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Advance Publication

Environ Health Perspect; DOI:10.1289/ehp.1307539

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

Jessica A. Wignall,1 Andrew J. Shapiro,1 Fred A. Wright,2 Tracey J. Woodruff,3 Weihsueh A. Chiu,4 Kathryn Z. Guyton,4 and Ivan Rusyn
Author Affiliations close
1Department of Environmental Sciences and Engineering, and 2Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA; 3Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of California, Oakland, California, USA; 4National Center for Environmental Assessment, United States Environmental Protection Agency, Washington, DC, USA
About This Article open

This EHP Advance Publication article has been peer-reviewed, revised, and accepted for publication. EHP Advance Publication articles are completely citable using the DOI number assigned to the article. This document will be replaced with the copyedited and formatted version as soon as it is available. Through the DOI number used in the citation, you will be able to access this document at each stage of the publication process.

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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Abstract

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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