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

Howell, Rowan; Clarke, Matthew A; Reuschl, Ann-Kathrin; Chen, Tianyi; Abbott-Imboden, Sean; Singer, Mervyn; Lowe, David M; ... Fisher, Jasmin; + view all (2022) Executable network of SARS-CoV-2-host interaction predicts drug combination treatments. npj Digital Medicine , 5 (1) , Article 18. 10.1038/s41746-022-00561-5. Green open access

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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 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.

Type: Article
Title: Executable network of SARS-CoV-2-host interaction predicts drug combination treatments
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41746-022-00561-5
Publisher version: https://doi.org/10.1038/s41746-022-00561-5
Language: English
Additional information: © 2022 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Cellular signalling networks, Dynamic networks, Regulatory networks, Virtual drug screening
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Haematology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
URI: https://discovery.ucl.ac.uk/id/eprint/10144118
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