Executable network of SARS-CoV-2-host interaction predicts drug combination treatments.

ABSTRACT: The COVID-19 pandemic has pushed healthcare systems globally to a breaking point. The urgent need for effective and affordable COVID-19 treatments calls for repurposing combinations of approved drugs. The challenge is to identify which combinations are likely to be most effective and at what stages of the disease. Here, we present the first disease-stage executable signalling network model of SARS-CoV-2-host interactions used to predict effective repurposed drug combinations for treating early- and late stage severe disease. Using our executable model, we performed in silico screening of 9870 pairs of 140 potential targets and have identified nine new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in

SUBMITTER: Howell R 

PROVIDER: S-EPMC8844383 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature