Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

被引:2
|
作者
Hwang, Woochang [1 ]
Han, Namshik [1 ,2 ]
机构
[1] Univ Cambridge, Milner Therapeut Inst, Cambridge, England
[2] Univ Cambridge, Cambridge Ctr AI Med, Dept Appl Math & Theoret Phys, Cambridge, England
关键词
COVID-19; SARS; Drug repurposing; Artificial neural network; Drug mechanism; Virus replication; Coronavirus; NETWORKS; INFECTION;
D O I
10.1016/j.ymeth.2021.11.002
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.
引用
收藏
页码:214 / 225
页数:12
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