Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

Abstract

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Keywords: Chemical curation; Computational exposure science; High-throughput; Machine learning; Non-targeted analysis (NTA); Predictive exposure modeling; Uncertainty.

Fig. 1.

Major components of computational exposure research program and their relation to one another.

Fig. 2.

Varying complexity in exposure-relevant data streams. The simplest data may be very general, while more detailed data may be specifically relevant to near-field and far-field exposure sources. Data availability dictates for which exposure pathways, exposure scenarios, and populations defensible exposure predictions can be developed. *GFR = Glomerular Filtration Rate.

Fig. 3.

Fit-for-purpose exposure models. As decision contexts move from priority-setting to screening to exposure assessment, descriptions of receptors and the built environment become more complex, while the level of aggregation of exposures may decrease, with all potential exposures considered in prioritization, whereas specific statute-based scenarios are considered in full assessments.