Combined In Silico Molecular Docking And In Vitro Experimental Assessment Of Drug Repurposing Candidates For Covid-19 (CovScreen)

Currently no vaccine or sufficiently validated pharmacological treatment is available for COVID-19. Drug-based strategies to reduce the viral load in patients with severe forms of COVID-19 include the repurposing of existing small molecule compounds that inhibit the activity of key viral proteins, or human proteins involved in mediating viral entry or release from the host cell. However, so far, the identified small molecule inhibitors for SARS-CoV-2 target proteins studied in -vitro have limitations in terms of either their binding affinity for the target protein, their bioavailability in the lung, known adverse effects, and high manufacturing costs. We therefore propose a combined computational and experimental approach to rank alternative candidate known drugs, antivirals and natural compounds, which are commercially available, inexpensive, and known to be safe in humans. We will focus on assessing , in terms of their ability to selectively bind to and inhibit the SARS-CoV-2 3CL protease (target 1), which is essential for viral replication, or the human protein TMPRSS2 (target 2), which is essential for viral entry into the host cell. For this purpose, we will screen and filter in silico x M~10k compounds using molecular docking and machine learning based lung bioavailability estimations, and conduct molecular dynamics simulations for refined binding affinity estimation of the 100 top-ranked compounds. The top 20 compounds per drug target in terms of predicted binding affinity will be validated experimentally in -vitro and cellular assays. As part of our prior work, we have already identified natural compounds which are safe in humans, reported to inhibit the replication of SARS-CoV (the predecessor82% identical to SARS-CoV-2, from the 2002/2003 outbreak), and for which our molecular docking and binding affinity estimation analyses predict similar inhibitory effects for the ortholog protein from SARS-CoV-2. This will enable us to start quickly with the first validation experiments for assessing ligand-binding, while selecting further candidate compounds in parallel through the computational screening., while conducting computational predictions for further candidate compounds in parallel. In summary, this project will provide a fast experimental validation of drug repurposing candidates for COVID-19 from a computational pre-selection of antivirals, drugs and natural compounds that are inexpensive, have known safety properties and high predicted bioavailability in the lung. These studies would pave the way for a quick progression to follow-up efficacy testing against virus infectivity in collaboration with a BSL-3 certified laboratory.