Belz Regina G, Cedergreen Nina, Sørensen Helle
University of Hohenheim, Institute of Phytomedicine, Department of Weed Science, Otto-Sander-Strasse 5, 70593 Stuttgart, Germany.
Sci Total Environ. 2008 Oct 1;404(1):77-87. doi: 10.1016/j.scitotenv.2008.06.008. Epub 2008 Jul 21.
Binary mixture studies are well established for mixtures of pollutants, pesticides, or allelochemicals and sound statistical methods are available to evaluate the results in relation to reference models. The majority of mixture studies are conducted to investigate the effect of one compound on the inhibitory action of another. However, since stimulatory responses to low concentrations of chemicals are gaining increased attention and improved statistical models are available to describe this phenomenon of hormesis, scientists are challenged by the question of what will happen in the low concentration range when all or some of the chemicals in a mixture induce hormesis? Can the mixture effects still be predicted and can the size and concentration range of hormesis be predicted? The present study focused on binary mixtures with one or both compounds inducing hormesis and evaluated six data sets of root length of Lactuca sativa L. and areal growth of Lemna minor L., where substantial and reproducible hormetic responses to allelochemicals and herbicides have been found. Results showed that the concentration giving maximal growth stimulatory effects (M) and the concentration where the hormetic effect had vanished (LDS) could be predicted by the most-used reference model of concentration addition (CA), if the growth inhibitory concentrations (EC50) followed CA. In cases of deviations from CA at EC50, the maximum concentration M and the LDS concentration followed the same deviation patterns, which were described by curved isobole models. Thus, low concentration mixture effects as well as the concentration range of hormesis can be predicted applying available statistical models, if both mixture partners induce hormesis. Using monotonic concentration-response models instead of biphasic concentration-response models for the prediction of joint effects, thus ignoring hormesis, slightly overestimated the deviation from CA at EC20 and EC50, but did not alter the general conclusion of the mixture study in terms of deviation from the reference model. Mixture effects on the maximum stimulatory response were tested against the hypothesis of a linear change with mixture ratio by constructing 95% prediction intervals based on the single concentration-response curves. Four out of the six data sets evaluated followed the model of linear interpolation reasonably well, which suggested that the size of the hormetic growth stimulation can be roughly predicted in mixtures from knowledge of the concentration-response relationships of the individual chemicals.
二元混合物研究在污染物、农药或化感物质的混合物方面已经很成熟，并且有完善的统计方法可用于根据参考模型评估结果。大多数混合物研究旨在调查一种化合物对另一种化合物抑制作用的影响。然而，由于对低浓度化学物质的刺激反应越来越受到关注，并且有改进的统计模型可用于描述这种兴奋效应现象，当混合物中的所有或某些化学物质在低浓度范围内诱导兴奋效应时会发生什么，这一问题给科学家带来了挑战。混合物效应仍然可以预测吗？兴奋效应的大小和浓度范围可以预测吗？本研究聚焦于一种或两种化合物诱导兴奋效应的二元混合物，并评估了六个关于生菜根长和浮萍面积生长的数据集，在这些数据集中发现了对化感物质和除草剂显著且可重复的兴奋效应反应。结果表明，如果生长抑制浓度（EC50）遵循浓度相加（CA）模型，那么通过最常用的浓度相加参考模型可以预测产生最大生长刺激效应的浓度（M）和兴奋效应消失的浓度（LDS）。在EC50处出现偏离CA模型的情况时，最大浓度M和LDS浓度遵循相同的偏离模式，这些模式由曲线等效线模型描述。因此，如果混合物的两种成分都诱导兴奋效应，那么可以应用现有的统计模型预测低浓度混合物效应以及兴奋效应的浓度范围。使用单调浓度 - 反应模型而非双相浓度 - 反应模型来预测联合效应，从而忽略兴奋效应，在EC20和EC50处略微高估了与CA模型的偏差，但在偏离参考模型方面并没有改变混合物研究的总体结论。通过基于单浓度 - 反应曲线构建95%预测区间，针对混合物比例线性变化的假设检验了混合物对最大刺激反应的影响。所评估的六个数据集中有四个相当合理地遵循线性插值模型，这表明根据各单个化学物质的浓度 - 反应关系，大致可以预测混合物中兴奋生长刺激的大小。
