To derive reference points (RPs) for health-based guidance values, the benchmark dose (BMD) approach increasingly replaces the no-observed-adverse-effect level approach. In the BMD approach, the RP corresponds to the benchmark dose lower confidence bounds (BMDLs) of a mathematical dose-response model derived from responses of animals over the entire dose range applied. The use of the entire dose range is seen as an important advantage of the BMD approach. This assumes that responses over the entire dose range are relevant for modeling low-dose responses, the basis for the RP. However, if part of the high-dose response was unnoticed triggered by a mechanism of action (MOA) that does not work at low doses, the high-dose response distorts the modeling of low-dose responses. Hence, we investigated the effect of high-dose specific responses on BMDLs by assuming a low- and a high-dose MOA. The BMDLs resulting from modeling fictitious quantal data were scattered over a broad dose range overlapping with the toxic range. Hence, BMDLs are sensitive to high-dose responses even though they might be irrelevant to low-dose response modeling. When applying the BMD approach, care should be taken that high-dose specific responses do not unduly affect the BMDL that derives from low doses.

中文翻译：

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高剂量引起的毒性反应可能会影响基准剂量。

为了得出基于健康的指导值的参考点（RPs），基准剂量（BMD）方法越来越多地取代了未观察到的不良影响水平方法。在BMD方法中，RP对应于数学剂量-反应模型的基准剂量下置信界（BMDL），该模型的剂量-反应模型源自在整个应用剂量范围内动物的反应。整个剂量范围的使用被视为BMD方法的重要优势。假设在整个剂量范围内的反应都与建模低剂量反应（RP的基础）有关。但是，如果高剂量反应的一部分未被在低剂量下不起作用的作用机制（MOA）触发而未被注意到，则高剂量反应会扭曲低剂量反应的模型。因此，我们通过假设低剂量和高剂量MOA来研究高剂量特异性应答对BMDL的影响。通过对虚拟的定量数据进行建模得到的BMDL分散在与毒性范围重叠的宽剂量范围内。因此，即使BMDL与低剂量反应模型无关，它们也对高剂量反应敏感。当采用BMD方法时，应注意高剂量的特异性反应不会不适当地影响低剂量的BMDL。