DOI: 10.6084/m9.figshare.29175905.v1
title: 0_bmd_ratios.zip

description: Project "BMD Ratios" contains data and code for a draft paper titled "Estimation of Benchmark Dose Ratio Distributions for Subchronic-to-Chronic Extrapolation Using Meta-Analysis". Contents of the zip files are described in "Zipfiles_ReadMe.txt".Authors:John Fox, Office of Research and Development emeritus employee, United States Environmental Protection Agency (US EPA), 1200 Pennsylvania Ave NW, Washington, DC 20460, USA, johnfoxatwork@gmail.com. https://orcid.org/0000-0003-4386-010XTodd Blessinger, Center for Public Health and Environmental Assessment (CPHEA), United States Environmental Protection Agency (US EPA), Mail code 8623R, 1200 Pennsylvania Ave NW, Washington, DC 20460, USA, blessinger.todd@epa.gov. *Jeffrey Dean, Center for Public Health and Environmental Assessment (CPHEA), United States Environmental Protection Agency (US EPA), 26 W. Martin Luther King Dr. (MS A-110), Cincinnati, OH 45268, USA, dean.jeff@epa.gov. <br>The datasets consist of benchmark dose estimates (BMDs) for subchronic and chronic exposures paired by chemical, route of administration, species, strain, sex, and measured effect ("endpoint"). R functions are included for processing data. <br>Data were acquired from the ToxRef database version 2.0 in April, 2019 (Watford et al. 2019a, 2019b) and from National Toxicology Program Technical reports (https://ntp.niehs.nih.gov/data/tr).Abstract of draft paper based on these data:Recently, the International Programme on Chemical Safety (IPCS) developed a unified probabilistic framework for deriving reference values, and a software tool, Approximate Probabilistic Analysis (APROBA), to help implement this framework. The distributions of multiple sources of uncertainty and variability were estimated, including uncertainty when extrapolating from subchronic to chronic data. The subchronic-to-chronic distribution was estimated using ratios between subchronic and chronic benchmark doses (BMD) and was determined to be approximately lognormal, with parameter values reported by IPCS. These parameters were estimated largely from historical data on body and organ weights from toxicological studies. We estimated the distribution using a larger collection of data, including histopathological and clinical endpoints. Our analysis determined that key assumptions of the method and the default values in APROBA are consistent with the results from the new data. However, the uncertainty of predictions for dichotomous response data was greater than assumed in APROBA. Also, APROBA's default parameter values do not account fully for the uncertainty of predicted chronic BMDs. Most importantly, uncertainty of the prediction can be much greater than assumed in APROBA if BMDs are accepted when they fall well outside the observed dose range or when an upper confidence limit (BMDU) is not quantifiable. In such cases, using the NOAEL as the point of departure is not a satisfactory alternative because it will underestimate uncertainty. Careful evaluation of dose-response model fit, including a number of indicators of model suitability in addition to standard goodness-of-fit statistics, is necessary to improve quantification of uncertainty.