National Institute of Public Health and the Environment (RIVM) , Bilthoven , The Netherlands and.
Crit Rev Toxicol. 2014 Mar;44(3):270-97. doi: 10.3109/10408444.2013.853726. Epub 2013 Nov 19.
A re-analysis of a large number of historical dose-response data for continuous endpoints indicates that an exponential or a Hill model with four parameters adequately describes toxicological dose-responses. No exceptions were found for the datasets considered, which related to a wide variety of endpoints and to both in vivo and in vitro studies of various types. For a given endpoint/study type dose-response shapes were found to be homogenous among chemicals in the in vitro studies considered, while a mild among-chemical variation in the steepness parameter seemed to be present in the in vivo studies. Our findings have various practical consequences. For continuous endpoints, model selection in the BMD approach is not a crucial issue. The often applied approach of using constraints on the model parameters to prevent "infinite" slopes at dose zero in fitting a model is not in line with our findings, and appears to be unjustified. Instead, more realistic ranges of parameter values could be derived from re-analyses of large numbers of historical dose-response datasets in the same endpoint and study type, which could be used as parameter constraints in future individual datasets. This approach will be particularly useful for weak datasets (e.g. few doses, much scatter). In addition, this approach may open the way to use fewer animals in future studies. In the discussion, we argue that distinctions between linear, sub/supralinear or thresholded dose-response shapes, based on visual inspection of plots, are not biologically meaningful nor useful for risk assessment.
对大量连续终点的历史剂量反应数据进行重新分析表明,四参数指数或 Hill 模型足以描述毒理学剂量反应。在所考虑的数据集没有发现例外,这些数据集涉及到广泛的终点和各种类型的体内和体外研究。对于给定的终点/研究类型,在考虑的体外研究中,化学物质之间的剂量反应形状是同质的,而在体内研究中,陡峭度参数似乎存在轻微的化学物质间变异。我们的发现有各种实际的影响。对于连续终点,BMD 方法中的模型选择不是一个关键问题。在拟合模型时,经常应用的方法是对模型参数施加约束,以防止在剂量为零时斜率“无穷大”,这与我们的发现不一致,似乎没有道理。相反,可以从同一终点和研究类型的大量历史剂量反应数据集的重新分析中得出更现实的参数值范围,并将其用作未来单个数据集的参数约束。这种方法对于弱数据集(例如剂量少,分散度大)尤其有用。此外,这种方法可能为未来的研究减少动物使用数量开辟道路。在讨论中,我们认为,基于绘图的直观观察来区分线性、亚线性或阈值剂量反应形状,在生物学上没有意义,也不利于风险评估。