Haggard et al. Supplemental Files

Haggard et al. Supplemental File

Predicting Rat Chronic Systemic Toxicity Using in vitro Bioactivity Data

Presented at the Annual Society of Toxicology meetin

ToxCast Assay Annotation: an ontology-based metadata resource for ToxCast HTS assays

Presented at Future Tox I

Predicting Hepatotoxicity Using ToxCast <i>in Vitro</i> Bioactivity and Chemical Structure

The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput <i>in vitro</i> assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict <i>in vivo</i> hepatotoxic effects. A set of 677 chemicals was represented by 711 <i>in vitro</i> 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 <i>in vitro</i> 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

Profiling 976 ToxCast Chemicals across 331 Enzymatic and Receptor Signaling Assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA’s ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical–assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical–assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical–target combinations clustered with known chemical–target interactions. Results from this large inventory of chemical–biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities

Profiling 976 ToxCast Chemicals across 331 Enzymatic and Receptor Signaling Assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA’s ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical–assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical–assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical–target combinations clustered with known chemical–target interactions. Results from this large inventory of chemical–biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities

An “EAR” on Environmental Surveillance and Monitoring: A Case Study on the Use of Exposure–Activity Ratios (EARs) to Prioritize Sites, Chemicals, and Bioactivities of Concern in Great Lakes Waters

Current environmental monitoring approaches focus primarily on chemical occurrence. However, based on concentration alone, it can be difficult to identify which compounds may be of toxicological concern and should be prioritized for further monitoring, in-depth testing, or management. This can be problematic because toxicological characterization is lacking for many emerging contaminants. New sources of high-throughput screening (HTS) data, such as the ToxCast database, which contains information for over 9000 compounds screened through up to 1100 bioassays, are now available. Integrated analysis of chemical occurrence data with HTS data offers new opportunities to prioritize chemicals, sites, or biological effects for further investigation based on concentrations detected in the environment linked to relative potencies in pathway-based bioassays. As a case study, chemical occurrence data from a 2012 study in the Great Lakes Basin along with the ToxCast effects database were used to calculate exposure–activity ratios (EARs) as a prioritization tool. Technical considerations of data processing and use of the ToxCast database are presented and discussed. EAR prioritization identified multiple sites, biological pathways, and chemicals that warrant further investigation. Prioritized bioactivities from the EAR analysis were linked to discrete adverse outcome pathways to identify potential adverse outcomes and biomarkers for use in subsequent monitoring efforts

Real-Time Growth Kinetics Measuring Hormone Mimicry for ToxCast Chemicals in T‑47D Human Ductal Carcinoma Cells

High-throughput screening (HTS) assays capable of profiling thousands of environmentally relevant chemicals for <i>in vitro</i> biological activity provide useful information on the potential for disrupting endocrine pathways. Disruption of the estrogen signaling pathway has been implicated in a variety of adverse health effects including impaired development, reproduction, and carcinogenesis. The estrogen-responsive human mammary ductal carcinoma cell line T-47D was exposed to 1815 ToxCast chemicals comprising pesticides, industrial chemicals, pharmaceuticals, personal care products, cosmetics, food ingredients, and other chemicals with known or suspected human exposure potential. Cell growth kinetics were evaluated using real-time cell electronic sensing. T-47D cells were exposed to eight concentrations (0.006–100 μM), and measurements of cellular impedance were repeatedly recorded for 105 h. Chemical effects were evaluated based on potency (concentration at which response occurs) and efficacy (extent of response). A linear growth response was observed in response to prototypical estrogen receptor agonists (17β-estradiol, genistein, bisphenol A, nonylphenol, and 4-<i>tert</i>-octylphenol). Several compounds, including bisphenol A and genistein, induced cell growth comparable in efficacy to that of 17β-estradiol, but with decreased potency. Progestins, androgens, and corticosteroids invoked a biphasic growth response indicative of changes in cell number or cell morphology. Results from this cell growth assay were compared with results from additional estrogen receptor (ER) binding and transactivation assays. Chemicals detected as active in both the cell growth and ER receptor binding assays demonstrated potencies highly correlated with two ER transactivation assays (<i>r</i> = 0.72; <i>r</i> = 0.70). While ER binding assays detected chemicals that were highly potent or efficacious in the T-47D cell growth and transactivation assays, the binding assays lacked sensitivity in detecting weakly active compounds. In conclusion, this cell-based assay rapidly detects chemical effects on T-47D growth and shows potential, in combination with other HTS assays, to detect environmentally relevant chemicals with potential estrogenic activity

Real-Time Growth Kinetics Measuring Hormone Mimicry for ToxCast Chemicals in T‑47D Human Ductal Carcinoma Cells

High-throughput screening (HTS) assays capable of profiling thousands of environmentally relevant chemicals for <i>in vitro</i> biological activity provide useful information on the potential for disrupting endocrine pathways. Disruption of the estrogen signaling pathway has been implicated in a variety of adverse health effects including impaired development, reproduction, and carcinogenesis. The estrogen-responsive human mammary ductal carcinoma cell line T-47D was exposed to 1815 ToxCast chemicals comprising pesticides, industrial chemicals, pharmaceuticals, personal care products, cosmetics, food ingredients, and other chemicals with known or suspected human exposure potential. Cell growth kinetics were evaluated using real-time cell electronic sensing. T-47D cells were exposed to eight concentrations (0.006–100 μM), and measurements of cellular impedance were repeatedly recorded for 105 h. Chemical effects were evaluated based on potency (concentration at which response occurs) and efficacy (extent of response). A linear growth response was observed in response to prototypical estrogen receptor agonists (17β-estradiol, genistein, bisphenol A, nonylphenol, and 4-<i>tert</i>-octylphenol). Several compounds, including bisphenol A and genistein, induced cell growth comparable in efficacy to that of 17β-estradiol, but with decreased potency. Progestins, androgens, and corticosteroids invoked a biphasic growth response indicative of changes in cell number or cell morphology. Results from this cell growth assay were compared with results from additional estrogen receptor (ER) binding and transactivation assays. Chemicals detected as active in both the cell growth and ER receptor binding assays demonstrated potencies highly correlated with two ER transactivation assays (<i>r</i> = 0.72; <i>r</i> = 0.70). While ER binding assays detected chemicals that were highly potent or efficacious in the T-47D cell growth and transactivation assays, the binding assays lacked sensitivity in detecting weakly active compounds. In conclusion, this cell-based assay rapidly detects chemical effects on T-47D growth and shows potential, in combination with other HTS assays, to detect environmentally relevant chemicals with potential estrogenic activity

Real-Time Growth Kinetics Measuring Hormone Mimicry for ToxCast Chemicals in T‑47D Human Ductal Carcinoma Cells

High-throughput screening (HTS) assays capable of profiling thousands of environmentally relevant chemicals for <i>in vitro</i> biological activity provide useful information on the potential for disrupting endocrine pathways. Disruption of the estrogen signaling pathway has been implicated in a variety of adverse health effects including impaired development, reproduction, and carcinogenesis. The estrogen-responsive human mammary ductal carcinoma cell line T-47D was exposed to 1815 ToxCast chemicals comprising pesticides, industrial chemicals, pharmaceuticals, personal care products, cosmetics, food ingredients, and other chemicals with known or suspected human exposure potential. Cell growth kinetics were evaluated using real-time cell electronic sensing. T-47D cells were exposed to eight concentrations (0.006–100 μM), and measurements of cellular impedance were repeatedly recorded for 105 h. Chemical effects were evaluated based on potency (concentration at which response occurs) and efficacy (extent of response). A linear growth response was observed in response to prototypical estrogen receptor agonists (17β-estradiol, genistein, bisphenol A, nonylphenol, and 4-<i>tert</i>-octylphenol). Several compounds, including bisphenol A and genistein, induced cell growth comparable in efficacy to that of 17β-estradiol, but with decreased potency. Progestins, androgens, and corticosteroids invoked a biphasic growth response indicative of changes in cell number or cell morphology. Results from this cell growth assay were compared with results from additional estrogen receptor (ER) binding and transactivation assays. Chemicals detected as active in both the cell growth and ER receptor binding assays demonstrated potencies highly correlated with two ER transactivation assays (<i>r</i> = 0.72; <i>r</i> = 0.70). While ER binding assays detected chemicals that were highly potent or efficacious in the T-47D cell growth and transactivation assays, the binding assays lacked sensitivity in detecting weakly active compounds. In conclusion, this cell-based assay rapidly detects chemical effects on T-47D growth and shows potential, in combination with other HTS assays, to detect environmentally relevant chemicals with potential estrogenic activity