Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, concentration-dependent responses of 1063 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a diverse battery of 821 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to better distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress / cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least two viability/cytotoxicity assays within the concentration range tested (typically up to 100 M) activated a median of 12% of assay endpoints while those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (e.g., receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering of specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), while intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment. This dataset is associated with the following publication: Judson , R., K. Houck , M. Martin , A. Richard , T. Knudsen , I. Shah , S. Little , J. Wambaugh , W. Setzer , P. Kothiya , J. Phuong , D. Filer , D. Smith , D. Reif, D. Rotroff, N. Kleinstreuer, N. Sipes, M. Xia, R. Huang, K. Crofton , and R. Thomas. (Toxicological Sciences) Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space. TOXICOLOGICAL SCIENCES. Society of Toxicology, 1-47, (2016).

Previous work identified a ‘cytotoxic burst’ (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared to the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). This dataset is associated with the following publication: Fay, K., J. Swintek, D. Villeneuve, S. Edwards, M. Nelms, B. Blackwell, and G. Ankley. Differentiating pathway-specific from non-specific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 163(2): 500-515, (2018).

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4382 in vivo studies for 1201 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. In order to address the complexity problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using Random Forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 20% of the variance reducing the root mean squared error (RMSE) from 0.96 log10 mg/kg/day to 0.85 log10 mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 38% of the variance with an RMSE of 0.76 log10 mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log10 mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.1 to 0.8 log10 mg/kg/day. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we have generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for thousands of chemicals. This dataset is associated with the following publication: Truong, L., G. Ouedraogo, L. Pham, J. Clouzeau, S. Loisel-Joubert, D. Blanchet, H. Noçairi, W. Setzer, R. Judson, C. Grulke, K. Mansouri, and M. Martin. (Archives of Toxicology) Predicting In Vivo Effect Levels for Repeat Dose Systemic Toxicity using Chemical, Biological, Kinetic and Study Covariates. Archives of Toxicology. Springer, New York, NY, USA, 92(2): 587-600, (2018).

Supplemental data for "Schaupp CM, Maloney EM, Mattingly K, Olker JH, Villeneuve DL. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment. Toxicol Sci. 2023 Jul 25:kfad072. doi: 10.1093/toxsci/kfad072. Epub ahead of print. PMID: 37490521.". This dataset is associated with the following publication: Schaupp, C., E. Maloney, K. Mattingly, J. Olker, and D. Villeneuve. Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 195(2): 145-154, (2023).

In the present study, we adapted an existing phenotypic profiling assay (“Cell Painting”, (Bray et al., 2016)) to be compatible with in-house microfluidics capabilities for 384-well culture format, chemical exposures and fluorescent cytochemistry in order to facilitate concentration-response screening of several hundred environmental chemicals. In this assay, human-derived cells were labeled with multiple fluorescent probes to visualize various subcellular organelles and structural features. High content image analysis workflows were used to measure hundreds of morphological features at the level of the individual cell (i.e. shape of the cells, intensity, texture and distribution of fluorescent labels, etc.). The resultant data were then used to calculate well-level summary values, perform high-throughput concentration-response modeling and generate phenotypic response profiles. First, we identified and screened a set of candidate phenotypic reference chemicals for use as plate-based controls for evaluating HTPP assay performance during large-scale screening studies and identified an optimal exposure duration for HTPP screening. Second, we screened a set of 462 environmental chemicals in the U-2 OS cell model and derived in vitro potency estimates for bioactivity of all active chemicals. In addition, we demonstrated the technical reproducibility of the HTPP assay in concentration-response screening mode using the previously identified phenotypic reference chemicals. Next, we used reverse dosimetry to calculate administered equivalent doses (AEDs) corresponding to the thresholds for chemical bioactivity and compared those values to in vivo effect values from mammalian toxicity studies. This dataset is associated with the following publication: Nyffeler, J., C. Willis, R. Lougee, A. Richard, K. Friedman, and J. Harrill. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 389: 114876, (2020).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity. This dataset is associated with the following publication: Karmaus , A., D. Filer , M. Martin , and K. Houck. (FOOD AND CHEMICAL TOXICOLOGY) Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 92: 188-196, (2016).

Thousands of chemicals are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The landscape of the food-relevant chemical universe was evaluated using cheminformatics, and subsequently the bioactivity of food-relevant chemicals across the publicly available ToxCast highthroughput screening program was assessed. In total, 8659 food-relevant chemicals were compiled including direct food additives, food contact substances, and pesticides. Of these food-relevant chemicals, 4719 had curated structure definition files amenable to defining chemical fingerprints, which were used to cluster chemicals using a selforganizing map approach. Pesticides, and direct food additives clustered apart from one another with food contact substances generally in between, supporting that these categories not only reflect different uses but also distinct chemistries. Subsequently, 1530 food-relevant chemicals were identified in ToxCast comprising 616 direct food additives, 371 food contact substances, and 543 pesticides. Bioactivity across ToxCast was filtered for cytotoxicity to identify selective chemical effects. Initiating analyses from strictly chemical-based methodology or bioactivity/cytotoxicity-driven evaluation presents unbiased approaches for prioritizing chemicals. Although bioactivity in vitro is not necessarily predictive of adverse effects in vivo, these data provide insight into chemical properties and cellular targets through which foodrelevant chemicals elicit bioactivity. This dataset is associated with the following publication: Karmaus , A., D. Filer , M. Martin , and K. Houck. (FOOD AND CHEMICAL TOXICOLOGY) Evaluation of food-relevant chemicals in the ToxCast high-throughput screening program. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 92: 188-196, (2016).

Background: Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use and the thousands of environmental chemicals lacking toxicity data. EPA's ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives: This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods: We tested 309 mostly pesticide active chemicals in 467 assays across 9 technologies, including high-throughput cell-free assays and cell-based assays in multiple human primary cells and cell lines, plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results: Chemicals display a broad spectrum of activity at the molecular and pathway levels. Many expected interactions are seen, including endocrine and xenobiotic metabolism enzyme activity. Chemicals range in promiscuity across pathways, from no activity to affecting dozens of pathways. We find a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also find associations between a small set in vitro assays and rodent liver lesion formation. Conclusions: This approach promises to provide meaningful data on the thousands of untested environmental chemicals, and to guide targeted testing of environmental contaminants.