Benchmark dose (BMD) modeling: current practice, issues, and challenges. (28th May 2018)

• Record Type: Journal Article
• Authors: Haber, Lynne T.; Dourson, Michael L.; Allen, Bruce C.; Hertzberg, Richard C.; Parker, Ann; Vincent, Melissa J.; Maier, Andrew; Boobis, Alan R.
• Is Part Of: Critical reviews in toxicology. Volume 48:Number 5(2018)
• Page Start: 387
• Page End: 415
• Publication Date: 2018-05-28
• Subjects: Benchmark dose -- BMD -- BMDS -- PROAST -- NOAEL -- risk assessment -- modeling issues -- model restriction -- model choice -- model averaging; Toxicology -- Periodicals; Poisons -- Physiological effect -- Periodicals; Toxicology -- Periodicals; 615.9
• DOI: 10.1080/10408444.2018.1430121
• Languages: English
• ISSNs: 1040-8444

Abstract

Abstract: Benchmark dose (BMD) modeling is now the state of the science for determining the point of departure for risk assessment. Key advantages include the fact that the modeling takes account of all of the data for a particular effect from a particular experiment, increased consistency, and better accounting for statistical uncertainties. Despite these strong advantages, disagreements remain as to several specific aspects of the modeling, including differences in the recommendations of the US Environmental Protection Agency (US EPA) and the European Food Safety Authority (EFSA). Differences exist in the choice of the benchmark response (BMR) for continuous data, the use of unrestricted models, and the mathematical models used; these can lead to differences in the final BMDL. It is important to take confidence in the model into account in choosing the BMDL, rather than simply choosing the lowest value. The field is moving in the direction of model averaging, which will avoid many of the challenges of choosing a single best model when the underlying biology does not suggest one, but additional research would be useful into methods of incorporating biological considerations into the weights used in the averaging. Additional research is also needed regarding the interplay between the BMR and the UF to ensure appropriate use for studies supporting a lower BMR than default values, such as for epidemiology data. Addressing these issues will aid in harmonizing methods and moving the field of risk assessment forward.