Contains values from pbpk models for each study on n-butanol effects. This dataset is associated with the following publication: Segal, D., A. Bale, L. Phillips, A. Sasso, P. Schlosser, C. Starkey, and S. Makris. Issues in Assessing the Health Risks of n-Butanol. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 40(1): 72-86, (2020).

R-language data files from "Carstens KE, Freudenrich T, Wallace K, Choo S, Carpenter A, Smeltz M, Clifton MS, Henderson WM, Richard AM, Patlewicz G, Wetmore BA, Paul Friedman K, Shafer T. Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity. Chem Res Toxicol. 2023 Mar 20;36(3):402-419. doi: 10.1021/acs.chemrestox.2c00344. Epub 2023 Feb 23. PMID: 36821828.". This dataset is associated with the following publication: Carstens, K., T. Freudenrich, K. Wallace, S. Choo, A. Carpenter, M. Smeltz, M. Clifton, M. Henderson, A. Richard, G. Patlewicz, B. Wetmore, K. Friedman, and T. Shafer. Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 402-419, (2023).

The data set includes source code that implements a PBPK template model applicable to PFAS. It includes data digitized from Kim et al. (2018), Kim et al. (2019), and Loccisano et al. (2012) used to show the capability of the template to replicate published PFAS PBPK models. The template model is described in a paper that is in review at the journal Toxicological Sciences.

The dataset contains the outputs for the analogue searches conducted for the chemical of interest, p,p'-DDD. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

The dataset includes source code that was used to perform analyses described in the manuscript "Evaluating Impact of Anatomical and Physiological Variability on Human Equivalent Doses Using PBPK Models" by Schacht et al.

Data for individual animals used to create the information demonstrated in Table 1 of the manuscript, including PFESA BP2 serum and liver concentrations and serum clinical chemistry values. This dataset is associated with the following publication: Jenkins-Hill, D., M. Strynar, A. Lindstrom, A. Farthing, H. Huang, J. Schmid, J. Lang, and N. Chernoff. Toxicity of Balb-c mice exposed to recently identified 1,1,2,2-tetrafluoro-2-[1,1,1,2,3,3-hexafluoro-3-(1,1,2,2-tetrafluoroethoxy)propan-2-yl]oxyethane-1-sulfonic acid (PFESA-BP2). TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 441(152529): 1, (2020).

The data set includes source code that implements a PBPK model template that has been extended with features capable of implementing models for volatile organic compounds (VOCs). It also includes data from the U.S. EPA IRIS assessments for DCM (2011) and methanol (2013) and data from Sasso et al. (2013), Ramsey and Andersen (1984), and Yoon et al. (2007) used to show the ability of the template to replicate published VOC PBPK models. The extension of the model template is described in a paper that will be submitted to the journal Toxicological Sciences.

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 experimental data were taken from Simon et al., who compiled potency data for effects related to neurotoxicity from four experimental datasets, Stenberg et al. [18] and Wigestrand et al. The measures of potency were EC50 (µM) or IC50 values for all the effects except Stenberg data, which were expressed as a percentage of the control uptake for different concentrations measured. This dataset is associated with the following publication: Pradeep, P., L. Carlson, R. Judson, G. Lehmann, and G. Patlewicz. Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 101: 12-23, (2019).

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).