German Federal Institute for Risk Assessment, Department of Food Safety, Max-Dohrn-Straße 8-10, 10589, Berlin, Germany.
German Federal Institute for Risk Assessment, Department Pesticides Safety, Max-Dohrn-Straße 8-10, 10589, Berlin, Germany.
Environ Pollut. 2020 May;260:113953. doi: 10.1016/j.envpol.2020.113953. Epub 2020 Jan 10.
Many different approaches have been proposed to evaluate and predict mixture effects. From a regulatory perspective, several guidance documents have been recently published and provide a strategy for mixture risk assessment based on valuable frameworks to investigate potential synergistic effects. However, some methodological aspects, e.g. for considering mathematical models, are not sufficiently defined. Therefore, the aim of this study was to examine the usefulness of five main mathematical models for mixture effect interpretation: theoretical additivity (TA), concentration addition (CA), independent action (IA), Chou-Talalay (CT), and a benchmark dose approach (BMD) were tested using a fictional data set depicting scenarios of additivity, synergism and antagonism. The synergism and antagonism scenarios were split in x-axis and y-axis synergism/antagonism, meaning a shift of the curve on x-axis or y-axis. The BMD approach was the only model which showed a perfect correspondence for dose addition. Regarding synergism and antagonism, all approaches correspond well for the x-axis synergism and antagonism with only few exceptions. In contrast, some limitations were observed in the particular scenarios of y-axis synergism and antagonism. Therefore our results show that each model has advantages and disadvantages, and that therefore no single model appears the best one for all kinds of application. We would recommend instead the parallel use of different models to increase confidence in the result of mixture effect evaluation.
许多不同的方法已经被提出用于评估和预测混合物效应。从监管的角度来看，最近已经发布了一些指导文件，提供了一种基于有价值的框架来调查潜在协同效应的混合物风险评估策略。然而，一些方法学方面的问题，例如考虑数学模型，还没有得到充分的定义。因此，本研究的目的是检验五种主要的数学模型在解释混合物效应中的有用性：理论加性（TA）、浓度加性（CA）、独立作用（IA）、Chou-Talalay（CT）和基准剂量法（BMD），使用一个描述加性、协同和拮抗情景的虚构数据集进行测试。协同和拮抗情景分为 x 轴和 y 轴协同/拮抗，意味着曲线在 x 轴或 y 轴上的移动。BMD 方法是唯一一种对剂量加性表现出完美对应关系的模型。关于协同和拮抗作用，所有方法都很好地对应了 x 轴协同和拮抗作用，只有少数例外。相比之下，在 y 轴协同和拮抗作用的特殊情况下，观察到了一些局限性。因此，我们的结果表明，每个模型都有其优点和缺点，因此没有一种单一的模型适用于所有类型的应用。我们建议相反，平行使用不同的模型来增加对混合物效应评估结果的信心。
