This study evaluated two anti-angiogenic agents, 5HPP-33 and TNP-470, across the ToxCastDB HTS assay platform and anchored the results to complex in vitro functional assays: the rat aortic explant assay (AEA), rat whole embryo culture (WEC), and the zebrafish embryotoxicity (ZET) assay. This dataset is not publicly accessible because: no EPA data; all the data generated by external organizations; EPA coauthors. It can be accessed through the following means: Data generated by external organizations. Format: N/A. This dataset is associated with the following publication: Ellis-Hutchings, R., R. Settivari, A. McCoy, N. Kleinstreuer, J. Franzosa, T. Knudsen, and E. Carney. (REPRODUCTIVE TOXICOLOGY) EMBRYONIC VASCULAR DISRUPTION ADVERSE OUTCOMES: LINKING HIGH THROUGHPUT SIGNALING SIGNATURES WITH FUNCTIONAL CONSEQUENCES. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 70: 82-96, (2017).

This study evaluated two anti-angiogenic agents, 5HPP-33 and TNP-470, across the ToxCastDB HTS assay platform and anchored the results to complex in vitro functional assays: the rat aortic explant assay (AEA), rat whole embryo culture (WEC), and the zebrafish embryotoxicity (ZET) assay. This dataset is not publicly accessible because: no EPA data; all the data generated by external organizations; EPA coauthors. It can be accessed through the following means: Data generated by external organizations. Format: N/A. This dataset is associated with the following publication: Ellis-Hutchings, R., R. Settivari, A. McCoy, N. Kleinstreuer, J. Franzosa, T. Knudsen, and E. Carney. (REPRODUCTIVE TOXICOLOGY) EMBRYONIC VASCULAR DISRUPTION ADVERSE OUTCOMES: LINKING HIGH THROUGHPUT SIGNALING SIGNATURES WITH FUNCTIONAL CONSEQUENCES. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 70: 82-96, (2017).

Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 μM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 μM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. This dataset is associated with the following publication: Shah , I., W. Setzer , J. Jack, K. Houck , R. Judson , T. Knudsen , J. Liu, M. Martin , D. Reif, A.M. Richard , R.S. Thomas , K. Crofton , D.J. Dix , and R.J. Kavlock. (Envir. Health Perspect.) Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-33, (2015).

The data presented here support the application of the Stemina devTOXqP platform for predictive toxicology and further demonstrate its value in ToxCast as a novel resource that can generate testable hypotheses aimed at characterizing potential pathways for teratogenicity and HTS prioritization of environmental chemicals for an exposure-based assessment of developmental hazard. The dataset from the Stemina (STM) assay is annotated in the ToxCast portfolio as STM. Major findings from the analysis of ToxCast_STM dataset include (1) 19% of 1065 chemicals yielded a prediction of developmental toxicity, (2) assay performance reached 79%-82% accuracy with high specificity (> 84%) but modest sensitivity (< 67%) when compared with in vivo animal models of human prenatal developmental toxicity, (3) sensitivity improved as more stringent weights of evidence requirements were applied to the animal studies, and (4) statistical analysis of the most potent chemical hits on specific biochemical targets in ToxCast revealed positive and negative associations with the STM response, providing insights into the mechanistic underpinnings of the targeted endpoint and its biological domain. The results of this study will be useful to improving our ability to predict in vivo developmental toxicants based on in vitro data and in silico models. This dataset is associated with the following publication: Zurlinden, T., K. Saili, N. Rush, P. Kothiya, R. Judson, K. Houck, E. Hunter, N. Baker, J. Palmer, R. Thomas, and T. Knudsen. Profiling the ToxCast Library With a Pluripotent Human (H9) Stem Cell Line-Based Biomarker Assay for Developmental Toxicity. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 174(2): 189-209, (2020).

We differentiated human induced pluripotent stem cells (hiPSCs) to embryonic endoderm and sought to identify a tipping point at which the developing system did not recover from perturbations caused by exposure to a known teratogen, all-trans retinoic acid (ATRA). Differentiating iPSC-derived endoderm was exposed to five concentrations of ATRA between 0.001 and 10 µM at 6h, 96h, or 192h and assessed for forkhead box A2 (FOXA2) protein expression and global gene transcript expression measured by RNA-sequencing. A tipping point of 17±11 nM was identified where patterns of differentially expressed genes supported a shift in the developmental trajectory away from embryonic endoderm in favor of mesoderm and extraembryonic endoderm. Five concentrations of all-trans retinoic acid (ATRA) between 0.001 and 10 µM were compared to time-matched 0.1% DMSO controls at three timepoints (6h, 96h, and 192h) in differentiating endoderm. Two biological replicates were used. Undifferentiated controls (not in DMSO) were also included in duplicate as internal controls for 6h, 96h, and 144h. This dataset is associated with the following publication: Saili, K., T. Antonijevic, T. Zurlinden, I. Shah, C. Deisenroth, and T. Knudsen. Molecular characterization of a toxicological tipping point during human stem cell differentiation. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 91(January 2020): 1-13, (2020).

The US EPA Toxicity Forecasting (ToxCast) project has involved the generation of large amount of high throughput in vitro data (Over 4000 chemicals tested in between 100 and 700 assays). This data is generated in a consistent manner, and includes a wide variety of chemicals including industrial and consumer products, food additives, pesticides, and drugs. These chemicals were not chosen because they were expected to be active, resulting in a database containing a balance of positive and negative data points. As such this data is useful for computational model construction. This in vitro data has been used at the EPA and elsewhere in modelling approaches, including computational modelling for specific target binding as biological descriptors for toxicity prediction, and pharmacokinetic modelling of human dose responses. All analyses in the generation of the burst flag hit-call matrix and extraction of chemicals for the targets in this study (AR and GR) were performed using R v3.1.2. This dataset is associated with the following publication: Allen, T.E., M.D. Nelms, S.W. Edwards, J.M. Goodman, S. Gutsell, and P.J. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(12): 7461-7470, (2020).

The US EPA Toxicity Forecasting (ToxCast) project has involved the generation of large amount of high throughput in vitro data (Over 4000 chemicals tested in between 100 and 700 assays). This data is generated in a consistent manner, and includes a wide variety of chemicals including industrial and consumer products, food additives, pesticides, and drugs. These chemicals were not chosen because they were expected to be active, resulting in a database containing a balance of positive and negative data points. As such this data is useful for computational model construction. This in vitro data has been used at the EPA and elsewhere in modelling approaches, including computational modelling for specific target binding as biological descriptors for toxicity prediction, and pharmacokinetic modelling of human dose responses. All analyses in the generation of the burst flag hit-call matrix and extraction of chemicals for the targets in this study (AR and GR) were performed using R v3.1.2. This dataset is associated with the following publication: Allen, T.E., M.D. Nelms, S.W. Edwards, J.M. Goodman, S. Gutsell, and P.J. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(12): 7461-7470, (2020).

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).

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).