Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Journal: Clinical pharmacology and therapeutics
Published Date: Feb 28, 2020

Abstract

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.

Authors

  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Ruo-Fan Ding
    State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
  • Han Xu
    1Institute of Microelectronics, Peking University, Beijing, 100871 China.
  • Yang-Mei Qin
    State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
  • Qiu-Shun He
    State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
  • Fei Du
    State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, China.
  • Yun Zhang
    Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
  • Li-Xia Yao
    Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
  • Pan You
    Xiamen Xianyue Hospital, Xiamen, Fujian, China.
  • Yan-Ping Xiang
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Zhi-Liang Ji
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China The Key Laboratory for Chemical Biology of Fujian Province, Xiamen University, Xiamen, Fujian 361005, P.R. China appo@xmu.edu.cn.

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