Qin Li-Tang, Liu Min, Zhang Xin, Mo Ling-Yun, Zeng Hong-Hu, Liang Yan-Peng
College of Environmental Science and Engineering, Guilin University of Technology, Guilin, China.
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, China.
Environ Toxicol Chem. 2021 May;40(5):1431-1442. doi: 10.1002/etc.4995. Epub 2021 Mar 31.
The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pK ). The toxicities of synergetic mixtures can be interpreted with the total energy (E ) and pK of the mixtures. Dipole moment and E are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.
卤乙酸（HAAs）作为常见的消毒副产物（DBPs），其潜在毒性已得到广泛研究；但其对淡水绿藻的联合效应仍知之甚少。本研究旨在调查HAA混合物对绿藻月牙藻的毒理学相互作用，并基于浓度相加、独立作用和定量构效关系（QSAR）模型预测DBP混合物的毒性。在96孔微孔板中分析了6种HAA（碘乙酸[IAA]、溴乙酸[BAA]、氯乙酸[CAA]、二氯乙酸[DCAA]、三氯乙酸[TCAA]和三溴乙酸[TBAA]）及其68种二元混合物对绿藻的急性毒性。结果表明，单个HAA的毒性顺序为CAA＞IAA≈BAA＞TCAA＞DCAA＞TBAA。以浓度相加作为参考相加模型，混合物效应在47.1%的实验中为协同作用，在25%的实验中为拮抗作用，而仅在27.9%的实验中观察到相加效应。诱导协同作用的主要成分是DCAA、IAA和BAA；CAA是导致拮抗作用的主要成分。浓度相加和独立作用预测表明，在有效浓度水平为50%时，这两个模型无法准确预测72%的混合物毒性。通过对描述符进行统计分析，建立了QSAR模型来模拟混合物，以确定其化学结构与50%有效浓度的负对数之间的关系。基于正辛醇-水分配系数和酸解离常数（pK）这两个参数的QSAR模型能够准确预测相加混合物毒性。协同混合物的毒性可以用混合物的总能量（E）和pK来解释。偶极矩和E是影响拮抗混合物毒性的量子描述符。因此，计算机模拟可能是预测消毒副产物混合物毒性的有用工具。《环境毒理学与化学》2021年；40:1431 - 1442。© 2021 SETAC。
