Broad‐Spectrum Profiling of Drug Safety via Learning Complex Network

• Published in: Clinical pharmacology and therapeutics 2020-06, Vol.107 (6), p.1373-1382
• Main Authors: Liu, Ke, Ding, Ruo‐Fan, Xu, Han, Qin, Yang‐Mei, He, Qiu‐Shun, Du, Fei, Zhang, Yun, Yao, Li‐Xia, You, Pan, Xiang, Yan‐Ping, Ji, Zhi‐Liang
• Format: Article
• Language: English
• Subjects: Adverse Drug Reaction Reporting Systems; Animals; Drug Discovery - methods; Drug Evaluation, Preclinical - methods; Drug-Related Side Effects and Adverse Reactions - epidemiology; Drug-Related Side Effects and Adverse Reactions - genetics; Humans; Machine Learning
• ISSN: 0009-9236; 1532-6535
• DOI: 10.1002/cpt.1750

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.