Data for "Breen, M., Wambaugh, J.F., Bernstein, A. et al. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. J Expo Sci Environ Epidemiol 32, 855–863 (2022). https://doi.org/10.1038/s41370-022-00491-0". This dataset is associated with the following publication: Breen, M., J. Wambaugh, A. Bernstein, M. Sfeir, and C. Ring. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 855-863, (2022).

httk: High-Throughput Toxicokinetics Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. 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. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK"). This dataset is associated with the following publication: Ring, C., R. Pearce, W. Setzer, B. Wetmore, and J. Wambaugh. (Environment International) Refining high-throughput prioritization of environmental chemicals to include inter-individual variability across subpopulations. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 106: 105-118, (2017).

These are the data associated with each of the figures in the publication. This dataset is associated with the following publication: Thursby, G., K. Sappington, and M. Etterson. Coupling Toxicokinetic-Toxicodynamic and Population Models for Assessing Aquatic Ecological Risks to Time-Varying Pesticide Exposures. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 37(10): 2633-2644, (2018).

These are the data associated with each of the figures in the publication. This dataset is associated with the following publication: Thursby, G., K. Sappington, and M. Etterson. Coupling Toxicokinetic-Toxicodynamic and Population Models for Assessing Aquatic Ecological Risks to Time-Varying Pesticide Exposures. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 37(10): 2633-2644, (2018).

The data used in this analysis was obtained from published literature and available through the high-throughput toxicokinetic (HTTK) R package. The dataset consists of 1486 chemicals that span a variety of use classes including pharmaceuticals, food-use chemicals, pesticides and industrial chemicals of which 1139 chemicals had experimental human in vitro fraction unbound data and 642 chemicals that had experimental human in vitro intrinsic clearance data. Structures were curated and obtained from the DSSTox database. The distribution of experimental values for fraction unbound and intrinsic clearance is shown in Supplementary Figure S1. Since the data were non-normally distributed they were appropriately transformed before any analysis was conducted. The details of the transformation and the transformed data distribution are presented in the results section and Supplementary Figures S2 and S3. A complete list of chemicals with CAS registry numbers (CASRN), DSSTox generic substance IDs (DTXSIDs), structure and experimental data for both parameters are included as supplemental data (1.ChemicalListData.csv and 1.ChemicalList-QSARready.sdf). This dataset is associated with the following publication: Pradeep, P., G. Patlewicz, R. Pearce, J. Wambaugh, B. Wetmore, and R. Judson. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16: 100136, (2020).

The data used in this analysis was obtained from published literature and available through the high-throughput toxicokinetic (HTTK) R package. The dataset consists of 1486 chemicals that span a variety of use classes including pharmaceuticals, food-use chemicals, pesticides and industrial chemicals of which 1139 chemicals had experimental human in vitro fraction unbound data and 642 chemicals that had experimental human in vitro intrinsic clearance data. Structures were curated and obtained from the DSSTox database. The distribution of experimental values for fraction unbound and intrinsic clearance is shown in Supplementary Figure S1. Since the data were non-normally distributed they were appropriately transformed before any analysis was conducted. The details of the transformation and the transformed data distribution are presented in the results section and Supplementary Figures S2 and S3. A complete list of chemicals with CAS registry numbers (CASRN), DSSTox generic substance IDs (DTXSIDs), structure and experimental data for both parameters are included as supplemental data (1.ChemicalListData.csv and 1.ChemicalList-QSARready.sdf). This dataset is associated with the following publication: Pradeep, P., G. Patlewicz, R. Pearce, J. Wambaugh, B. Wetmore, and R. Judson. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16: 100136, (2020).

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"

The dataset provides growth and toxicokinetic parameters for the simulations presented in the manuscript by Pollesch et al. Developing Integral Projection Models for Aquatic Ecotoxicology. This dataset is associated with the following publication: Pollesch, N., K. Flynn, S. Kadlec, J. Swintek, S. Raimondo, and M. Etterson. Developing integral projection models for ecotoxicology. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, NETHERLANDS, 464: 109813, (2022).

The dataset provides growth and toxicokinetic parameters for the simulations presented in the manuscript by Pollesch et al. Developing Integral Projection Models for Aquatic Ecotoxicology. This dataset is associated with the following publication: Pollesch, N., K. Flynn, S. Kadlec, J. Swintek, S. Raimondo, and M. Etterson. Developing integral projection models for ecotoxicology. ECOLOGICAL MODELLING. Elsevier Science BV, Amsterdam, NETHERLANDS, 464: 109813, (2022).

Chemical concentrations, exposures, health risks by census tract for the United States from National Scale Air Toxics Assessment (NATA). This dataset is associated with the following publication: Huang, H., and T. Barzyk. Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates. International Journal of Environmental Research and Public Health. Molecular Diversity Preservation International, Basel, SWITZERLAND, 14(1): 1-15, (2017).