Using Evidence Integration Techniques in the Development of Health-Based Occupational Exposure Levels.

Development of toxicology-based criteria such as occupational exposure levels (OELs) are rarely straightforward. This process requires a rigorous review of the literature, searching for patterns in toxicity, biological plausibility, coherence, and dose-response relationships. Despite the direct applicability, human data are rarely used primarily because of imprecise exposure estimates, unknown influence of assumptions, and confounding factors. As a result, high reliance is often placed on laboratory animal data. Often, data from a single study is typically used to represent an entire database to extrapolate an OEL, even for data-rich compounds. Here we present a holistic framework for evaluating epidemiological, controlled , mechanistic/, and computational evidence that can be useful in deriving OELs. It begins with describing a documented review process of the literature, followed by sorting of data into either controlled laboratory , /read-across, mechanistic/, or epidemiological/field data categories. Studies are then evaluated and qualified based on rigor, risk of bias, and applicability for point of departure development. Other data (eg, , estimates, read-across data and mechanistic information, and data that failed to meet the former criteria) are used alongside qualified epidemiological exposure estimates to help inform points of departure or human-equivalent concentrations that are based on toxic end points. Bayesian benchmark dose methods are used to estimate points of departure and for estimating uncertainty factors (UFs) to develop preliminary OELs. These are then compared with epidemiological data to support the OEL and the use and magnitude of UFs, when appropriate.