Network reinforcement driven drug repurposing for Covid-19 by exploiting disease-gene-drug associations

作     者：Nam, Yonghyun Yun, Jae-Seung Lee, Seung Mi Park, Ji Won Chen, Ziqi Lee, Brian Verma, Anurag Ning, Xia Shen, Li Kim, Dokyoon 

作者机构：Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia United States Division of Endocrinology and Metabolism Department of Internal Medicine St. Vincent’s Hospital College of Medicine Catholic University of Korea Seoul Korea Republic of Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul Korea Republic of Department of Surgery Seoul National University College of Medicine Seoul Korea Republic of Computer Science and Engineering Department College of Engineering Ohio State University Columbus United States Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia United States Biomedical Informatics Department College of Medicine Ohio State University Columbus United States Translational Data Analytics Institute Ohio State University Columbus United States Institute for Biomedical Informatics University of Pennsylvania Philadelphia United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2020年

核心收录：

主　　题：Diseases 

摘      要：Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19. Copyright © 2020, The Authors. All rights reserved.