Preprint Article Version 1 This version is not peer-reviewed

Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

Version 1 : Received: 17 March 2020 / Approved: 18 March 2020 / Online: 18 March 2020 (08:50:10 CET)

How to cite: Li, X.; Yu, J.; Zhang, Z.; Ren, J.; Peluffo, A.E.; Zhang, W.; Zhao, Y.; Yan, K.; Cohen, D.; Wang, W. Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019. Preprints 2020, 2020030286 (doi: 10.20944/preprints202003.0286.v1). Li, X.; Yu, J.; Zhang, Z.; Ren, J.; Peluffo, A.E.; Zhang, W.; Zhao, Y.; Yan, K.; Cohen, D.; Wang, W. Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019. Preprints 2020, 2020030286 (doi: 10.20944/preprints202003.0286.v1).

Abstract

The COVID-2019 disease caused by the SARS-CoV-2 virus (aka 2019-nCoV) has raised significant health concerns in China and worldwide. While novel drug discovery and vaccine studies are long, repurposing old drugs against the COVID-2019 epidemic can help identify treatments, with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter Phase 3 or 4 or can be used directly in clinical settings. In this study, we presented a novel network based drug repurposing platform to identify potential drugs for the treatment of COVID-2019. We first analysed the genome sequence of SARS-CoV-2 and identified SARS as the closest disease, based on genome similarity between both causal viruses, followed by MERS and other human coronavirus diseases. Using our AutoSeed pipeline (text mining and database searches), we obtained 34 COVID-2019-related genes. Taking those genes as seeds, we automatically built a molecular network for which our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules and finally suggested 78 drugs to repurpose. Following manual filtering based on clinical knowledge, we re-prioritized 30 potential repurposable drugs against COVID-2019 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide) . We hope that this data can provide critical insights into SARS-CoV-2 biology and help design rapid clinical trials of treatments against COVID-2019.

Subject Areas

COVID-2019; SARS-CoV-2; repurposing; network bioinformatics

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