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Characterization and Integration of Uncertainty Across Alternative Methods for Hazard, Toxicokinetics, and Exposure
Citation:
Thomas, R. Characterization and Integration of Uncertainty Across Alternative Methods for Hazard, Toxicokinetics, and Exposure. Presented at Society of Toxicology annual meeting, Baltimore, MD, March 10 - 14, 2019.
Impact/Purpose:
Abstract for a Society of Toxicology 2019 meeting symposium
Description:
A critical component of any regulatory decision is an understanding of the uncertainty and variability surrounding the important factors underlying the decision. In this context, uncertainty is defined as a lack of knowledge, while variability measures the true differences across a population. For decades, the uncertainty and variability associated with decision based on traditional animal studies were typically captured using standardized adjustment factors called “uncertainty factors.” In contrast, efforts to characterize uncertainty and variability in alternative methods for hazard, toxicokinetic, and exposure are varied in their approach, but have generally focused on chemical-specific methods. In the analysis of in vitro assay results, statistical methods are being developed and evaluated to establish uncertainty bounds around the measured potency and efficacy values. For the toxicokinetic modeling, the experimental uncertainty in the experimental measurements is being propagated through the toxicokinetic models to the final summary values (e.g., Cmax, AUC) while inter-individual variability is incorporated into toxicokinetic models using Monte Carlo methods that vary physiological parameters (such as age and bodyweight) based on a population distribution. In exposure, models are being calibrated against human biomonitoring data while capturing uncertainty in the exposure estimates and variability across demographic groups. Taken together, the chemical-specific variability and uncertainty estimates for the different components can be integrated to provide better-informed chemical safety decisions, as well as the ability to identify where in the process additional data may be useful for reducing uncertainty to inform risk decisions. This abstract does not necessarily reflect U.S. EPA policy.