DDT) and Dr. Guanhua Du (Chinese Academy of Medical Science, China; Editor of DDT). Fifty-nine titles were presented in 6 specialized sessions (Research Advances in Drug Discoveries and Therapeutics, Drug Synthesis/Clinical Therapeutics, Medicinal Chemistry/Natural Products, Anti-influenza Drugs, Anti-infection/antiviral Drugs, Biochemistry/Molecular Biology /Pharmacology) and a poster session (Drug Discov Ther 2008; 2, Suppl; available at http://www.ddtjournal.com/Announce/index.htm). An annual outbreak of avian influenza in Asian countries including China and Japan has sparked fears that the virus will mutate and then cause an epidemic in humans. Therefore, Asian researchers need to work together to control this infection. This year's JCMWDDT helped provide an opportunity to reiterate the crucial role of medicinal chemistry in conquering influenza and created an environment for cooperative
LEGO-inspired drug design: Discovery of novel fungal Plasma membrane H+-ATPase (Pma1) inhibitors from small molecule libraries: An introduction of HFSA-SBS_DOS-RD strategy in drug discovery
DEFF Research Database (Denmark)
Tung, Truong Thanh; Dao, Trong Tuan; Palmgren, Michael B.
to extracellular, this enzyme generates a transmembrane electrochem. gradient, as a consequence, fungi can uptake nutrients by secondary transport systems. Until now, only low resoln. of protein structure has been reported, and notably there a no report of co-crystal structure of Pma1 with inhibitors. Therefore......-oriented synthesis (SBS_DOS) and rational design (RD), so called HFSA-SBS_DOS-RD strategy in drug discovery and development process. Using HFSA-SBS_DOS-RD, our group successfully designed, synthesized, and performed SAR studies of novel compds. potent Pma1 inhibitors. An expeditious, high yield and scalable...... microwave-assisted synthesis was developed and applied for synthesis of library compds. To our delight, ours compd. libraries were able to inhibit Pma1 activity and growth inhibitory activity of C. albican and S. cerevisiae revealed the most promising example for future development of antifungal drugs...
Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery
Directory of Open Access Journals (Sweden)
Daura Xavier
Full Text Available Abstract Background With the classical, active-site oriented drug-development approach reaching its limits, protein ligand-binding sites in general and allosteric sites in particular are increasingly attracting the interest of medicinal chemists in the search for new types of targets and strategies to drug development. Given that allostery represents one of the most common and powerful means to regulate protein function, the traditional drug discovery approach of targeting active sites can be extended by targeting allosteric or regulatory protein pockets that may allow the discovery of not only novel drug-like inhibitors, but activators as well. The wealth of available protein structural data can be exploited to further increase our understanding of allosterism, which in turn may have therapeutic applications. A first step in this direction is to identify and characterize putative effector sites that may be present in already available structural data. Results We performed a large-scale study of protein cavities as potential allosteric and functional sites, by integrating publicly available information on protein sequences, structures and active sites for more than a thousand protein families. By identifying common pockets across different structures of the same protein family we developed a method to measure the pocket's structural conservation. The method was first parameterized using known active sites. We characterized the predicted pockets in terms of sequence and structural conservation, backbone flexibility and electrostatic potential. Although these different measures do not tend to correlate, their combination is useful in selecting functional and regulatory sites, as a detailed analysis of a handful of protein families shows. We finally estimated the numbers of potential allosteric or regulatory pockets that may be present in the data set, finding that pockets with putative functional and effector characteristics are widespread across
Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors.
Frett, Brendan; McConnell, Nick; Smith, Catherine C; Wang, Yuanxiang; Shah, Neil P; Li, Hong-yu
The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
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Christodoulos A. Floudas
Full Text Available Biclustering has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the ``best'' grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters. In the first part of the presentation, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric [1,2]. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. The performance of OREO is tested on several important data matrices arising in systems biology to validate the ability of the proposed method and compare it to existing biclustering and clustering methods. In the second part of the talk, we will focus on novel methods for clustering of data matrices that are very sparse [3]. These types of data matrices arise in drug discovery where the x- and y-axis of a data matrix can correspond to different functional groups for two distinct substituent sites on a molecular scaffold. Each possible x and y pair corresponds to a single molecule which can be synthesized and tested for a certain property, such as percent inhibition of a protein function. For even moderate size matrices, synthesizing and testing a small fraction of the molecules is labor intensive and not economically feasible. Thus, it is of paramount importance to have a reliable method for guiding the synthesis process to select molecules that have a high probability of success. In the second part of the presentation, we introduce a new strategy to enable efficient substituent reordering and descriptor-free property estimation. Our approach casts
Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery
Yu, Pulan
Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…
Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions.
Lipinski, Christopher A
The rule of five (Ro5), based on physicochemical profiles of phase II drugs, is consistent with structural limitations in protein targets and the drug target ligands. Three of four parameters in Ro5 are fundamental to the structure of both target and drug binding sites. The chemical structure of the drug ligand depends on the ligand chemistry and design philosophy. Two extremes of chemical structure and design philosophy exist; ligands constructed in the medicinal chemistry synthesis laboratory without input from natural selection and natural product (NP) metabolites biosynthesized based on evolutionary selection. Exceptions to Ro5 are found mostly among NPs. Chemistry chameleon-like behavior of some NPs due to intra-molecular hydrogen bonding as exemplified by cyclosporine A is a strong contributor to NP Ro5 outliers. The fragment derived, drug Navitoclax is an example of the extensive expertise, resources, time and key decisions required for the rare discovery of a non-NP Ro5 outlier. Copyright © 2016 Elsevier B.V. All rights reserved.
Assessment of berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning.
Karaosmanoglu, Kubra; Sayar, Nihat Alpagu; Kurnaz, Isil Aksan; Akbulut, Berna Sariyar
Postgenomics drug development is undergoing major transformation in the age of multi-omics studies and drug repositioning. Rather than applications solely in personalized medicine, omics science thus additionally offers a better understanding of a broader range of drug targets and drug repositioning. Berberine is an isoquinoline alkaloid found in many medicinal plants. We report here a whole genome microarray study in tandem with proteomics techniques for mining the plethora of targets that are putatively involved in the antimicrobial activity of berberine against Escherichia coli. We found DNA replication/repair and transcription to be triggered by berberine, indicating that nucleic acids, in general, are among its targets. Our combined transcriptomics and proteomics multi-omics findings underscore that, in the presence of berberine, cell wall or cell membrane transport and motility-related functions are also specifically regulated. We further report a general decline in metabolism, as seen by repression of genes in carbohydrate and amino acid metabolism, energy production, and conversion. An involvement of multidrug efflux pumps, as well as reduced membrane permeability for developing resistance against berberine in E. coli was noted. Collectively, these findings offer original and significant leads for omics-guided drug discovery and future repositioning approaches in the postgenomics era, using berberine as a multi-omics case study.
Membrane Interactions of Phytochemicals as Their Molecular Mechanism Applicable to the Discovery of Drug Leads from Plants
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Hironori Tsuchiya
Full Text Available In addition to interacting with functional proteins such as receptors, ion channels, and enzymes, a variety of drugs mechanistically act on membrane lipids to change the physicochemical properties of biomembranes as reported for anesthetic, adrenergic, cholinergic, non-steroidal anti-inflammatory, analgesic, antitumor, antiplatelet, antimicrobial, and antioxidant drugs. As well as these membrane-acting drugs, bioactive plant components, phytochemicals, with amphiphilic or hydrophobic structures, are presumed to interact with biological membranes and biomimetic membranes prepared with phospholipids and cholesterol, resulting in the modification of membrane fluidity, microviscosity, order, elasticity, and permeability with the potencies being consistent with their pharmacological effects. A novel mechanistic point of view of phytochemicals would lead to a better understanding of their bioactivities, an insight into their medicinal benefits, and a strategic implication for discovering drug leads from plants. This article reviews the membrane interactions of different classes of phytochemicals by highlighting their induced changes in membrane property. The phytochemicals to be reviewed include membrane-interactive flavonoids, terpenoids, stilbenoids, capsaicinoids, phloroglucinols, naphthodianthrones, organosulfur compounds, alkaloids, anthraquinonoids, ginsenosides, pentacyclic triterpene acids, and curcuminoids. The membrane interaction's applicability to the discovery of phytochemical drug leads is also discussed while referring to previous screening and isolating studies.
Drug discovery for male subfertility using high-throughput screening: a new approach to an unsolved problem.
Martins da Silva, Sarah J; Brown, Sean G; Sutton, Keith; King, Louise V; Ruso, Halil; Gray, David W; Wyatt, Paul G; Kelly, Mark C; Barratt, Christopher L R; Hope, Anthony G
Can pharma drug discovery approaches be utilized to transform investigation into novel therapeutics for male infertility? High-throughput screening (HTS) is a viable approach to much-needed drug discovery for male factor infertility. There is both huge demand and a genuine clinical need for new treatment options for infertile men. However, the time, effort and resources required for drug discovery are currently exorbitant, due to the unique challenges of the cellular, physical and functional properties of human spermatozoa and a lack of appropriate assay platform. Spermatozoa were obtained from healthy volunteer research donors and subfertile patients undergoing IVF/ICSI at a hospital-assisted reproductive techniques clinic between January 2012 and November 2016. A HTS assay was developed and validated using intracellular calcium ([Ca2+]i) as a surrogate for motility in human spermatozoa. Calcium fluorescence was detected using a Flexstation microplate reader (384-well platform) and compared with responses evoked by progesterone, a compound known to modify a number of biologically relevant behaviours in human spermatozoa. Hit compounds identified following single point drug screen (10 μM) of an ion channel-focussed library assembled by the University of Dundee Drug Discovery Unit were rescreened to ensure potency using standard 10 point half-logarithm concentration curves, and tested for purity and integrity using liquid chromatography and mass spectrometry. Hit compounds were grouped by structure activity relationships and five representative compounds then further investigated for direct effects on spermatozoa, using computer-assisted sperm assessment, sperm penetration assay and whole-cell patch clamping. Of the 3242 ion channel library ligands screened, 384 compounds (11.8%) elicited a statistically significant increase in calcium fluorescence, with greater than 3× median absolute deviation above the baseline. Seventy-four compounds eliciting ≥50% increase
Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing
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Francis A. Ortega
Full Text Available Dynamic clamp, a hybrid-computational-experimental technique that has been used to elucidate ionic mechanisms underlying cardiac electrophysiology, is emerging as a promising tool in the discovery of potential anti-arrhythmic targets and in pharmacological safety testing. Through the injection of computationally simulated conductances into isolated cardiomyocytes in a real-time continuous loop, dynamic clamp has greatly expanded the capabilities of patch clamp outside traditional static voltage and current protocols. Recent applications include fine manipulation of injected artificial conductances to identify promising drug targets in the prevention of arrhythmia and the direct testing of model-based hypotheses. Furthermore, dynamic clamp has been used to enhance existing experimental models by addressing their intrinsic limitations, which increased predictive power in identifying pro-arrhythmic pharmacological compounds. Here, we review the recent advances of the dynamic clamp technique in cardiac electrophysiology with a focus on its future role in the development of safety testing and discovery of anti-arrhythmic drugs.
Integrated quantitative and qualitative workflow for in vivo bioanalytical support in drug discovery using hybrid Q-TOF-MS.
Ranasinghe, Asoka; Ramanathan, Ragu; Jemal, Mohammed; D'Arienzo, Celia J; Humphreys, W Griffith; Olah, Timothy V
UHPLC coupled with orthogonal acceleration hybrid quadrupole-TOF (Q-TOF)-MS is an emerging technique offering new strategies for the efficient screening of new chemical entities and related molecules at the early discovery stage within the pharmaceutical industry. In the first part of this article, we examine the main instrumental parameters that are critical for the integration of UHPLC-Q-TOF technology to existing bioanalytical workflows, in order to provide simultaneous quantitative and qualitative bioanalysis of samples generated following in vivo studies. Three modern Q-TOF mass spectrometers, including Bruker maXis™, Agilent 6540 and Sciex TripleTOF™ 5600, all interfaced with UHPLC systems, are evaluated in the second part of the article. The scope of this work is to demonstrate the potential of Q-TOF for the analysis of typical small molecules, therapeutic peptides (molecular weight <6000 Da), and enzymatically (i.e., trypsin, chymotrypsin and pepsin) cleaved peptides from larger proteins. This work focuses mainly on full-scan TOF data obtained under ESI conditions, the major mode of TOF operation in discovery bioanalytical research, where the compounds are selected based on their pharmacokinetic/pharmacodynamic behaviors using animal models prior to selecting a few desirable candidates for further development. Finally, important emerging TOF technologies that could potentially benefit bioanalytical research in the semi-quantification of metabolites without synthesized standards are discussed. Particularly, the utility of captive spray ionization coupled with TripleTOF 5600 was evaluated for improving sensitivity and providing normalized MS response for drugs and their metabolites. The workflow proposed compromises neither the efficiency, nor the quality of pharmacokinetic data in support of early drug discovery programs.
In Silico Predictions of hERG Channel Blockers in Drug Discovery
DEFF Research Database (Denmark)
Taboureau, Olivier; Sørensen, Flemming Steen
The risk for cardiotoxic side effects represents a major problem in clinical studies of drug candidates and regulatory agencies have explicitly recommended that all new drug candidates should be tested for blockage of the human Ether-a-go-go Related-Gene (hERG) potassium channel. Indeed, several ...
Novel strategies in drug discovery of the calcium-sensing receptor based on biased signaling
DEFF Research Database (Denmark)
Thomsen, Alex Rojas Bie; Smajilovic, Sanela; Bräuner-Osborne, Hans
SR activating drug that selectively activates signaling pathways that inhibit PTH secretion while having no effect on signaling pathways involved in calcitonin secretion. Such a drug would have the same therapeutic value as cinacalcet in lowering PTH secretion while eliminating the side effect of hypocalcemia...
Zika antiviral chemotherapy: identification of drugs and promising starting points for drug discovery from an FDA-approved library [version 1; referees: 2 approved
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Bruno S. Pascoalino
Full Text Available Background The recent epidemics of Zika virus (ZIKV implicated it as the cause of serious and potentially lethal congenital conditions such microcephaly and other central nervous system defects, as well as the development of the Guillain-Barré syndrome in otherwise healthy patients. Recent findings showed that anti-Dengue antibodies are capable of amplifying ZIKV infection by a mechanism similar to antibody-dependent enhancement, increasing the severity of the disease. This scenario becomes potentially catastrophic when the global burden of Dengue and the advent of the newly approved anti-Dengue vaccines in the near future are taken into account. Thus, antiviral chemotherapy should be pursued as a priority strategy to control the spread of the virus and prevent the complications associated with Zika. Methods Here we describe a fast and reliable cell-based, high-content screening assay for discovery of anti-ZIKV compounds. This methodology has been used to screen the National Institute of Health Clinical Collection compound library, a small collection of FDA-approved drugs. Results and conclusion From 725 FDA-approved compounds triaged, 29 (4% were found to have anti-Zika virus activity, of which 22 had confirmed (76% of confirmation by dose-response curves. Five candidates presented selective activity against ZIKV infection and replication in a human cell line. These hits have abroad spectrum of chemotypes and therapeutic uses, offering valuable opportunities for selection of leads for antiviral drug discovery.
Scaffold-free 3D bio-printed human liver tissue stably maintains metabolic functions useful for drug discovery.
Kizawa, Hideki; Nagao, Eri; Shimamura, Mitsuru; Zhang, Guangyuan; Torii, Hitoshi
The liver plays a central role in metabolism. Although many studies have described in vitro liver models for drug discovery, to date, no model has been described that can stably maintain liver function. Here, we used a unique, scaffold-free 3D bio-printing technology to construct a small portion of liver tissue that could stably maintain drug, glucose, and lipid metabolism, in addition to bile acid secretion. This bio-printed normal human liver tissue maintained expression of several kinds of hepatic drug transporters and metabolic enzymes that functioned for several weeks. The bio-printed liver tissue displayed glucose production via cAMP/protein kinase A signaling, which could be suppressed with insulin. Bile acid secretion was also observed from the printed liver tissue, and it accumulated in the culture medium over time. We observed both bile duct and sinusoid-like structures in the bio-printed liver tissue, which suggested that bile acid secretion occurred via a sinusoid-hepatocyte-bile duct route. These results demonstrated that our bio-printed liver tissue was unique, because it exerted diverse liver metabolic functions for several weeks. In future, we expect our bio-printed liver tissue to be applied to developing new models that can be used to improve preclinical predictions of long-term toxicity in humans, generate novel targets for metabolic liver disease, and evaluate biliary excretion in drug development.
Colworth prize lecture 2016: exploiting new biological targets from a whole-cell phenotypic screening campaign for TB drug discovery.
Moynihan, Patrick Joseph; Besra, Gurdyal S
Mycobacterium tuberculosis is the aetiological agent of tuberculosis (TB) and is the leading bacterial cause of mortality and morbidity in the world. One third of the world's population is infected with TB, and in conjunction with HIV represents a serious problem that urgently needs addressing. TB is a disease of poverty and mostly affects young adults in their productive years, primarily in the developing world. The most recent report from the World Health Organisation states that 8 million new cases of TB were reported and that ~1.5 million people died from TB. The efficacy of treatment is threatened by the emergence of multi-drug and extensively drug-resistant strains of M. tuberculosis. It can be argued that, globally, M. tuberculosis is the single most important infectious agent affecting mankind. Our research aims to establish an academic-industrial partnership with the goal of discovering new drug targets and hit-to-lead new chemical entities for TB drug discovery.
Drug discovery strategies in the field of tumor energy metabolism: Limitations by metabolic flexibility and metabolic resistance to chemotherapy.
Amoedo, N D; Obre, E; Rossignol, R
The search for new drugs capable of blocking the metabolic vulnerabilities of human tumors has now entered the clinical evaluation stage, but several projects already failed in phase I or phase II. In particular, very promising in vitro studies could not be translated in vivo at preclinical stage and beyond. This was the case for most glycolysis inhibitors that demonstrated systemic toxicity. A more recent example is the inhibition of glutamine catabolism in lung adenocarcinoma that failed in vivo despite a strong addiction of several cancer cell lines to glutamine in vitro. Such contradictory findings raised several questions concerning the optimization of drug discovery strategies in the field of cancer metabolism. For instance, the cell culture models in 2D or 3D might already show strong limitations to mimic the tumor micro- and macro-environment. The microenvironment of tumors is composed of cancer cells of variegated metabolic profiles, supporting local metabolic exchanges and symbiosis, but also of immune cells and stroma that further interact with and reshape cancer cell metabolism. The macroenvironment includes the different tissues of the organism, capable of exchanging signals and fueling the tumor 'a distance'. Moreover, most metabolic targets were identified from their increased expression in tumor transcriptomic studies, or from targeted analyses looking at the metabolic impact of particular oncogenes or tumor suppressors on selected metabolic pathways. Still, very few targets were identified from in vivo analyses of tumor metabolism in patients because such studies are difficult and adequate imaging methods are only currently being developed for that purpose. For instance, perfusion of patients with [ 13 C]-glucose allows deciphering the metabolomics of tumors and opens a new area in the search for effective targets. Metabolic imaging with positron emission tomography and other techniques that do not involve [ 13 C] can also be used to evaluate tumor
Large-scale cross-species chemogenomic platform proposes a new drug discovery strategy of veterinary drug from herbal medicines.
Directory of Open Access Journals (Sweden)
Chao Huang
Full Text Available Veterinary Herbal Medicine (VHM is a comprehensive, current, and informative discipline on the utilization of herbs in veterinary practice. Driven by chemistry but progressively directed by pharmacology and the clinical sciences, drug research has contributed more to address the needs for innovative veterinary medicine for curing animal diseases. However, research into veterinary medicine of vegetal origin in the pharmaceutical industry has reduced, owing to questions such as the short of compatibility of traditional natural-product extract libraries with high-throughput screening. Here, we present a cross-species chemogenomic screening platform to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future veterinary medicines, thereby increasing the number of treatment options. First, based on critically examined pharmacology and text mining, we build a cross-species drug-likeness evaluation approach to screen the lead compounds in veterinary medicines. Second, a specific cross-species target prediction model is developed to infer drug-target connections, with the purpose of understanding how drugs work on the specific targets. Third, we focus on exploring the multiple targets interference effects of veterinary medicines by heterogeneous network convergence and modularization analysis. Finally, we manually integrate a disease pathway to test whether the cross-species chemogenomic platform could uncover the active mechanism of veterinary medicine, which is exemplified by a specific network module. We believe the proposed cross-species chemogenomic platform allows for the systematization of current and traditional knowledge of veterinary medicine and, importantly, for the application of this emerging body of knowledge to the development of new drugs for animal diseases.
Development of novel, 384-well high-throughput assay panels for human drug transporters: drug interaction and safety assessment in support of discovery research.
Tang, Huaping; Shen, Ding Ren; Han, Yong-Hae; Kong, Yan; Balimane, Praveen; Marino, Anthony; Gao, Mian; Wu, Sophie; Xie, Dianlin; Soars, Matthew G; O'Connell, Jonathan C; Rodrigues, A David; Zhang, Litao; Cvijic, Mary Ellen
Transporter proteins are known to play a critical role in affecting the overall absorption, distribution, metabolism, and excretion characteristics of drug candidates. In addition to efflux transporters (P-gp, BCRP, MRP2, etc.) that limit absorption, there has been a renewed interest in influx transporters at the renal (OATs, OCTs) and hepatic (OATPs, BSEP, NTCP, etc.) organ level that can cause significant clinical drug-drug interactions (DDIs). Several of these transporters are also critical for hepatobiliary disposition of bilirubin and bile acid/salts, and their inhibition is directly implicated in hepatic toxicities. Regulatory agencies took action to address transporter-mediated DDI with the goal of ensuring drug safety in the clinic and on the market. To meet regulatory requirements, advanced bioassay technology and automation solutions were implemented for high-throughput transporter screening to provide structure-activity relationship within lead optimization. To enhance capacity, several functional assay formats were miniaturized to 384-well throughput including novel fluorescence-based uptake and efflux inhibition assays using high-content image analysis as well as cell-based radioactive uptake and vesicle-based efflux inhibition assays. This high-throughput capability enabled a paradigm shift from studying transporter-related issues in the development space to identifying and dialing out these concerns early on in discovery for enhanced mechanism-based efficacy while circumventing DDIs and transporter toxicities.
Animal Models of Seizures and Epilepsy: Past, Present, and Future Role for the Discovery of Antiseizure Drugs.
Löscher, Wolfgang
The identification of potential therapeutic agents for the treatment of epilepsy requires the use of seizure models. Except for some early treatments, including bromides and phenobarbital, the antiseizure activity of all clinically used drugs was, for the most part, defined by acute seizure models in rodents using the maximal electroshock and subcutaneous pentylenetetrazole seizure tests and the electrically kindled rat. Unfortunately, the clinical evidence to date would suggest that none of these models, albeit useful, are likely to identify those therapeutics that will effectively manage patients with drug resistant seizures. Over the last 30 years, a number of animal models have been developed that display varying degrees of pharmacoresistance, such as the phenytoin- or lamotrigine-resistant kindled rat, the 6-Hz mouse model of partial seizures, the intrahippocampal kainate model in mice, or rats in which spontaneous recurrent seizures develops after inducing status epilepticus by chemical or electrical stimulation. As such, these models can be used to study mechanisms of drug resistance and may provide a unique opportunity for identifying a truly novel antiseizure drug (ASD), but thus far clinical evidence for this hope is lacking. Although animal models of drug resistant seizures are now included in ASD discovery approaches such as the ETSP (epilepsy therapy screening program), it is important to note that no single model has been validated for use to identify potential compounds for as yet drug resistant seizures, but rather a battery of such models should be employed, thus enhancing the sensitivity to discover novel, highly effective ASDs. The present review describes the previous and current approaches used in the search for new ASDs and offers some insight into future directions incorporating new and emerging animal models of therapy resistance.
Use of biomarkers in the discovery of novel anti-schizophrenia drugs
DEFF Research Database (Denmark)
Mikkelsen, Jens D; Thomsen, Morten S; Hansen, Henrik H
Schizophrenia is characterized by a diverse symptomatology that often includes positive, cognitive and negative symptoms. Current anti-schizophrenic drugs act at multiple receptors, but little is known about how each of these receptors contributes to their mechanisms of action. Screening of novel...... anti-schizophrenic drug candidates targeting single receptors will be based on biomarker assays that measure signalling pathways, transcriptional factors, epigenetic mechanisms and synaptic function and translate these effects to behavioural effects in animals and humans. This review discusses current...
Multi-Step Usage of in Vivo Models During Rational Drug Design and Discovery
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Charles H. Williams
Full Text Available In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design.
Approach to Drug Latentiation as a Tool for Discovery of New Antichagasic
Directory of Open Access Journals (Sweden)
Eliana Ometto Pavan Serafim
Full Text Available Chagas disease, discovered more than one century ago by Carlos Chagas, is still a serious Public Health problem. It is considered an extremely neglected disease, for it affects specially the population of developing countries. The patients with this disease have, in the great majority, low income and, for not representing market, they are excluded from the aims and efforts of research and development of pharmaceutical industries. About 8 to 11 million people may be infected with Trypanosoma cruzi, the etiological agent of the disease and about 100 million people are in risk in Latin America. The treatment of the disease is still a challenge, for only two nytroheterociclic drugs are commercialized in the world: nifurtimox and benznidazole, being this last one, the only available drug in the Brazilian market. However, these drugs are active only in the acute phase of the disease, and the treatment is not efficient in patients in the chronic phase. Consequently it is relevant to develop efficient anti-chagas compounds, particularly for the chronic phase of the disease. This paper discusses the importance of latency for the development of new pro-drugs. The literature describes several methodological techniques that have enabled significant advances in the planning and development of new anti-chagas agents, with emphasis on the search for pro-drugs that allow the enhancement of drug matrices.
PharMillenium '99--the second world pharmaceutical congress and exhibition. Accelerating the pipeline: from drug discovery to market. 1-3 February 1999, Washington DC, USA.
Fernandes, M
This highly interactive meeting effectively covered critical issues on every transaction from drug discovery through to development and commercialization. The program included company-specific descriptions of new discovery products, together with seminars by clinical research and site management organizations on the acceleration of development, pharmaco-economics, branding of products, direct-to-consumer advertising, global marketing, management, information technology and business strategy. There were approximately 50 sessions covered by 70 speakers.
Lab-on-a-chip platform for high throughput drug discovery with DNA-encoded chemical libraries
Grünzner, S.; Reddavide, F. V.; Steinfelder, C.; Cui, M.; Busek, M.; Klotzbach, U.; Zhang, Y.; Sonntag, F.
The fast development of DNA-encoded chemical libraries (DECL) in the past 10 years has received great attention from pharmaceutical industries. It applies the selection approach for small molecular drug discovery. Because of the limited choices of DNA-compatible chemical reactions, most DNA-encoded chemical libraries have a narrow structural diversity and low synthetic yield. There is also a poor correlation between the ranking of compounds resulted from analyzing the sequencing data and the affinity measured through biochemical assays. By combining DECL with dynamical chemical library, the resulting DNA-encoded dynamic library (EDCCL) explores the thermodynamic equilibrium of reversible reactions as well as the advantages of DNA encoded compounds for manipulation/detection, thus leads to enhanced signal-to-noise ratio of the selection process and higher library quality. However, the library dynamics are caused by the weak interactions between the DNA strands, which also result in relatively low affinity of the bidentate interaction, as compared to a stable DNA duplex. To take advantage of both stably assembled dual-pharmacophore libraries and EDCCLs, we extended the concept of EDCCLs to heat-induced EDCCLs (hi-EDCCLs), in which the heat-induced recombination process of stable DNA duplexes and affinity capture are carried out separately. To replace the extremely laborious and repetitive manual process, a fully automated device will facilitate the use of DECL in drug discovery. Herein we describe a novel lab-on-a-chip platform for high throughput drug discovery with hi-EDCCL. A microfluidic system with integrated actuation was designed which is able to provide a continuous sample circulation by reducing the volume to a minimum. It consists of a cooled and a heated chamber for constant circulation. The system is capable to generate stable temperatures above 75 °C in the heated chamber to melt the double strands of the DNA and less than 15 °C in the cooled chamber
Integrating scientific data for drug discovery and development using the Life Sciences Grid.
Dow, Ernst R; Hughes, James B; Stephens, Susie M; Narayan, Vaibhav A; Bishop, Richard W
There are many daunting challenges for companies who wish to bring novel drugs to market. The information complexity around potential drug targets has increased greatly with the introduction of microarrays, high-throughput screening and other technological advances over the past decade, but has not yet fundamentally increased our understanding of how to modify a disease with pharmaceuticals. Further, the bar has been raised in getting a successful drug to market as just being new is no longer enough: the drug must demonstrate improved performance compared with the ever increasing generic pharmacopeia to gain support from payers and government authorities. In addition, partly as a consequence of a climate of concern regarding the safety of drugs, regulatory authorities have approved fewer new molecular entities compared to historical norms over the past few years. To overcome these challenges, the pharmaceutical industry must fully embrace information technology to bring better understood compounds to market. An important first step in addressing an unmet medical need is in understanding the disease and identifying the physiological target(s) to be modulated by the drug. Deciding which targets to pursue for a given disease requires a multidisciplinary effort that integrates heterogeneous data from many sources, including genetic variations of populations, changes in gene expression and biochemical assays. The Life Science Grid was developed to provide a flexible framework to integrate such diverse biological, chemical and disease information to help scientists make better-informed decisions. The Life Science Grid has been used to rapidly and effectively integrate scientific information in the pharmaceutical industry and has been placed in the open source community to foster collaboration in the life sciences community.
Drug safety is a barrier to the discovery and development of new androgen receptor antagonists.
Foster, William R; Car, Bruce D; Shi, Hong; Levesque, Paul C; Obermeier, Mary T; Gan, Jinping; Arezzo, Joseph C; Powlin, Stephanie S; Dinchuk, Joseph E; Balog, Aaron; Salvati, Mark E; Attar, Ricardo M; Gottardis, Marco M
Androgen receptor (AR) antagonists are part of the standard of care for prostate cancer. Despite the almost inevitable development of resistance in prostate tumors to AR antagonists, no new AR antagonists have been approved for over a decade. Treatment failure is due in part to mutations that increase activity of AR in response to lower ligand concentrations as well as to mutations that result in AR response to a broader range of ligands. The failure to discover new AR antagonists has occurred in the face of continued research; to enable progress, a clear understanding of the reasons for failure is required. Non-clinical drug safety studies and safety pharmacology assays were performed on previously approved AR antagonists (bicalutamide, flutamide, nilutamide), next generation antagonists in clinical testing (MDV3100, BMS-641988), and a pre-clinical drug candidate (BMS-501949). In addition, non-clinical studies with AR mutant mice, and EEG recordings in rats were performed. Non-clinical findings are compared to disclosures of clinical trial results. As a drug class, AR antagonists cause seizure in animals by an off-target mechanism and are found in vitro to inhibit GABA-A currents. Clinical trials of candidate next generation AR antagonists identify seizure as a clinical safety risk. Non-clinical drug safety profiles of the AR antagonist drug class create a significant barrier to the identification of next generation AR antagonists. GABA-A inhibition is a common off-target activity of approved and next generation AR antagonists potentially explaining some side effects and safety hazards of this class of drugs. Copyright © 2010 Wiley-Liss, Inc.
Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.
de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong
CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.
Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action
Boot, Maikel
Due to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and shorten treatment regimens. Recently, drug screens using whole cell analyses have been shown to be successful. However, current high-throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint. Notably, this fingerprint was more distinctive in M. marinum. We decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed to respond to DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so, we could identify the putative mode of action for three novel compounds, which confirms our approach.
From Medicinal Chemistry to Human Health: Current Approaches to Drug Discovery for Cancer and Neglected Tropical Diseases
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Full Text Available ABSTRACT Scientific and technological breakthroughs have compelled the current players in drug discovery to increasingly incorporate knowledge-based approaches. This evolving paradigm, which has its roots attached to the recent advances in medicinal chemistry, molecular and structural biology, has unprecedentedly demanded the development of up-to-date computational approaches, such as bio- and chemo-informatics. These tools have been pivotal to catalyzing the ever-increasing amount of data generated by the molecular sciences, and to converting the data into insightful guidelines for use in the research pipeline. As a result, ligand- and structure-based drug design have emerged as key pathways to address the pharmaceutical industry's striking demands for innovation. These approaches depend on a keen integration of experimental and molecular modeling methods to surmount the main challenges faced by drug candidates - in vivo efficacy, pharmacodynamics, metabolism, pharmacokinetics and safety. To that end, the Laboratório de Química Medicinal e Computacional (LQMC of the Universidade de São Paulo has developed forefront research on highly prevalent and life-threatening neglected tropical diseases and cancer. By taking part in global initiatives for pharmaceutical innovation, the laboratory has contributed to the advance of these critical therapeutic areas through the use of cutting-edge strategies in medicinal chemistry.
The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity
International Nuclear Information System (INIS)
Amacher, David E.
Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in
Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.
Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola
Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.
Nanotechnology in Drug Delivery and Tissue Engineering: From Discovery to Applications
Shi, Jinjun; Votruba, Alexander R.; Farokhzad, Omid C.; Langer, Robert
The application of nanotechnology in medicine, referred to as nanomedicine, is offering numerous exciting possibilities in healthcare. Herein, we discuss two important aspects of nanomedicine—drug delivery and tissue engineering—highlighting the advances we have recently experienced, the challenges we are currently facing, and what we are likely to witness in the near future.
Use of biomarkers in the discovery of novel anti-schizophrenia drugs