Standardizing Benchmark Dose Calculations to Improve Science-Based Decisions in Human Health Assessments

Background: Benchmark dose (BMD) modeling computes the dose associated with a prespecified response level. While offering advantages over traditional points of departure (PODs), 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 limits [10% extra risk, or change in the mean equal to 1 SD (BMD/L(10/1SD))] for each chemical in a standardized way with prespecified criteria for model fit acceptance. We identified study design features associated with acceptable model fits. Results: We derived values for 255 (72%) of the chemicals. Batch-calculated BMD/L(10/1SD) values were significantly and highly correlated (R(2) of 0.95 and 0.83, respectively, n = 42) with PODs previously used in human health assessments, with values similar to reported NOAELs. Specifically, the median ratio of BMDs(10/1SD):NOAELs was 1.96, and the median ratio of BMDLs(10/1SD):NOAELs was 0.89. We also observed a significant trend of increasing model viability with increasing number of dose groups. Conclusions: BMD/L(10/1SD) values can be calculated in a standardized way for use in health assessments on a large number of chemicals and critical effects. This facilitates the 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. Citation: Wignall JA, Shapiro AJ, Wright FA, Woodruff TJ, Chiu WA, Guyton KZ, Rusyn I. 2014. Standardizing benchmark dose calculations to improve science-based decisions in human health assessments. Environ Health Perspect 122:499–505; http://dx.doi.org/10.1289/ehp.1307539