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Significance testing of synergistic/antagonistic, dose level-dependent, or dose ratio-dependent effects in mixture dose-response analysis.

机译:混合物剂量反应分析中协同/拮抗,剂量水平依赖性或剂量比率依赖性效应的意义测试。

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摘要

In ecotoxicology, the state of the art for effect assessment of chemical mixtures is through multiple dose-response analysis of single compounds and their combinations. Investigating whether such data deviate from the reference models of concentration addition and/or independent action to identify overall synergism or antagonism is becoming routine. However, recent data show that more complex deviation patterns, such as dose ratio-dependent deviation and dose level-dependent deviation, need to be addressed. For concentration addition, methods to detect such deviation patterns exist, but they are stand-alone methods developed separately in literature, and conclusions derived from these analyses are therefore difficult to compare. For independent action, hardly any methods to detect such deviations from this reference model exist. This paper describes how these well-established mixture toxicity principles have been incorporated in a coherent data analysis procedure enabling detection and quantification of dose level-and dose ratio-specific synergism or antagonism from both the concentration addition and the independent action models. Significance testing of which deviation pattern describes the data best is carried out through maximum likelihood analysis. This analysis procedure is demonstrated through various data sets, and its applicability and limitations in mixture research are discussed.
机译:在生态毒理学中,对化学混合物进行效果评估的最新技术是通过对单个化合物及其组合进行多次剂量响应分析。研究这种数据是否偏离浓度附加和/或独立作用的参考模型以识别总体协同作用或拮抗作用已成为日常工作。但是,最新数据表明,需要解决更复杂的偏差模式,例如剂量比相关偏差和剂量水平相关偏差。对于添加浓度,存在检测这种偏差模式的方法,但是它们是文献中单独开发的独立方法,因此很难比较这些分析得出的结论。对于独立动作,几乎没有任何方法可以检测到与该参考模型的偏差。本文描述了如何将这些公认的混合物毒性原理纳入相干数据分析程序,从而能够从浓度添加和独立作用模型中检测和定量确定剂量水平和剂量比特异性协同作用或拮抗作用。通过最大似然分析对偏差模式最能说明数据的意义进行测试。通过各种数据集演示了该分析程序,并讨论了其在混合物研究中的适用性和局限性。

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