We have performed high-throughput experimental estimates of five physicochemical properties for a set of 200 chemicals to evaluate the consistency with previous measurements, factors impacting consistency and experimental success, and the applicability domain of the new data in relation to previously measured data and predictive models. This dataset is associated with the following publication: Nicolas, C., K. Mansouri, K. Phillips, C. Grulke, A. Richard, A. Williams, J. Rabinowitz, K. Isaacs, A. Yau, and J. Wambaugh. (ENVIRONMENTAL SCIENCE and TECHNOLOGY) Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 636: 901-909, (2018).

The MS Excel file (Dawson et al S2 Supporting information.xlsx) contains multiple sheets containing the training sets, test sets, and predictions for intrinsic metabolic clearance (Clint), fraction unbound in plasma (fup), and bioactivity-exposure ratios (BER), for ToxCast and pharmaceutical-like chemicals. The Word file (Dawson et al S1 Supporting Information.docx) provides additional supporting information on assembly of the training and test sets for Clint, fup, and BER. The data dictionary describes the terms used in the supporting information, S1 and S2. 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, (2021).

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).

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).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

Links to data for "The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations". This dataset is associated with the following publication: Handa, S., K. Isaacs, J. Wall, A. Larger, S. Burns, L. Koval, K. Baron-Furuyama, C. Elonen, D. Lyons, K. Dionisio, M.B. Horton, and K. Phillips. The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations. Scientific Data. Springer Nature, LONDON, UK, 12: 950, (2025).

In this study, a major effort was undertaken to compile a large genotoxicity dataset (54,805 records for 9299 substances) from several public sources (e.g., TOXNET, COSMOS, eChemPortal). The names and outcomes of the different assays were harmonized, and assays were annotated by type: gene mutation in Salmonella bacteria (Ames assay) and chromosome mutation (clastogenicity) in vitro or in vivo (chromosome aberration, micronucleus, and mouse lymphoma Tk+/- assays). This dataset was then evaluated to assess genotoxic potential using a categorization scheme, whereby a substance was considered genotoxic if it was positive in at least one Ames or clastogen study. The categorization dataset comprised 8442 chemicals, of which 2728 chemicals were genotoxic, 5585 were not and 129 were inconclusive. QSAR models (TEST and VEGA) and the OECD Toolbox structural alerts/profilers (e.g., OASIS DNA alerts for Ames and chromosomal aberrations) were used to make in silico predictions of genotoxicity potential. The performance of the individual QSAR tools and structural alerts resulted in balanced accuracies of 57-73%. A Naïve Bayes consensus model was developed using combinations of QSAR models and structural alert predictions. The ‘best’ consensus model selected had a balanced accuracy of 81.2%, a sensitivity of 87.24% and a specificity of 75.20%. This in silico scheme offers promise as a first step in ranking thousands of substances as part of a prioritization approach for genotoxicity. This dataset is associated with the following publication: Pradeep, P., R. Judson, D. DeMarini, N. Keshava, T. Martin, J. Dean, C. Gibbons, A. Simha, S. Warren, M. Gwinn, and G. Patlewicz. An Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 18: 100167, (2021).

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).

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).

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates”. The use of surrogate training data can yield diminished performance of concentration estimation approaches. In order to evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from chemical surrogates. This dataset is associated with the following publication: Pu, S., J. McCord, J. Bangma, and J. Sobus. Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 416: 1249-1267, (2024).