Standardizing Benchmark Dose Calculations to Improve Science-Based Decisions in Human Health Assessments
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Citation: Wignall JA, Shapiro AJ, Wright FA, Woodruff TJ, Chiu WA, Guyton KZ, Rusyn I. Standardizing Benchmark Dose Calculations to Improve Science-Based Decisions in Human Health Assessments. Environ Health Perspect; http://dx.doi.org/10.1289/ehp.1307539.
Received: 23 August 2013
Accepted: 24 February 2014
Advance Publication: 25 February 2014
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Background: Benchmark dose (BMD) modeling computes the dose associated with a pre-specified response level. While offering advantages over traditional points of departure (POD), such as no-observed-adverse-effect-levels (NOAELs), BMD methods have lacked consistency and transparency in application, interpretation and reporting in human health assessments of chemicals.
Objectives: We aimed to apply a standardized process for conducting BMD modeling to reduce inconsistencies in model fitting and selection.
Methods: We evaluated 880 dose-response data sets for 352 environmental chemicals with existing human health assessments. We calculated benchmark doses and their lower limit [10% extra risk, or change in the mean equal to 1 standard deviation (SD), BMD/L10/1SD] for each chemical in a standardized way with pre-specified criteria for model fit acceptance. We identified study design features associated with acceptable model fits.
Results: We derived values for 255 (72%) of chemicals. Batch-calculated BMD/L10/1SD values were significantly and highly correlated (R2 of 0.95 and 0.83, respectively, n=42) with points of departure previously used in human health assessments, with values similar to reported NOAELs. Specifically, the median ratio of BMDs10/1SD:NOAELs was 1.96, and the median ratio of BMDLs10/1SD:NOAELs was 0.89. We also observed a significant trend of increasing model viability with increasing number of dose groups.
Conclusions: BMD/L10/1SD values can be calculated in a standardized way for use in health assessments on a large number of chemicals/critical effects. This facilitates exploration of health effects across multiple studies of a given chemical, or when chemicals need to be compared, providing greater transparency and efficiency than current approaches.
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