Discovering Affective Drug Combinations for Treating Covid-19

Division Of Basic Sciences - Nci

Investigators

Linked publications & trials

Abstract

Efforts of anti-COVID-19 drug discovery encompass hundreds of ongoing clinical trials and published pre-clinical drug screening studies, with many employing drug repurposing to meet urgent clinical needs. It is well established that effective viral therapies frequently employ combinations of drugs, however, as the space of possible drug combinations is prohibitively large, rational strategies for predicting effective combinations are needed. Viruses, including coronaviruses, are known to hijack various host metabolic pathways to facilitate their own proliferation, making targeting host metabolism an interesting anti-viral approach. Here, we aim to harness our expertise in genome-scale metabolic modeling (GEM) to address these challenges. Using both published data and our own RNA sequencing data on SARS-CoV-2-infected samples of cell lines and patients, we shall apply our published high-performance GEM-based computational pipeline to predict host metabolism-targeting anti-COVID-19 drugs and their combinations. We first aim to find treatments that effectively reverse the metabolic alterations induced by SARS-CoV-2 in the human host cells. Second, we shall predict their most synergistic combinations, and predict the combinations of metabolic drugs with emerging non-metabolic antiviral drugs under clinical investigation, such as remdesivir. In close collaboration with the Sumit Chanda lab, which has performed the largest high-throughput drug screening to date, we shall experimentally validate our predictions in vitro and in vivo. Our pipeline is the first that integrates drug screening and GEM analysis for antiviral drug discovery focusing on host metabolism. It will contribute to the anti-COVID-19 endeavor but also elucidate host metabolism-targeting as a novel and potentially generalizable antiviral strategy.