Publication related to RSI or an RSI staff member
Beyond the RfD: Broad Application of a Probabilistic Approach to Improve Chemical Dose-Response Assessments for Noncancer Effects.
Authors
- Chiu, Weihsueh A, Chiu WA, Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA.
- Axelrad, Daniel A, Axelrad DA, Office of Policy (1809T), U.S. Environmental Protection Agency, Washington, District of Columbia, USA.
- Dalaijamts, Chimeddulam, Dalaijamts C, Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA.
- Dockins, Chris, Dockins C, Office of Policy (1809T), U.S. Environmental Protection Agency, Washington, District of Columbia, USA.
- Shao, Kan, Shao K, Department of Environmental and Occupational Health, Indiana University School of Public-Bloomington, Bloomington, Indiana, USA.
- Shapiro, Andrew J, Shapiro AJ, National Toxicology Program, National Institute for Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
- Paoli, Greg, Paoli G, Risk Sciences International, Ottawa, Ontario, Canada.
BACKGROUND: The National Academies recommended risk assessments redefine the traditional noncancer Reference Dose (RfD) as a probabilistically derived risk-specific dose, a framework for which was recently developed by the World Health Organization (WHO). OBJECTIVES: Our aim was to assess the feasibility and implications of replacing traditional RfDs with probabilistic estimates of the human dose associated with an effect magnitude M and population incidence I (HD(M)I). METHODS: We created a comprehensive, curated database of RfDs derived from animal data and developed a standardized, automated, web-accessible probabilistic dose-response workflow implementing the WHO framework. RESULTS: We identified 1,464 RfDs and associated endpoints, representing 608 chemicals across many types of effects. Applying our standardized workflow resulted in 1,522 HD(M)I values. Traditional RfDs are generally within an order of magnitude of the HD(M)I lower confidence bound for I=1% and M values commonly used for benchmark doses. The greatest contributor to uncertainty was lack of benchmark dose estimates, followed by uncertainty in the extent of human variability. Exposure at the traditional RfD frequently implies an upper 95% confidence bound of several percent of the population affected. Whether such incidences are considered acceptable is likely to vary by chemical and risk context, especially given the wide range of severity of the associated effects, from clinical chemistry to mortality. CONCLUSIONS: Overall, replacing RfDs with HD(M)I estimates can provide a more consistent, scientifically rigorous, and transparent basis for risk management decisions, as well as support additional decision contexts such as economic benefit-cost analysis, risk-risk tradeoffs, life-cycle impact analysis, and emergency response. https://doi.org/10.1289/EHP3368.
