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).

This is a new, open, and transparent database of toxicokinetic data supporting EPA decision making. The database has already become the basis of research efforts within EPA to improve HTTK modeling using generic TK models and has facilitated the creation and validation of models for new exposure routes. Publishing the database supports open, transparent science and this database (the largest public database for this domain) will spur improvement and development of TK models by external experts in the field. Future efforts to improving the accessibility of this database (with a graphical user interface) and encouraging crowdsourcing to expand the size and scope of the database will lead to larger validation sets for our modeling efforts and likely lower uncertainties when estimating TK. This dataset is associated with the following publication: Sayre, R., J. Wambaugh, and C. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data. Springer Nature Group, New York, NY, 7: 122, (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).

Data and code for "Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas, Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing, Computational Toxicology, Volume 24, 2022, 100250, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2022.100250.". This dataset is associated with the following publication: Patlewicz, G., A. Richard, A. Williams, R. Judson, and R. Thomas. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 24: 100250, (2022).

The files and .R scripts here are provided to aid in understanding and reproduction of the analyses in the submitted paper, "Examining the Utility of In Vitro Bioactivity as a Protective Point of Departure: A Case Study." The scripts in the .R folder reference source files in the other folders. This dataset is associated with the following publication: Friedman, K., M. Gagne, L. Loo, P. Karamertzanis, T. Netzeva, T. Sobanski, J. Franzosa, A. Richard, R. Lougee, A. Gissi, J.J. Lee, M. Angrish, J. Dorne, S. Foster, K. Raffaele, T. Bahadori, M. Gwinn, J. Lambert, M. Whelan, M. Rasenberg, T. Barton-Maclaren, and R. Thomas. Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 173(1): 202-225, (2020).

The supplemental information for this paper includes chemical-specific analytical methods, raw instrument data for chemical concentration analysis, processed data for experiments on intrinsic hepatic clearance (CLint -- metabolism) and chemical fraction unbound in the presence of human plasma protein (fup). Figures showing the curve fits for determining CLint are provided. Finally, all data were released publicly as HTTK R Package v1.10.1. This dataset is associated with the following publication: Wambaugh, J., B. Wetmore, C. Ring, C. Nicolas, R. Pearce, G. Honda, R. Dinallo, D. Angus, J. Gilbert, T. Sierra, A. Badrinarayanan, B. Snodgrass, A. Brockman, C. Strock, R. Setzer, and R. Thomas. Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 172(2): 235-251, (2019).

The data presented here support the application of the Stemina devTOXqP platform for predictive toxicology and further demonstrate its value in ToxCast as a novel resource that can generate testable hypotheses aimed at characterizing potential pathways for teratogenicity and HTS prioritization of environmental chemicals for an exposure-based assessment of developmental hazard. The dataset from the Stemina (STM) assay is annotated in the ToxCast portfolio as STM. Major findings from the analysis of ToxCast_STM dataset include (1) 19% of 1065 chemicals yielded a prediction of developmental toxicity, (2) assay performance reached 79%-82% accuracy with high specificity (> 84%) but modest sensitivity (< 67%) when compared with in vivo animal models of human prenatal developmental toxicity, (3) sensitivity improved as more stringent weights of evidence requirements were applied to the animal studies, and (4) statistical analysis of the most potent chemical hits on specific biochemical targets in ToxCast revealed positive and negative associations with the STM response, providing insights into the mechanistic underpinnings of the targeted endpoint and its biological domain. The results of this study will be useful to improving our ability to predict in vivo developmental toxicants based on in vitro data and in silico models. This dataset is associated with the following publication: Zurlinden, T., K. Saili, N. Rush, P. Kothiya, R. Judson, K. Houck, E. Hunter, N. Baker, J. Palmer, R. Thomas, and T. Knudsen. Profiling the ToxCast Library With a Pluripotent Human (H9) Stem Cell Line-Based Biomarker Assay for Developmental Toxicity. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 174(2): 189-209, (2020).

Here are all of the data files used for this manuscript. Please note that this is all published data. Imran Shah 1.1060+ Chemicals and Chemical controls 2. Chemical descriptors (chm): 2048 Morgan (mrgn) 2048 Topological Torsion (tptr) 729 ToxPrints (toxp) 3. Transcriptomic descriptors(bio): 95 Gene (ge) 189 Assay (asy) 4. 922 Toxicity outcomes(tox) 5. 86 Predefined Chemical Clusters

Dataset for "Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies". This dataset is associated with the following publication: Lowe, K., J. Dawson, K. Phillips, J. Minucci, J. Wambaugh, H. Qian, T. Ramanarayanan, P. Egeghy, B. Ingle, R. Brunner, E. Mendez, M. Embry, and C. Tan. Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 127: 105073, (2021).

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. This dataset is associated with the following publication: Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).