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A metabolic modeling approach reveals promising therapeutic targets and antiviral drugs to combat COVID-19.
Santos-Beneit, Fernando; Raskevicius, Vytautas; Skeberdis, Vytenis A; Bordel, Sergio.
  • Santos-Beneit F; Institute of Sustainable Processes, Universidad de Valladolid, Valladolid, Spain.
  • Raskevicius V; Cell Culture Laboratory, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
  • Skeberdis VA; Cell Culture Laboratory, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
  • Bordel S; Institute of Sustainable Processes, Universidad de Valladolid, Valladolid, Spain. sergio.bordel@uva.es.
Sci Rep ; 11(1): 11982, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1260953
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / Virus Replication / Molecular Docking Simulation / SARS-CoV-2 / COVID-19 Drug Treatment Type of study: Prognostic study Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-91526-3

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / Virus Replication / Molecular Docking Simulation / SARS-CoV-2 / COVID-19 Drug Treatment Type of study: Prognostic study Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-91526-3