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Combinatorial Chemistry & High Throughput Screening

Author(s): Mahija K.C.* and Abdul Nazeer K.A.

DOI: 10.2174/1386207325666220210125923

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Repurposing Ayush-64 for COVID-19: A Computational Study Based on Network Pharmacology and Molecular Docking

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Abstract

Background: As COVID-19 pandemic continues to affect people’s lives, the government of India gave emergency use approval to the ayurvedic antimalarial drug Ayush-64 in April 2021 to treat asymptomatic COVID-19 positive and mild COVID-19 positive patients.

Objective: This study aims to explore the therapeutic potential of Ayush-64 to treat COVID-19 and provide a new approach for repurposing Ayurvedic drugs.

Methods: The bioactives present in Ayush-64 were found along with their targets, and a plantbioactive- target network was created. A protein-protein interaction network of the common targets of Ayush-64 and COVID-19 was constructed and analyzed to find the key targets of Ayush-64 associated with the disease. Gene ontology and pathway enrichment analysis were performed to find COVID-19 related biological processes and pathways involved by the key targets. The key bioactives were docked with SARS-CoV-2 main protease 3CL, native Human Angiotensin-converting Enzyme ACE2, Spike protein S1, and RNA-dependent RNA polymerase RdRp.

Results: From the 336 targets for Ayush-64, we found 38 key targets. Functional enrichment analysis of the key targets resulted in 121 gene ontology terms and 38 pathways. When molecular docking was performed with four receptors, thirteen bioactives showed good binding affinity comparable to that of the eight drugs presently used to treat COVID-19.

Conclusion: Network pharmacological analysis and molecular docking study of Ayush-64 revealed that it can be recommended to treat COVID-19. Further in vitro and in vivo studies are needed to confirm the results. The study demonstrated a new approach for repurposing Ayurvedic drugs.

Keywords: Drug repurposing, Ayush-64, Ayurveda, COVID-19, SARS-CoV-2, network pharmacology, molecular docking.

Graphical Abstract

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