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Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2

This study aimed to evaluate the pharmacological and toxicological potential of five known antiviral agents and their derivatives through computational modeling. Ritonavir, remdesivir, chloroquine, lopinavir, lieckol, and their derivatives were subjected to molecular docking analysis against five SA...

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Autores principales: Adegboyega, Abayomi Emmanuel, Johnson, Titilayo Omolara, Omale, Simeon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137999/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00018-6
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author Adegboyega, Abayomi Emmanuel
Johnson, Titilayo Omolara
Omale, Simeon
author_facet Adegboyega, Abayomi Emmanuel
Johnson, Titilayo Omolara
Omale, Simeon
author_sort Adegboyega, Abayomi Emmanuel
collection PubMed
description This study aimed to evaluate the pharmacological and toxicological potential of five known antiviral agents and their derivatives through computational modeling. Ritonavir, remdesivir, chloroquine, lopinavir, lieckol, and their derivatives were subjected to molecular docking analysis against five SARS-CoV-2 target proteins. The absorption, distribution, metabolism, excretion, and toxicity properties of the compounds with high affinity for the SARS-CoV-2 target proteins were predicted. Dieckol demonstrated the highest binding affinity for all the SARS-CoV-2 target proteins, while lopinavir and ritonavir showed a relatively high-binding affinity for 3-chymotrypsin-like protease, main protease, and RNA-dependent RNA polymerase. The compounds fulfilled the Lipinski rule, possess moderate water solubility, gastrointestinal absorption absorption, bioavailability, synthetic accessibility, and optimum lipophilicity. Hence, this study proves the therapeutic potential of dieckol, lopinavir, and ritonavir.
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spelling pubmed-81379992021-05-21 Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2 Adegboyega, Abayomi Emmanuel Johnson, Titilayo Omolara Omale, Simeon Data Science for COVID-19 Article This study aimed to evaluate the pharmacological and toxicological potential of five known antiviral agents and their derivatives through computational modeling. Ritonavir, remdesivir, chloroquine, lopinavir, lieckol, and their derivatives were subjected to molecular docking analysis against five SARS-CoV-2 target proteins. The absorption, distribution, metabolism, excretion, and toxicity properties of the compounds with high affinity for the SARS-CoV-2 target proteins were predicted. Dieckol demonstrated the highest binding affinity for all the SARS-CoV-2 target proteins, while lopinavir and ritonavir showed a relatively high-binding affinity for 3-chymotrypsin-like protease, main protease, and RNA-dependent RNA polymerase. The compounds fulfilled the Lipinski rule, possess moderate water solubility, gastrointestinal absorption absorption, bioavailability, synthetic accessibility, and optimum lipophilicity. Hence, this study proves the therapeutic potential of dieckol, lopinavir, and ritonavir. 2021 2021-05-21 /pmc/articles/PMC8137999/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00018-6 Text en Copyright © 2021 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 Article
Adegboyega, Abayomi Emmanuel
Johnson, Titilayo Omolara
Omale, Simeon
Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title_full Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title_fullStr Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title_full_unstemmed Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title_short Computational modeling of the pharmacological actions of some antiviral agents against SARS-CoV-2
title_sort computational modeling of the pharmacological actions of some antiviral agents against sars-cov-2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137999/
http://dx.doi.org/10.1016/B978-0-12-824536-1.00018-6
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