Influence of Distribution of Animals between Dose Groups on Estimated Benchmark Dose and Animal Welfare for Continuous Effects

Abstract

The benchmark dose (BMD) approach is increasingly used as a preferred approach for doseeffect analysis, but standard experimental designs are generally not optimized for BMD analysis. The aim of this study was to evaluate how the use of unequally sized dose groups affects the quality of BMD estimates in toxicity testing, with special consideration of the total burden of animal distress. We generated continuous dose-effect data by Monte Carlo simulation using two dose-effect curves based on endpoints with different shape parameters. Eighty-five designs, each with four dose groups of unequal size, were examined in scenarios ranging from low-to high-dose placements and with a total number of animals set to 40, 80, or 200. For each simulation, a BMD value was estimated and compared with the "true" BMD. In general, redistribution of animals from higher to lower dose groups resulted in an improved precision of the calculated BMD value as long as dose placements were high enough to detect a significant trend in the dose-effect data with sufficient power. The improved BMD precision and the associated reduction of the number of animals exposed to the highest dose, where chemically induced distress is most likely to occur, are favorable for the reduction and refinement principles. The result thereby strengthen BMD-aligned design of experiments as a means for more accurate hazard characterization along with animal welfare improvements.

A Review of Recent Advances in Benchmark Dose Methodology

“…The rest of the doses should be spread over the resulting dose scale. More generally, it has repeatedly been shown to be advantageous to have studies with one or more doses near the level of the true BMD (Piegorsch et al., ; Ringblom, Kalantari, Johanson, & Öberg, ; U.S. EPA, ). Obviously, these suggestions require some prior data to be practically applied.…”

“…Although pairwise comparison strategies, such as NOAEL and the lowest adverse effect level (LOAEL), benefit from designs with equal group sizes, this is not necessarily the case for the BMD approach (Öberg, ). In the context of animal experiments, two recent studies (Ringblom et al., ) showed that varying the number of animals used between doses while retaining the same number of doses may reduce animal stress without changing the precision of the estimated BMD. A similar finding was reported by Shao and Small (), who found only a limited effect on BMD precision by unbalanced designs, but a tendency toward increased precision by placing more animals in the lower dose groups and fewer in the higher dose groups, resulting in higher animal welfare (Öberg, ).…”

“…A lot of methodological work has focused on model averaging for BMDL estimates. Current practices to derive a BMDL for a model‐averaged BMD estimate include calculating simple averages of the BMDL estimates from the individual models (Ringblom et al., ; Ritz, Gerhard, & Hothorn, ; Wheeler & Bailer, ), to use an approach based on a variance inequality proposed by Buckland et al. () (Namata et al., ; Piegorsch et al., ) or using bootstrap approaches (Buckley et al., ; Roberts & Martin, ; Wheeler & Bailer, ).…”

Toxic Responses Induced at High Doses May Affect Benchmark Doses

“…Most publications about the BMD approach focus on the mathematical/statistical aspects of modeling 27 and the study design effects such as group sizes. 28 In this study, we want to critically discuss BMD modeling in the context of potentially different MOAs associated with different subpopulations inadvertently included in the study contributing to the observed total response of interest.…”