Reference : DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung thro...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/48557
DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling.
English
Kishk, Ali mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Pires Pacheco, Maria Irene mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
2021
iScience
24
11
103331
Yes (verified by ORBilu)
International
2589-0042
2589-0042
[en] Bioinformatics ; Pharmaceutical science ; Virology
[en] The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA 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 virus replication within the host tissue. Making use of expression datasets 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, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (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 (https://github.com/sysbiolux/DCcov).
http://hdl.handle.net/10993/48557
© 2021 The Authors.

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