DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling

Area of Science:

• Computational biology
• Virology
• Metabolic modeling

Background:

• The 2019 coronavirus disease (COVID-19) pandemic lacks approved antiviral therapies.
• Metabolic reprogramming in host cells is crucial for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication.

Purpose of the Study:

• To identify host-specific metabolic targets for drug repositioning against SARS-CoV-2.
• To predict existing drugs and drug combinations effective against COVID-19.

Main Methods:

• Reconstruction of genome-scale metabolic models for SARS-CoV-2-infected human lung cells using expression datasets.
• In silico gene knockouts to identify essential host genes and gene pairs crucial for viral replication.
• Screening of Food and Drug Administration (FDA)-approved drugs against identified targets.

Main Results:

• Key metabolic pathways implicated in COVID-19 severity include oxidative stress, ferroptosis, and pyrimidine metabolism.
• Identification of 85 FDA-approved drugs and 52 drug combinations as potential COVID-19 therapeutics.
• Computational models and predicted drug candidates are available at https://github.com/sysbiolux/DCcov.

Conclusions:

• Flux balance analysis (FBA) is a viable method for identifying host-directed therapies for viral infections.
• The study provides a computational framework and a list of candidate drugs for COVID-19 treatment development.