COVID-19 and the Importance of Being Prepared: A Multidisciplinary Strategy for the Discovery of Antivirals to Combat Pandemics

Abstract

During an emergency, such as a pandemic in which time and resources are extremely scarce, it is important to find effective and rapid solutions when searching for possible treatments. One possibility in this regard is the repurposing of available “on the market” drugs. This is a proof of the concept study showing the potential of a collaboration between two research groups, engaged in computer-aided drug design and control of viral infections, for the development of early strategies to combat future pandemics. We describe a QSAR (quantitative structure activity relationship) based repurposing study on molecular topology and molecular docking for identifying inhibitors of the main protease (Mpro) of SARS-CoV-2, the causative agent of COVID-19. The aim of this computational strategy was to create an agile, rapid, and efficient way to enable the selection of molecules capable of inhibiting SARS-CoV-2 protease. Molecules selected through in silico method were tested in vitro using human coronavirus 229E as a surrogate for SARS-CoV-2. Three strategies were used to screen the antiviral activity of these molecules against human coronavirus 229E in cell cultures, e.g., pre-treatment, co-treatment, and post-treatment. We found >99% of virus inhibition during pre-treatment and co-treatment and 90–99% inhibition when the molecules were applied post-treatment (after infection with the virus). From all tested compounds, Molport-046-067-769 and Molport-046-568-802 are here reported for the first time as potential anti-SARS-CoV-2 compounds.

QSAR and molecular docking studies on designing potent inhibitors of SARS-CoVs main protease

“…The literature indicates that a variety of QSAR methods has been widely applied in anti-SARS inhibitor researches. Masand et al (2020) discovered a genetic algorithm-multi-linear regression methodology to modify the peptide-type compounds for SARS-CoV; Zaki et al (2021) identified anti-SARS-CoV-2 compounds from food by virtual screening, molecular Docking, and molecular dynamics simulation analysis; Galvez-Llompart et al (2022) described a novel QSAR model based repurposing study on molecular topology and molecular docking for identifying inhibitors of the main protease of SARS-CoV-2; Toropov et al (2022) obtained new designed anti-SARS CoVs inhibitors based on statistical and probability quality of molecular alerts extracted from SMILES.…”