Innovative Research for a Sustainable Future www.epa.gov/research Keith Houck | houck.keith@epa.gov | 919-541-5519 Predicting Toxic and Therapeutic Mechanisms of the ToxCast Chemical Library by Phenotypic Screening Houck K1, Kleinstreuer N2, Yang J3, Berg E3, Knudsen T1, Richard A1, Martin M1, Reif D4, Judson R1, Polokoff M3 1 National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC; 2ILS, Inc., Research Triangle Park, NC; 3BioSeek, a Division of DiscoveRx, South San Francisco, CA; 4NCSU, Raleigh, NC Abstract Summary Results The ToxCast Phase II library was screened in 8 complex cell culture systems measuring endpoints relevant to inflammatory signaling and vascular biology. Assays showed strong reproducibility across technical replicates and built-in test compounds. The BioMAP system identified potential targets, modes of action, and clinical side effects for compounds based on the reference database. Assays provided coverage of mechanisms/targets not directly represented in assay endpoints, e.g. AhR Phenotypic screening and computational analysis provides a unique opportunity to survey environmental chemicals for potential human bioactivity Methods Addressing safety aspects of drugs and environmental chemicals relies extensively on animal testing. However the quantity of chemicals needing assessment and challenges of species extrapolation require development of alternative approaches. Using 8 primary human cell systems (BioMAP), we screened in concentration-response format 776 chemicals from the ToxCast Phase II library (http://epa.gov/ncct/toxcast/chemicals.html) for perturbation of physiologically important pathways. Cell systems consisted of combinations of endothelial, peripheral blood mononuclear, bronchial epithelial and coronary artery smooth muscle cells; fibroblasts and keratinocytes. Chemical-response signatures from 87 endpoints covering molecular functions relevant to toxic and therapeutic pathways were generated. Assessment of profiling data by unsupervised clustering using Self Organizing Maps and supervised analysis using Support Vector Machine algorithms grouped chemical/concentration by potential mechanism class providing insight into polypharmacology and potential off-target effects of drugs. Clusters contained diverse mechanistic activity including kinase, TNFα, phosphodiesterase and Hsp90 inhibitors; Ah, estrogen and glucocorticoid receptor modulators; disruptors of mitochondrial and tubulin function; histamine antagonists; and statins. Novel associations identified included induction of tissue factor in endothelial cells by ER antagonists, AhR agonists and mTOR inhibitors, all chemical classes with susceptibility to venous thrombosis. Further, structure-based analysis demonstrated associations between chemical categories and mechanism class predictions. Our results yielded an extensive list of potential toxicological targets and biological pathways that we are incorporating into a chemical prioritization strategy for chemicals of concern to the Agency. Objective Use primary human cell phenotypic responses to classify and predict compound mechanisms of action and potential toxicities Primary Cell Systems Used SVM Models and Top Scoring Compounds • Compounds were tested at 4 (or 8) concentrations in duplicate, 200 mM high concentration with half-log dilutions. • Cells treated with compounds followed at one hr by stimulation of signaling pathways • Cells harvested at 24 hr and endpoints measured by ELISA or staining (SRB) • Data normalized to log10 Fold Change over DMSO controls • AC50 values calculated using 4-parameter Hill model • Compounds tested in blinded fashion and included internal replicates • Predictive models for 28 mechanism classes were built using a two class approach with SVM using R SVM package e1071 (Berg et al., JBS 18:1260, 2013). • Unsupervised clustering of all compounds at the individual concentration level was conducted