This is a manuscript developed by a group at University of Berlin and Danish Technical University, using previously published data from EPA. The only EPA interaction has been providing advice on how to correctly use the data. This dataset is not publicly accessible because: The data were not collected by EPA and are hosted external to the agency. It can be accessed through the following means: Contact the corresponding author Yasmine Emara at the Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany. Email: y.emara@tu-berlin.de. Format: Not available. This dataset is associated with the following publication: Emara, Y., P. Fantke, R. Judson, X. Chang, P. Pradeep, A. Lehmann, M. Siegert, and M. Finkbeiner. Integrating endocrine-related health effects into comparative human toxicity characterization. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 762: 143874, (2021).

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

No novel data were reported in association with this product. This dataset is not publicly accessible because: The associated publication is a review/forum-type article. No novel scientific data are reported. All data cited have been previously published elsewhere. It can be accessed through the following means: Not applicable. Format: This article is a review/forum-type article. No novel scientific data are included. This dataset is associated with the following publication: Knapen, D., E. Stinckens, J. Cavallin, G. Ankley, H. Holbech, D. Villeneuve, and L. Vergauwen. Toward an AOP network-based tiered testing strategy for the assessment of thyroid hormone disruption. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(16): 8491-8499, (2020).

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).

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).

The data are acquired from publicly available literature and model simulations. This dataset is not publicly accessible because: The manuscript is being peer-reviewed and will become available later. It can be accessed through the following means: The data are stored in the EPA IRIS Sharepoint website. Format: The data are in the Excel format. This dataset is associated with the following publication: Lin, Y., K. Chialton, P. White, V. Morozov, and A. Persad. Uncovering the connection: ethylene exposure and endogenous ethylene oxide levels in humans. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 1-14, (2025).

This research was designed to evaluate whether a biologically-based computational model aligned with an adverse outcome pathway (AOP) could effectively predict animal (in vivo) responses to chemicals shown to inhibit the enzyme aromatase in a non-animal (in vitro) screening assays. Aromatase is an enzyme that plays a critical role in the synthesis of estrogens, an important class of hormones, and chemicals that inhibit aromatase are viewed as probable endocrine disrupting compounds. Although the model was not able to accurately predict the average in vivo responses observed for all chemicals tested, there was strong qualitative to semi-quantitative agreement with the proposed AOP and predictions did fall within the distribution of measured values. Consequently, this “new approach methodology” likely has utility for screening-level assessments. This work helps to establish the confidence and limitations of this approach. The data set includes: 1) High throughput screening results for chemicals identified as aromatase inhibitors. 2) Novel in vitro aromatase inhibition data for six chemicals. 3) Modeled predictions of impacts on 17b-estradiol and vitellogenin concentrations over a range of concentrations. 4) Measured biological effects of 3 aromatase inhibitors in fathead minnows exposed for 24 h. 5) Measured plasma and water concentrations of the test chemicals. This dataset is associated with the following publication: Villeneuve, D., B. Blackwell, J. Cavallin, W. Cheng, R. Conolly, D. Feifarek, K. Jensen, M. Kahl, R. Milsk, S. Poole, E. Randolph, T. Saari, and G. Ankley. Case study in 21st century ecotoxicology: Using in vitro aromatase inhibition data to predict short term in vivo responses in adult female fish. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 40(4): 1155-1170, (2021).

This research was designed to evaluate whether a biologically-based computational model aligned with an adverse outcome pathway (AOP) could effectively predict animal (in vivo) responses to chemicals shown to inhibit the enzyme aromatase in a non-animal (in vitro) screening assays. Aromatase is an enzyme that plays a critical role in the synthesis of estrogens, an important class of hormones, and chemicals that inhibit aromatase are viewed as probable endocrine disrupting compounds. Although the model was not able to accurately predict the average in vivo responses observed for all chemicals tested, there was strong qualitative to semi-quantitative agreement with the proposed AOP and predictions did fall within the distribution of measured values. Consequently, this “new approach methodology” likely has utility for screening-level assessments. This work helps to establish the confidence and limitations of this approach. The data set includes: 1) High throughput screening results for chemicals identified as aromatase inhibitors. 2) Novel in vitro aromatase inhibition data for six chemicals. 3) Modeled predictions of impacts on 17b-estradiol and vitellogenin concentrations over a range of concentrations. 4) Measured biological effects of 3 aromatase inhibitors in fathead minnows exposed for 24 h. 5) Measured plasma and water concentrations of the test chemicals. This dataset is associated with the following publication: Villeneuve, D., B. Blackwell, J. Cavallin, W. Cheng, R. Conolly, D. Feifarek, K. Jensen, M. Kahl, R. Milsk, S. Poole, E. Randolph, T. Saari, and G. Ankley. Case study in 21st century ecotoxicology: Using in vitro aromatase inhibition data to predict short term in vivo responses in adult female fish. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 40(4): 1155-1170, (2021).

This paper uses EPA public data to build new datasets and analysis by non-EPA authors. 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: This paper uses EPA public data to build new datasets and analysis by non-EPA authors. Format: N/A. This dataset is associated with the following publication: Theunissen, P., S. Beken, B. Beyer, W. Breslin, G. Cappon, C. Chen, G. Chmielewski, L. De Schaepdrijver, B. Enright, J. Foreman, W. Harrouk, K. Hew, A. Hoberman, J. Hui, T. Knudsen , S. Laffan, S. Makris , M. Martin , M. McNerney, C. Siezen, D. Stanislaus, J. Stewart, K. Thompson, B. Tornesi, G. Weinbauer, S. Wood, J. Van der Laan, and A. Piersma. (Crit. Rev. Tox.) Comparison of rat and rabbit embryo-fetal developmental toxicity data for 379 pharmaceuticals: on the nature and severity of developmental effects. CRITICAL REVIEWS IN TOXICOLOGY. CRC Press LLC, Boca Raton, FL, USA, 1-11, (2016).