Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea; Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea.
Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea.
NanoImpact. 2022 Jan;25:100383. doi: 10.1016/j.impact.2022.100383. Epub 2022 Jan 21.
During emission, TiO nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO nanoparticles (NPs) or mixtures of TiO NPs and four metal ions (Cu, Cd, Ni and Zn). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC of 76 nano-mixtures containing TiO NPs and one of eight inorganic/organic compounds (i.e., AgNO, Cd(NO), Cu(NO), CuSO, NaHAsO, NaAsO, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO NPs and one of eleven inorganic/organic compounds (i.e., AgNO, Cd(NO), Cu(NO), CuSO, NaHAsO, NaAsO, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (D) combing quantum descriptors of mixture components (e.g., TiO NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC (Adj.R = 0.955 ± 0.003, RMSE = 0.016 ± 0.002, and MAE = 0.008 ± 0.001) and immobilization (Adj.R = 0.888 ± 0.011, RMSE = 11.327 ± 0.730, and MAE = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO based nano-mixtures.
在排放过程中，TiO 纳米颗粒 (NPs) 可能会遇到各种化学物质，包括水生环境中的金属离子和有机化合物（例如地表水、沉积物）。在环境安全浓度下，TiO NPs 与这些化学物质的组合可能会对水生生物产生鸡尾酒效应（即混合物毒性）。以前的模型，如浓度加和和独立作用，需要混合物中单一组分的剂量-反应曲线来预测混合物毒性。结构-活性关系 (QSAR) 模型可以预测纳米混合物的毒性，而无需混合物中单一组分的剂量-反应曲线。然而，目前的定量结构-活性关系 (QSAR) 模型主要集中于预测异质金属 TiO 纳米颗粒 (NPs) 或 TiO NPs 与四种金属离子（Cu、Cd、Ni 和 Zn）混合物的细胞毒性（即细胞活力）。为了最大限度地降低纳米混合物风险评估的实验成本，在这项研究中，我们开发了新的纳米混合物 QSAR 模型，以预测 i）76 种包含 TiO NPs 和八种无机/有机化合物之一（即 AgNO、Cd(NO)、Cu(NO)、CuSO、NaHAsO、NaAsO、对羟基苯甲酸甲酯和二苯甲酮-3）的纳米混合物对大型溞（D. magna）的半数有效浓度 (EC)，和 ii）暴露于 98 种混合物之一的大型溞（D. magna）的固定化，所述混合物包含 TiO NPs 和十一种无机/有机化合物之一（即 AgNO、Cd(NO)、Cu(NO)、CuSO、NaHAsO、NaAsO、对羟基苯甲酸甲酯、二苯甲酮-3、吡虫啉、五溴二苯醚和 Triton X-100）。纳米混合物 QSAR 模型是通过使用不同的机器学习技术（即随机森林、神经网络、支持向量机和多元线性回归）将混合物成分（例如 TiO NPs 及其伙伴）的量子描述符（D）组合起来而开发的。使用随机森林算法和提出的混合物描述符构建的纳米混合物 QSAR 模型在预测 logEC（调整后的 R = 0.955 ± 0.003，RMSE = 0.016 ± 0.002，MAE = 0.008 ± 0.001）和固定化方面表现出良好的性能（调整后的 R = 0.888 ± 0.011，RMSE = 11.327 ± 0.730，MAE = 5.933 ± 0.442）。本研究中开发的模型已在一个用户友好的应用程序中实施，用于评估基于 TiO 的纳米混合物的水生毒性。
