By Lynn L. Bergeson and Carla N. Hutton
On August 19, 2021, the U.S. Environmental Protection Agency (EPA) announced a collaborative agreement with Unilever to explore better ways to assess chemical risks associated with consumer products. According to EPA, this agreement builds on prior cooperation between EPA and Unilever regarding New Approach Methods (NAM), “which are a promising alternative to conventional toxicity testing that are intended to reduce reliance on the use of animals.” EPA states that the collaboration aims to establish a framework for the Next Generation of Risk Assessments based on NAMs. The collaboration will bring together more than $2 million in both monetary and in-kind contributions, including scientific expertise and equipment, to develop a comprehensive NAMs dataset for a minimum of 40 chemicals. According to EPA, the chemicals will be selected and grouped such that half will be benign and the other half will have known adverse implications for human health. These chemicals will be tested using a wide variety of NAMs, and the results will be compared between the two groups to determine how well particular NAMs can infer differences in risk. EPA states that these data will be used in case studies to evaluate the potential to use NAMs in regulatory decisions. All data generated through the collaboration will be in the public domain, allowing academic, corporate, government, and nonprofit scientists to use the project results in their own research.
EPA states that in addition to the data generated through the collaboration, EPA and Unilever will use chemical data from EPA’s high-throughput screening efforts and the federal government’s Tox21 consortium, which is a collaboration among EPA, the National Institutes of Health (NIH), and the U.S. Food and Drug Administration (FDA). According to EPA, “[t]hese automated chemical screening technologies rapidly test thousands of chemicals for their effects on human cells or cellular components that are critical to normal function.” EPA notes that data from these technologies are then incorporated into computational models to predict potential adverse health effects and estimate the amount of chemical that may cause these effects.