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多种污染物样本中的混合物效应 - 多生物测定法的实验室间研究。

Mixture effects in samples of multiple contaminants - An inter-laboratory study with manifold bioassays.

机构信息

UFZ - Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany; Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany.

Institute for the Environment, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom.

出版信息

Environ Int. 2018 May;114:95-106. doi: 10.1016/j.envint.2018.02.013. Epub 2018 Feb 28.

Abstract

Chemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findings support the ongoing development of bioanalytical tools for (i) compiling comprehensive effect-based batteries for water quality assessment, (ii) designing tailored surveillance methods to safeguard specific water uses, and (iii) devising strategies for effect-based diagnosis of complex contamination.

摘要

环境中的化学物质以混合物的形式存在,而不是作为单个实体存在。因此,环境质量监测面临着全面评估多种污染物和潜在不良影响的挑战。基于效应的方法已被提议作为对复杂污染模式的化学分析特征的补充。由于污染物在野外产生的不良影响,化学分析和生物评估之间经常存在差异,这种差异可能是由于未识别的污染物造成的,也可能是由于混合物中化合物的相互作用造成的。在这里,我们进行了一项实验室间研究,使用 19 种不同的生物测定方法,通过广泛的浓度-效应分析研究了单个化合物及其混合物。该测定小组由 5 种测量顶端效应的整体生物测定法和 14 种基于细胞和生物体的生物测定法组成,这些生物测定法具有更具体的效应观察。研究了 12 种结构不同、作用机制独特的有机水污染物,这些污染物单独作为混合物进行研究,以模拟淡水暴露情景。我们将观察到的混合物效应与基于成分的混合物效应预测进行了比较,这些预测是基于加和性预期(假设无相互作用)得出的。大多数生物测定法都检测到了活性成分的混合物反应,即使在其他非活性污染物的背景下也是如此。当混合物中的任何成分本身都没有活性时,混合物也没有效果。使用顶端终点观察到的混合物效应位于浓度加和和独立作用的加和性预测定义的预测窗口中间,很好地反映了预期作用模式的多样性。在一种情况下,由于混合物中一种成分的溶解度降低,导致混合物的反应低于两种加和性混合物模型的预测。大多数基于特定细胞和生物体的终点都产生了与浓度加和的加和性预期一致的混合物反应。例外情况是,由于其他化合物的一般毒性等掩蔽效应,预期(加和)混合物反应没有发生。通常,由于实验因素、效应终点的特定局限性或细胞毒性等掩蔽副作用,可以解释与加和性预期的偏差。大多数生物测定法能够定量检测出针对样品中其他复杂化学物质混合物背景下具有特定生物活性的化合物的非相互作用、加和性组合效应。这支持了使用化学和生物分析监测工具的组合来识别驱动特定混合物效应的化学物质。此外,我们证明了一组生物测定法可以为复杂污染样品提供多样化的效应反应谱。这可以扩展到代表混合物不良结局途径。我们的研究结果支持生物分析工具的不断发展,用于(i) 为水质评估编制综合基于效应的电池,(ii) 设计专门的监测方法来保护特定的水用途,以及 (iii) 制定基于效应的复杂污染诊断策略。

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