Three recent IARC Working Groups pioneered inclusion of the US Environmental Protection Agency (EPA) ToxCast program high-throughput screening (HTS) data to supplement other mechanistic evidence. In Monograph V110, HTS profiles were compared between perfluorooctanoic acid (PFOA) and prototypical activators across multiple nuclear receptors. For Monograph V112-113, HTS assays were mapped to 10 key characteristics of carcinogens identified by an IARC expert group, and systematically considered as an additional mechanistic data stream. This dataset is not publicly accessible because: The data is generated by external authors from existing public data sources. It can be accessed through the following means: Data is available in existing public data sources. Format: N/A. This dataset is associated with the following publication: Chiu, W., K. Guyton, M. Martin, D. Reif, and I. Rusyn. (ALTEX) Use of High-throughput in vitro toxicity screening data in cancer hazard evaluations by the IARC Monograph Working Groups. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 35(1): 51-64, (2018).

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).

The paper has data generated by NIH and the EPA coauthors provided input into the preparation of the manuscript. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: Data generated by NIH. Format: N/A. This dataset is associated with the following publication: Lynch, C., S. Sakamuru, R. Huang, D.A. Stavea, L. Varticovski, G.L. Hagar, R.S. Judson, K.A. Houck, N.C. Kleinstreuer, W. Casey, R.S. Paules, A. Simeonov, and M. Xia. (Toxicology) Identifying Environmental Chemicals as Agonists of the Androgen Receptor by Applying a Quantitative High-throughput Screening Platform. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 385: 48-58, (2017).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

Merged product weight fraction and chemical function data. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

All data is generated by the external authors or taken from public data sources. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: All data is generated by the external authors or taken from public data sources. Format: N/A. This dataset is associated with the following publication: Mahapatra, D., J.A. Franzosa, K. Roell, M.A. Kuenemann, K.A. Houck, D.M. Reif, D. Fourches, and S.W. Kullman. Confirmation of High-Throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays. Scientific Reports. Nature Publishing Group, London, UK, 8(8883): 1-16, (2018).

Data for publication Turley et al. "Applying the RISK21 approach to assess predictivity of new approach methodologies in toxicity testing and exposure assessment: a case study on food contact chemicals". Includes food concentration predictions from the model of Biryol et al. (2017) and SHEDS-HT exposure predictions. This dataset is associated with the following publication: Turley, A., K. Isaacs, B. Wetmore, A. Karmaus, M. Embry, and M. Krishan. Incorporating new approach methodologies in toxicity testing and exposure assessment for tiered risk assessment using the RISK21 approach: Case studies on food contact chemicals. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 134: 110819, (2019).

There are five different files for this dataset: 1. A dataset listing the reported functional uses of chemicals (FUse) 2. All 729 ToxPrint descriptors obtained from ChemoTyper for chemicals in FUse 3. All EPI Suite properties obtained for chemicals in FUse 4. The confusion matrix values, similarity thresholds, and bioactivity index for each model. 5. The functional use prediction, bioactivity index, and prediction classification (poor prediction, functional substitute, candidate alternative) for each Tox21 chemical. This dataset is associated with the following publication: Phillips, K., J. Wambaugh, C. Grulke, K. Dionisio, and K. Isaacs. High-throughput screening of chemicals as functional substitutes using structure-based classification models. GREEN CHEMISTRY. Royal Society of Chemistry, Cambridge, UK, 19: 1063-1074, (2017).

Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") as in Pearce et al. (2017) . Chemical-specific in vitro data have been obtained from relatively high throughput experiments. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. This dataset is associated with the following publication: Pearce , R., C. Strope , W. Setzer , N. Sipes , and J. Wambaugh. (Journal of Statistical Software) HTTK: R Package for High-Throughput Toxicokinetics. Journal of Statistical Software. American Statistical Association, Alexandria, VA, USA, 79(4): 1-26, (2017).

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C0) and chemical properties. The most predictive variables in the resulting model were C0, molecular weight, log Kow, and food type (R2=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C0 based on the functional role of chemical in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R2=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority–setting. This dataset is associated with the following publication: Biryol, D., C. Nicolas, J. Wambaugh, K. Phillips, and K. Isaacs. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 108: 185-194, (2017).

In zebrafish, 161 compounds were screened and 34 were identified by visual inspection as VDCs, of which 28 were confirmed as VDCs by quantitative image analysis. Testing of the zebrafish VDCs for their capacity to inhibit endothelial tube formation in the murine yolk-sac-derived endothelial cell line C166 identified 22 compounds that both disrupted zebrafish vascular development and murine endothelial in vitro tubulogenesis. This dataset is associated with the following publication: McCollum, C., J. Conde Vancells, C. Hans, M. Vazquez-Chantada, N. Kleinstreuer, T. Tal , T. Knudsen, S. Shah, F. Merchant, R. Finnell, J. Gustafsson, R. Cabrera, and M. Bondesson. (Reproductive Toxicology) Identification of vascular disruptor compounds by a tiered analysis in zebrafish embryos and mouse embryonic endothelial cells. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 70: 60-69, (2017).

Importance of random forest predictors for all classification models of chemical function. This dataset is associated with the following publication: Isaacs , K., M. Goldsmith, P. Egeghy , K. Phillips, R. Brooks, T. Hong, and J. Wambaugh. Characterization and prediction of chemical functions and weight fractions in consumer products. Toxicology Reports. Elsevier B.V., Amsterdam, NETHERLANDS, 3: 723-732, (2016).

List of chemicals used for model evaluation, their MW, log KOW, and references for the original data source(s), the review(s) the data was collected from, and reference for log KOW as cited in the reviews. [Table SI-3 of research article]. This dataset is associated with the following publication: Brown, T., J. Armitage, P. Egeghy, I. Kircanski, and J. Arnot. Dermal permeation data and models for the prioritization and screening-level exposure assessment of organic chemicals. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 94: 424-435, (2016).

This data set contains ToxPrint finger prints for all chemicals in FUse that had QSAR-ready SMILES strings as well as select physicochemical properties from the Estimation Program Interface Suite (EPI Suite) program. This dataset is associated with the following publication: Phillips, K., J. Wambaugh, C. Grulke, K. Dionisio, and K. Isaacs. High-throughput screening of chemicals as functional substitutes using structure-based classification models. GREEN CHEMISTRY. Royal Society of Chemistry, Cambridge, UK, 19: 1063-1074, (2017).

This file contains value of the model training set confusion matrix, domain of applicability evaluation based on training set to predicted chemicals structural similarity, and 75th percentile bioactivity index values for each QSUR model. This dataset is associated with the following publication: Phillips, K., J. Wambaugh, C. Grulke, K. Dionisio, and K. Isaacs. High-throughput screening of chemicals as functional substitutes using structure-based classification models. GREEN CHEMISTRY. Royal Society of Chemistry, Cambridge, UK, 19: 1063-1074, (2017).