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).

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: Pearce, R., W. Setzer, J. Davis, and J. Wambaugh. Evaluation and Calibration of High-Throughput Predictions of Chemical Distribution to Tissues. JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS. Springer, New York, NY, USA, 44(6): 549-565, (2017).

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).

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: 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).

This paper describes a model to take chemical structures and predict a property (the point of departure) for a new chemical. No new data were generated. The contents of this zip file contains metadata that you could use to make a model prediction. It does contain all of the code and a help file describing how to run the model. This dataset is associated with the following publication: Pradeep, P., K. Paul-Friedman, and R. Judson. Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16(November 2020): 100139, (2020).

This paper describes a model to take chemical structures and predict a property (the point of departure) for a new chemical. No new data were generated. The contents of this zip file contains metadata that you could use to make a model prediction. It does contain all of the code and a help file describing how to run the model. This dataset is associated with the following publication: Pradeep, P., K. Paul-Friedman, and R. Judson. Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16(November 2020): 100139, (2020).

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).

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).

This investigation was broken down into three interrelated steps: data collection, model building, and model evaluation. R software (v. 3.5.1) with the httk package (v. 1.9) was used for data organization, analysis, and visualization. All models and data associated with this manuscript are available in httk vX. This dataset is associated with the following publication: Linakis, M., R. Sayre, R. Pearce, M.A. Sfeir, N. Sipes, H. Pangburn, J. Gearhart, and J. Wambaugh. Development and Evaluation of a High Throughput Inhalation Model for Organic Chemicals. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 30(5): 866-877, (2020).

Additional details used in the methods are found in the MS Word file “S1_Dawson et al._Supporting_Information.docx”. The MS Excel file “S2_Dawson et al. Supporting Information.xlsx” contains datasets and graphical results. The Excel file sheets are as follows: S2.1 illustrates Clint hepatic flow calculations, S2.2 - 5 include training and test data sets; S2.6-7 include figures illustrating Clint model selection criteria and assemblages of model descriptors; S2.8 includes confusion matrices for evaluation Clint model, S2.9-10 include figures illustrating fup model selection criteria and assemblages of model descriptors (with ranges); S2.11 includes tables of model assessments of the Clint test set, S2.12 includes information relevant to BER calculations for the ToxCast test set, S2.13 includes information relevant to BER calculations for Tox21 chemicals, and S2.14 provides information on different transformations for fup. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505, (6517).