title: Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure.
abstract: The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance.
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.|‡Department of Information Science, University of Arkansas at Little Rock, Arkansas 72204, United States.|§Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831, United States.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.|§Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831, United States.
authors.forename: Kamel
authors.initials: K
authors.lastname: Mansouri
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
authors.forename: Richard S
authors.initials: RS
authors.lastname: Judson
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Martin
authors.affiliation: ∥Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States.
authors.forename: Huixiao
authors.initials: H
authors.lastname: Hong
authors.affiliation: ∥Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States.
authors.forename: Minjun
authors.initials: M
authors.lastname: Chen
authors.affiliation: ‡Department of Information Science, University of Arkansas at Little Rock, Arkansas 72204, United States.|∥Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Xu
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
authors.forename: Russell S
authors.initials: RS
authors.lastname: Thomas
authors.affiliation: †National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
authors.forename: Imran
authors.initials: I
authors.lastname: Shah
mesh_terms: D000818:Animals; D066298:In Vitro Techniques; D008099:Liver / Q000187:drug effects*; D015394:Molecular Structure; D051381:Rats; D018675:Toxicity Tests