Evidence supporting Camostat as repurposing drug against COVID-19 using CoV-DrugX Pipeline
Yogalakshmi Sabapathy, Prashant Singh1, Robin Sinha1, Preeti P.1, Trapti Sharma1, Swarsat Kaushik Nath1, Kamal Rawal#1
1. Amity 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 upsurge of the SARS-CoV-2 infection continues even after two years of its inception due to inadequate and unequal distribution of approved vaccines. There are only ten approved vaccines as per the WHO report as of Jan 2022. Hence, there is a large demand for antivirals designed to combat SARS-CoV-2. However, the development and approval of a new drug will take nearly 10 to 15years. Thus, repurposing of existing drugs developed against other diseases is considered the appropriate action to meet the global demand.
Methods: The computational approach based drug repurposing is an emerging field and it screens large numbers of drugs within a short span of time utilizing knowledge and omics data available in pharmaceutical research. The CoV-DrugX Pipeline server is a computational tool for repositioning existing drugs for COVID-19 treatment.
Results and conclusions: The camostat drug was found to be effective against COVID-19 based on CoV-DrugX analysis and the CoV-DrugX Pipeline could be a productive computational tool for drug repositioning in terms of usage and function.
Keywords: Drug repurposing, artificial intelligence, COVID-19, Camostat
Pipeline Access : http://drugx.kamalrawal.in/drugx/
Introduction
The outbreak of the novel coronavirus (COVID-19) has posed a global health crisis since 2020. The highly contagious COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which belongs to RNA viruses. SARS-CoV-2 rapidly spread across the world in a short period and WHO declared it as a global pandemic on March 11, 2020 [Aleem et al., 2021]. The virus continues to circulate throughout the world even today due to its genetic evolution, which leads to the development of mutant variants such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) [Thye et al., 2021]. The prevention of COVID-19 spread and global mass vaccination efforts against COVID-19 has been overturned by the emerging SARS-CoV-2 mutant variants. Thus, the researchers are on the lookout for new ways to control its expansion.
The search for effective COVID-19 management has paved the way for drug repurposing. Drug repurposing is the process of reassessing the efficacy of licensed and experimental drugs developed against other diseases to treat newly emerging diseases [Singh et al., 2020]. The scientific data such as drug-target interaction, gene expression, and drug adverse events, required for drug repositioning can be obtained through three different approaches such as computational approaches, biological experimental approaches, and mixed approaches [Zhou et al., 2020]. The computational methods integrate all the knowledge and data available in pharmaceutical research in finding the new signaling pathways and give rise to novel insights into drug mechanisms, drug-target interactions, and side effects which further accelerate the drug discovery. The significance of the computational approach enabled us to develop a user-friendly server, known as CoV-DrugX Pipeline. The server is written using Python, HTML, CSS, and JS. The external libraries such as flask web framework, celery, Redis queue, snakemake, obabel, AutoDockFR, AutoDock Vina, and p2rank were also used in this server [Rawal et al., 2021].
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 system [Rawal et al. 2021], next-generation sequencing analysis system for cancer and other genomes [Preeti et al.,2021, Rawal et al 2011, Mandal et al 2006].
Camostat, a synthetic serine protease inhibitor, may inhibit SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2 [Ragia et al., 2020]. The host cell factors such as angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2) have been used by SARS-CoV-2 spike protein (S) for entry into target cells (see Figure 1). The activation of viral S protein has been initiated by the cleavage of TMPRSS2, a cellular type II transmembrane serine protease (TTSP), expressed in the human respiratory epithelium. Activation is essential for viral infectivity and thus the protease inhibitor camostat mesylate, which is known to block TMPRSS2 activity, may inhibit SARS-CoV-2 infection of lung cells. Camostat mesylate has been approved for the treatment of pancreatitis in Japan and it is currently being investigated as a treatment of COVID-19 in several clinical trials in Denmark, Israel, the USA [Hoffmann et al., 2020]. Thus, we evaluate the camostat drug using a computational server to check its activity against COVID-19 and its related host targets for limiting the COVID-19 infection.
Implementation
The CoV-DrugX Pipeline is constructed in our lab using deep learning, machine learning and artificial intelligence. The server includes 14 different modules such as drug_dl_11, drug_dl_200, drug_condition, drug_phenotype, drug_side_effect, drug_symptom, drug_circuit, drug_target, drug_dock_human, drug_dock_KG, drug_dock_viral, drug_AI_ranking, drug_side_effect_neighbours and drug_gene_expression. The drug_dl_11 and drug_dl_200 modules check the query drug for various druglikeness properties including molecular weight, polar surface area (PSA), mutagenicity, number of H donors, number of H acceptors and rule of five. The drug_condition, drug_phenotype, drug_side_effect, drug_symptom and drug_target modules analyse the given drug for COVID-19 associated conditions, phenotype, side effects, symptoms and viral targets. The drug_dock_human module docks the given drug with the human targets such as ACE2 and TMPRSS2, targeted by SARS-CoV-2. The drug_dock_viral module docks the query drug with the 23 SARS-CoV-2 targets covering spike protein, membrane protein, envelop small membrane protein, nucleocapsid protein, papain-like protease and RNA dependent RNA polymerase . The drug_dock_KG module docks the test drug with the human protein kinases such as AAK1, GAK and JAK12. The drug_gene_expression module examines the given drug for any abnormal human gene expression during COVID-19.
Usage
The SMILE format of camostat is used for this purpose. The CoV-DrugX server consists of 14 modules to test the given drug against SARS-CoV-2 targets, human targets targeted by SARS-CoV-2 and also to check its activity on any COVID-19 condition, phenotype, symptom and side effect (see Figure 2) . Each module is assigned with a score of 0 and 1, where 1 indicates pass and 0 denotes fail. The SI score gives the sum of all the modules and PI score gives the average of all the modules. Greater the score, greater the chances of being considered as a repurposing drug against COVID-19.
Result
The modules of the CoV-DrugX server are indicated as A, B, C, D, E, F, G, H, I, J, K, L, M and N (see Table 1). The modules with score 1 are A, B, G and J, which indicates that the camostat has drug likeness properties as given in Table 2, along with abnormal human gene expression during COVID-19 as represented in Table 3. The SI score and PI score for the camostat drug are 4.96 and 0.38 respectively. Subsequently, Table 4 summarizes that camostat has no effect on COVID-19 condition, COVID-19 phenotype, COVID-19 side effect, COVID-19 target and COVID-19 symptom. The corresponding modules such as C, D, E, F and N displays score 0 (refer Table 1).
The camostat exhibits best binding against viral target Npro (Nucleocapsid Protein) and Nsp14 (Nonstructural protein14) as shown in Figure 3. The predicted binding energy was -9.6 kcal/mol and -9.5 kcal/mol respectively, with mean affinity of -7.24 kcal/mol against 23 SARS-CoV-2 targets ( see Table 5). The camostat demonstrates abnormal expression of TMPRSS2, PRSS1, ST14, CCK, TFF2 genes as indicated in Table 3. The average affinity of camostat against human targets such as ACE2 and TMPRSS2 was found to be -7.15 kcal/mol (see Table 6). The camostat binding affinity towards ACE2 was found to be more than that of TMPRSS2 as shown in Figure 4. The camostat effect on human protein kinases such as AAK1, GAK and JAK12 was studied as these proteins play a vital role in the endocytosis process. We found that the camostat has good binding affinity against AAK1 as presented in Figure 5 and the mean affinity was found to be -8.8 kcal/mol as given in Table 7.
Discussion
The CoV-DrugX server is exclusively built for those who are interested in drug discovery against COVID-19 and includes vast amounts of data under fourteen different modules to categorize the drug as repurposing or non-repurposing against COVID-19. The server is user-friendly and computes the data within a short time, thereby enabling the user to test a large number of drugs against COVID-19. The theoretical conclusion given by the server based on SI score and PI score mostly coincides with the in vitro data and thus paves the way for the next generation drug discovery using deep learning, machine learning, and artificial intelligence.
Conclusion
Camostat was considered to play a vital role in the inhibition of SARS-CoV-2 infection by blocking its binding with the human epithelial cells lining the respiratory tract. The analysis by the CoV-DrugX server data proved the camostat effectiveness against COVID-19. Thus, CoV-DrugX server data could be a deciding factor in the drug repositioning against COVID-19.
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.
Supplementary Information
Camostat_Supplementary Figures
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