Benchmark dose (BMD) modeling: current practice, issues, and challenges

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.

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“…The BMC values were selected from the accepted model with the lowest Akaike's information criterion (Haber et al, 2018;Kamelia et al, 2019). 7.8 minutes) as the major metabolite formed from BaP (retention time: 9.5 minutes) is readily observed (Figure 2A).…”

“…The benchmark response was set at 50%, representing either a 50% decrease in cell viability (BMCv50) or 50% inhibition of cardiomyocyte differentiation (BMC d 50). The performance of each fitted model was evaluated based on the scaled residuals, the Akaike's information criterion, the visual inspection of the model fit and the global goodness‐of‐fit, with fitted models showing a goodness‐of‐fit P ≥ .05 being accepted (Gift & Davis, ; Haber et al, ). The BMC values were selected from the accepted model with the lowest Akaike's information criterion (Haber et al, ; Kamelia et al, ).…”

“…On day 5, the EBs were transferred to a 24-well plate (Greiner Bio-One) (1 EB/well) containing 1 mL exposure medium/well, and incubated at 37°C, 5% CO 2 . On day 10, the number of wells (of (Gift & Davis, 2017;Haber et al, 2018).…”

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“…Ideally, the BMR for continuous data represents the smallest change in response that is considered biologically significant; however, change points greater than this level may be necessary because of statistical limitations. Conversely, a statistically significant change may not indicate an adverse effect when low variability in the data is observed [Haber et al 2018].…”

“…Conversely, divergence in model estimates is indicative of model dependence. The EPA guidance does not explicitly define "divergence"; however, it has been suggested by others that BMDL values within a factor of 3 are sufficiently close [Haber et al 2018].…”