Supplementary Files 1-15 contain the generated assay data that was used to establish BMAD and determine treatment effects. The tabbed spreadsheet data is formatted so that it can be directly analyzed, once converted to individual comma-separated values (.csv) files, using the R code provided in Supplementary File 16. Column headings are described in the supplemental file 'Metadata Glossary.docx'. This dataset is associated with the following publication: Angrish, M., C. McQueen, E. Hubal, M. Bruno, Y. Ge, and B. Chorley. Mechanistic Toxicity Tests Based on an Adverse Outcome Pathway Network for Hepatic Steatosis. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 159(1): 159-169, (2017).

Dataset includes overview text document (accepted version of manuscript) and tables, figures, and supplementary materials. Supplementary tables provide summary data underlying figures, as noted in the text. This dataset is associated with the following publication: Wehmas, L., A. Deangelo, S. Hester, B. Chorley, G. Carswell, G. Olson, M. George, J. Carter, S. Eldridge, A. Fisher, B. Vallanat, and C. Wood. Metabolic Disruption Early in Life is Associated With Latent Carcinogenic Activity of Dichloroacetic Acid in Mice. TOXICOLOGICAL SCIENCES. Society of Toxicology, 159(2): 354-365, (2017).

Supplementary data for "Tia Tate, Grace Patlewicz, Imran Shah, A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data, Computational Toxicology, Volume 29, 2024, 100301, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2024.100301.". This dataset is associated with the following publication: Tate, T., G. Patlewicz, and I. Shah. A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 29: 100301, (2024).

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).

This repository contains the necessary data, python source code and jupyter notebooks to reproduce the results from our manuscript, "Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes." Using in vitro data to estimate point of departure (POD) values is an important component of new approach method (NAM)-based chemical risk assessments. In this case study we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI) and in vitro to in vivo extrapolation (IVIVE). This dataset is associated with the following publication: Shah, I., T. Antonijevic, B. Chambers, J. Harrill, and R. Thomas. Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 183(2): 285-301, (2021).

This repository contains the necessary data, python source code and jupyter notebooks to reproduce the results from our manuscript, "Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes." Using in vitro data to estimate point of departure (POD) values is an important component of new approach method (NAM)-based chemical risk assessments. In this case study we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI) and in vitro to in vivo extrapolation (IVIVE). This dataset is associated with the following publication: Shah, I., T. Antonijevic, B. Chambers, J. Harrill, and R. Thomas. Estimating Hepatotoxic Doses Using High-content Imaging in Primary Hepatocytes. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 183(2): 285-301, (2021).

Datasets used in RD-023418: Adverse Outcome Pathway-Driven Identification of Rat Hepatocarcinogens in Short-Term Assays. This dataset is associated with the following publication: Rooney, J., T. Hill, C. Qin, F. Sistare, and C. Corton. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 356: 99-113, (2018).

Datasets used in RD-023418: Adverse Outcome Pathway-Driven Identification of Rat Hepatocarcinogens in Short-Term Assays. This dataset is associated with the following publication: Rooney, J., T. Hill, C. Qin, F. Sistare, and C. Corton. Adverse outcome pathway-driven identification of rat liver tumorigens in short-term assays. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 356: 99-113, (2018).

Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level2_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Gene expression data from the Fluidigm qRT-PCR arrays was analyzed in R (v3.6.1; R Foundation for Statistical Computing, 2019). Prior to processing through the tcpl package, each qRT-PCR primer set was annotated as an individual assay endpoint (aeid) for analyses. For each plate, well types were designated for test compound wells (t), positive controls (c), (that is phenobarbital) and neutral controls (n, DMSO). Fold-change in the number of amplification cycles needed to pass the background threshold (Ct) for 96 transcripts to (ftp://newftp.epa.gov/COMPTOX/CCTE_Publication_Data/CCED_Publication_Data/Wambaugh/ToxCast_LTEA, file LTEA_Level5_20191119.zip) were normalized to the geometric mean of three housekeeping genes (ACTB, GAPDH, POLR2A) to generate ΔCt values (cval). Prior to calculating the response values (rval), or ΔΔCt, for each transcript (n = 96) per well, the baseline value (bval), the plate-wise median of the neutral control wells, was generated for each plate (the normalization process is described in detail in supplemental file SupFile4-DeltaCTCalculation.docx). The bval was subtracted from the cval to yield the rval or log2 Fold Change per transcript. Supplemental File LTEA_Inucyte_Images.zip is comprised of 20,493 images totaling more than 15 gigabytes. Cell morphology images were acquired for each well/plate with an Essen IncuCyte™ FLR automated phase-contrast microscope located inside a tissue culture incubator. Six 96-well culture plates were loaded into the instrument and imaged for an elapsed time (~24 minutes). The IncuCyte™ software was used for image capturing and export of images in JPEG format. This dataset is associated with the following publication: Franzosa, J., J. Bonzo, J. Jack, N.C. Baker, P. Kothiya, R. Witek, P. Hurban, S. Siferd, S. Hester, I. Shah, S. Ferguson, K. Houck, and J. Wambaugh. High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures. npj Systems Biology and Applications. Springer Nature Group, New York, NY, 7: Article 7, (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).