Citation:

Blessinger, T., Allen Davis, W. Chiu, J. Stanek, G. Woodall, J. Gift, K. Thayer, AND D. Bussard. Application of a Unified Probabilistic Framework to the Dose-Response Assessment of Acrolein. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, Netherlands, 143:105953, (2020). https://doi.org/10.1016/j.envint.2020.105953

Impact/Purpose:

This journal article demonstrates the utility of Approximate Probabilistic Analysis (APROBA) by applying it to the critical endpoint (nasal lesions) for acrolein. Quantitative uncertainty analysis (QUA) has been recommended by multiple organizations outside EPA as a way of increasing transparency in our risk assessments. APROBA is a spreadsheet tool that computes references values probabilistically, thus allowing the risk assessor to make probabilistic statements on reference values. The application of APROBA and a subsequent sensitivity analysis to acrolein is a case example.

Description:

Background: In quantitative chemical risk assessment, a reference value is an estimate of an exposure to a chemical that is considered sufficiently low-risk. Because current “deterministic” approaches do not quantitatively characterize the likelihood or severity of harm, the National Academies has recommended using reference values derived from a risk-specific dose that is treated as a random variable, with a probability distribution characterizing uncertainty and variability, Objectives: In order to build familiarity and address issues needed for routine and standardized derivation of probabilistic risk-specific dose distributions, a case example applying the unified probabilistic framework presented in Chiu and Slob (2015) is developed for acrolein. This case study is based on an updated systematic review of literature (see Part 1 of this manuscript) identifying nasal lesions reported in Dorman et al. (2008) as the most appropriate endpoint and study for reference value derivation. Methods: The probability distribution was estimated for the risk-specific dose, defined as the human dose at which 1% of the human population experiences minimal lesions, and a probabilistic reference value was computed as the 5th percentile of this distribution. A deterministic reference value was also derived for comparison, and a sensitivity analysis of the probabilistic reference value was conducted investigating alternative assumptions for the point of departure type and exposure duration. Results: The probabilistic reference value of 6×10-4 mg/m3 was slightly lower than the deterministic reference value of 8×10-4 mg/m3, and the risk-specific dose distribution had an uncertainty spanning a factor of 137 (95th-5th percentile ratio). Sensitivity analysis yielded probabilistic reference values that were between 9×10-4 mg/m3 and 2×10-3 mg/m3. Conclusions: Using a probabilistic approach for deriving a reference value allows quantitative characterization of the severity, incidence, and uncertainty of effects at a given dose. The results can be used to inform risk management decisions and improve risk communication.

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