The chemical landscape of high-throughput new approach methodologies for exposure

Abstract

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Keywords: Exposure Modeling, New Approach Methodologies (NAMs), Chemicals in Products, Biomonitoring, Empirical/Statistical Models.

Fig. 1. Characterization of the chemical inventory in terms of known sectors of use, function, and structure.

This tableplot visualizes the multidimensional inventory dataset sorted by inventory; each column represents a variable, and each row of each column shows the distribution of the variable values for aggregate bins of 150 chemicals. Chemicals were assigned to a single inventory category here for ease of interpretation; chemicals that occurred in multiple inventories were combined. These chemicals were largely common organic commercial chemicals and had a range of use sectors, chemistries, and functions (including “ubiquitous” chemicals with more than 10 reported functions).

Fig. 2. Availability of traditional (reported) use data and NAM chemical use descriptors for the combined chemical inventory.

Not illustrated but available: reported chemical functions for the full FDA-SAF inventory, limited chemicals in EPA’s National Emissions Inventory and Health Canada’s National Pollutant Release Inventory. Each horizontal line on the heatmap represents a single chemical; chemicals are ordered by inventory and then kingdom as indicated in the right-side bars (and then clustered within inventory-kingdom pair to optimize visibility of patterns of existing and missing data).

Fig. 3. Monitoring information for inventory chemicals, organized by media category.

Only 2780 inventory chemicals have any monitoring data; the heatmap indicates coverage of chemicals monitored (not necessarily detected) in various harmonized media categories in EPA’s MMDB and chemicals tentatively identified in recent non-targeted analysis (NTA) studies. Less than 50% of inventory chemicals in each use sector have traditional measurement data (inset).

Fig. 4. HT exposure model and evaluation NAMs have greatly improved the number of chemicals in regulatory inventories with exposure estimates.

A Predictions of relevant exposure pathways and estimated quantitative intakes in mg/kg-BW/day are available for thousands of inventory chemicals, with many of the chemicals without data being UVCBs. Organic chemicals without SEEM3 model predictions (green boxes) are chemicals with structures that differ significantly from those in the training set of the pathway predictor models, and thus likely have exposure pathways not currently represented by available HT exposure models. B Coverage of inventory by use sector. Pharmaceuticals and industrial chemicals still have the least coverage.

Fig. 5. Availability of traditional TK data, TK NAMs, and exposure NAMs for 9404 chemicals with in vitro data.

A TK data by use sector. B Chemical space of SEEM3 exposure and in silico TK predictions. There is a subset of chemicals having a range of octanol-water partition coefficients and water solubilities, but relatively high molecular weights, for which SEEM3 predictions are unavailable. These chemicals include pharmaceutical and industrial chemicals outside the domain of the SEEM3 model.