  The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with
currently no effective antiviral drug except treatments for symptomatic
therapy. Flux balance analysis is an efficient method to analyze metabolic
networks. It allows optimizing for a metabolic function and thus e.g.,
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, sphingolipid metabolism, cysteine metabolism,
and fat digestion. By in-silico screening of FDA approved drugs on the putative
disease-specific essential genes and gene-pairs, 45 drugs and 99 drug
combinations were predicted as promising candidates for COVID-19 focused drug
repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug
candidates, six antiviral drugs were found and seven drugs that are being
tested in clinical trials against COVID-19. Other drugs like gemcitabine,
rosuvastatin and acetylcysteine, and drug combinations like
azathioprine-pemetrexed might offer new chances for treating COVID-19.
