Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.
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.


CONCLUSIONS
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.
Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee 37232, USA.
Mei
M
Liu
Yonghui
Y
Wu
Yukun
Y
Chen
Jingchun
J
Sun
Zhongming
Z
Zhao
Xue-wen
XW
Chen
Michael Edwin
ME
Matheny
Hua
H
Xu
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016015:Logistic Models; D004364:Pharmaceutical Preparations / Q000737:chemistry; D012372:ROC Curve; D060388:Support Vector Machine
D004364:Pharmaceutical Preparations