The dataset contains the outputs for the analogue searches conducted for the chemical of interest, p,p'-DDD. 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).

Data and code for "Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas, Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing, Computational Toxicology, Volume 24, 2022, 100250, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2022.100250.". This dataset is associated with the following publication: Patlewicz, G., A. Richard, A. Williams, R. Judson, and R. Thomas. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 24: 100250, (2022).

Data and code for "Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas, Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing, Computational Toxicology, Volume 24, 2022, 100250, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2022.100250.". This dataset is associated with the following publication: Patlewicz, G., A. Richard, A. Williams, R. Judson, and R. Thomas. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 24: 100250, (2022).

Data processing was conducted using the Anaconda distribution of Python 3.9 and associated libraries. Jupyter notebooks are available at https://github.com/patlewig/nts_pfas. Datasets supporting the manuscript are accessible at https://doi.org/10.23645/epacomptox.26524327. This dataset is associated with the following publication: Patlewicz, G., R. Judson, A. Williams, T. Butler, S. Barone, K. Carstens, J. Cowden, J. Dawson, S. Degitz, K. Fay, A. Lowit, S. Padilla, K. Friedman, M. Phillips, D. Turk, J. Wambaugh, B. Wetmore, and R. Thomas. Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 31: 100327, (2024).

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).

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).

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).

Contains values from pbpk models for each study on n-butanol effects. This dataset is associated with the following publication: Segal, D., A. Bale, L. Phillips, A. Sasso, P. Schlosser, C. Starkey, and S. Makris. Issues in Assessing the Health Risks of n-Butanol. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 40(1): 72-86, (2020).

Contains values from pbpk models for each study on n-butanol effects. This dataset is associated with the following publication: Segal, D., A. Bale, L. Phillips, A. Sasso, P. Schlosser, C. Starkey, and S. Makris. Issues in Assessing the Health Risks of n-Butanol. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 40(1): 72-86, (2020).

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