Loading...
Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Liu, Mei Wu, Yonghui Chen, Yukun Sun, Jingchun Zhao, Zhongming Chen, Xue-wen Matheny, Michael Edwin Xu, Hua
Liu, Mei
Wu, Yonghui
Chen, Yukun
Sun, Jingchun
Zhao, Zhongming
Chen, Xue-wen
Matheny, Michael Edwin
Xu, Hua
Citations
Altmetric:
Abstract
Abstract
Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance.
Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared.
Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin.
Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.
Description
Date
2012-06-19
Journal Title
Journal ISSN
Volume Title
Publisher
American Medical Informatics Association
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Liu, Mei, Yonghui Wu, Yukun Chen, Jingchun Sun, Zhongming Zhao, Xue-wen Chen, Michael Edwin Matheny, and Hua Xu. 2012. “Large-Scale Prediction of Adverse Drug Reactions Using Chemical, Biological, and Phenotypic Properties of Drugs.” Journal of the American Medical Informatics Association : JAMIA 19 (e1): e28–e35. http://dx.doi.org/10.1136/amiajnl-2011-000699.