title: Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.
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.
authors.affiliation: Laboratory for Molecular Modeling, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
authors.forename: Yen
authors.initials: Y
authors.lastname: Low
authors.forename: Takeki
authors.initials: T
authors.lastname: Uehara
authors.forename: Yohsuke
authors.initials: Y
authors.lastname: Minowa
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Yamada
authors.forename: Yasuo
authors.initials: Y
authors.lastname: Ohno
authors.forename: Tetsuro
authors.initials: T
authors.lastname: Urushidani
authors.forename: Alexander
authors.initials: A
authors.lastname: Sedykh
authors.forename: Eugene
authors.initials: E
authors.lastname: Muratov
authors.forename: Viktor
authors.initials: V
authors.lastname: Kuz'min
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Ivan
authors.initials: I
authors.lastname: Rusyn
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000818:Animals; D056486:Chemical and Drug Induced Liver Injury / Q000378:metabolism* / Q000473:pathology; D016208:Databases, Factual; D051557:Hepatocyte Nuclear Factor 4 / Q000378:metabolism; D008297:Male; D015233:Models, Statistical; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D017207:Rats, Sprague-Dawley; D054852:Small Molecule Libraries / Q000737:chemistry / Q000633:toxicity; D043922:Toxicogenetics
chemical_list: D051557:Hepatocyte Nuclear Factor 4; C496594:Hnf4a protein, rat; D054852:Small Molecule Libraries