Abstract

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. https://doi.org/10.1289/EHP8610

中文翻译：

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挖掘消费品成分和采购数据以识别潜在的化学暴露

摘要

背景：

消费品中的化学物质是导致人类化学物质共同暴露的主要因素。消费者购买和使用的产品种类繁多，可能含有数千种化学物质。有必要确定潜在的现实世界化学共同暴露，以优先进行体外毒性筛查。然而，由于大量潜在的化学组合，这种鉴定一直是一个重大挑战。

目标：

我们的目标是开发和实施一种数据驱动的程序，用于识别人类通过购买和使用消费品而接触到的普遍化学组合。

方法：

我们将频繁项集挖掘应用于将消费品化学成分数据与来自 60,000 个家庭的产品购买数据联系起来的集成数据集，以识别由消费品共同使用产生的化学组合。

结果：

我们根据种族/民族、收入、教育和家庭构成确定了所有家庭以及特定于人口群体的化学品共现模式。我们还通过确定同一家庭使用的多种产品中出现的化学物质，确定了总体接触可能性最高的化学物质。最后，在计算机模型中对雌激素和雄激素受体中活性化学物质的案例研究揭示了优先化学组合共同靶向参与重要生物信号通路的受体。

讨论：

家庭购买数据和产品化学信息的整合和综合分析提供了一种评估人类近场暴露的方法，并为体外分析中高通量筛选的化学组合选择提供信息。https://doi.org/10.1289/EHP8610