(2022) Screening of potential inhibitors of COVID-19 with repurposing approach via molecular docking. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS. ISSN 2192-6662 2192-6670 J9 - NETW MODEL ANAL HLTH
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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 Papainlike 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 |
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Keywords: | COVID-19 Abl kinase inhibitors Janus kinase inhibitor Dipeptidyl peptidase 4 inhibitors RNA-dependent RNA polymerase inhibitors Papain-like protease inhibitors ABL KINASES CORONAVIRUS |
Journal or Publication Title: | NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS |
Journal Index: | ISI |
Volume: | 11 |
Number: | 1 |
Identification Number: | https://doi.org/10.1007/s13721-021-00341-3 |
ISSN: | 2192-6662 2192-6670 J9 - NETW MODEL ANAL HLTH |
Depositing User: | Zahra Otroj |
URI: | http://eprints.mui.ac.ir/id/eprint/16266 |
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