Citation:

Stanfield, Z., C. Addington, K. Dionisio, D. Lyons, R. Tornero-Velez, K. Phillips, T. Buckley, AND K. Isaacs. Mining of Consumer Product Ingredient and Purchasing Data to Identify Potential Chemical Coexposures. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 129(6):N/A, (2021). https://doi.org/10.1289/EHP8610

Impact/Purpose:

In this work, we present a complementary approach to biomonitoring-based mixture identification that utilizes consumer product ingredient and purchasing data streams. We integrated consumer product ingredient and product purchasing data via unique product identifiers to develop a large dataset of chemicals introduced to specific households and apply frequent itemset mining to identify relevant co-occurring chemicals within households; a case study of potential endocrine active chemicals is performed. Based on our results, we provide recommended sets of chemical combinations to be prioritized for bioactivity testing in in vitro HTS assays.

Description:

Background: Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge. Objectives: We aimed 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 to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 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. Last, 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.

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