Potential Drugs Against COVID-19 Revealed by Gene Expression Profile, Molecular Docking and Molecular Dynamic Simulation

Abstract

Abstract BackgroundSARS-CoV-2 coronavirus, an emerging Betacoronavirus, is the causative agent of the severe acute respiratory distress syndrome outbreak in 2019 (COVID-19). Currently, there are neither specific and selective antiviral drugs for the treatment nor vaccines to prevent contagion. Here we propose a bioinformatic approach in order to test in silico the efficacy of existing drugs for COVID-19. ResultsIn the first step of our study we identified, through a gene expression analysis, several drugs that could act on the biological pathways altered in COVID-19. In the second step, we performed a docking simulation in order to test the properties of the identified drugs to target the 3CL main protease of SARS-CoV-2. The drugs that showed higher binding affinity are bardoxolone (-8.78 kcal/ mol), Irinotecan (-8.40 kcal/mol) and Pyrotinib (-8.40 kcal/mol). Molecular dynamics simulations were carried out on the three selected drugs to validate the stability and interactions of the complexes. Among other promising drugs we found also AZD-8055, Olaparib, Tyrphostin AG 879, Topotecan Hydrochloride, MP-412, S-222611, Allitinib, 7-Ethyl-10-Hydroxy-Camptothecin, and Falnidamol. ConclusionsWe suggested some drugs that could efficient in COVID-19. However further studies are suggested to confirm the affinity of these drugs with 3CL main protease of SARS-CoV-2.