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).

Frequent itemset mining (FIM), a technique used for finding patterns in consumer purchasing behavior, can be applied to data from large-scale biomonitoring studies to identify combinations of chemicals that frequently co-occur in people. As a proof of concept, we applied FIM to biomonitoring data from the National Health and Nutrition Examination Survey. In this way, we identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the US population, as well as 3 super-combinations consisting of relatively many chemicals that occur in a small but non-negligible proportion of the US population. Thus, we have demonstrated a technique for narrowing a large number of possible chemical combinations down to a much smaller collection of prevalent chemical combinations. This dataset is associated with the following publication: Kapraun, D.F., J.F. Wambaugh, R. Tornero-Velez, and R.W. Setzer. (ENVIRONMENTAL HEALTH PERSPECTIVES) Identifying Prevalent Chemical Mixtures in the US Population. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(8): 1-16, (2017).

Frequent itemset mining (FIM), a technique used for finding patterns in consumer purchasing behavior, can be applied to data from large-scale biomonitoring studies to identify combinations of chemicals that frequently co-occur in people. As a proof of concept, we applied FIM to biomonitoring data from the National Health and Nutrition Examination Survey. In this way, we identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the US population, as well as 3 super-combinations consisting of relatively many chemicals that occur in a small but non-negligible proportion of the US population. Thus, we have demonstrated a technique for narrowing a large number of possible chemical combinations down to a much smaller collection of prevalent chemical combinations. This dataset is associated with the following publication: Kapraun, D.F., J.F. Wambaugh, R. Tornero-Velez, and R.W. Setzer. (ENVIRONMENTAL HEALTH PERSPECTIVES) Identifying Prevalent Chemical Mixtures in the US Population. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(8): 1-16, (2017).

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).

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).

생활화학제품 사용시 흡입, 피부접촉, 음용을 통해 화학물질에 노출될 수 있는 양에 대하여 경로별 시나리오를 가정하고 표준노출계수와 사용량 정보 등을 이용하여 노출량을 계산하여 제공합니다. 사용자가 주어진 조건에서의 계산된 노출량 값을 보고 그와 유사한 조건에서의 노출량을 추정하는데 도움을 주며, 기타 노출량 결과값과 비교해 볼 수 있습니다.

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.

유해화학물질 사용량의 지속적인 증가로 인해 일반국민 및 화학사고 대응기관을 위한 인체 노출 위험성, 화학물질 사고시 초동대응정보, 2차 피해확산 장비를 위한 방제 정보등

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).