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).

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).

Per Imran Shah, this was the Data used in and published as supplemental material for this manuscript. Table S1. Aggregated point of departure (POD) data obtained from ToxRefDB v2.0. Table S2. Chemical structure descriptor data from DSSTox. Table S3. Chemical cluster membership. Table S5. GenRA optimal predictions for each endpoint category and cluster.

Background: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. Objectives: Our goal was to analyze dynamic cellular changes using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 μM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. Results: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 μM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. Conclusions: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. This dataset is associated with the following publication: Shah , I., W. Setzer , J. Jack, K. Houck , R. Judson , T. Knudsen , J. Liu, M. Martin , D. Reif, A.M. Richard , R.S. Thomas , K. Crofton , D.J. Dix , and R.J. Kavlock. (Envir. Health Perspect.) Using ToxCast data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-33, (2015).

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).

Dataset for A Comparison of In Vitro Points of Departure with Human Biomonitoring Levels for Per- and Polyfluoroalkyl Substances (PFAS), to be published by Environmental Health Perspectives (EHP). This dataset is associated with the following publication: Judson, R., D. Smith, M. Devito, J. Wambaugh, B. Wetmore, K. Friedman, G. Patlewicz, R. Thomas, R. Sayre, J. Olker, S. Degitz, S. Padilla, J. Harrill, T. Shafer, and K. Carstens. A Comparison of In Vitro Points of Departure with Human Blood Levels for Per- and Polyfluoroalkyl Substances (PFAS) (Toxics). Toxics. MDPI, Basel, SWITZERLAND, 12(4): 271, (2024).

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

Previous work identified a ‘cytotoxic burst’ (CTB) phenomenon wherein large numbers of the ToxCast assays begin to respond at or near test chemical concentrations that elicit cytotoxicity, and a statistical approach to defining the bounds of the CTB was developed. To focus AOP development on the molecular targets corresponding to ToxCast assays indicating pathway-specific effects, we conducted a meta-analysis to identify which assays most frequently respond at concentrations below the CTB. A preliminary list of potentially important, target-specific assays was determined by ranking assays by the fraction of chemical hits below the CTB compared to the number of chemicals tested. Additional priority assays were identified using a diagnostic-odds-ratio approach which gives greater ranking to assays with high specificity but low responsivity. Combined, the two prioritization methods identified several novel targets (e.g., peripheral benzodiazepine and progesterone receptors) to prioritize for AOP development, and affirmed the importance of a number of existing AOPs aligned with ToxCast targets (e.g., thyroperoxidase, estrogen receptor, aromatase). This dataset is associated with the following publication: Fay, K., J. Swintek, D. Villeneuve, S. Edwards, M. Nelms, B. Blackwell, and G. Ankley. Differentiating pathway-specific from non-specific effects in high-throughput toxicity data: A foundation for prioritizing adverse outcome pathway development. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 163(2): 500-515, (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).