Wang Na, Wang Xiaochang C, Ma Xiaoyan
Key Laboratory of Northwest Water Resource, Environment and Ecology, Ministry of Education, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055 China.
Key Laboratory of Northwest Water Resource, Environment and Ecology, Ministry of Education, School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055 China.
Ecotoxicol Environ Saf. 2015 Mar;113:176-82. doi: 10.1016/j.ecoenv.2014.12.008. Epub 2014 Dec 11.
The concentration addition (CA) model has been widely applied to predict mixture toxicity. However, its applicability is difficult to evaluate due to the complexity of interactions among substances. Considering that the concentration-response curve (CRC) of each component of the mixture is closely related to the prediction of mixture toxicity, mathematical treatments were used to derive a characteristic index kECx (k was the slope of the tangent line of a CRC at concentration ECx). The implication is that the CA model would be applicable for predicting the mixture toxicity only when chemical components have similar kECx in the whole or part of the concentration range. For five selected chemicals whose toxicity was detected using luminescent bacteria, sodium dodecyl benzene sulfonate (SDBS) showed much higher kECx values than the others and its existence in the binary mixtures brought about overestimation of the mixture toxicity with the CA model. The higher the mass ratio of SDBS in a multi-mixture was, the more the toxicity prediction deviated from measurements. By applying the method proposed in this study to analyze some published data, it is confirmed that some components having significantly different kECx values from the other components could explain the large deviation of the mixture toxicity predicted by the CA model.
浓度相加（CA）模型已被广泛应用于预测混合物毒性。然而，由于物质间相互作用的复杂性，其适用性难以评估。考虑到混合物各组分的浓度-反应曲线（CRC）与混合物毒性预测密切相关，采用数学处理方法得出一个特征指数kECx（k为CRC在浓度ECx处切线的斜率）。这意味着只有当化学成分在整个或部分浓度范围内具有相似的kECx时，CA模型才适用于预测混合物毒性。对于五种用发光细菌检测毒性的选定化学品，十二烷基苯磺酸钠（SDBS）的kECx值远高于其他化学品，并且它在二元混合物中的存在导致CA模型对混合物毒性的高估。在多组分混合物中，SDBS的质量比越高，毒性预测与测量值的偏差就越大。通过应用本研究提出的方法分析一些已发表的数据，证实了某些kECx值与其他组分有显著差异的组分可以解释CA模型预测的混合物毒性的较大偏差。
