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

By Mei Liu, Yonghui Wu, Yukun Chen, Jingchun Sun, Zhongming Zhao, Xue-wen Chen, M. Matheny, Hua Xu. 2012

278 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Toxicology

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Citations in the corpus

Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “In addition, Liu et al. [141] integrated chemical, biological, phenotypic properties of drugs to predict ADR associated with it via machine learning analysis.”
Linking Biochemical Pathways and Networks to Adverse Drug Reactions (2014, methodology): “In comparison to other computational approaches such as support vector machines and -nearest neighbours [7], the SCCA-based approach used in this study has a clear advantage in terms of interpretability of results [26].”
ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms (2014, unknown): “Conventionally, the drug safety predictions were demonstrated on ADR terms themselves that derived from databases like SIDER2 and MedDRA (18,19).”
Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. (2014, background): “In our exploration of several novel predicted DDIs involving antipsychotic drugs, we demonstrated the potential utility of the HNAI framework for the identi ﬁ cation of anti-psychotic DDIs.”