PT  - JOURNAL ARTICLE
AU  - Rowan Howell
AU  - Matthew A. Clarke
AU  - Ann-Kathrin Reuschl
AU  - Tianyi Chen
AU  - Sean Abbott-Imboden
AU  - Mervyn Singer
AU  - David M. Lowe
AU  - Clare L. Bennett
AU  - Benjamin Chain
AU  - Clare Jolly
AU  - Jasmin Fisher
TI  - Executable Network of SARS-CoV-2-Host Interaction Predicts Drug Combination Treatments
AID  - 10.1101/2021.07.27.453973
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.07.27.453973
4099  - http://biorxiv.org/content/early/2021/07/27/2021.07.27.453973.short
4100  - http://biorxiv.org/content/early/2021/07/27/2021.07.27.453973.full
AB  - 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 12 new drug combinations. Camostat and Apilimod were predicted to be the most promising combination in effectively supressing viral replication in the early stages of severe disease and were validated experimentally in human Caco-2 cells. Our study further demonstrates the power of executable mechanistic modelling to enable rapid pre-clinical evaluation of combination therapies tailored to disease progression. It also presents a novel resource and expandable model system that can respond to further needs in the pandemic.Competing Interest StatementThe authors have declared no competing interest.