In Silico Prediction of Drug-Induced Liver Injury Based on Adverse Drug Reaction Reports

Toxicological Sciences : an Official Journal of the Society of Toxicology
Xiang-Wei Zhu, Shao-Jing Li

Abstract

Drug-induced liver injury (DILI) is a major cause of drug attrition. Currently existing Quantitative Structure-Activity Relationship models have limited predictive capabilities for DILI. Furthermore, their practical applications were limited by lack of new hepatotoxicity data. In this study, we first collected and curated a novel set of 122 DILI-positive and 932 DILI-negative drugs from online adverse drug reports using proportional reporting ratios as the signal detection method. Second, three strategies (under-sampling the majority class, synthetic minority over-sampling technique, and adjusting decision threshold approach) were employed to develop predictive classification models to cope with the unbalanced dataset. Random forest (RF) models using CDK, MACCS, and Mold2 descriptors based on the under-sampling and over-sampling strategies afforded correct classification ratio (CCR) of ∼0.77 and 0.78, respectively. Recursive RF models based on the last strategy tremendously reduced modeling descriptors (at most 95.4% for Mold2) while apparently improved the predictability with a consensus CCR of 0.84 (sensitivity of 0.88 and specificity of 0.79). Structural analysis showed that pyrimidine derivatives, purine derivatives, and ha...Continue Reading

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Citations

Jan 26, 2020·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Benjamin Bajželj, Viktor Drgan
Apr 20, 2019·International Journal of Molecular Sciences·Shuaibing HeXiaobo Sun
Aug 25, 2019·International Journal of Molecular Sciences·Yangyang WangBing Wang
Feb 15, 2020·Biology Direct·Marco ChiericiCesare Furlanello
Dec 29, 2020·Chemical Research in Toxicology·Ting LiShraddha Thakkar
Aug 4, 2021·Cell Regeneration·Do Thuy Uyen Ha LamHuck-Hui Ng

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