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Integrating real‐world data to accelerate and guide drug development: A clinical pharmacology perspective

Abstract Pharmaceutical products in the current accelerated drug development landscape can benefit from tools beyond data generated from randomized control trials. We have seen an abundance of real‐world data (RWD) and real‐world evidence, driven by the digitalization of healthcare systems and an increased awareness that has inspired a heightened interest in their potential use. Literature review suggest leveraging RWD as a promising tool to answer key questions in the areas of clinical pharmacology and translational science. RWD may increase our understanding regarding the impact of intrinsic (e.g., liver, renal impairment, or genetic polymorphisms) and extrinsic (e.g., food consumption or concomitant medications) factors on the clearance of administered drugs. Changes in clearance may lead to clinically relevant changes in drug exposure that may require clinical management strategies, such as change in dose or dosing regimen. RWD can be leveraged to potentially bridge the gaps among research, development, and clinical care. This paper highlights promising areas of how RWD have been used to complement clinical pharmacology throughout various phases of drug development; case examples will include dose/regimen extrapolation, dose adjustments for special populations (organ impairment, pediatrics, etc.), and pharmacokinetic/pharmacodynamic models to assess impact of prognostic factors on outcomes. In addition, this paper will also juxtapose limitations and promises of utilizing RWD to answer key scientific questions in drug development and articulate challenges posed by quality issues, data availability, and integration from various sources as well as the increased need for multidimensional‐omics data that can better guide the development of personalized and predictive medicine.


INTRODUCTION
In recent years, there has been an increased interest in the use of real-world data RWD and real-world evidence (RWE) to facilitate drug discovery, development, and regulatory decision making. The increased utilization of RWD is a result of a few key factors. First, the Affordable Care Act (ACA) mandated digitization of health records resulting in democratization and commercialization of hundreds of millions of pieces of patient data, including billions of records and data points. Recent estimates determined around 2314 exabytes of new healthcare data were generated in 2020 and a single patient can generate as much as 80 megabytes of health data each year. 1,2 Second, technological advancement in the form of cloud computing and machine learning have made storage, manipulation, and analysis of large-scale data efficient and feasible. Finally, increased encouragement by regulatory policies (Prescription Drug User Fee Act [PDUFA] VI, 21st Century Cures Act) resulted in improvements to data interoperability and data quality that further has translated to first use of RWD/RWE for regulatory submission. 3 The US Food and Drug Administration (FDA) defines RWD as "data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources" and RWE as "the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD." 3 Sources of RWD include administrative/insurance claims data, patients' electronic health records (EHRs), disease/product registries, health data linked with genomic data, etc. (Figure 1a). Although development and advancement in RWD has been discussed in this paper mostly in the context of the United States, there are important initiatives underway by global regulatory agencies to further integrate RWE in drug development outside of the United States as well. 4 Historically, RWD has been applied primarily to address gaps and questions related to safety and efficacy of a treatment in later stages of development and/or in postmarketing evaluations. However, with advances in digital technology and analytics, emergence of large populationbased data sources that include both clinical and genomic data combined with increased interoperability between data platforms have allowed for a new era of integration of RWD throughout the product's lifecycle. These efforts have generated further interest across the pharmaceutical industry to address questions beyond safety and efficacy. 5,6 Clinical pharmacology plays an essential role in advancing the development of safe and effective drug treatments and optimization of dose and dose regimens specific for different strata of patient populations. This paper highlights some of the potential applications of RWD/RWE to address clinical pharmacology questions throughout the life cycle of a therapeutic product. Additionally, the authors also discuss a few opportunities and challenges pertaining to RWD/RWE.

INTEGRATION OF RWD/RWE IN CLINICAL PHARMACOLOGY THROUGHOUT DRUG DEVELOPMENT
Clinical pharmacology can help improve decision making at critical drug development milestones. Innovative clinical pharmacology tools (e.g., quantitative or mechanistic) are increasingly leveraged from discovery of new target molecules to determine the effect of drugs to accelerate the development of safe and effective drugs across the therapeutic landscape. RWD can also be used by clinical pharmacologists to bridge potential information gaps between clinical trials and daily clinical practice. Figure 1b provides an overview of critical clinical pharmacology questions at different stages of early and late drug development and highlights opportunities to utilize RWD for functional characterization of disease, facilitating biomarker development, informing drug-drug interactions, and drug usage in special or understudied populations, as well as optimizing treatment and dosing regimens. In the following sections, we provide examples with a focus on the value of RWD to inform clinical pharmacology knowledge in three areas: (1) improving and optimizing dosing regimens; (2) facilitating pharmacogenomic biomarker development; and (3) informing drug usage in understudied subgroups, and finally we discuss some challenges and opportunities.

Improve and optimize dosing regimens
One key task for clinical pharmacologists is to determine the safe and effective doses and dosing regimens for drug products. Clinical pharmacologists use integrated pharmacokinetics (PKs), pharmacodynamics (PDs), safety, and efficacy data to establish exposure-response relationships that can impact dosing regimens with optimum benefit-risk profile. A drug can be determined to be safe and effective only when the exposure-response relationships are well-understood. 7 Dose and regimen recommendations for the new drug product usually reflect the dosing schema tested in the pivotal phase III clinical studies, where patients are selected based on specific inclusion and exclusion criteria and treatment arms are well-defined and controlled. However, these pivotal clinical trials have a limited number of study participants that are not a representation of the overall patient population or specific subgroups. As a result, initially approved drug dosing regimens may not be optimal for a more heterogenous real-world population commonly excluded from the strictly controlled clinical efficacy and safety trials or have multiple characteristics likely to influence treatment outcomes (e.g., patients with organ impairment or pediatric population). Inclusion of RWD from routine clinical practice experience provides an enhanced opportunity to continuously improve drug dosing recommendations even after treatment is approved. Examples of studies from the literature based on RWD to inform alternative or optimal dosing regimens are summarized in Table 1.
In April 2021, the FDA approved an alternate biweekly (Q2W) dosing regimen for cetuximab (Erbitux) in addition to the previously approved once weekly (Q1W) dosing regimen. 8 Efficacy results from overall survival analyses using Flatiron Health RWD in patients with metastatic colorectal cancer, who received either the weekly or biweekly dosing regimens, supported the population PK model prediction of similar cetuximab exposures between two schedules. The approval was also supported by pooled efficacy analyses from the published literature. This alternate Q2W regimen allows physicians to provide patients with the same efficacious cetuximab treatment with less frequent clinic visits. The approval was a testimony to the successful use of RWD to fill evidence gaps in the postapproval setting for regulatory decision making.
Dosing for select population subgroups may specifically benefit from evidence and insight generated through RWD analysis. Pediatric dosing has often been extrapolated from adults based on systemic exposure (PK) matching, largely due to challenges associated with performing clinical trials in children. With an increased use of EHR, data from the clinical practice setting provides an opportunity to guide pediatric dosing via PK/PD analysis. For example, in a pediatric population PK analysis for fentanyl, F I G U R E 1 Real world data source and its contribution to clinical pharmacology characterization across the drug development process. (a) Selected real-world data (RWD) sources. RWD can come from a number of sources, such as electronic health records, medical insurance claims, patient-generated health data, medical product/device/disease registries, laboratory/diagnostic testing, clinicogenomic data and others. (b) An overview of critical clinical pharmacology questions at different stages of the drug development process highlighted opportunities for using RWD for functional characterization of disease, facilitating biomarker development, informing drug-drug interactions, and drug usage in special or understudied populations, as well as optimizing treatment and dosing regimens. DDI, drug-drug interaction; IND, investigational new drug; PD, pharmacodynamics; PK, pharmacokinetics; RP2D, recommended phase II dose; RWD, real-world data; SEER, surveillance, epidemiology, and end results. Van Driest et al. used RWD without any additional blood sampling in children after cardiac surgery to demonstrate that model-driven weight-adjusted per kg fentanyl dosing led to more consistent therapeutic concentrations than standard fixed per kg weight dosing. 9 Model-predicted fentanyl concentrations during standard fixed weightbased dosing were not consistently within the therapeutic concentration range for pediatric patients of different weights due to the nonlinear relationship of fentanyl PKs with weight. Therefore, further weight adjustment on per kg dosing resulted in a higher proportion of simulated 6.4 and 20 kg patients within the targeted drug therapeutic range and led to more consistent fentanyl concentrations in the therapeutic range across the size spectrum. This work shows the value of using remnant clinical specimens and EHR data.

T A B L E 1 Selected examples of RWD to improve and optimize dosing regimens
In another example, a pediatric study for continuous erythropoietin receptor activators (CERA) in patients with anemia of chronic kidney disease, RWD on CERA doses and hemoglobin levels obtained from the International Pediatric Dialysis Network registries was leveraged to validate PK/PD model simulations and supported a subcutaneous starting dose in pediatric patients similar to the intravenous dose. 10 A model-based approach confirmed by RWD helped to optimize a pediatric drug development plan approved by health authorities, with reduced drug exposure and treatment burden for children. RWE has become increasingly accepted to inform optimal dosing regimens for clinical practice, and, in some cases, to provide complementary evidence to support regulatory decisions.

Facilitate pharmacogenomic biomarker development
Clinical pharmacology plays an essential role in therapeutic individualization by incorporating individual heterogeneity in intrinsic (e.g., genes and organ function) and extrinsic (e.g., food and concomitant medication) factors on the trajectory of response to medications. Information on pharmacogenomic biomarkers is regularly incorporated in FDA product-approved labeling to guide precise dosing, reduce adverse events, and maximize benefit to patients. 11 The availability of large clinical and genomic biomarker data from real-world patients provides a unique opportunity to understand drug efficacy and safety beyond the clinical trial population and to better inform treatment decisions in routine care. Selected literature examples on the use of RWD to support pharmacogenomic biomarker identification are summarized in Table 2.
Large scale real-world longitudinal patient-level data, including baseline demographic, clinical, and laboratory characteristics, is now routinely used to identify prognostic markers associated with disease outcomes and/ or biomarkers that are predictive of response to specific drugs. 12 This has significantly enhanced the exploration of novel drug targets and strategies to develop combination therapeutics, especially in the field of oncology. Simultaneously, lower costs of genomic sequencing and the increasing linkage of genomic data to patient EHR records have led to the development of clinicogenomic databases that can provide a better understanding of patient response, disease heterogeneity, and mechanisms of resistance to specific therapies. This practice is highlighted in the search for the association of patient characteristics and tumor genomics with clinical outcomes for non-small cell lung cancer (NSCLC). 13 As an example, a Flatiron Health Analytic Database of patients with NSCLC who underwent comprehensive genomic profiling was linked to de-identified EHR data. Close to 400 genes were included along with tumor mutation burden (TMB) stratification, and PD-L1 expression. The results demonstrated the power of TMB as a predictor of cancer immunotherapy response and confirmed that combining the real-world dataset, clinical outcomes, and genomic profiling can enable novel biomarker discovery and identify potential treatment responders to help guide clinical practice and the design of future trials. 13 Similarly, another example includes the use of geographically diverse clinicogenomic databases to identify a small cohort of real-world patients with neurotrophic tropomyosin receptor kinase (NTRK)mutant tumors, complementing traditional clinical trial evidence for this rare population and improving generalizability of trial results. 14 Overall, the integration of patients' genomic and molecular profiles with RWD has focused on identifying population subgroups likely to demonstrate maximum effectiveness in trials or likely to experience adverse events, which translates into more evidence-based clinical decisions. Its influence on regulatory decisions remains to be seen, however, the future impact on informing clinical trial design and precision medicine cannot be ignored.

Informed drug use in understudied or excluded populations
Another dimension of RWE's value to clinical pharmacology is better defining dose/dosing regimen in historically under-represented groups, whether due to disease rarity, trial eligibility criteria, or conservation for the vulnerable populations (e.g., children, pregnant women, the elderly, and those with organ dysfunction). Numerous efforts have been made in clinical pharmacology to help understand the effect of gender, body size metrics, and pathophysiological characteristics (e.g., body size, CYP450 ontogeny,  Table 3. The rarity of men diagnosed with breast cancer presents as a challenge for conducting timely and adequately powered randomized clinical trials (RCTs). Although guidelines recommend treating men with metastatic breast cancer (mBC) similarly to postmenopausal women, there are limited data on the effectiveness and safety of palbociclib in men. Three independent real-world studies were conducted to assess palbociclib treatment in men with mBC. 15 The first one leveraged commercially available pharmacy and medical claims data to describe real-world treatment patterns and duration. The second approach retrospectively analyzed demographically and geographically diverse EHR-derived data to understand real-world response. Furthermore, the global safety dataset was searched for any adverse events reported in this setting. Overall, RWD contributed to the totality of evidence and supported an expansion of the palbociclib US label to include men with mBC. Considering the challenges of conducting RCTs in some disease settings or subpopulations, RWD highlights a potential new pathway to expand the body of evidence for regulatory decision making and improve patient access to effective therapies in real-world settings.
The need to understand dose compliance and treatment outcomes also expands to patients with organ dysfunction/impairment, and clinical pharmacology is likewise tasked with identifying how disposition of drugs is altered by modified intrinsic factors. For example, varying degrees of renal or hepatic impairment is often an exclusion criterion in clinical trials. However, safety and efficacy data in patients with some degree of organ impairment can often help determine if dose adjustments and/or contraindications are warranted in lieu of dedicated organ impairment clinical pharmacology studies. RWD can also be leveraged to streamline the eligibility criteria of clinical trials and inform dosing for dedicated organ impairment studies if needed. In an RWD retrospective analysis of the efficacy and safety of sorafenib in patients with advanced renal cell carcinoma with renal impairment, patients were divided into two groups by an estimated glomerular filtration rate cutoff of 45 ml/min/1.73 m 2 and propensity scores matching was conducted to adjust for key demographic factors that could affect the prognosis. 16 The results confirmed similar efficacy and safety for patients regardless of renal function and thus warranted a continuation of treatment without adjustment and eliminated the need to conduct a clinical trial in this subset of the population.

DISCUSSION
Although the momentum to leverage RWE to inform drug development decisions is undeniable, its evidentiary value and the place in regulatory decision making remains an area of active research. It is important to appreciate that RWE can provide important complementary evidence for current drug development processes including RCTs and fill the gaps in knowledge. 17 More recently, to encourage and guide the use of RWD for regulatory submissions, the FDA issued a series draft guidance on data standards and considerations for the use of RWD/RWE in regulatory submissions. 3 The European Medicines Agency also outlined the vision for use of RWE in EU medicines regulation. 4 However, issues related to data availability, data quality (e.g., accuracy, completeness, and traceability), lack of standardization in data from different sources (e.g., EHRs, claims, and registries), inherent biases, and lack of best practices in methodology or analysis when interrogating RWE are challenges that can often limit the ability to fully leverage such data for decision making. There are also limitations in RWD that are specific to answering clinical pharmacology related research questions. Often special or understudied patient populations with organ impairment (e.g., hepatic or renal impairment) are defined by biomarkers in different laboratory measures. Most RWD sources have limited (e.g., EHRs) or no data (e.g., administrative claims) on laboratory parameters. This can significantly reduce the patient pool for studying special populations and can introduce unknown biases due to missing or incomplete data. Similarly, it is difficult to obtain all important pregnancy and pregnancy-related information (e.g., exact gestational age) from most RWD sources. Information on race and ethnicity could be incomplete or missing, making it difficult to study racial or ethnic subgroups. Complete data on administered doses, number of prescription days, or the quantity of drugs dispensed are extremely important for dosing or dose adjustment studies or drug-drug interaction studies. Most RWD sources have varying degrees of data availability for these parameters, especially regarding the number of prescriptions days or quantity actually dispensed. In many cases, statistical imputations or data driven algorithms are used to derive relevant clinical information but validation of such algorithms is often incomplete or resource intensive and rather relied upon expert opinions and accuracy of external data driven assumptions. EHR datasets can also have limited longitudinal patient data needed for long follow-up studies. Smaller sample sizes for patients with genomic information, lack of standardization of data across assays or platforms, and relatively high price points for clinicogenomic data sources are some challenges for widespread use of pharmacogenomic studies for patient selection, disease heterogeneity, efficacy, or toxicity indicators. There are many ongoing efforts to mitigate some of these issues: common data models are being used to ensure interoperability between data sources, digital tokenization can link multiple data sources of different types (claims data with EHR data) providing a more complete and more longitudinal picture of patient journeys, availability of genomic data has also been increasing exponentially. RWD sources usually include a wide array of clinical and demographic data on millions of patients that allows the flexibility of stratifying patients, exploring research questions, and confirming pre-established hypotheses. The promise it holds for use in clinical pharmacology is abundant and yet vastly untapped.
In this paper, we provided some key examples in the literature of RWE to guide clinical decisions on dosing expansion and treatment for certain subgroups of patients, however, further research is still required to harness its potential in areas such as dose predictions for drugs with similar mechanism of actions, food effect, formulation optimization, dose adherence, compliance, and more. In one particular area, off-label use of drugs in neonates is more common than an exception due to insufficient new drug development in this high-risk population. However, there is an urgency to generate the evidence to support safety, efficacy, and dosage in the neonatal population. Innovative methodologies, increased needs for better health care, data-sharing collaboration, and recent regulatory use cases provide the opportunity to accelerate quality RWD collection to support drug development for this underserved population. The availability of new treatment modalities and epidemiological changes make the periodic reassessment of prognostic factors using RWE of great relevance to guide clinical practice and the design of future trials.
The development and availability of mRNA vaccines for coronavirus disease 2019 (COVID-19) would not have been possible without the insights gathered from RWE where, among others, a Centers for Disease Control and Prevention (CDC)-led study confirmed a COVID-19 risk reduction of over 90% after second dose of mRNA vaccines among healthcare personnel, first responders, and other essential workers supporting a full FDA approval of mRNA vaccines. 18 The pandemic accelerated the expansion of RWE by uncovering much of its untapped potential in an unprecedented situation where expedited rollouts were vital to the public health, but now it is time to apply those learnings to other unmet areas. However, the lessons learned on the challenges of utilizing RWD during the pandemic, for example, incomplete patient care information, missing data or clinical outcomes, and lack of validation or standardization, should also be considered carefully to mitigate these issues. The growing landscape of RWD provides an opportune moment to embrace RWE and leverage its full potential for clinical pharmacology strategies. This will allow clinical pharmacologists to further advance scientific innovation and promote optimization and individualization for current and future generations of drug therapies.

FUNDING INFORMATION
No funding was received for this work.