Screening of potential inhibitors of COVID-19 with repurposing approach via molecular docking

(2022) Screening of potential inhibitors of COVID-19 with repurposing approach via molecular docking. Network modeling and analysis in health informatics and bioinformatics. p. 11. ISSN 2192-6662 (Print) 2192-6670 (Electronic) 2192-6670 (Linking)

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Abstract

SARS-CoV-2 (COVID-19) is the causative organism for a pandemic disease with a high rate of infectivity and mortality. In this study, we aimed to assess the affinity between several available small molecule and proteins, including Abl kinase inhibitors, Janus kinase inhibitor, dipeptidyl peptidase 4 inhibitors, RNA-dependent RNA polymerase inhibitors, and Papain-like protease inhibitors, using binding simulation, to test whether they may be effective in inhibiting COVID-19 infection through several mechanisms. The efficiency of inhibitors was evaluated based on docking scores using AutoDock Vina software. Strong ligand-protein interactions were predicted among some of these drugs, that included: Imatinib, Remdesivir, and Telaprevir, and this may render these compounds promising candidates. Some candidate drugs might be efficient in disease control as potential inhibitors or lead compounds against the SARS-CoV-2. It is also worth highlighting the powerful immunomodulatory role of other drugs, such as Abivertinib that inhibits pro-inflammatory cytokine production associated with cytokine release syndrome (CRS) and the progression of COVID-19 infection. The potential role of other Abl kinase inhibitors, including Imatinib in reducing SARS-CoV and MERS-CoV viral titers, immune regulatory function and the development of acute respiratory distress syndrome (ARDS), indicate that this drug may be useful for COVID-19, as the SARS-CoV-2 genome is similar to SARS-CoV.

Item Type: Article
Keywords: Abl kinase inhibitors Covid-19 Dipeptidyl peptidase 4 inhibitors Janus kinase inhibitor Papain-like protease inhibitors RNA-dependent RNA polymerase inhibitors
Page Range: p. 11
Journal or Publication Title: Network modeling and analysis in health informatics and bioinformatics
Journal Index: Pubmed
Volume: 11
Number: 1
Identification Number: https://doi.org/10.1007/s13721-021-00341-3
ISSN: 2192-6662 (Print) 2192-6670 (Electronic) 2192-6670 (Linking)
Depositing User: خانم ناهید ضیائی
URI: http://eprints.mui.ac.ir/id/eprint/25491

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