Three recent IARC Working Groups pioneered inclusion of the US Environmental Protection Agency (EPA) ToxCast program high-throughput screening (HTS) data to supplement other mechanistic evidence. In Monograph V110, HTS profiles were compared between perfluorooctanoic acid (PFOA) and prototypical activators across multiple nuclear receptors. For Monograph V112-113, HTS assays were mapped to 10 key characteristics of carcinogens identified by an IARC expert group, and systematically considered as an additional mechanistic data stream. This dataset is not publicly accessible because: The data is generated by external authors from existing public data sources. It can be accessed through the following means: Data is available in existing public data sources. Format: N/A. This dataset is associated with the following publication: Chiu, W., K. Guyton, M. Martin, D. Reif, and I. Rusyn. (ALTEX) Use of High-throughput in vitro toxicity screening data in cancer hazard evaluations by the IARC Monograph Working Groups. ALTEX. Society ALTEX Edition, Kuesnacht, SWITZERLAND, 35(1): 51-64, (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).

Dataset consists of high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) project. This dataset is associated with the following publication: Wegner, S., C. Pinto, C. Ring, and J. Wambaugh. High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 137: 105470, (2020).

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC–TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC–TOF/molecular feature data (match score ≥ 90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media. This dataset is associated with the following publication: Rager, J.E., M. Strynar , S. Liang, R.L. McMahen, A. Richard , C.M. Grukle, J. Wambaugh , K. Isaacs , R. Judson , A. Williams , and J. Sobus. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 88: 269-280, (2016).

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC–TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC–TOF/molecular feature data (match score ≥ 90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media. This dataset is associated with the following publication: Rager, J.E., M. Strynar , S. Liang, R.L. McMahen, A. Richard , C.M. Grukle, J. Wambaugh , K. Isaacs , R. Judson , A. Williams , and J. Sobus. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 88: 269-280, (2016).

We screened 44 chemicals in MCF7 cells in concentration response and generated HTTr data using the TempO-Seq hWTv1 assay. First, we provide an outline of the quality of the HTTr data based on a set of QC metrics we developed for this platform. Second, we evaluate the reproducibility and mechanistic accuracy of the HTTr platform using inter-plate analysis of reference samples and comparison of reference chemical treatment effects with CMAP signatures, respectively. Third, we summarize the concentration-dependent HTTr responses for all 44 chemicals to stratify them in terms of their overall effect on the transcriptome. Fourth, we present a new gene signature based concentration-response analysis that provides potency estimates for perturbation of cellular biology (ie, BPACs). This dataset is associated with the following publication: Harrill, J., L. Everett, D. Haggard, T. Sheffield, J. Bundy, C. Willis, R. Thomas, I. Shah, and R. Judson. High-Throughput Transcriptomics Platform for Screening Environmental Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 181(1): 68-89, (2021).

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).

Tables, Figures, and Supplemental Materials. This dataset is associated with the following publication: Hill III, T., M. Nelms, S. Edwards, M. Martin, R. Judson, C. Corton, and C. Wood. Negative Predictors of Carcinogenicity for Environmental Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 155(1): 157-169, (2017).

Tables, Figures, and Supplemental Materials. This dataset is associated with the following publication: Hill III, T., M. Nelms, S. Edwards, M. Martin, R. Judson, C. Corton, and C. Wood. Negative Predictors of Carcinogenicity for Environmental Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 155(1): 157-169, (2017).

Data for the exposure-response arrays comparing effect levels for non-cancer and cancer endpoints for p,p'-DDD and analogues were sourced from the links provided. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).