Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China.
Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China.
Chemosphere. 2022 Feb;289:133190. doi: 10.1016/j.chemosphere.2021.133190. Epub 2021 Dec 6.
At present, the toxicity prediction of mixtures mainly focuses on the concentration addition (CA) and independent action (IA) based on individual toxicants to predict the toxicity of multicomponent mixtures. This process of predicting the toxicity of multicomponent mixtures based on single substances or low component mixtures is called down-to-top method in this study. However, due to the particularity of some toxicants, we have to use the top-to-down idea to obtain or eliminate the toxicity of some components from mixtures. For example, the toxicity of toxicants is obtained from the toxicity of a mixture with, especially toxic, cosolvent added. In the study, two top-to-down methods, the inverse CA (ICA) and inverse IA (IIA) models, were proposed to eliminate the effects of a certain component from multicomponent mixtures. Furthermore, taking the eight binary mixtures consisting of different shapes of cosolvents (isopropyl alcohol (IPA) having hormesis and dimethyl sulfoxide (DMSO)) and toxicants (two ionic liquids and two pesticides) as an example, combined with the interaction evaluated by CA and IA model, the influence of different shapes of components on top-to-down toxicity prediction was explored. The results showed that cosolvent IPA having hormesis may cause unpredictable effects, even at low concentrations, and should be used with caution. For DMSO, most of the toxicant's toxicity obtained by ICA and IIA models were almost in accordance with those observed experimentally, which showed that ICA and IIA could effectively eliminate the effects of cosolvent, even if toxic cosolvent, from the mixture. Ultimately, a frame of cosolvent use and toxicity correction for the hydrophobic toxicant were suggested based on the top-to-down toxicity prediction method. The proposed methods improve the existing framework of mixture toxicity prediction and provide a new idea for mixture toxicity evaluation and risk assessment.
目前，混合物的毒性预测主要集中在基于单个毒物的浓度加和（CA）和独立作用（IA）上，以预测多组分混合物的毒性。在本研究中，基于单一物质或低组分混合物预测多组分混合物毒性的过程称为自上而下方法。然而，由于某些毒物的特殊性，我们不得不使用自下而上的思路从混合物中获得或消除某些成分的毒性。例如，通过添加具有毒性的共溶剂的混合物来获得毒物的毒性。在研究中，提出了两种自下而上的方法，即反浓度加和（ICA）和反独立作用（IIA）模型，以消除混合物中特定成分的影响。此外，以由不同形状的共溶剂（具有激素作用的异丙醇（IPA）和二甲基亚砜（DMSO））和毒物（两种离子液体和两种农药）组成的八个二元混合物为例，结合 CA 和 IA 模型评估的相互作用，探索了不同形状成分对自上而下毒性预测的影响。结果表明，具有激素作用的共溶剂 IPA 可能会导致不可预测的影响，即使在低浓度下也是如此，因此应谨慎使用。对于 DMSO，ICA 和 IIA 模型获得的大多数毒物毒性几乎与实验观察到的毒性一致，这表明 ICA 和 IIA 可以有效地从混合物中消除溶剂的影响，即使是有毒的溶剂。最终，基于自上而下的毒性预测方法，提出了一种疏水毒物的共溶剂使用和毒性修正框架。所提出的方法改进了现有的混合物毒性预测框架，并为混合物毒性评价和风险评估提供了新的思路。
