Exploring potential inhibitor of SARS-CoV2 replicase from FDA approved drugs using insilico drug discovery methods

Abstract

Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV2) is responsible for fetal pneumonia called COVID19. SARS-CoV2 emerged in Wuhan, Hubei Province of China in December 2019. The COVID19 pandemic has now gripped the entire world with more than 70 million cases and over 1.5 million deaths so far. There no treatment option for COVID19 is in term of a drug or vaccine is currently available. Therefore drug repurposing may only provide a quick method for utilizing existing drugs for a therapeutic option. The virus genome contains several non-structural proteins (NSP) which serve as target for designing of antiviral agents. NSP9 of SARS-CoV2 encodes for a replicase enzyme which is essential for the virus replication in the host cell. In search of potent inhibitors, we have screened FDA approved drugs against NSP9 using in silico methods. Five drugs fluspirilene, troglitazone, alvesco, dihydroergotoxine and avodart were found to have highest affinities with the replicase. The molecular dynamics simulation (MDS) studies demonstrated strong drugs binding and stable NSP9-drugs complexes formation. The findings are also strongly supported by root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and hydrogen bond analysis of the complexes. Principal component analysis showed the stable conformation of NSP9 upon drug binding. It could be inferred that these five drugs individually or in combinations may be used as potential inhibitors of NSP9 of SARS-CoV-2 after exploring their in vivo antiviral potential.

Communicated by Ramaswamy H. Sarma

Keywords:

• Anti-viral compounds
• COVID-19
• SARS-CoV-2
• molecular docking
• molecular dynamics
• virtual screening

Acknowledgements

The authors MZA and ASA extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no (RG-1441-461). Authors are also thankful to ProteinInsights (www.proteininsights.com) for providing computational resources.

Disclosure statement

No potential conflict of interest was reported by the authors.