Bart Sylvain, Jager Tjalling, Robinson Alex, Lahive Elma, Spurgeon David J, Ashauer Roman
Department of Environment and Geography, University of York, Heslington, York, YO10 5NG, U.K.
UK Centre for Ecology and Hydrology, Maclean Building, Benson Lane, Wallingford OX10 8BB, Oxfordshire, U.K.
Environ Sci Technol. 2021 Feb 16;55(4):2430-2439. doi: 10.1021/acs.est.0c05282. Epub 2021 Jan 26.
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 and previously published data for and , 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.
当前评估化学混合物对生物体影响的方法忽略了时间维度。通用统一生存阈值模型(GUTS)提供了一个推导毒代动力学-毒效动力学(TKTD)模型的框架,该模型考虑了毒物暴露对生存随时间的影响。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物毒性概念相对应的GUTS简化(GUTS-RED)模型的方程,并继续展示其应用。利用针对[具体物质1]和[具体物质2]的实验二元混合物研究以及先前发表的数据,我们评估了扩展的GUTS-RED框架用于混合物评估的预测能力。扩展模型准确地预测了混合物效应。关于单一暴露数据的GUTS参数、混合物模型校准以及混合物暴露数据的预测能力分析提供了新的诊断工具,以了解化学作用模式,特别是混合物成分是否造成相似或不同形式的损害。最后,观察到的与模型预测的偏差可以识别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,而这些是模型未考虑的。因此,诸如GUTS-RED之类的TKTD模型提供了一个在混合物危害评估中纳入新的机制知识的框架。