Drug repurposing: identification of SARS-CoV-2 potential inhibitors by virtual screening and pharmacokinetics strategies.


Journal

Journal of infection in developing countries
ISSN: 1972-2680
Titre abrégé: J Infect Dev Ctries
Pays: Italy
ID NLM: 101305410

Informations de publication

Date de publication:
30 Apr 2024
Historique:
received: 08 03 2023
accepted: 17 09 2023
medline: 10 5 2024
pubmed: 10 5 2024
entrez: 10 5 2024
Statut: epublish

Résumé

The coronavirus disease 2019 (COVID-19) pandemic caused global health, economic, and population loss. Variants of the coronavirus contributed to the severity of the disease and persistent rise in infections. This study aimed to identify potential drug candidates from fifteen approved antiviral drugs against SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike protein (6M0J) using virtual screening and pharmacokinetics to gain insights into COVID-19 therapeutics. We employed drug repurposing approach to analyze binding performance of fifteen clinically approved antiviral drugs against the main protease of SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike proteins bound to ACE-2 receptor (6M0J), to provide an insight into the therapeutics of COVID-19. AutoDock Vina was used for docking studies. The binding affinities were calculated, and 2-3D structures of protein-ligand interactions were drawn. Rutin, hesperidin, and nelfinavir are clinically approved antiviral drugs with high binding affinity to proteins 6LU7, 5B6O, and 6M0J. These ligands have excellent pharmacokinetics, ensuring efficient absorption, metabolism, excretion, and digestibility. Hesperidin showed the most potent interaction with spike protein 6M0J, forming four H-bonds. Nelfinavir had a high human intestinal absorption (HIA) score of 0.93, indicating maximum absorption in the body and promising interactions with 6LU7. Our results indicated that rutin, hesperidin, and nelfinavir had the highest binding results against the proposed drug targets. The computational approach effectively identified SARS-CoV-2 inhibitors. COVID-19 is still a recurrent threat globally and predictive analysis using natural compounds might serve as a starting point for new drug development against SARS-CoV-2 and related viruses.

Identifiants

pubmed: 38728643
doi: 10.3855/jidc.18189
doi:

Substances chimiques

Antiviral Agents 0
Spike Glycoprotein, Coronavirus 0
spike protein, SARS-CoV-2 0
Coronavirus 3C Proteases EC 3.4.22.28

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

520-531

Informations de copyright

Copyright (c) 2024 Zubia Rashid, Amal Fatima, Areeba Khan, Jennifer Matthew, Muhammad Z Yousaf, Neha Nadeem, Tarique N Hasan, Mati Ur Rehman, Syed S Naqvi, Saad J Khan.

Déclaration de conflit d'intérêts

No Conflict of Interest is declared

Auteurs

Zubia Rashid (Z)

Pure Health Laboratory, Mafraq Hospital, Abu Dhabi, United Arab Emirates.

Amal Fatima (A)

Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.

Areeba Khan (A)

Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.

Jennifer Matthew (J)

Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.

Muhammad Z Yousaf (MZ)

KAM School of Life Sciences, Forman Christian College University, Lahore 54600, Pakistan.

Neha Nadeem (N)

KAM School of Life Sciences, Forman Christian College University, Lahore 54600, Pakistan.

Tarique N Hasan (TN)

School of Life Science, Manipal Academy of Higher Education, Dubai, United Arab Emirates.

Mati Ur Rehman (MU)

Pure Health Laboratory, Mafraq Hospital, Abu Dhabi, United Arab Emirates.

Syed S Naqvi (SS)

Pure Health Laboratory, Mafraq Hospital, Abu Dhabi, United Arab Emirates.

Saad J Khan (SJ)

Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.

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Classifications MeSH