Slob Wout
National Institute of Public Health and the Environment (RIVM), Bilthoven , The Netherlands.
Crit Rev Toxicol. 2014 Aug;44(7):557-67. doi: 10.3109/10408444.2014.925423. Epub 2014 Jul 7.
Evaluating dose-response data using the Benchmark dose (BMD) approach rather than by the no observed adverse effect (NOAEL) approach implies a considerable step forward from the perspective of the Reduction, Replacement, and Refinement, three Rs, in particular the R of reduction: more information is obtained from the same number of animals, or, vice versa, similar information may be obtained from fewer animals. The first part of this twin paper focusses on the former, the second on the latter aspect. Regarding the former, the BMD approach provides more information from any given dose-response dataset in various ways. First, the BMDL (= BMD lower confidence bound) provides more information by its more explicit definition. Further, as compared to the NOAEL approach the BMD approach results in more statistical precision in the value of the point of departure (PoD), for deriving exposure limits. While part of the animals in the study do not directly contribute to the numerical value of a NOAEL, all animals are effectively used and do contribute to a BMDL. In addition, the BMD approach allows for combining similar datasets for the same chemical (e.g., both sexes) in a single analysis, which further increases precision. By combining a dose-response dataset with similar historical data for other chemicals, the precision can even be substantially increased. Further, the BMD approach results in more precise estimates for relative potency factors (RPFs, or TEFs). And finally, the BMD approach is not only more precise, it also allows for quantification of the precision in the BMD estimate, which is not possible in the NOAEL approach.
使用基准剂量(BMD)方法而非无观察到不良反应水平(NOAEL)方法来评估剂量反应数据,从减少、替代和优化(3R原则)的角度来看意味着向前迈出了重要一步,特别是在减少原则方面:从相同数量的动物中可获得更多信息,或者反过来,从更少的动物中可获得类似的信息。这篇姊妹论文的第一部分聚焦于前者,第二部分聚焦于后者。关于前者,BMD方法以各种方式从任何给定的剂量反应数据集中提供更多信息。首先,BMDL(=BMD下限置信区间)通过其更明确的定义提供了更多信息。此外,与NOAEL方法相比,BMD方法在推导暴露限值的起始点(PoD)值时具有更高的统计精度。虽然研究中的部分动物对NOAEL的数值没有直接贡献,但所有动物都被有效利用并对BMDL有贡献。此外,BMD方法允许在单一分析中合并同一化学物质(例如,两性)的类似数据集,这进一步提高了精度。通过将剂量反应数据集与其他化学物质的类似历史数据相结合,精度甚至可以大幅提高。此外,BMD方法对相对效力因子(RPFs,或TEFs)的估计更精确。最后,BMD方法不仅更精确,还允许对BMD估计中的精度进行量化,而这在NOAEL方法中是不可能的。