Cargando…

A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19

In this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by dock...

Descripción completa

Detalles Bibliográficos
Autores principales: Santos-Beneit, Fernando, Raškevičius, Vytautas, Skeberdis, Vytenis A., Bordel, Sergio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184994/
https://www.ncbi.nlm.nih.gov/pubmed/34099831
http://dx.doi.org/10.1038/s41598-021-91526-3
_version_ 1783704691332349952
author Santos-Beneit, Fernando
Raškevičius, Vytautas
Skeberdis, Vytenis A.
Bordel, Sergio
author_facet Santos-Beneit, Fernando
Raškevičius, Vytautas
Skeberdis, Vytenis A.
Bordel, Sergio
author_sort Santos-Beneit, Fernando
collection PubMed
description In this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by docking calculations. In total, 10 targets and 12 bioactive molecules have been predicted. Among the most promising molecules we identified Triacsin C, which inhibits ACSL3, and which has been shown to be very effective against different viruses, including positive-sense single-stranded RNA viruses. Similarly, we also identified the drug Celgosivir, which has been successfully tested in cells infected with different types of viruses such as Dengue, Zika, Hepatitis C and Influenza. Finally, other drugs targeting enzymes of lipid metabolism, carbohydrate metabolism or protein palmitoylation (such as Propylthiouracil, 2-Bromopalmitate, Lipofermata, Tunicamycin, Benzyl Isothiocyanate, Tipifarnib and Lonafarnib) are also proposed.
format Online
Article
Text
id pubmed-8184994
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-81849942021-06-08 A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19 Santos-Beneit, Fernando Raškevičius, Vytautas Skeberdis, Vytenis A. Bordel, Sergio Sci Rep Article In this study we have developed a method based on Flux Balance Analysis to identify human metabolic enzymes which can be targeted for therapeutic intervention against COVID-19. A literature search was carried out in order to identify suitable inhibitors of these enzymes, which were confirmed by docking calculations. In total, 10 targets and 12 bioactive molecules have been predicted. Among the most promising molecules we identified Triacsin C, which inhibits ACSL3, and which has been shown to be very effective against different viruses, including positive-sense single-stranded RNA viruses. Similarly, we also identified the drug Celgosivir, which has been successfully tested in cells infected with different types of viruses such as Dengue, Zika, Hepatitis C and Influenza. Finally, other drugs targeting enzymes of lipid metabolism, carbohydrate metabolism or protein palmitoylation (such as Propylthiouracil, 2-Bromopalmitate, Lipofermata, Tunicamycin, Benzyl Isothiocyanate, Tipifarnib and Lonafarnib) are also proposed. Nature Publishing Group UK 2021-06-07 /pmc/articles/PMC8184994/ /pubmed/34099831 http://dx.doi.org/10.1038/s41598-021-91526-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Santos-Beneit, Fernando
Raškevičius, Vytautas
Skeberdis, Vytenis A.
Bordel, Sergio
A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title_full A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title_fullStr A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title_full_unstemmed A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title_short A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19
title_sort metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184994/
https://www.ncbi.nlm.nih.gov/pubmed/34099831
http://dx.doi.org/10.1038/s41598-021-91526-3
work_keys_str_mv AT santosbeneitfernando ametabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT raskeviciusvytautas ametabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT skeberdisvytenisa ametabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT bordelsergio ametabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT santosbeneitfernando metabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT raskeviciusvytautas metabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT skeberdisvytenisa metabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19
AT bordelsergio metabolicmodelingapproachrevealspromisingtherapeutictargetsandantiviraldrugstocombatcovid19