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Assessment of merimepodib drug for repurposing against COVID-19 using the CoV-DrugX pipeline

Hema Palanisamy¹, Aruna Vigneshwari¹, Preeti P.¹, Prashant Singh¹, Robin Sinha¹, Trapti Sharma¹, Swarsat Kaushik Nath¹, Kamal Rawal^#1

¹Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India.

#Corresponding Author

Email ID: kamal.rawal@gmail.com

Centre for Computational Biology and Bioinformatics,

AIB Amity University, Noida

ABSTRACT

Background

The COVID-19 pandemic has resulted in a higher number of deaths and has not ended yet. This emphasizes the need for effective treatment and drugs against the disease. Traditional approaches to drug discovery will take years and result in an exorbitant cost to get the drug on the market. To hasten the process, repurposing the available drugs is an effective alternative. In-silico method of drug repositioning involves two steps: i) collection of data about the drugs and their targets ii) creation of pipeline by integrating collected data. The developed pipeline could predict the repurposing properties of the given drug. In this research, we are analyzing the repurposing potential of a merimepodib drug using the CoV-DrugX pipeline that has been developed by our research team. Merimepodib inhibits the enzyme involved in the de novo synthesis of guanine nucleotides in viral replication which has been discovered to treat the Hepatitis C virus. In this paper, we have used our Cov-DrugX pipeline and predicted the potential of merimepodib for repurposing in COVID-19 treatment.

Methods

We have developed a pipeline (CoV-DrugX) that integrates many modules based on artificial intelligence. We have taken Merimepodib for our analysis. This tool has predicted and resulted in scores for the individual modules and a cumulative PI score comprising the scores from all modules.

Results

The results suggested that merimepodib has properties that could be considered for repurposing against COVID-19 with a probability of 71.43%. This drug is also involved in dysregulating the expression of the human gene Inosine-5'- Monophosphate Dehydrogenase (IMPDH2), which is the main target of the drug and also satisfies 11 pharmacological properties that are needed for repurposing. Our results show that Merimepodib drug had a strong binding affinity with viral proteins like Nsp14 (binding affinity of 9.4 kcal/mol), human proteins targeted by virus including Angiotensin-Converting Enzyme 2 (ACE2) receptor (binding affinity of -8.7 kcal/mol), and protein kinases like AP2-associated protein kinase (AKK1) (binding affinity of -9.2 kcal/mol), Janus Kinase (JAK14) (binding affinity of -9.0 kcal/mol). The overall result shows the involvement of merimepodib drug during COVID-19 infection and side effects.

Keywords: CoV-DrugX, COVID-19, docking, Inosine-5'- Monophosphate Dehydrogenase (IMPDH), drug repurposing, Merimepodib.

1. INTRODUCTION

The global pandemic COVID-19 caused by the SARS-CoV-2 virus has affected more than 340 million people and recorded 5.5 million show deaths worldwide as of 24 January 2022 (https://covid19.who.int/). This situation urges the researchers to discover novel and effective therapeutics to battle against the disease. The traditional approaches of developing new drugs to combat COVID-19 would take a long period, which could be shortened by repurposing the existing drugs. (Sultana et al., 2020). This approach also allows us to skip the long phase of the drug discovery process including the clinical trials, thereby reducing the cost required (Pushpakom et al., 2018).

In-silico drug repurposing is an emerging approach for drug repurposing in this pandemic where many researchers cannot access laboratories. It takes less time to get the drug for treatment. It is a broader sector of computational pharmacology that encloses in-silico techniques to analyze how drugs act in the biological system. There are two key steps involved in this in-silico drug repurposing approach (Shim and Liu, 2014; Wu et al., 2013). One is the collection of various data related to the diseases, symptoms, genomic, proteomic data, clinical data of patients, causative agent information, etc. This information reveals the mechanism of action of the disease and its impact on the host which is essential to find the potential therapeutic targets (Vanhaelen et al., 2017). Another step is converting this data into databases and developing algorithms to integrate tools and data. The pipeline developed out of these steps could find potential drugs based on the information provided on drugs and diseases.

Mermimepodib (C₂₃H₂₄N₄O₆) has a molecular weight of 452.5 g/mol. We show the three-dimensional structure of merimepodib in Supplemenatry_figure1. Merimepodib was originally developed against HCV (Hepatitis C Virus) and had greater potency against many viruses (Marcellin et al., 2007). It can act against several viruses in vitro despite its genetic material (DNA and RNA) (Markland et al., 2000). It suppresses viral replication and growth by inhibiting IMPDH, the primary enzyme for the de novo guanine nucleotide synthesis (Sintchak et al., 2000). This drug also inhibits RNA replication in ZIKV (Zika virus) cell culture (Tong et al., 2018). Here we predicted the properties of Merimepodib drug for repurposing against COVID-19.

A superior interpretation of the disease is necessary to identify the drug candidate for repurposing it against the disease. Drugs selected for repurposing must either inhibit the lifecycle of COVID-19 or should have the potential to counteract the effect caused by the virus.

We have developed several tools that use machine learning algorithms and integrate with bioinformatics platforms. These tools can be used for text mining and network biology-based systems [Jagannadham et al., 2016], vaccine discovery systems (Rawal et al. 2021), next-generation sequencing analysis systems for analyzing cancer and other genomes (Preeti et al., 2021, Rawal et al 2011, Mandal et al 2006). In this work, we have used our own Pipeline CoV-DrugX (http://drugx.kamalrawal.in/drugx/), developed with various modules and tools that decipher properties needed for drug repurposing (Rawal et al., 2021). This tool gives the score for the given drug that replicates the probability of repurposing the drug for COVID-19 treatment. The major advantage is that, the CoV-DrugX is a web-based tool and does not need supercomputing systems or coding knowledge and gives efficient results with the SMILES of the drug alone.

2. IMPLEMENTATION

The CoV-DrugX pipeline takes the SMILES of the merimepodib drug as input. This pipeline has 14 modules that analyze the merimepodib for all the properties considered for repurposing it for COVID-19. They provide information like the drug has any interactions with the COVID-19 target, elicit any abnormal human gene expressions like ACE2, Transmembrane Serine protease 2 (TMPRSS2) which participates in the COVID-19 pathway, and interacts with main protein kinases. They also give the binding affinities by docking with COVID-19 targets, human targets targeted by COVID-19, and protein kinases.

The simple execution of the CoV-DrugX pipeline for merimepodib is shown in Supplementary_figure 2.

2.1. Modules for repurposing properties

The Drug X pipeline has two Deep learning (DL) based modules (Drug DL 11, Drug DL 200) that search the given drug for properties necessary for repurposing against COVID-19. Drug DL 11 module was trained with the properties of 262 COVID-19 related drugs and could predict 11 biological properties. Drug DL 200 could predict 200 chemoinformatic properties related to COVID-19, and this module was trained with the chemical properties extracted from a similar dataset as Drug DL 11. These modules were used to predict whether Merimepodib has properties that are necessary for repurposing.

2.2. COVID-19 Condition module

This module has 34 conditions related to COVID-19 and conditions cured by the drug in its database which was developed for the pipeline. The data for this database were collected from websites like DrugBank (https://go.drugbank.com/), RxList (https://www.rxlist.com/). It compares the conditions which could be cured by merimepodib with the COVID-19 conditions. This module predicts and gives the output whether mermipodib has any cure for conditions of COVID-19

.2.3. COVID-19 Phenotype Module

The module was built using the database that contains 1952 phenotypes observed in the persons affected by SARS-CoV-2 and phenotypes of the other diseases for which the drugs are prescribed. This module takes the Merimepodib phenotype and compares it with the COVID-19 phenotype. The result of the module is phenotypes of the drug that matched with the COVID-19 phenotype. The module also provides a score based on the matches.

2.4. Drug Side Effect Module

SIDER ((http://sideeffects.embl.de/) and OFFSIDES ((http://tatonettilab.org/offsides/) databases were used to prepare this Drug-side effect module. This module comprises a total of 6123 side effects of the drug for COVID-19. It compares the side effects of COVID-19 with those of merimepodib and gives a cumulative score based on the number of side effects.

2.5. Drug and COVID-19 Target Interaction Module

This module has 377 targets of COVID-19 and compares them with the merimepodib drug targets. It finds similar targets with the query drug and COVID-19 virus and scores the module based on them. If the target of the drug matches the target molecule of COVID-19, the output will be 1, otherwise 0.

2.6. Gene expression module

This module was built based on the DGIdb, MsigDb databases which have information about whether the human gene expression is dysregulated (upregulated or downregulated) by the drug. If the drug is interacting with any of the genes and altering their expression, then it would predict the output as 1, otherwise 0..

2.7. Modules for docking

Docking is one of the significant approaches to asses the drug repurposing potential of the query drug, The DrugX pipeline has three docking modules, docking with SARS-CoV-2 targets, docking with human targets targeted by SARS-CoV-2, and docking with knowledge graph targets. Scoring for these modules is based on the mean binding affinity. These modules give a score of 1 for an average binding affinity < -8 kcal/mol and decreases the score as mean affinity decreases.

2.7.1. Interaction with SARS-CoV-2 targets

The module has 20 proteins of SARS-CoV-2 and retrieves the binding affinity of merimepodib with all targets from the database. The drug will be docked against the viral proteins, Epro, Mapro, Mepro, Npro, Nsp1, Nsp2, Nsp3, Nsp4, Nsp6, Nsp9s2, Nsp10, Nsp12, Nsp13, Nsp14, Nsp15, Nsp16, ORF3A, ORF6, ORF8, ORF10, PLpro_m, S_2, S_trimer.

2.7.2. Docking with human targets targeted by SARS-CoV-2

This docking module has two main human targets targeted by SARS-CoV-2 (ACE2 and TMPRSS2), and it provides the binding affinity of merimepodib with these targets.

2.7.3. Docking with knowledge graph targets

This module has two kinase targets: AP2-associated protein kinase (AAK1) and Janus kinase-1/2 (JAK12). It provides the binding affinity of merimepodib with these kinases. Inhibiting these kinases suppresses the immune functions that are important in COVID-19 treatment.

2.8. Drug response with the SARS-CoV-2-pathway circuit module

This module contains the curated database of different drug-gene networks which are associated with COVID-19. This provides information about the involvement of merimepodib in the SARS-CoV-2 pathway circuit such as, gene-protein interaction, host-virus interaction, inflammatory response, immune activity, antiviral defense, endocytosis, replication, and energetics.

2.9. Euclidean distance between drug and SARS-CoV-2 disease module

The module takes the merimepodib drug and the SARS-CoV-2 disease and calculates the euclidean distance. It returns four models with their rank, value, and distance between the drug and disease.

2.10. COVID-19 symptom module

This module has 105 symptom data points and compares them with the merimepodib drug. It provides a score in the module based on the matching symptoms between disease and symptoms cured by the drug.

2.11. Drug interactions with cell lines module

This module has information on drug effects on three cell lines Caco-2 cells (human colorectal adenocarcinoma cells), Vero E6 cells, which is a clone of Vero (African Green Monkey Kidney Epithelial cells) 76, and HRCE cells (Human Renal Cortical Epithelial Cells). These modules provide information on the pharmacodynamics of merimepodib on these cell lines.

3. USAGE

We collected the SMILES Notation of merimepodib drug from the DrugBank (https://go.drugbank.com/) and uploaded the SMILES as an input in a .txt file format to the CoV-DrugX Pipeline (CoV-DrugX). There are two options for executing i) To select one module at a time for analyzing the particular properties of the drug ii) To select all the modules for a cumulative analysis of drug on all modules. After submitting drug SMILES to the pipeline server, job ID and other details will be shown and the execution starts within a few minutes. The pipeline gives the results page, displaying the score for each module when we run it by selecting all modules. These results can be downloadable as a CSV format file. The result page shows the modules and their scores for the merimepodib drug in the range of 0 and 1 where 1 refers to the drug can be considered in drug repositioning for COVID-19, while 0 implies, that the drug can not be considered to be repurposed for COVID-19 based on the properties of that module. It gives a valid score for the successful completion of that particular module. We show the overall representation of the CoV-DrugX pipeline from collection to execution for merimepodib in Supplementary_figure3.

4. RESULTS

The results from the pipeline can be visualized in the pipeline-interface and are downloadable. The results obtained for merimepodib are presented in the supplementary file (Supplementary_tables). The pipeline provides information for merimepodib in 7 out of 14 modules.

4.1. Module for Repurposing Properties

The drug merimepodib satisfied the Drug DL 11 module and has 11 properties needed for repurposing it for COVID-19, and hence the module is scored as 1 in the pipeline. We listed all the properties of mermepodib in Supplementary_table1 given by the pipeline. Synthetic_Accessibility_score, Quantitative_Estimation of Drug_likeness, Rule of Five, MolecularWeight, ALogP, PSA, NumHDonors, NumHAcceptors, Mutagenicity, Mutagenicity_prediction_prob, Mutagenicity_Processed are all listed.

4.2. Abnormal human gene expression module

The results showed that merimepodib is involved in the upregulation of IMPDH2 gene expression (Supplementary_table7). Therefore, it could act against the replication of the COVID-19. This module scored 1 point in the pipeline because the gene is the main target of merimepodib.

4.3. Docking Modules

The three modules on docking in the pipeline have results for the merimepodib drug and detailed results of all three modules discussed below.

4.3.1. Merimepodib's interaction with SARS-CoV-2 targets

Merimepodib has a 0.5 score in the pipeline for docking with SARS-CoV-2 targets. We show the binding affinity of merimepodib with Sars-CoV-2 proteins in supplemenatry_figure4 and supplementary_table 8. Among all the considered proteins, merimepodib has a greater binding affinity with Nsp14 at -9.7 kcal/mol. The 0.5 score deciphers that this drug binds with all considered viral proteins with an average binding affinity of -8 kcal/mol to -7 kcal/mol (the average affinity score is -7.44 kcal/mol for mermipodib). Npro and Nsp2 have a high binding affinity of -9 kcal/mol. Nsp1 has the least binding affinity of -3.8 kcal/mol. When viruses enter the cell, they release their genomic RNA to produce non-structural proteins (Lim et al., 2016). These proteins help viruses translate their mRNA into proteins and favor the conditions for their infection (Prentice et al., 2004). Non-Structural Protein 14 (NSP-14) of SARS-CoV-2 is an exoribonuclease and performs a proofreading mechanism. It also functions in mRNA capping with its methyltransferase activity. Thus, it has been involved in both replication and transcription of viral RNA (Ma et al., 2015). The results suggest that merimepodib could efficiently bind to the major therapeutic targets of COVID-19 to inhibit viral infection and proliferation.

4.3.2 Docking of merimepodib with human targets targeted by SARS-CoV-2

The pipeline gives a score of 0.5 for the docking of merimepodib with human targets targeted by SARS-CoV-2. We show the binding affinities of the merimepodib with various targets in supplementary_figure5 and supplementary_table 9. Among two human targets (ACE2, TMPRSS2), merimepodib has a higher binding affinity to ACE2, with a binding affinity of -8.9 kcal/mol. It also has a moderate binding affinity of -6.5 kcal/mol with TMPRSS2. The average binding affinity of -7.7kcal/mol resulted in 0.5 score in the pipeline. ACE2 (angiotensin-converting enzyme 2) is a gateway through which Sars-CoV-2 enters cells of the host(Zhou et al., 2020). Mermepodib has a good binding affinity with the ACE2 receptor and can block the viral entry. After the viral entry through the ACE2 receptor, TMPRSS2 (a cellular protease) facilitates virus-host cell fusion by priming the Sars-CoV-2 viral spike protein (Hoffmann et al., 2020). Mermipodib could strongly influence the viral entry and moderately affect the viral infection through TMPRSS2.

4.3.3. Docking with knowledge graph targets

Merimepodib scores 1 in the pipeline with protein kinases, which shows its average binding affinity <-8kcal/mol. We show the binding score of merimepodib with the three main protein kinases, AAK1, JAK12 in Supplementary_figure6 and supplementary_table 10. Among the two kinases, the drug has a greater binding affinity with AAK1 with a value of -9.2 kcal/mol, and the average binding affinity is -9.1 kcal/mol. JAK12 has a binding affinity of -9 kcal/mol. After Sars-CoV-2 entry, it infects a host cell through clathrin-mediated endocytosis (Bayati et al., 2021). Recruitment of AP2 (adaptor protein) complex initiates the Clathrin-mediated endocytosis. AKK1 regulates clathrin-mediated endocytosis through phosphorylation of AP2 (Conner et al., 2002). JAK-12 inhibitors had shown to lower IL6 production during COVID-19 infection. IL6 is a cytokine involved in the immune response. As COVID-19 is associated with an elevated immune response, these JAK12 inhibitors could be a better therapeutic agent (Molina et al., 2021). Mermimepodib has a good binding affinity with AAK1 and JAK12, showing the action of merimepodib on both prevention of COVID-19 infection and reduction of the immune response.

4.4. Drug interactions with cell lines module

Among three cell lines, the pipeline has information only in the CoCa (human colorectal adenocarcinoma cells) cell lines for merimepodib. This cell line demonstrates the activity of the drug in the epithelial cells of the intestine. The module gave a score of 22.07 and had no information for the other two cell lines.

All the other (8) modules have no information for mermipodib in the database, and we tabulated their results in the supplementary_tables. The overall pipeline results with all modules and their corresponding scores are present in Supplementary_table15.

Out of 14 modules, seven modules have information about the merimepodib drug. Fully satisfied modules give a score of 1, whereas four modules have a score of 1 for merimepodib out of all. Two modules with a score of 0.5 are partially satisfied. Then the total score for merimepodib is five, mentioned as a cumulative SI score. The likelihood consideration of merimepodib for repurposing is 71.43% (5/7 *100). A study that concluded that a low concentration of merimepodib inhibits viral replication of Sars-CoV2 in the Vero cell lines supports the prediction (Bukreyeva et al., 2020).

It is docked with knowledge graph targets and has information on the drug effect in the cell lines. Merimepodib involves abnormal human gene expression during COVID-19 indicated by a score of 1 in the respective module. It partially docked with SARS-CoV-2 targets and human targets targeted by SARS-CoV-2 with 0.5 scores from these modules. The decrease in score for these modules represents the lowered binding affinities of the merimepodib with the target proteins. Merimepodib has an SI score of around five and a PI score of 0.36. The drug smiles have passed through all the modules in the CoV-DrugX pipeline shown by ti value 14.

5. CONCLUSION

Merimepodib has 11 properties needed for consideration for repurposing it for COVID-19 treatment. It is also involved in upregulated expression of the IMPDH2 gene, which is an important enzyme involved in denovo guanine biosynthesis, hence inhibiting viral replication. Merimepodib has a higher binding affinity with the Sars-CoV-2 protein Nsp14 with a -9.7 kcal/mol which implies that the drug could have an effect in inhibiting viral transcription and replication. They also bind with good affinity to human targets targeted by Sars-CoV-2, which has an ACE2 score of -8.7 kcal/mol, thereby blocking viral entry. Merimepodib may inhibit viral infection via clathrin-mediated endocytosis by binding to AAK1 (-9.2 kcal/mol) and suppressing immune responses by binding to JAK14 (-9.0kcal/mol). The drug effect was studied in Caco-2 cell lines. Hence, the CoV-DrugX pipeline predicts that Mermipodib could be a therapeutic drug against COVID-19 with a probability of 71.43%. Merimrpodib is a multitargeted drug that acts on viral proteins essential for viral growth and on human targets involved in viral entry, infection, and replication. In addition, it also helps with immune suppression, which is much needed to reduce the severity of the disease. The CoV-DrugX tool suggests that mermipodib could be efficiently considered for repurposing in COVID-19 treatment.

6. ADDITIONAL INFORMATION

Supplementary_tables

Supplementary_figures

7. ACKNOWLEDGEMENT

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) and 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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40. Rawal, K., Khurana T, Sharma H, . 2019. An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7:e27538v1 https://doi.org/10.7287/peerj.preprints.27538v1
    

41. Development of a Web Based Weight Loss Programme. K. Rawal, P. Gaur and K. Kashive, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, October, 2014
    

42. Developing Networks in Obesity using Text Mining. K. Rawal, S. Agarwal and J. Jagannadham, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, September, 2014.