New model to enable drug predictions against COVID-19

Researchers have developed a computational model of a human lung cell which has been used to understand how SARS-CoV-2, the virus behind COVID-19 disease, uses the host to survive, and to enable drug predictions for treating the virus.

Using a computer model of a human lung cell metabolism, the study published in the journal Life Science Alliance, have captured the stoichiometric amino and nucleic acid requirements of SARS-CoV-2.

Their model has identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction, highlighting reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways.

In fact, researchers found that only a few of these metabolic perturbations are able to selectively inhibit virus reproduction.

"We have created a stoichiometric biomass function for the Covid-19-causing SARS-CoV-2 virus and incorporated this into a human lung cell genome-scale metabolic model," said study author Orkun Soyer, from the University of Warwick in the UK.

"We then predicted reaction perturbations that can inhibit SARS-CoV-2 reproduction in general or selectively, without inhibiting the host metabolic maintenance," Soyer added.

The predicted reactions primarily fall onto glycolysis and oxidative phosphorylation pathways, and their connections to amino acid biosynthesis pathways.

Together, these results highlight the possibility of targeting host metabolism for inhibition of SARS-CoV-2 reproduction in human cells in general and in human lung cells specifically.

"More research needs to be carried out to explore SARS-CoV-2 infected cells and their metabolism, however, the model developed here by the researchers can be used as a starting point for testing out specific drug predictions," said author Hadrien Delattre.