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Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review

BACKGROUND: Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in finding a potential therapeutic agent for the disease. Considering the matter of time, the computational methods of drug repurposing offer the best chance of selecting one...

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Autores principales: Mohamed, Kawthar, Yazdanpanah, Niloufar, Saghazadeh, Amene, Rezaei, Nima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676368/
https://www.ncbi.nlm.nih.gov/pubmed/33261845
http://dx.doi.org/10.1016/j.bioorg.2020.104490
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author Mohamed, Kawthar
Yazdanpanah, Niloufar
Saghazadeh, Amene
Rezaei, Nima
author_facet Mohamed, Kawthar
Yazdanpanah, Niloufar
Saghazadeh, Amene
Rezaei, Nima
author_sort Mohamed, Kawthar
collection PubMed
description BACKGROUND: Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in finding a potential therapeutic agent for the disease. Considering the matter of time, the computational methods of drug repurposing offer the best chance of selecting one drug from a list of approved drugs for the life-threatening condition of COVID-19. The present systematic review aims to provide an overview of studies that have used computational methods for drug repurposing in COVID-19. METHODS: We undertook a systematic search in five databases and included original articles in English that applied computational methods for drug repurposing in COVID-19. RESULTS: Twenty-one original articles utilizing computational drug methods for COVID-19 drug repurposing were included in the systematic review. Regarding the quality of eligible studies, high-quality items including the use of two or more approved drug databases, analysis of molecular dynamic simulation, multi-target assessment, the use of crystal structure for the generation of the target sequence, and the use of AutoDock Vina combined with other docking tools occurred in about 52%, 38%, 24%, 48%, and 19% of included studies. Studies included repurposed drugs mainly against non-structural proteins of SARS-CoV2: the main 3C-like protease (Lopinavir, Ritonavir, Indinavir, Atazanavir, Nelfinavir, and Clocortolone), RNA-dependent RNA polymerase (Remdesivir and Ribavirin), and the papain-like protease (Mycophenolic acid, Telaprevir, Boceprevir, Grazoprevir, Darunavir, Chloroquine, and Formoterol). The review revealed the best-documented multi-target drugs repurposed by computational methods for COVID-19 therapy as follows: antiviral drugs commonly used to treat AIDS/HIV (Atazanavir, Efavirenz, and Dolutegravir Ritonavir, Raltegravir, and Darunavir, Lopinavir, Saquinavir, Nelfinavir, and Indinavir), HCV (Grazoprevir, Lomibuvir, Asunaprevir, Ribavirin, and Simeprevir), HBV (Entecavir), HSV (Penciclovir), CMV (Ganciclovir), and Ebola (Remdesivir), anticoagulant drug (Dabigatran), and an antifungal drug (Itraconazole). CONCLUSIONS: The present systematic review provides a list of existing drugs that have the potential to influence SARS-CoV2 through different mechanisms of action. For the majority of these drugs, direct clinical evidence on their efficacy for the treatment of COVID-19 is lacking. Future clinical studies examining these drugs might come to conclude, which can be more useful to inhibit COVID-19 progression.
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spelling pubmed-76763682020-11-20 Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review Mohamed, Kawthar Yazdanpanah, Niloufar Saghazadeh, Amene Rezaei, Nima Bioorg Chem Review Article BACKGROUND: Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in finding a potential therapeutic agent for the disease. Considering the matter of time, the computational methods of drug repurposing offer the best chance of selecting one drug from a list of approved drugs for the life-threatening condition of COVID-19. The present systematic review aims to provide an overview of studies that have used computational methods for drug repurposing in COVID-19. METHODS: We undertook a systematic search in five databases and included original articles in English that applied computational methods for drug repurposing in COVID-19. RESULTS: Twenty-one original articles utilizing computational drug methods for COVID-19 drug repurposing were included in the systematic review. Regarding the quality of eligible studies, high-quality items including the use of two or more approved drug databases, analysis of molecular dynamic simulation, multi-target assessment, the use of crystal structure for the generation of the target sequence, and the use of AutoDock Vina combined with other docking tools occurred in about 52%, 38%, 24%, 48%, and 19% of included studies. Studies included repurposed drugs mainly against non-structural proteins of SARS-CoV2: the main 3C-like protease (Lopinavir, Ritonavir, Indinavir, Atazanavir, Nelfinavir, and Clocortolone), RNA-dependent RNA polymerase (Remdesivir and Ribavirin), and the papain-like protease (Mycophenolic acid, Telaprevir, Boceprevir, Grazoprevir, Darunavir, Chloroquine, and Formoterol). The review revealed the best-documented multi-target drugs repurposed by computational methods for COVID-19 therapy as follows: antiviral drugs commonly used to treat AIDS/HIV (Atazanavir, Efavirenz, and Dolutegravir Ritonavir, Raltegravir, and Darunavir, Lopinavir, Saquinavir, Nelfinavir, and Indinavir), HCV (Grazoprevir, Lomibuvir, Asunaprevir, Ribavirin, and Simeprevir), HBV (Entecavir), HSV (Penciclovir), CMV (Ganciclovir), and Ebola (Remdesivir), anticoagulant drug (Dabigatran), and an antifungal drug (Itraconazole). CONCLUSIONS: The present systematic review provides a list of existing drugs that have the potential to influence SARS-CoV2 through different mechanisms of action. For the majority of these drugs, direct clinical evidence on their efficacy for the treatment of COVID-19 is lacking. Future clinical studies examining these drugs might come to conclude, which can be more useful to inhibit COVID-19 progression. Elsevier Inc. 2021-01 2020-11-19 /pmc/articles/PMC7676368/ /pubmed/33261845 http://dx.doi.org/10.1016/j.bioorg.2020.104490 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Review Article
Mohamed, Kawthar
Yazdanpanah, Niloufar
Saghazadeh, Amene
Rezaei, Nima
Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title_full Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title_fullStr Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title_full_unstemmed Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title_short Computational drug discovery and repurposing for the treatment of COVID-19: A systematic review
title_sort computational drug discovery and repurposing for the treatment of covid-19: a systematic review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676368/
https://www.ncbi.nlm.nih.gov/pubmed/33261845
http://dx.doi.org/10.1016/j.bioorg.2020.104490
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