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An Integrative in Silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS-CoV-2 Main Protease
preprintsubmitted on 27.06.2020, 20:57 and posted on 30.06.2020, 08:13 by Nemanja Djokovic, Dusan Ruzic, Teodora Djikic, Sandra Cvijic, Jelisaveta Ignjatovic, Svetlana Ibric, Katarina Baralic, Aleksandra Buha Djordjevic, Marijana Curcic, Danijela Djukic-Cosic, Katarina Nikolic
Aims: An infectious disease (COVID-19) caused by the coronavirus SARS-CoV-2 emerged in Wuhan, China in December 2019. Currently, SARS-CoV-2 infected more than 9 million people and caused more than 450 000 deaths. Considering the urgent need for novel therapeutics, drug repurposing approach might offer rapid solutions comparing to de novo drug design. In this study, we investigated an integrative in silico drug repurposing approach as a valuable tool for rapid selection of potential candidates against SARS-CoV-2 Main Protease (Mpro).
Main methods: To screen FDA-approved drugs, we designed an integrative in silico drug repurposing approach implementing structure-based molecular modelling techniques, physiologically-based pharmacokinetic (PBPK) modelling of drugs disposition and data-mining analysis of drug-gene-COVID-19 association.
Key findings: Through the presented approach, 43 candidates with potential inhibitory effect on Mpro were selected and further evaluated according to the predictions of tissue disposition, drug-gene-COVID-19 associations and potential pleiotropic effects. We singled out 9 FDA approved drugs as the most promising for their profiling in COVID-19 drug discovery campaigns. Our results were in agreement with current experimental findings, which validate the applied integrative approach and may support clinical decisions for a novel epidemic wave of COVID-19.
Significance: To the best of our knowledge, this is the first integrative in silico repurposing study for COVID-19 with a clear advantage in linking structure-based molecular modeling of Mpro inhibitors with predictions of tissue disposition, drug-gene-COVID-19 associations and prediction of pleiotropic effects of selected candidates.