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Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review

Graphical abstract


Introduction
The 21st century has allowed for coronaviruses to become well perfected and higher pathogenic to humans. In 2003 the world experienced severe acute respiratory syndrome of coronavirus (SARS-CoV) outbreak. It started from China and spread to five continents, with the calculated fatality rate of 9.6% during the outbreak period [1]. In 2012, the second outbreak of the Middle East respiratory syndrome-related coronavirus (MERS-CoV) occurred in the Arabian Peninsula, with the fatality rate of about 34.4% [2]. An outbreak of the novel coronavirus disease  has emerged in Wuhan, Hubei Province, China, in December 2019. It is expanding at a remarkable pace so that on March 11th, 2020, the World Health Organization (WHO) declared COVID-19 as a pandemic [3]. As of writing this, there are more than one million people infected with, and more than 80,000 people died from COVID- 19. Having multiple routes of transmission and lack of full adherence to social distancing guidelines are important barriers making its prevention difficult [4][5][6][7]. Also, despite widespread efforts, finding the origin, diagnosis, treatment, and management of COVID-19 has been a challenge for the health care system [8][9][10][11]. Such efforts have so far occurred separately in different biomedical disciplines, particularly immunology, genetics, medical biotechnology, molecular engineering, nutrition, picotechnology, and regenerative medicine , highlighting the need for an integrated view of COVID-19 [33][34][35] aimed at doing science of high-quality [36]. With respect to the absence of specific treatment for COVID-19, we can provide only the combination of symptomatic treatment and supportive measures [37,38], that for a significant proportion of cases, will not suffice. The fact is given as the disease can cause hyper inflammation affecting multiple systems and organs making it difficult to treat [26,[39][40][41][42][43][44][45].
The process of de novo drug design is hugely time-consuming. Drug repurposing, also known as drug repositioning or drug re-profiling, works as an alternate, systematic method in drug discovery that can aid in determining the new indications for the existing drugs. It is of high importance that this method repurposes drugs which their safety and pharmacokinetics have been recognized so far. Hence, it would confidently reduce the risk of adverse side effects, drug interactions, and drug development time and expenditure. The fastness of the computational or in silico techniques has made them an exciting approach to the drug repurposing world [46]. There are two main approaches to the computational drug repurposing process: target-based and diseasebased [47]. The former allows the drug and the target to interact with each other leading to the establishment of drug-target interactions. The latter utilizes datasets to determine new indications for already approved drugs from comparisons of characteristics of diseases. Computational drug repurposing approaches differ from each other in some points, such as target modeling, algorithms, and the drug bank or data sets [48].
Since the beginning of the COVID-19 outbreak, there has been an increasing interest in finding a potential therapeutic agent for COVID-19. Considering the matter of time that this pandemic is not the time of trial and error [49] and also the possibility of re-infection [50], the computational methods of drug repurposing offer the best chance of selecting one drug from a list of approved drugs for the life-threatening condition of COVID-19. The present systematic review aims to provide an overview of studies that have used computational methods for COVID-19 drug repurposing.

Methods
We prepared this review according to the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) statement [51]. Before beginning the database search, the protocol of the present systematic review was developed and submitted to PROSPERO.

Study selection
The present systematic review included original articles in English that applied computational methods for COVID-19drug repurposing. Eligible studies should repurpose drugs that are already approved by at least one of the following authorities: the European Medicines Agency (EMA), the Japan Pharmaceuticals and Medical Devices Agency (PMDA), or the U.S. Food and Drug Administration (FDA). Studies that either investigated biologic agents (such as interleukins, vaccines, and miRNA), nutritional supplements, and traditional medicines or focused on protein structure prediction or determination rather than having a drug discovery project were not eligible to be included in this review. Also, studies examining a wet lab approach were excluded. By considering the above criteria, two authors (K.M. and N.Y.) independently performed title/abstract screening and detailed review. In the case of disagreement, the two authors discussed the reasons to reach a consensus. When they were unable to reach a consensus, the third author (A.S.) was consulted.

Data extraction
The first two reviewers (K.M. and N.Y.) extracted the following data from each included publication: the first author, year of publication, country of origin, drug repurposing method, sequence alignment, target preparation, the resource for approved drugs, the visualization tool, molecular docking tool, coronavirus strain, target structures, candidate therapeutic agents, and the authors' conclusions. A consensus discussion between the two authors was made on items of discrepancies. The third author (A.S.) was involved in the points on which consensus did not happen.

Quality assessment
The idea of bias in computational drug research studies is not well established. However, few statements can aid in assessing bias systematically. Cleves et al. [4] argue about a bias that is produced by two dimensional (2D) descriptors in compound screening. The use of the method would reduce the probability of finding novel compounds. Also, Hert et al. [5] have introduced a bias that occurs when screening drug libraries, and therefore the result of screening would be restricted to the compounds with known biological effects. It is a threat to novelty, and a possible solution to this bias is the development of useful decoys (DUD), which use a standard set of ligands to make comparisons of different docking methods simultaneously [6]. Recently, Scannell et al. [7] have presented a bias that occurs when targeting a single molecule with a single compound and can be avoided with a multi-target approach. Eventually, it is crucial to point out the molecular flexibility of the targets. This point is usually missed out when one docking method is applied. The best way of putting this bias into account is to use the molecular dynamics simulations, a robust method with many functions that can predict the drug-target interaction in a better way. The most crucial function of molecular dynamics simulations is to provide multiple receptor conformations, in addition to many other sophisticated analyses that can accurately differentiate between a proper docking and an inadequate docking [52].
According to the potential issues of bias, a tool was designed for the assessment of five main aspects of quality of studies included in the present systematic review: design (mono-target vs. multi-target), target template modeling (crystal structure, homology modeling, and cocrystal ligand), docking tools, molecular dynamics simulation (yes vs. no), and the resource for approved drugs. The quality of each eligible article was independently appraised by two authors (K.M. and N.Y.) and then was double-checked by the third author (A.S.).

Study quality
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Data synthesis
Conserved structures in the viral genome represent a high potential to be target candidates. While the phylogenetic tree inevitably undergoes evolutionary changes, a highly conserved structure can maintain its sequence among strains. Targeting such a highly conserved site will provide cross-reactive protection in different strains. Highly conserved elements of the 2019-nCoV (SARS-CoV2) include nonstructural proteins such as 3CLP, RdRp, and PLP, and structural proteins, such as the S protein [78,124]. Below is a target-based synthesis of data for COVID-19 drug repurposing as summarized in Table 4.

3CLPa
The main protease of the 2019-nCoV, also known as 3CLP or the C30 endopeptidase, is a highly conserved element that shares 96.1% similarity with the main protease of SARS-CoV. This protease is a member of the coronavirus polyproteins. When translation takes place, it is the first one that is auto-cleaved from the polyprotein. Then, it, in turn, would mediate the cleavage of the other 11 non-structural proteins that are vital for viral replication and transcription. Thus, 3CLP might serve as a marvelous target for COVID-19 therapy [78].
Lopinavir and Ritonavir are the most documented candidate drugs that target 3CLP (Table 3). However, Wu et al. claimed that Lopinavir and Ritonavir have not an excellent binding score in docking compared

Table 2
Quality of studies included in the systematic review.
to other drugs [144]. Moreover, Contini et al. developed molecular dynamics simulations, which are known to be more potent than docking in the prediction of the drug-target binding [147]. The authors showed that Ritonavir failed to form the interaction with the 3CLP [130]. The good binding energy and docking score for each one of Elbasvir, Simeprevir, Indinavir, and Atanzavir might nominate these drugs as candidates for the inhibition of 3CLP of SARS-CoV2. Interestingly, both Indinavir and Remdesivir might be effective against SARS-CoV2 infection due to their excellent docking scores and limited toxicity, as confirmed by Chang et al. [128]. Nelfinavir was also mentioned as one of the best drugs binding to the 3CLP. However, it appeared less efficient than Tegobuvir and Bictegravir when the affinity and the physical-chemical analysis parameters were calculated using SeeSAR [136,145]. Nguyen et al. suggested an unusual inhibitor of 3CLP, Clocortolone, a medium-strength steroid that is used for dermatitis and has good binding scores making it a promising cleanser against SARS-CoV2 contaminated surfaces [139].

RdRp
Viral RdRp is an enzyme that accelerates the replication of RNA from a template RNA and shares around 97.08% of its sequence with SARS-CoV [131].
Among the drugs that target this protein, Remdesivir is a bold one which, by a triphosphate nucleotide, would act as an ATP-competitive inhibitor of RdRp and intervene with the viral RNA synthesis. Also, Remdesivir can bind to the human TMPRSS2, a protein that mediates the cleavage of the viral S protein and promotes the entry of SARS-CoV2 into the host cells [144]. Ribavirin is another drug reported in two studies to interact with RdRp. It appeared not to be a very effective drug compared to other drugs [128,131].

PLPa
PLP plays a crucial part in the initiation of infection through its ability to antagonize interferon (IFN) activity and deubiquitinate viral and cellular proteins [148].
Eight different structures attached to SARS-CoV PLP comprise sequences that are at least 82.17% identical to that of SARS-CoV2 PLP [132]. Two studies mainly focused on PLP drug candidates. Elfiky et al.
pointed out drugs against SARS (GRL-0667, GRL-0617, and Mycophenolic acid) and HCV NS3 (Telaprevir, Boceprevir, and Grazoprevir) that have acceptable binding energy for SARS-CoV2 PLP but lack an excellent binding score [132]. Lin et al. demonstrated conformational changes of PLP upon binding to Darunavir and suggested Darunavir as a competitive inhibitor of PLP [137]. Arya et al. also reported two drugs that affect the SARS-CoV2 PLP: Chloroquine, an anti-malarial agent, and Formoterol, a drug that mainly works as a bronchodilator [126].

Discussion
Multi-target therapeutic (Table 3) agents are more useful than monotarget drugs ( Table 5) in terms of better predictive pharmacokinetics, better patient compliance, and reduced risk of drug interactions [149]. Simultaneously impacting different targets is, in particular, advantageous to approach individuals that express intrinsic or induced variability in drug response due to modifications in key disease-relevant biological pathways and activation of compensatory mechanisms [150,151]. Below is a discussion of drugs repurposed for COVID-19 that target multiple viral elements (see Fig. 3).
It is worth mentioning that this category contains the highest number of repetitions among the multi-target drugs repurposed for COVID-19 therapy. Initially, each one of Entecavir, Penciclovir, and Ganciclovir similarly hit RdRp, helicase, and 3 ′ -to-5 ′ exonuclease proteins. The percentages of high-quality items Fig. 2. The percentage of studies reported high-quality items.

Table 4
The main findings of studies included in the systematic review. -Through the DTI model, the viral proteinase-targeting drugs were predicted to act better on the viral replication process than viral proteinase. -An antiviral drug, such as guanosine analogs (e.g., acyclovir, ganciclovir, and penciclovir), reverse transcriptase inhibitors, and integrase inhibitors, were more than proteinase inhibitors in the results. -Dual-component HCV drugs, Epclusa (velpatasvir/ sofosbuvir), and Harvoni (ledipasvir/sofosbuvir) act on two viral proteins, thus reducing the viral resistance ability. In addition to their easy administration (orally) with minimal side effects. -Diosmin and hesperidin, which are flavonoid glycosides from citrus fruits, fit amazingly into the substrate-binding site and block it. However, these chemotherapy drugs have many adverse effects and should be administered intravenously. -Venetoclax, which is also a chemotherapy drug, is loaded with side effects, including upper respiratory tract infection. Contini 2020 -Indinavir was selected by both COVID-19 main protease (PDB code 6LU7) and COVID-19 3CL-PRO proteinase. -Lopinavir and Atazanavir were best selected for 3CL-PRO -Cobicistat was chosen as a potential COVID-19 primary protease inhibitor. -Angiotensin II and GHRP-2 were selected too. Although these might bind the target, they are cleaved by the target too because these are peptides, and they are just served as substrates.  -Virtual screening introduced many compounds able to bind the ACE2 target. However, none of these drugs bind with the contact surface of the ACE2-Spike complex. Thus, these compounds could merely inhibit ACE2 enzyme activities rather than the inhibition of the viral infections caused by ACE2. -2-PROTAC technology might be the right choice for the proteins that finding their inhibitor is quite hard. In this technology, the proteins are first degraded, then this leads the inhibition if the viral infection, e.g., natural hesperidin was selected for Spike protein. In this condition, the viral infection process could be inhibited by any small molecule that interferes with the re-folding of the spike when it is bound to it.
(continued on next page) K. Mohamed et al. Danoprevir has the same targets as the drugs mentioned above, with an exception to helicase, which is replaced by endoRNAse. Moreover, Dabigatran, Itraconazole, and Saquinavir hit RdRp, helicase, and spike proteins; however, the later one hits 3CLP instead of the spike protein.
Furthermore, Sempremivir and Nelfinavir commonly hit 3CLP and helicase, and distinctively hit each one of the 3 ′ -to-5 ′ exonuclease and endoRNAse, respectively. Indinavir and Remdesivir are the most documented drugs among dual-target drugs. These two drugs similarly target the 3CLP and desperately target each one of the helicase and RdRp, respectively. Also, Ribavirin is a drug with a dual effect; it targets RdRp and PLVP.
Smith et al. mentioned that Nitrofurantoin, Isoniazid pyruvate, Eriodictyol, are the top three candidates that bind to the ACE2 part of the ACE2 receptor-spike protein interface. Therefore, it might be expected that these drugs might restrict the binding of SARS-CoV2 spike protein to the ACE2 receptor and hence, inhibit the spread of infection [140]. However, this might be quite controversial if we consider what Wu et al. have declared about the inability of these drugs to block the viral infection and their restricted effect on the ACE2 Enzyme activity [144].
Chloroquine is another doubtful drug. Three studies have suggested that this drug might inhibit the PLP and the E-channel [126,133,144]. In all of these studies, the binding energy of Chloroquine is acceptable, but it lacks an excellent value and as declared by Wu and colleagues who emphasize on not considering the drugs that do not have a specific target like Chloroquine [144].
Wu et al. [144] have claimed that they found probable druggable compounds through the docking process of their structural model of helicase. The authors predicted that anti-bacterial drugs (Lymecycline, Cefsulodine, and Rolitetracycline), anti-fungal drug Itraconazole, antihuman immunodeficiency virus-1 (HIV-1) drug Saquinavir, anticoagulant drug Dabigatran, and diuretic drug Canrenoic acid could act as potential coronavirus helicase inhibitors with high mfScores. Beck et al. [127] have also applied helicase as the drug repurposing target. The five top potential inhibitors have turned out to be Simeprevir, Atazanavir, Grazoprevir, Asunaprevir, and Telaprevir; with K d (nM): 23.34, 25.92, 26.28, and 28.20, respectively.
Drug-target interaction (DTI) prediction results of repurposing of approved drugs indicate a list of possible inhibitors of 3 ′ to 5 ′ exonuclease, and the four top candidates were Simeprevir, Efavirenz, Danoprevir, and Ganciclovir [127].
2 ′ -O-MTase, methylates the ribose 2 ′ -O position of the first and second nucleotide in viral mRNA structure to sequester it from the host immune system [135]. The reported docking results in two separate studies that have utilized 2 ′ -OMTase as their target was different. These two studies were performed by applying two different methodologies. Khan et al. [135] have performed homology modeling, molecular docking, and molecular dynamics simulations and have reported Dolutegravir and Bictegravir as probable candidates against SARS-CoV2. While Beck et al. [127] have conducted molecule transformer-drug target Interaction (MT-DTI) and have introduced Atazanavir, Efavirenz, and Boceprevir as potential inhibitors of SARS-CoV2 2 ′ -OMTase.
EndoRNAse is an enzyme that can cleave both single-stranded and double-stranded RNA. In all 21 included studies, only Beck et al. [127] have used endoRNAse as a target for computational drug repurposing and proposed a list of approved drugs as potential candidates among which the top-ranked ones are Efavirenze and Atazanavir.
Atazanavir (ATV) is an antiretroviral protease inhibitor primarily introduced for the treatment of HIV. When it is administered intravenously, it can reach the lungs and help to cure pulmonary fibrosis. An in vitro study has shown that ATZ lessens SARS-CoV2 replication in both Vero cells and human epithelial pulmonary cells (A549). ATZ can particularly attenuate the unwanted inflammatory response to SARS-CoV2 in infected monocytes, as measured by the reduced levels of pro-inflammatory cytokines, including IL6 and TNFA [152].
In vitro studies indicate the anti-SARS activities of Lopinavir. Ritonavir is a potent inhibitor of cytochrome P450. When combined with Lopinavir, Ritonavir can help reduce cytochrome P450-mediated metabolism of Lopinavir in the liver that will increase plasma half-life of and biological effects of Lopinavir. As evidenced by a randomized controlled trial (RCT), no difference in clinical outcomes appeared following Lopinavir-Ritonavir treatment in adult patients with severe COVID-19 [153]. Moreover, patients receiving Lopinavir-Ritonavir treatment developed gastrointestinal adverse events more than those who underwent standard care.
An in vitro study [154] compared the safety and efficacy of nine HIV-1 protease inhibitors on SARS-CoV2 in VeroE6 cells. Amprenavir, darunavir, and indinavir could provide inhibition of SARS-CoV2 replication at a high 50% effective concentration (EC 50 (21.52). Also, the C trough /EC 50 ratio higher than one, which indicates that the compound can reach a trough serum concentration of higher than 50% effective concentration, occurred in only three of nine drugs Nelfinavir, Lopinavir, and Tipranavir. Overall, Nelfinavir seems to be the best among different anti-protease inhibitors, with both the lowest EC 50 and the highest SI as well as the C trough /EC 50 ratio higher than one.
Because of the absence of any effect of Darunavir on SARS-CoV cultured in Caco-2 cells, there is no SI attached to Darunavir [155]. By contrast, Remdesivir could reduce the cytopathogenic effect (CPE) of SARS-CoV at low doses (EC 50 = 0.11 μM) and represent a noticeable SI higher than 900.
The study [156] evaluated the efficacy of seven drugs against SARS-CoV2 in Vero E6 cells. The 50% effective concentration at which the inhibitory effects of Ribavirin, Penciclovir, and Favipiravir on SARS-CoV2 replication appeared was as high as 109.5, 95.96, and 61.88 μM,  Like Chloroquine, hydroxychloroquine can have anti-malarial and immunomodulatory effects in a manner useful to patients with autoimmune diseases. Both Chloroquine and hydroxychloroquine have shown to help antiviral immunity through inhibition of the fusion between viral and host-cell membranes, virus replication, and viral glycosylation and assembly. However, hydroxychloroquine has become more important for fewer adverse effects and drug-drug interactions. In vitro investigation points out a lower 50% effective concentration required for Chloroquine compared to hydroxychloroquine to exert anti-SARS-COv2 effects in Vero cells [157]. It would indicate the more potency of Chloroquine than its analog, hydroxychloroquine, in inhibiting SARS-CoV2 replication.
In conclusion, at this growing rate of COVID-19 pandemic and increasing mortality rate, it seems quite unachievable to design a novel specific drug for it. Therefore, it puts a spotlight on the drug repurposing system, and if we consider the matter of time, computational methods are our best shots. Since the incidence of this outbreak, many investigations have been conducted to present an appropriate therapeutic agent for COVID-19 utilizing computation drug repurposing approaches. Therefore, the review intended to cover all these studies to help build a concise framework for future research into COVID-19 therapy.
All methods have inherent limitations, and computational methods are not exceptional in this field. Thus, the use of models -which are illustrations of the real world-in running computational research, might be an inherent limitation. Besides, the results reported by docking score, binding energy, binding affinity, and other numerical variants were immensely divergent across twenty-one studies included in the review. Having the fact in mind that there is not any specific tool for quality assessment and comparison of computational repurposing methods; it would not be possible to evaluate the potential candidates from different studies to declare which is more significant.

Conclusion
Despite its limitations, the present systematic review provides a list of existing drugs that have the potential to influence SARS-CoV2 through different mechanisms of action. And in the near future, clinical studies examining these drugs might come to conclude, which can be more useful to inhibit COVID-19 progression.
Ethics approval and consent to participate Not applicable.

Consent for publication
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Availability of data and materials
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Funding
There is no funding for the present study. Authors' contributions K.M. conceptualized the study. K.M. and N.Y. conducted database search, search results screening, detailed review, data extraction, quality assessment, and prepared the initial draft. A.S. prepared the final draft. N.R. supervised the project and critically appraised the manuscript.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. K. Mohamed et al.