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Artificial

Molnupiravir: A potential candidate drug for COVID-19 treatment

Preeti P.¹, Prashant Singh¹, Robin Sinha¹, Priya Kumari¹, Swarsat Kaushik Nath¹, Trapti Sharma¹, Ridhima¹, Sweety Dattatraya Shinde¹, Aruna vigneshwari, Rutuja, Kamal Rawal^#1

1. Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
   
   #Corresponding Author
   Email ID: kamal.rawal@gmail.com
   Center for Computational Biology and Bioinformatics, AIB
   Amity University, Noida.

Website: http://drugx.kamalrawal.in/drugx/

Abstract

Background: A rapid increase in cases due to the SARS-CoV-2 Omicron (B.1.1.529) variant in highly vaccinated populations has raised concerns about the effectiveness of current vaccines. There is an urgent requirement for new drugs for the treatment of COVID-19. MSD has reported that molnupiravir (antiviral) has reduced the risk of admission to hospital or death by around 50% in non-hospitalized adults who had mild to moderate COVID-19 and were at risk of poor outcomes. Computational drug-repositioning approaches applied to COVID-19 have helped to identify new drugs (e.g. molnupiravir) for COVID-19 in the past two years. The drug repositioning strategies can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) based approaches.

Methods: We have implemented a multi-modal pipeline (COV-DrugX) that utilizes machine learning strategies, chemical information, molecular docking, clinical data, gene expression, and molecular data to screen drugs for their suitability against COVID-19. Here we applied COV-DrugX on molnupiravir to understand its physicochemical properties, interactions with viral proteins (targets), role in human network datasets as well as a potential role in the change of gene expression profiles induced due to COVID-19.

Results and conclusions: We found that the molnupiravir had shown the best binding affinity (-8.2 Kcal/Mol) with Nucleocapsid protein (Npro). Using the deep learning module, we found that molnupriavir shows potential physicochemical features similar to candidate COVID-19 drugs. Molnupiravir also scored high in other modules of the pipeline indicating its suitability in repurposing in the COVID-19 situation. Through large-scale docking with over 20000 proteins, we found important targets by which we could explain the potential side effects of molnupiravir.

Keywords: Bioinformatics, drug repurposing, artificial intelligence, COVID-19, molnupiravir

1. Introduction

The worldwide outbreak of SARS-CoV-2 has sparked significant health concerns and global socio-economic impacts. Despite the invention of therapeutic vaccines, the threat posed by the emerging SARS-CoV-2 variants such as delta and omicron still questions the effectiveness of existing vaccines. Furthermore, considering the mutation rate of the virus, the world should be prepared for the emergence of future variants and other pathogenic viruses which could be a bigger threat to the community. Therefore, the need for effective strategies like drug repurposing is still pertinent to develop a treatment to reduce viral transmission, risk of hospitalization, and disease progression.

Previously, we have developed several machine learning and bioinformatics platforms. These include text mining and network biology-based systems [Jagannadham et al., 2016], vaccine discovery systems [Rawal et al. 2021], next-generation sequencing analysis systems for cancer and other genomes [Preeti et al.,2021, Rawal et al 2011, Mandal et al 2006].

1.1 Drug repurposing - an effective strategy for COVID-19 drug discovery

Drug repurposing is also known as drug repositioning which is defined as a process of identification of new pharmacological indications of approved or investigational drugs beyond their intended therapeutic use [Ashburn and Thor, 2004]. Drug repurposing has several advantages over traditional drug discovery processes including the short period required for R&D and low cost. Since the world has started battling the pandemic, this approach has embarked on a new avenue in meeting the increasing demand for new drugs [Ng et al., 2021]. However, to achieve high success rates of repositioned drugs, it is necessary to develop integrated computational and experimental approaches. Herein, we report the developed pipeline DrugX and its efficacy in studying the properties of the molnupiravir drug against SARS-CoV-2.

1.2 Molnupiravir - an effective antiviral drug for SARS-CoV-2

Molnupiravir is an antiviral drug that has been issued emergency use authorization (EUA) by the FDA in the treatment of COVID-19 [U.S. Food and Drug Administration, 2021]. Molnupiravir (Emory Institute of Drug Development-2801/MK-4482) has emerged as a promising oral antiviral drug for COVID-19 [Benkovics et al., 2020]. This drug molecularly targets the RNA polymerase of SARS-CoV-2, the key enzyme in the viral replication cycle. Molnupiravir is the isopropyl prodrug of the ribonucleoside analog β-D-N4-hydroxycytidine (NHC, EIDD-1931) [Agostini et al., 2019]. Molnupiravir was developed by Drug Innovation Ventures at Emory University which was later acquired by Ridgeback Therapeutics in partnership with Merck & Co, USA. It is found to be effective against SARS-CoV-2 infections in Syrian hamsters [Abdelnabi et al., 2021], mice [Wahl et al., 2021], and ferrets [Cox et al., 2021]. Molnupiravir in phase I/II/III clinical trials has demonstrated good efficacy and safety and, data explained a good safety profile [Vitiello et al., 2021]. The UK’s medicines regulator has issued temporary authorization of molnupiravir for the treatment of mild to moderate COVID-19 in adults with at least one risk factor for severe illness [Mahase, 2021a]. It has been confirmed that molnupiravir could reduce the risk of admission to hospital or death by around 50% in non-hospitalized adults with mild to moderate CTP infection and the people at risk of poor outcomes [Mahase, 2021b].

1.3 RNA-Dependent RNA-Polymerase (RdRp) – an attractive therapeutic target

The RNA-dependent RNA polymerase (RdRp) is a multidomain protein encoded by nonstructural protein 12 (nsp12), and nsp7 and nsp8 as accessory proteins [Venkataraman et al., 2018]. This belongs to the viral polymerase family and consists of three subdomains: a fingers subdomain, a palm subdomain, and a thumb subdomain [Venkataraman et al., 2018]. The highly conserved residues at the C-terminal domain of nsp12 serve as an active site [Gao et al., 2020]. This enzyme plays a significant role in the SARS-CoV-2 replication and transcription process [Imran et al., 2021] (Figure 1). Though the emerging variants of concern B.1.1.7 and B.1.351 have considerable mutations in spike proteins, the active sites of RdRp remain conserved [Abdelnabi et al., 2021a]. The approved RdRp inhibitors remdesivir and favipiravir have been proved effective in reducing the progression of COVID-19. But the disadvantages are remdesivir has to be administered intravenously and favipiravir has a poor pharmacokinetic profile [Ghasemnejad-Berenji, 2021]. Molnupiravir has emerged as an effective alternative as it has a favorable pharmacokinetic profile and can be administered orally [Vangeel et al., 2021] Molnupiravir has also been proved effective against Variants of concern [Abdelnabi et al., 2021b, Prince et al., 2021].

1.4 Molnupiravir - Mechanism of action against SARS-CoV-2

Molnupiravir prevents the spread of COVID-19 by inhibiting viral propagation through a mechanism known as lethal mutagenesis [Malone and Campbell, 2021]. Molnupiravir in its active form molnupiravir triphosphate (MTP) in the cell, competes effectively with Cytidine triphosphate for incorporation into the product RNA in the form of monophosphorylated MNP (Figure 2a) [Kabinger et al., 2021, Gordon et al., 2021]. Monophosphorylated Molnupiravir (MNP) when localized in the template strand leads to indiscriminate incorporation of either Adenosine Triphosphate (ATP) (mutagenesis) or Guanosine Triphosphate (GTP) (Figure 2b). This erroneous incorporation of Adenosine Monophosphate (AMP) subsequently incorporates template Uridine Triphosphate (UTP) causing downstream C-to-U mutations (Figure 2c). The accumulation of these mutations makes the viral replication over the ‘error threshold’ which causes error catastrophe by demarcating the replication fidelity required for viability [Malone and Campbell, 2021]. Studies showed that the treatment with molnupiravir has failed to induce viral-resistance mutations, suggesting a higher genetic barrier to immune evasion (Yoon et al., 2018).

2. Implementation

We have developed a pipeline named DrugX (http://drugx.kamalrawal.in/drugx/) based on different computational drug-repurposing approaches. This pipeline was built using various modules utilizing different sources from the different databases and literature-based-text mining approaches for COVID-19. There are 14 modules named as drug-circuit, drug-target, drug_dock_human, drug_dock_viral, drug_dock_KG, drug_phenotype, drug_AI_ranking, drug_condition, drug_side_effet, drug_side_effect_neighbours, drug_gene_expression, drug_dl_11, drug_dl_200, and drug_gene_network have been constructed using various strategies and different sources of data. These modules provide results in the form of scores 0 and 1 to the query drug, taking input in the SMILES format of the drug.

2.1 Drug_Feature Module:

Drug_dl_11 module was created using a deep learning approach using a dataset of 262 drugs related to COVID-19 which were extracted using literature mining). A total of 11 biological properties of drugs were used for the training (See ST1). Further, 200 physicochemical properties were included in a specialized module named Drug_dl_200. These 200 chemoinformatics properties were extracted from RDkit (https://rdkit.org/).

2.2 Drug_Circuit Module :

Drug_circuit module was built using the drug-genes interaction data which was downloaded from the DGIdb. Additional data regarding the circuits associated with genes were collected from literature mining and used as a source file.

2.3 Docking module:

Molecular docking is an important technique in drug repurposing studies for COVID-19. Therefore, we have used docking as one of the strategies to study the interaction of molnupiravir with significant proteins involved in COVID-19. Drug_dock_human, drug_dock_viral, and drug_dock_KG modules were built upon a docking-based drug-repositioning approach. We have included 23 viral proteins from SARS-COV-2 for docking in a module named drug_dock_viral. The viral proteins include spike protein, membrane protein, envelop small membrane protein, nucleocapsid protein, main protease, papain-like protease, nsp3 (207–379), RNA dependent RNA polymerase (RdRp, nsp12/7/8 complex), nsp7, nsp8, nsp12, helicase, nsp14, nsp15 (endoribonuclease), nsp10, nsp16, nsp16/10 (2′-O-methyltransferase), nsp1, nsp2, nsp4, nsp6, nsp9, ORF3A, ORF6, ORF7A, ORF8, and ORF10.

We have also included the two important human proteins Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease (TMPRSS) which play a significant role in the entry of SARS-CoV-2 [Lan et al., 2020; Mollica et al., 2020], We included these proteins for docking against the query drug molnupiravir in an independent module named drug_dock_human. Another module named drug_dock_KG works on the proteins extracted using a knowledge graph This module incorporates AP2-associated protein kinase 1 (AAK1), Cyclin G-associated kinase (GAK), and Janus kinase (JAK1/2). These proteins have been reported to play a role in viral endocytosis [Neveu et al., 2015].

2.3 Side effect module:

Drug_side_effect module data source has been prepared using SIDER (http://sideeffects.embl.de/) and OFFSIDES (http://tatonettilab.org/offsides/) databases. In the drug_side_effect module, we have compiled the 6,123 side effects of drugs related to COVID-19 (See Supp table). Also, we have gathered a list of various side effects observed in 3,052 drugs obtained from the SIDER & OFFSIDES database. The working of this module is such that it predicts if the drug is associated with COVID-19 or not, based on drug- side effect association. If the module got the side effect in the listed dataset for COVID-19, then the module predicts a score of 1 otherwise it will give 0.

2.4 Target Module:

The Drug_target module uses 31,359 drugs and their targets obtained from the TTD database (http://db.idrblab.net/ttd/). Furthermore, this also utilizes 378 targets related to COVID-19. The targets were collected using the drugs displaying some functional role in the treatment of COVID-19 (positive drug dataset). The working of the module is such that it would provide the target for the query drug if it is associated with COVID-19. If the module got the target name in the COVID-19 dataset which was obtained from the TTD dataset then the module predicts a score of 1 otherwise it will give 0.

2.5 Drug_Circuit module:

The Source of the Drug_circuit module is the DGIdb (https://www.dgidb.org/downloads) database, from which we have extracted information of 100,274 genes and their associated drugs. Also, we have a total of 299 circuits and their associated gene-protein derived from the research paper [Loucera et al., 2020]. The functioning of this module is such that it provides the circuit for an input query drug that is associated with COVID-19. Users are allowed to provide either drug name (separated by pipe in a text file) or SMILE notation of the drugs (separated by newline character in a text file). The module would accept drug names as a query and search its associated interactions (genes), and further associated circuit information in the datasets provided in the module. The module picks the gene associated with the provided drug and further checks on the associated circuit with the gene and its functional information such as Host-virus interaction, Inflammatory response, Immune activity, Antiviral defense, Endocytosis, Replication, and Energetics. If the module has an associated circuit to our query drug in the dataset, then the module predicts a score of docking-based 1 otherwise it will give 0.

2.6 Drug_Phenotype Module:

Webmd scraped dataset (https://www.kaggle.com/rohanharode07/webmd-drug-reviews-dataset) serves as a data source of the Drug phenotype module. This dataset comprises a list of 6,147 genes along with their associated drugs and the observed phenotypes in the condition when the drug is prescribed. Furthermore, we have extracted 2,009 phenotypes from the literature which were observed in COVID-19 patients. This module provides the phenotype of the input drug if it is associated with COVID-19 and also provides the number of matching phenotypes of the drug observed in COVID-19 affected patients.

2.7 Gene Expression Module:

In the Drug Gene expression module, data has been derived from multiple sources such as DGIdb, drug_central database where the list of drugs and their interacting genes is extracted from DGIdb database, and Drug target interaction data from drug_central database. In the processed file, only gene and drug names are included from two source files from DGIdb and Drug central database. Apart from this data, we have included COVID-19 gene Expression data from various sources which include a study conducted by [Blanco-Melo et al., 2020], MsigDb (https://www.gsea-msigdb.org/gsea/msigdb/), and COVID-19 drug-gene set library (Mayanlab (https://maayanlab.cloud/covid19/)). The process file of COVID-19 gene Expression contains information about gene symbols, gene expression data as a result of drug administration. This module would provide information about genes associated with the drug and their expression pattern which denotes whether these genes are upregulated or downregulated. Gene associated with the query drug is searched in the database and the drugs that interact with genes associated with COVID-19 are assigned a score of 1. If the gene is not found in the database of DEGs, the query drug is assigned a score of 0 which implies that the query drug does not interact with any genes associated with COVID-19.

3. Usage

Molnupiravir drug (Figure 3) having drug bank ID “DB15661 '' an isopropyl ester prodrug of N4-hydroxycytidine is hydrolyzed in vivo and incorporated into the genome of RNA viruses. Its SMILES were taken from DrugBank database “CC(C)C(=O)OC[C@H]1O[C@H]([C@H](O)[C@@H]1O)N1C=C\C(NC1=O)=N\O”. These SMILES when uploaded in the online server of our pipeline DrugX and selected all 14 modules named as drug-circuit, drug-target, drug_dock_human, drug_dock_viral, drug_dock_KG, drug_phenotype, drug_AI_ranking, drug_condition, drug_side_effet, drug_side_effect_neighbours, drug_gene_expression, drug_dl_11, drug_dl_200, and drug_gene_network. The scores are produced in the range 0 and 1 where 0 refers to the drug not to be considered in drug repositioning for COVID-19, while 1 symbolizes that the drug should be considered into drug repurposing for COVID-19. Also, intermediate files are generated for each module, providing more functional and elaborate information found in the module related to the input drug.

4. Result and Discussion

We found our modules are based on various computational drug repurposing based approaches where we have two modules predicting the drug is COVID19 repurposable drug on the basis of 11 biological properties and 200 chemoinformatics properties of SARS COVID-19 virus. Then, we have a set of modules analyzing the drugs based on conditions related to COVID19, phenotypes observed in COVID19, drugs Shox-special/nautilus-clipboard

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wing symptoms similar to COVID19, side effects of drugs related to cure and prevent COVID19, and target, gene expression-based approach as well. Another approach used in our pipeline is docking-based and has built 3 docking-based modules where we have a human target-based docking module, viral proteins target-based module, and protein kinases associated with human proteins as targets for the query drugs. Not just these, we have added a module on calculating the euclidean distance between drug and COV-2 disease and a module checking the query drug if it has any association with the SARS COV-2 circuit.

Molnupiravir when docked against 23 viral proteins, using drug_dock_viral, results are interpreted in the form of Binding Affinities(which shows the strength of binding interaction). The average binding affinity came out to be -6.4 KCal/mol. Amongst the 5 highest interacting viral proteins with molnupiravir are Npro, Nsp2, Nsp14, Nsp15, and Orf3A with -8.2, -7.8, -7.9, -7.7, -7.6 as their respective binding energies. While, on the other hand, the minimum binding energy was observed in the case of Nsp1 and Orf10 with -3.3 and -4.7 KCal/mol of binding energy. The histogram showing the distribution of binding affinities for viral proteins is shown in Figure 3.

Similarly, Molnupiravir when docked against human proteins (ACE2, TMPRSS2) and their associated protein kinases (AAK1, JAK1/2, and GAK) we find the average binding energy to be -7.22 kcal/mol. The output binding affinity values of the human proteins and their associated protein kinases are listed in table 2. The histogram plot for better analyzing the binding affinity values obtained is shown in figure 4.

The biological and categorical properties enlisted in deep learning modules drug_dl_11 and drug_dl_200 give the scores as recorded in tables 3, 4 for drug molnupiravir. Amongst 277 targets listed in the drug_targets module for COVID-19, we found a target similar to molnupiravir drug target which is RNA dependent RNA polymerase. Table 6 here shows the results of an intermediate file formed in module drug_side-effect where all the side effects of drug molnupiravir, and the side effects of molnupiravir similar to those observed in COVID-19. Here, amongst the 6,123 side effects listed related to COVID-19 we found 4 similar side effects of molnupiravir, and these were namely dizziness, headache, nausea, and diarrhea.

Compiled scores for all the 14 modules are reported in (Table 7) where we get the final score in form of 0 and 1 for almost all the modules except docking modules which gives the score within the range of 0 and 1, considering the cumulative score for the docked proteins considered in the docking modules. Amongst the total 8 modules reporting a score of 0, the modules Drug 11 properties, COVID-19 Drug_Condition, COVID-19 drug_phenotype, and human_gene_expression during COVID-19, drug_circuit, drug_knowledge_graph and drug_AI_ranking, etc give a score of 0 because of lacking any evident information on the drug in the source databases associated with these modules as there has been no gene associated to the drug reported yet. The modules having the data on the drug Molnupiravir (6 in number ), all report the score of 1, recognizing the drug to be considered as a COVID-19 repurposable drug.

Number of modules having data related to Molnupiravir = 6

Number of modules giving a score of 1 = 6

Number of modules giving a score of 0 = 6

Average overall score = No. of module giving score 1/ Total number of modules having data related to drug

= 6/6*100= 100%

Hence, based upon the average score, we find that there is a 100% probability for the drug to be considered for repurposing against COVID-19.

5. Conclusion

The pandemic has forced scientists to invent novel strategies for drug discovery, vaccine development, and effective treatment methods in a short duration. Implementation of AI is one of the effective strategies, which makes virtual screening of potential drugs possible, in a cheaper and faster way. CoV-DrugX is one of the evidence-based efficient pipelines which was built using Machine learning to assess the potential candidates for drug repurposing against SARS-CoV-2. As the results of Molnupiravir drug against our DrugX database extensively built for repurposing of COVID-19 has yielded good results, we support the studies claiming Molnupiravir to be a probable cure for COVID-19. Researchers were looking to identify a treatment that would be effective, cheap, and easy to use and molnupiravir looks like it can hit all these targets.

Acknowledgment

We extend our sincere gratitude to Amity University for providing the administrative and technical support required in the conduct of this study.

Contribution of Authors

This study was conducted under the overall guidance of KR, who contributed to the protocol, critical evaluation of data, and manuscript. The pipeline was designed, constructed, and validated by RS and PS. Manuscript writing was done by R.

Financial Support and Sponsorship

Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016). SKN, PP, R, SS, SDS, NG, and TS have received financial support from grants obtained from Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.

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