Predicting Mixture Effects over Time with Toxicokinetic-Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power.
Predicting Mixture Effects over Time with Toxicokinetic-Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power.
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DOI：
10.1021/acs.est.0c05282
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发表时间：
2021-02-16
影响因子：
11.4
通讯作者：
Ashauer R
中科院分区：
文献类型：
--
作者：
Bart S;Jager T;Robinson A;Lahive E;Spurgeon DJ;Ashauer R
目前评估化学混合物对生物体影响的方法忽略了时间维度。通用生存阈值模型(GUTS)为推导毒物动力学-毒物动力学(TKTD)模型提供了一个框架，该模型考虑了毒物暴露对存活时间的影响。从独立作用和浓度相加的经典假设出发，我们推导了与这些混合毒性概念对应的GUTS还原(GUTS-RED)模型的方程，并进一步展示了它们的应用。使用对Enchytraeus crypticus的实验二元混合研究以及之前发表的大型水蚤和意大利蜜蜂的数据，我们评估了扩展的Guts-Red框架对混合物评估的预测能力。扩展后的模型准确地预测了混合效应。单一暴露数据的肠道参数、混合物模型校准和混合物暴露数据的预测能力分析提供了新的诊断工具，以告知化学作用模式，特别是类似或不同形式的损害是由混合物成分造成的。最后，观察到的与模型预测的偏差可以确定混合物中化学物质之间的相互作用，例如，协同作用或拮抗作用，这些作用没有被模型考虑在内。TKTD模型，如GUTS-RED，因此提供了在混合物危险评估中实施新的机械知识的框架。
Current methods to assess the impact of chemical mixtures on organisms ignore the temporal dimension. The General Unified Threshold model for Survival (GUTS) provides a framework for deriving toxicokinetic–toxicodynamic (TKTD) models, which account for effects of toxicant exposure on survival in time. Starting from the classic assumptions of independent action and concentration addition, we derive equations for the GUTS reduced (GUTS-RED) model corresponding to these mixture toxicity concepts and go on to demonstrate their application. Using experimental binary mixture studies with Enchytraeus crypticus and previously published data for Daphnia magna and Apis mellifera, we assessed the predictive power of the extended GUTS-RED framework for mixture assessment. The extended models accurately predicted the mixture effect. The GUTS parameters on single exposure data, mixture model calibration, and predictive power analyses on mixture exposure data offer novel diagnostic tools to inform on the chemical mode of action, specifically whether a similar or dissimilar form of damage is caused by mixture components. Finally, observed deviations from model predictions can identify interactions, e.g., synergism or antagonism, between chemicals in the mixture, which are not accounted for by the models. TKTD models, such as GUTS-RED, thus offer a framework to implement new mechanistic knowledge in mixture hazard assessments.
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影响因子：
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作者：
Arlos, Maricor J.;Focks, Andreas;Stamm, Christian
通讯作者：
Stamm, Christian
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Ashauer, Roman;Escher, Beate I.
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