The Toxic Substances Control Act (TSCA) mandates the US EPA perform risk-based prioritisation of chemicals in commerce and then, for high-priority substances, develop risk evaluations that integrate toxicity data with exposure information. One approach being considered for chemicals with limited chemical-specific toxicity data is a Threshold of Toxicological Concern (TTC)-to-Exposure ratio. Here, TTC values derived using oral (sub)chronic No Observable (Adverse) Effect Level (NO(A)EL) data from the EPA’s Toxicity Values database (ToxValDB) were compared with published TTC values from Munro et al. (1996). 4554 chemicals with structures present in ToxValDB were assigned into their respective TTC categories using the Toxtree software tool. Chemicals were assigned into the five TTC classes (Cramer structural class I, II, III, containing alerts for genotoxicity and acetylcholinesterase inhibitors). 114 (2.5%) chemicals were determined to be not appropriate for TTC. The TTC values derived from the ToxValDB were similar, but not identical to the Munro TTC values: Cramer I (37.3 compared to 30 ug/kg-day), Cramer II (34.6 compared to 9 ug/kg-day) and Cramer III (3.9 compared to 1.5 ug/kg-day). The 5th percentile values of Cramer classes I and II for the ToxValDB and Munro datasets were not statistically different whereas the class III 5th percentile values were different. Chemical features of the two class III datasets were evaluated to account for the differences in TTC values. The revised Kroes workflow was then applied to a large set of chemicals (~45,000). TTC values derived from this expanded dataset of toxicity values substantiated the original TTC values derived by Munro et al. (1996), reaffirming the utility of TTC as a promising tool in a risk-based prioritisation approach. This dataset is associated with the following publication: Nelms, M., P. Pradeep, and G. Patlewicz. Evaluating potential refinements to existing Thresholds of Toxicological Concern (TTC) values for environmentally-relevant compounds. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 109: 104505, (2019).

The Toxic Substances Control Act (TSCA) mandates the US EPA perform risk-based prioritisation of chemicals in commerce and then, for high-priority substances, develop risk evaluations that integrate toxicity data with exposure information. One approach being considered for chemicals with limited chemical-specific toxicity data is a Threshold of Toxicological Concern (TTC)-to-Exposure ratio. Here, TTC values derived using oral (sub)chronic No Observable (Adverse) Effect Level (NO(A)EL) data from the EPA’s Toxicity Values database (ToxValDB) were compared with published TTC values from Munro et al. (1996). 4554 chemicals with structures present in ToxValDB were assigned into their respective TTC categories using the Toxtree software tool. Chemicals were assigned into the five TTC classes (Cramer structural class I, II, III, containing alerts for genotoxicity and acetylcholinesterase inhibitors). 114 (2.5%) chemicals were determined to be not appropriate for TTC. The TTC values derived from the ToxValDB were similar, but not identical to the Munro TTC values: Cramer I (37.3 compared to 30 ug/kg-day), Cramer II (34.6 compared to 9 ug/kg-day) and Cramer III (3.9 compared to 1.5 ug/kg-day). The 5th percentile values of Cramer classes I and II for the ToxValDB and Munro datasets were not statistically different whereas the class III 5th percentile values were different. Chemical features of the two class III datasets were evaluated to account for the differences in TTC values. The revised Kroes workflow was then applied to a large set of chemicals (~45,000). TTC values derived from this expanded dataset of toxicity values substantiated the original TTC values derived by Munro et al. (1996), reaffirming the utility of TTC as a promising tool in a risk-based prioritisation approach. This dataset is associated with the following publication: Nelms, M., P. Pradeep, and G. Patlewicz. Evaluating potential refinements to existing Thresholds of Toxicological Concern (TTC) values for environmentally-relevant compounds. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 109: 104505, (2019).

The dataset that was evaluated in this approach was taken from Wambaugh et al [29] who filtered the Tox21 library to reflect substances with similar uses to those in NHANES. The zip file contains the supplementary information being provided for the re-analysis performed in this dataset. There was no specific code as such developed for the analysis aside from using KNIME to help combine different outputs from different tools including Leadscope in order to arrive at the counts reflected in Table 2 of the manuscript. Instead of this very laborious approach, we re-did the analysis using Toxtree alone and streamlined the processing of the outcomes with R. This is documented in the supplementary information file. List of files: SMARTS Toxtree schemes use to identify carbamates, OPs and steroids Carbamates.tml OPs.tml Steroids.tml R code used to manipulate the various outputs derived from processing the associated sdf through the Kroes, specific Toxtree schemes and Cramer scheme within Toxtree TTC_HTTK.R R data file HTTK_TTC_070218.RData sdf file used in the analysis HTTK_7K_mod_kekule.sdf. This dataset is associated with the following publication: Patlewicz, G., J. Wambaugh, S. Felter, T. Simon, and R. Becker. Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 58-67, (2018).

Dataset for Jesse Rogers et al., 'Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening' published in Environmental Health Perspectives, Vol 133, Issue 6, 067013, June 2025. DOI: https://doi.org/10.1289/EHP16024, PMC12165737 R scripts for reproducing all analyses are available on Github (https://github.com/USEPA/CompTox-HTTr-RCAS). All sequencing data are available via the Gene Expression Omnibus repository (accessionnumbers GSE274318 for U-2 OS and GSE284321 for HepaRG). High-throughput screening assay data are available from InvitroDB via download 29 or the USEPA CompTox Chemicals Dashboard(https://comptox.epa.gov/dashboard/). This dataset is associated with the following publication: Rogers, J., J. Bundy, J. Harrill, R. Judson, K. Friedman, and L. Everett. Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 133(6): 067013, (2025).

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"

Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"

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).

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).

An effort was made to derive new inhalation TTC values using the EPA’s Toxicity Values database, ToxValDB. A total of 4703 substances captured in ToxValDB were assigned into their respective TTC categories using the Kroes module within the Toxtree software tool and custom profilers developed in Nelms et al (2019) and Patlewicz et al (2018). For the substances assigned into the 3 Cramer classes, the 5th percentiles were calculated from the empirical cumulative distributions of No observed (adverse) effect level (concentration) values. The 5th percentiles were converted to their respective TTC values and compared with published values reported by Escher et al (2010) and Carthew et al (2009). The TTC values derived from ToxValDB were orders of magnitude more conservative, further Cramer classification was not found to be effective at discriminating potencies. This dataset is associated with the following publication: Nelms, M., and G. Patlewicz. Derivation of New Threshold of Toxicological Concern Values for Exposure via Inhalation for Environmentally-Relevant Chemicals. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 2: 580347, (2020).