Improved Methods for Estimating Chemical Exposure
Chemicals are present in every product we use—from hand sanitizer to kitchen cutting boards to the drywall in our homes. These chemicals ensure that the products serve their purposes. But the market is flooded with new chemicals every day, and there is little information available about which chemicals (and how much of them) are present in many products—and how much we are exposed to them over time. One of the ways EPA works to protect public health is by conducting research to develop improved methods for estimating chemical exposure.
Traditional practices for estimating chemical exposure cannot keep up with the volume of new chemicals introduced to the market, nor can they quickly accommodate changes in human behavior that determine which products we use and how long we are exposed to them. To keep up with these changes, we must develop faster and more comprehensive ways to find out if, when, and how much exposure to chemicals occurs—and how that may lead to health effects.
EPA is already taking steps to address this need. Recently, EPA scientists published a paper titled Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment, which defines the growing field of computational exposure science. As a whole, computational exposure science helps to create a more complete picture of how and in what amounts chemicals enter the human body. To compose this “picture,” researchers used new technologies that collect data on thousands of chemicals, computational modeling to make predictions for untested chemicals and exposure pathways, as well as big data analytics to identify unexplored connections between thousands of variables, such as human interaction with common chemicals. Lead author Dr. Peter Egeghy describes the paper as a framework for future research efforts in the field.
The ultimate goal of this paper, according to Dr. Egeghy, is to help researchers examine a chemical they know very little about and figure out what kinds of products it could be in, how consumers use it, and how much exposure is safe for consumers. The paper recommends using novel resources such as market research data, machine learning programs (like the ones Netflix uses to personalize your show preferences), and behavior informatics, in addition to traditional exposure data and tools.
By combining computational exposure science methods with traditional chemical screening practices, researchers can evaluate a larger volume of chemicals at an even faster rate, making it easier for scientists to keep pace with the new chemicals brought to market.
EPA scientists are already using the framework to fill in product ingredient information in EPA’s Chemical Product Categorization (CPCat) database, which provides information on the use of more than 40,000 chemicals. The methods described in the paper are also being used in EPA’s Endocrine Disruptor Screening Program to quickly screen large amounts of chemicals and help determine which chemicals should be prioritized for further study. This paper is a small part of what EPA researchers do to help protect public health, and the framework will inform future exposure science studies.
Understanding how exposure to chemicals affects us is important to both human and environmental health. Creating a healthier world starts with ensuring that what we put in, on, and around our bodies won’t have negative consequences, whether that be in three months or 30 years. By designing the framework for faster and more efficient exposure screening methods, EPA’s researchers will continue to advance computational exposure science and its benefits to society.