Selection of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Metabolic Modeling
The 2019 coronavirus disease (COVID-19) became a global pandemic with emerging variants that might escape the vaccine-induced immunity. Despite the global effort for mass vaccination with the newly developed vaccines, there is no effective antiviral drug except treatments for symptomatic therapy. Combining both whole genome sequences with our previous knowledge of biochemical reactions, metabolic networks can be reconstructed on a genome scale. Flux balance analysis is an efficient method to analyze metabolic networks such as predicting the growth rate of a specific cell or the production rate of a metabolite of interest. Here, flux balance analysis was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the replication of the SARS-CoV-2 virus within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene-pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, as well as ferroptosis, pyrimidine metabolism, and fat digestion. By in silico screening of FDA-approved drugs on the putative disease-specific essential genes and gene-pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 focused drug repositioning (https://github.com/sysbiolux/DCcov). Among the 85 drug candidates, five broad-spectrum antiviral drugs were found, and nine drugs are being tested in clinical trials against COVID-19.