Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
Milner Therapeutics Institute, University of Cambridge, Cambridge, UK; Cambridge Centre for AI in Medicine, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Methods. 2022 Jul;203:214-225. doi: 10.1016/j.ymeth.2021.11.002. Epub 2021 Nov 9.
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.
在过去的 20 年中,有几种人类传染病是由人畜共患的冠状病毒引起的。新出现的传染病疫情,如当前的 2019 年冠状病毒病(COVID-19)大流行,需要在确定严重患者的有效治疗方法方面迅速做出反应。重新利用其他治疗领域的已批准药物是最实用的方法之一。在这里,我们提出了一种系统的基于网络的药物再利用方法,该方法探讨了病毒-人类、人类蛋白质-蛋白质和药物-蛋白质互作组数据。我们确定了 196 种适合用于 COVID-19 再利用的已批准药物和 102 种用于相关冠状病毒(严重急性呼吸系统综合征(SARS-CoV)的已批准药物。我们基于 COVID-19 和 SARS 多组学数据集的疾病特征构建了蛋白质-蛋白质相互作用(PPI)网络。对该 PPI 网络的分析揭示了关键途径。在预测针对 COVID-19 相关途径的 196 种药物中,有 44 种(超几何 p 值:1.98e-04)已在 COVID-19 临床试验中,证明了我们方法的有效性。我们使用人工神经网络为每个已识别的药物提供作用机制和治疗价值的信息,以促进它们快速重新用于临床试验。