Laboratory of Environmental Toxicology and Aquatic Ecology, Environmental Toxicology Unit (GhEnToxLab), Ghent, Ghent University, Belgium.
Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Heverlee, Belgium.
Environ Toxicol Chem. 2021 Nov;40(11):3034-3048. doi: 10.1002/etc.5176. Epub 2021 Sep 17.
Environmental risk assessment of metal mixtures is challenging due to the large number of possible mixtures and interactions. Mixture toxicity data cannot realistically be generated for all relevant scenarios. Therefore, methods for prediction of mixture toxicity from single-metal toxicity data are needed. We tested how well toxicity of Cu-Ni-Zn mixtures to Daphnia magna populations can be predicted based on the Dynamic Energy Budget theory with an individual-based model (DEB-IBM), assuming non-interactivity of metals on the physiological level. We exposed D. magna populations to Cu, Ni, and Zn and their mixture at a fixed concentration ratio. We calibrated the DEB-IBM with single-metal data and generated blind predictions of mixture toxicity (population size over time), with account for uncertainty. We compared the predictive performance of the DEB-IBM with respect to mixture effects on population density and population growth rates with that of two reference models applied on the population level, independent action and concentration addition. Our inferred physiological modes of action (pMoA) differed from literature-reported pMoAs, raising the question of whether this is a result of different model selection approaches, intraspecific variability, or whether different pMoAs might actually drive toxicity in a population context. Observed mixture effects were concentration- and endpoint-dependent. The independent action was overall more accurate than the concentration addition but concentration addition-predicted effects on population growth rate were slightly better. The DEB-IBM most accurately predicted effects on 6-week density, including antagonistic effects at high concentrations, which emerged from non-interactivity at the physiological level. Mixture effects on initial population growth rate appear to be more difficult to predict. To explain why model accuracy is endpoint-dependent, relationships between individual-level and population-level endpoints should be illuminated. Environ Toxicol Chem 2021;40:3034-3048. © 2021 SETAC.
由于可能的混合物和相互作用数量众多，金属混合物的环境风险评估具有挑战性。实际上，不可能为所有相关情况生成混合物毒性数据。因此，需要从单一金属毒性数据预测混合物毒性的方法。我们基于个体基础模型（DEB-IBM）的动态能量预算理论，测试了基于假设金属在生理水平上非相互作用的假设，用铜-镍-锌混合物对大型溞种群的毒性进行预测的效果。我们用固定浓度比例的铜、镍和锌及其混合物暴露大型溞种群。我们用单一金属数据对 DEB-IBM 进行校准，并生成混合物毒性（随时间推移的种群大小）的盲目预测，同时考虑不确定性。我们比较了 DEB-IBM 相对于种群密度和种群增长率的混合物效应的预测性能与应用于种群水平的两个参考模型（独立作用和浓度加和）的预测性能。我们推断的生理作用模式（pMoA）与文献报道的 pMoA 不同，这引发了一个问题，即这是由于不同的模型选择方法、种内变异性还是不同的 pMoA 实际上可能在种群环境中引起毒性。观察到的混合物效应取决于浓度和终点。独立作用总体上比浓度加和更准确，但浓度加和预测的对种群增长率的影响稍好。DEB-IBM 最准确地预测了 6 周密度的影响，包括高浓度时的拮抗作用，这是由于生理水平的非相互作用所致。混合物对初始种群增长率的影响似乎更难预测。为了解释为什么模型准确性取决于终点，应该阐明个体水平和种群水平终点之间的关系。环境毒理化学 2021；40:3034-3048。版权所有 2021 SETAC。
