This dataset is a project file generated by BMDExpress 2.2 SW (Sciome, Research Triangle Park, NC). It contains gene expression data for livers of rats exposed to 4 chemicals (crude MCHM, neat MCHM, DMPT, p-toluidine) and kidneys of rats exposed to PPH. The project file includes normalized expression data (GeneChip Rat 230 2.0 Array) using 7 different pre-processing methods (RMA, GCRMA, MAS5.0, MAS5.0_noA calls, PLIER, PLIER16, and PLIER16_noA calls); differentially expressed probe-sets detected by William's method (p<0.05, and minimum fold change of 1.5); probeset-level and pathway-level BMD and BMDL values from transcriptomic dose-response modeling. This dataset is associated with the following publication: Mezencev, R., and S. Auerbach. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 15(5): e0232955, (2020).

This dataset is a project file generated by BMDExpress 2.2 SW (Sciome, Research Triangle Park, NC). It contains gene expression data for livers of rats exposed to 4 chemicals (crude MCHM, neat MCHM, DMPT, p-toluidine) and kidneys of rats exposed to PPH. The project file includes normalized expression data (GeneChip Rat 230 2.0 Array) using 7 different pre-processing methods (RMA, GCRMA, MAS5.0, MAS5.0_noA calls, PLIER, PLIER16, and PLIER16_noA calls); differentially expressed probe-sets detected by William's method (p<0.05, and minimum fold change of 1.5); probeset-level and pathway-level BMD and BMDL values from transcriptomic dose-response modeling. This dataset is associated with the following publication: Mezencev, R., and S. Auerbach. The sensitivity of transcriptomics BMD modeling to the methods used for microarray data normalization. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 15(5): e0232955, (2020).

The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. This dataset is associated with the following publication: Mav, D., R.R. Shah, B.E. Howard, S.S. Auerbach, P.R. Bushel, J.B. Collins, D.L. Gerhold, R. Judson, A.L. Karmaus, E.A. Maull, D.L. Mendrick, B.A. Merrick, N.S. Sipes, D. Svoboda, and R.S. Paules. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(2): 1-17, (2018).

The U.S. Tox21 Federal collaboration, which currently quantifies the biological effects of nearly 10,000 chemicals via quantitative high-throughput screening(qHTS) in in vitro model systems, is now making an effort to incorporate gene expression profiling into the existing battery of assays. Whole transcriptome analyses performed on large numbers of samples using microarrays or RNA-Seq is currently cost-prohibitive. Accordingly, the Tox21 Program is pursuing a high-throughput transcriptomics (HTT) method that focuses on the targeted detection of gene expression for a carefully selected subset of the transcriptome that potentially can reduce the cost by a factor of 10-fold, allowing for the analysis of larger numbers of samples. To identify the optimal transcriptome subset, genes were sought that are (1) representative of the highly diverse biological space, (2) capable of serving as a proxy for expression changes in unmeasured genes, and (3) sufficient to provide coverage of well described biological pathways. A hybrid method for gene selection is presented herein that combines data-driven and knowledge-driven concepts into one cohesive method. This dataset is associated with the following publication: Mav, D., R.R. Shah, B.E. Howard, S.S. Auerbach, P.R. Bushel, J.B. Collins, D.L. Gerhold, R. Judson, A.L. Karmaus, E.A. Maull, D.L. Mendrick, B.A. Merrick, N.S. Sipes, D. Svoboda, and R.S. Paules. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(2): 1-17, (2018).

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).

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).

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).

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).

First, we curated and selected experimental animal toxicity data and split them into two distinct data sets covering general noncancer effects and reproductive/developmental effects. Second, we collected POD values from regulatory data sources (PODreg) and compared these PODreg with the curated dose–response toxicity data to identify a statisti-cal approach for deriving surrogate oral PODs. Third, we systematically applied this approach to determine a surrogate POD for each substance in the two curated data sets. We then characterized the uncertainty around each of the surrogate PODs that was due to intrastudy and interstudy variability through a bootstrapping approach. Finally, using the surrogate PODs and their uncertainty, we derived both probabilistic RfDs and human population effect doses (I =10%) for use in health-based or comparative risk assessments and LCIA, respectively. This dataset is associated with the following publication: Aurisano, N., O. Jolliet, W. Chiu, R. Judson, S. Jang, A. Unnikrishnan, M. Kosnik, and P. Fantke. Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 131(3): 037016, (2023).

Data for "Breen, M., Wambaugh, J.F., Bernstein, A. et al. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. J Expo Sci Environ Epidemiol 32, 855–863 (2022). https://doi.org/10.1038/s41370-022-00491-0". This dataset is associated with the following publication: Breen, M., J. Wambaugh, A. Bernstein, M. Sfeir, and C. Ring. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 855-863, (2022).