Data for Reardon AJF, et al., From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Front. Toxicol. 5:1194895. doi: 10.3389/ftox.2023.1194895. PMC10242042. This dataset is associated with the following publication: Reardon, A., R. Farmahin, A. Williams, M. Meier, G. Addicks, C. Yauk, G. Matteo, E. Atlas, J. Harrill, L. Everett, I. Shah, R. Judson, S. Ramaiahgari, S. Ferguson, and T. Barton-Maclaren. From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 5: 1194895, (2023).

Dataset for 'Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates' published in Toxicology December 2023, DOI https://doi.org/10.1016/j.tox.2023.153694. This dataset is associated with the following publication: Harrill, J., L. Everett, D. Haggard, J. Bundy, C. Willis, I. Shah, K. Friedman, D. Basili, A. Middleton, and R. Judson. Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 501: 153694, (2024).

To determine the best way to select predictive groups of genes, we used published microarray data from dose-response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). This dataset is not publicly accessible because: The research which produced this data was not funded by EPA. The EPA coauthor helped write the manuscript. It can be accessed through the following means: Data generated by other authors. Format: N/A. This dataset is associated with the following publication: Farmahin, R., A. Williams, B. Kuo, N.L. Chepelev, R.S. Thomas, T.S. Burton-Maclaren, I.H. Curran, A. Nong, M.G. Wade, and C.L. Yauk. (Archives of Toxicology) Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Archives of Toxicology. Springer, New York, NY, USA, 91(5): 2045-2065, (2017).

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

All of the code used to analyze and report the data as well as build confidence in the approach is available as a supplementary RMarkdown report, and a tool to derive PODBioactivity and PODRead-Across is available as an RShiny web-application. The data used in the workflow are either available on public databases or are included in the supplementary material to allow for reproducibility of results. The results and output of the workflow (i.e., chemical info, PODs, etc.) are provided in the supplementary material (available as a download from the journal article). This dataset is associated with the following publication: Beal, M., M. Gagne, S. Kulkarni, G. Patlewicz, R. Thomas, and T. Barton-Maclaren. Implementing in vitro Bioactivity Data to Modernize Priority Setting of Chemical Inventories. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 39(1): 123-139, (2022).

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).

Whole transcriptomics dose response data is collected and storedthrough public facing Gene Expression Omnibus database. Raw collected viability/cytotoxicity data for each chemical are collected and presented on separate spreadsheets. Portions of this dataset are inaccessible because: The transcriptomics full data file is too large to be uploaded alone onto ScienceHub, impeding ease of access. They can be accessed through the following means: Raw and processed transcriptomics data is available through Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession GSE199794. Format: The full transcriptomics data set for all chemical conditions tested. This dataset is associated with the following publication: Speen, A., J. Murray, T. Krantz, D. Davies, P. Evansky, J. Harrill, L. Everett, J. Bundy, L. Dailey, W. Zander, E. Carlsten, M. Monsees, J. Hill, J. Zavala, and M. Higuchi. Benchmark Dose Modeling Approaches for Volatile Organic Chemicals using a Novel Air-Liquid Interface In Vitro Exposure System. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 188(1): 88-107, (2022).

Whole transcriptomics dose response data is collected and storedthrough public facing Gene Expression Omnibus database. Raw collected viability/cytotoxicity data for each chemical are collected and presented on separate spreadsheets. Portions of this dataset are inaccessible because: The transcriptomics full data file is too large to be uploaded alone onto ScienceHub, impeding ease of access. They can be accessed through the following means: Raw and processed transcriptomics data is available through Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession GSE199794. Format: The full transcriptomics data set for all chemical conditions tested. This dataset is associated with the following publication: Speen, A., J. Murray, T. Krantz, D. Davies, P. Evansky, J. Harrill, L. Everett, J. Bundy, L. Dailey, W. Zander, E. Carlsten, M. Monsees, J. Hill, J. Zavala, and M. Higuchi. Benchmark Dose Modeling Approaches for Volatile Organic Chemicals using a Novel Air-Liquid Interface In Vitro Exposure System. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 188(1): 88-107, (2022).

This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (fup) and intrinsic hepatic clearance (CLint) parameters were estimated in silico and incorporated in a 3-compartment toxicokinetic (TK) model to first predict Cmax for in vivo corroboration using therapeutic scenarios. This dataset is associated with the following publication: Sipes, N., J. Wambaugh, R. Pearce, S. Auerbach, B. Wetmore, J. Hsieh, A. Shapiro, D. Sboboda, M. DeVito, and S. Ferguson. (ENVIRONMENTAL SCIENCE and TECHNOLOGY) An Intuitive Approach for Predicting Human Risk with the Tox21 10k Library. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, issue}: 10786-10796, (2017).

This study represents the first report applying IVIVE approaches and exposure comparisons using the entirety of the Tox21 federal collaboration chemical screening data, incorporating assay response efficacy and quality of concentration-response fits, and providing quantitative anchoring to first address the likelihood of human in vivo interactions with Tox21 compounds. This likelihood was assessed using a maximum blood concentration to in vitro response ratio approach (Cmax/AC50), analogous to decision-making methods for clinical drug-drug interactions. Fraction unbound in plasma (fup) and intrinsic hepatic clearance (CLint) parameters were estimated in silico and incorporated in a 3-compartment toxicokinetic (TK) model to first predict Cmax for in vivo corroboration using therapeutic scenarios. This dataset is associated with the following publication: Sipes, N., J. Wambaugh, R. Pearce, S. Auerbach, B. Wetmore, J. Hsieh, A. Shapiro, D. Sboboda, M. DeVito, and S. Ferguson. (ENVIRONMENTAL SCIENCE and TECHNOLOGY) An Intuitive Approach for Predicting Human Risk with the Tox21 10k Library. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, issue}: 10786-10796, (2017).