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An isobole-based statistical model and test for synergism/antagonism in binary mixture toxicity experiments

机译:基于等边的统计模型和二元混合物毒性实验中协同/拮抗作用的测试

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Synergism and antagonism are often defined in relation to the model of Concentration Addition (CA). Hence, it is vital for the conclusion of mixture toxicity studies to be able to test whether an observed deviation from CA reflects a true deviation or whether it is simply due to random variation. In this paper we consider a non-linear regression model for the classical ray designs for binary mixture experiments. The model combines dose–response curves for each mixture in the experiment with an isobole model, describing possible deviations from CA. The method allows us to test whether the chosen isobole model is reasonable for the data and to test the hypothesis of CA. Furthermore, it provides us with a measure of the degree of synergism/antagonism. The method is flexible since both the dose–response relationships and the isobole model can be chosen arbitrarily. We demonstrate the use of the method on datasets where combinations of pesticides are tested on a floating plant, Lemna minor, and an algae, Pseudokirchneriella subcapitata. Furthermore, we conduct a simulation study in order to explore the power with which a specific deviation from CA can be distinguished in different test-systems.
机译:协同作用和拮抗作用通常是相对于浓度增加(CA)模型定义的。因此,对于混合物毒性研究的结论至关重要的是,能够测试观察到的与CA的偏差是否反映了真实的偏差或仅仅是由于随机变化而引起的。在本文中,我们考虑了用于二元混合实验的经典射线设计的非线性回归模型。该模型将实验中每种混合物的剂量反应曲线与等边线模型相结合,描述了可能偏离CA的情况。该方法允许我们测试所选等腰线模型对于数据是否合理,并测试CA的假设。此外,它为我们提供了协同/拮抗程度的度量。该方法是灵活的,因为可以任意选择剂量反应关系和等容线模型。我们证明了该方法在数据集上的使用,该数据集是在漂浮植物小Lemna和藻类Pseudokirchneriella subcapitata上测试农药组合的。此外,我们进行了仿真研究,以探索在不同的测试系统中可以区分出CA的特定偏差的能力。

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