New Approach Methodologies for Exposure Science

Abstract

Chemical risk assessment relies on knowledge of hazard, the dose-response relationship, and exposure to characterize potential risks to public health and the environment. A chemical with minimal toxicity might pose a risk if exposures are extensive, repeated, and/or occurring during critical windows across the human life span. Exposure assessment involves understanding human activity, and this activity is confounded by interindividual variability that is both biological and behavioral. Exposures further vary between the general population and susceptible or occupationally exposed populations. Recent computational exposure efforts have tackled these problems through the creation of new tools and predictive models. These tools include machine learning to draw inferences from existing data and computer-enhanced screening analyses to generate new data. Mathematical models provide frameworks describing chemical exposure processes. These models can be statistically evaluated to establish rigorous confidence in their predictions. The computational exposure tools reviewed here are oriented toward 'high-throughput' application, that is, they are suitable for dealing with the thousands of chemicals in commerce with limited sources of chemical exposure information. These new tools and models are moving chemical exposure and risk assessment forward in the 21st century.

Figure 1:

Chemical exposure arises from a diversity of pathways that involve human interactions and physical processes.

Figure 2

Systematic empirical evaluation of models (SEEM) uses Intake rates inferred from biomonitoring data to evaluate and calibrate exposure predictors across as many chemicals as possible. Exposure predictors include HTE models for predicting intake rates as well as presence on the chemicals on various lists (such as high production or banned). SEEM provides a quantitative estimate of uncertainty. HTE, high-throughput exposure.

Figure 3:

High-throughput methods may trade off precisions for speed, but if the uncertainty in the methods can be quantified, then the methods may still be useful for separating chemicals based on likelihood of risk.