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).

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).

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).

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).

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 revised paper dataset includes data shown in the main paper and supplemental file and are derived from Excel files associated with this Science Hub entry. The Word file "Science Hub Record summary Revised.docx" lists all main paper and supplemental tables and figures, and which specific Excel files are associated with each one. Further details on navigating individual Excel files are found within each Excel file. This dataset is associated with the following publication: Jackson, T., J. Murray, C. Schacht, P. Evansky, M. Monsees, J. Harrill, I. Gilmour, A. Johnstone, W. Williams, R. Grindstaff, M. Schladweiler, S. Vance, and S. Gavett. Bridging in vitro and in vivo inhalation toxicity: Volatile organic compounds elicit similar transcriptomic points of departure in human airway cells and mouse respiratory tract. ENVIRONMENTAL POLLUTION. Elsevier Science Ltd, New York, NY, USA, 387: 127342, (2025).

This investigation was broken down into three interrelated steps: data collection, model building, and model evaluation. R software (v. 3.5.1) with the httk package (v. 1.9) was used for data organization, analysis, and visualization. All models and data associated with this manuscript are available in httk vX. This dataset is associated with the following publication: Linakis, M., R. Sayre, R. Pearce, M.A. Sfeir, N. Sipes, H. Pangburn, J. Gearhart, and J. Wambaugh. Development and Evaluation of a High Throughput Inhalation Model for Organic Chemicals. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 30(5): 866-877, (2020).

This investigation was broken down into three interrelated steps: data collection, model building, and model evaluation. R software (v. 3.5.1) with the httk package (v. 1.9) was used for data organization, analysis, and visualization. All models and data associated with this manuscript are available in httk vX. This dataset is associated with the following publication: Linakis, M., R. Sayre, R. Pearce, M.A. Sfeir, N. Sipes, H. Pangburn, J. Gearhart, and J. Wambaugh. Development and Evaluation of a High Throughput Inhalation Model for Organic Chemicals. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 30(5): 866-877, (2020).