Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles

作     者：Han, Lu Shan, Guangcun Chu, Bingfeng Wang, Hongyu Wang, Zhongjian Gao, Shengqiao Zhou, Wenxia 

作者机构：Beijing Institute of Pharmacology and Toxicology State Key Laboratory of Toxicology and Medical Countermeasures Beijing100850 China School of Instrumentation Science and Opto-electronics Engineering Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100083 China First Medical Center of PLA General Hospital Beijing100853 China Chengdu Jianshu Technology Co. Ltd Chengdu610015 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2020年

核心收录：

主　　题：Coronavirus 

摘      要：The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanisms of action (MoA) based on the cell image features of 1105 drugs in the LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoA. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19. Copyright © 2020, The Authors. All rights reserved.