This dataset represents quantitative data on product chemical composition for >75,000 chemicals contained in >15,000 consumer products. The dataset provided at the FigShare link is fully described in the associated Scientific Data publication (Dionisio et al.). The dataset is presented in the form of a MySQL relational database, which mimics CPDat data available under the 'Exposure' tab of the CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard). This dataset is associated with the following publication: Dionisio, K., K. Phillips, P. Price, C. Grulke, A. Williams, D. Biryol, T. Hong, and K. Isaacs. The Chemical and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Scientific Data. Springer Nature Group, New York, NY, 5: 180125, (2018).

The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

Background: Chemicals in consumer products are a major contributor to human chemical co-exposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical co-exposures in order to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this has been a major challenge. Objectives: We aim to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. Methods: We applied frequent itemset mining on an integrated dataset linking consumer product chemical ingredient data with product purchasing data from sixty thousand households to identify chemical combinations resulting from co-use of consumer products. Results: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Lastly, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. Discussion: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. This dataset is associated with the following publication: Stanfield, Z., C. Addington, K. Dionisio, D. Lyons, R. Tornero-Velez, K. Phillips, T. Buckley, and K. Isaacs. Mining of consumer product and purchasing data to identify potential chemical co-exposures.. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 129(6): N/A, (2021).

Background: Chemicals in consumer products are a major contributor to human chemical co-exposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical co-exposures in order to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this has been a major challenge. Objectives: We aim to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. Methods: We applied frequent itemset mining on an integrated dataset linking consumer product chemical ingredient data with product purchasing data from sixty thousand households to identify chemical combinations resulting from co-use of consumer products. Results: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Lastly, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. Discussion: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays. This dataset is associated with the following publication: Stanfield, Z., C. Addington, K. Dionisio, D. Lyons, R. Tornero-Velez, K. Phillips, T. Buckley, and K. Isaacs. Mining of consumer product and purchasing data to identify potential chemical co-exposures.. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 129(6): N/A, (2021).

The contents of the chemistry database, presently containing ~ 760,000 substances, are available as public domain data for download. The chemistry content underpinning the Dashboard has been aggregated over the past 15 years by both manual and auto-curation techniques within EPA’s DSSTox project.These data include physicochemical, environmental fate and transport, exposure, usage, in vivo toxicity, and in vitro bioassay data, surfaced through an integration hub with link-outs to additional EPA data and public domain online resources. This dataset is associated with the following publication: Williams, A., C. Grulke, J. Edwards, A. McEachran, K. Mansouri, N. Baker, G. Patlewicz, I. Shah, J. Wambaugh, R. Judson, and A. Richard. (Journal of Cheminformatics) The CompTox Chemistry Dashboard - A Community Data Resource for Environmental Chemistry. Journal of Cheminformatics. Springer, New York, NY, USA, 9(61): 1-27, (2017).

Links to data for "The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations". This dataset is associated with the following publication: Handa, S., K. Isaacs, J. Wall, A. Larger, S. Burns, L. Koval, K. Baron-Furuyama, C. Elonen, D. Lyons, K. Dionisio, M.B. Horton, and K. Phillips. The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations. Scientific Data. Springer Nature, LONDON, UK, 12: 950, (2025).

The increasing number of chemicals for which SHEDS probabilistic exposure assessment has been performed over the years. This dataset is associated with the following publication: Egeghy , P., L. Sheldon, K. Isaacs , H. Ozkaynak, M. Goldsmith, J. Wambaugh , R. Judson , and T. Buckley. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 124(6): 697–702, (2016).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).