Article

Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Laboratory for Molecular Modeling, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
Chemical Research in Toxicology (Impact Factor: 3.53). 06/2011; 24(8):1251-62. DOI: 10.1021/tx200148a
Source: PubMed

ABSTRACT

Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

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    • "Zhu et al. (2008) first combined the in vitro assay data on cell viability with conventional chemical descriptors which greatly improved the prediction accuracy of rodent carcinogenicity. In subsequent studies, the hybrid classification models (i.e. using both biological and chemical descriptors) were developed in predicting an acute toxicity half-maximal lethal dose (Sedykh et al., 2011) and shortterm drug hepatotoxicity (Low et al., 2011) in rats. Compared with traditional, purely chemical (e.g. "
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    • "Cheng et al. (2010) examined similarities between chemical structures and molecular targets of 37 drugs that were clustered based on their bioactivity profiles. Low et al. (2011) classified 127 rat liver samples to toxic versus non-toxic responses, based on combined drug-induced expression profiles and chemical descriptors, and identified chemical substructures and genes that were responsible for liver toxicity. In a broader setting, when the goal is to find dependencies between two data sources (chemical structures and genomic responses), correlation-type approaches match the goal directly, and have the additional advantage that a predefined classification is not required. "
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