Humans are potentially exposed to thousands of commercial chemicals from a variety of sources. For example, the public active inventory of chemicals regulated by the U.S. Environmental Protection Agency (EPA) under the Toxic Substances Control Act (TSCA) currently contains more than 31,000 active, nonconfidential substances. To address the challenges associated with characterizing the toxicity for these large numbers of chemicals, thousands of high-throughput (HT) cell and cell-free bioactivity assays have been conducted under the EPA’s Toxicity Forecaster (ToxCast).
Although HT screening (HTS) approaches are more efficient and less expensive than animal testing, developing a strategy for addressing mixtures is still challenging. The number of potential chemical combinations is huge (there are more than one million possible combinations when considering just 20 chemicals), meaning HTS of all or even a fraction of the potential combinations is impossible. An alternative approach would be to predict bioactivity of chemical mixtures from component chemical responses via modeling; however, some experimental testing of mixtures is needed to evaluate those predictions and inform selection or refinement of models.
This presentation describes a complementary approach to biomonitoring-based mixture identification that uses consumer product ingredient and purchasing data streams. This approach integrates consumer product ingredient and product purchasing data via unique product identifiers to develop a large data set of chemicals introduced to specific households and apply frequent itemset mining to identify relevant co-occurring chemicals within households.
Speaker: Dr. Zachary Stanfield, Postdoctoral Research Scientist in the EPA Center for Computational Toxicology and Exposure