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Prediction of Adverse Drug Reactions Using Decision Tree Modeling
Author(s) -
Hammann F,
Gutmann H,
Vogt N,
Helma C,
Drewe J
Publication year - 2010
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2009.248
Subject(s) - pharmacovigilance , drug , drug reaction , medicine , pharmacology , intensive care medicine , pharmacotherapy , adverse effect , clinical pharmacology , decision tree , postmarketing surveillance , machine learning , computer science
Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure–activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs ( n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9–90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end‐organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance. Clinical Pharmacology & Therapeutics (2010) 88 1, 52–59. doi: 10.1038/clpt.2009.248