College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.
College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.
Ecotoxicol Environ Saf. 2021 Jan 15;208:111634. doi: 10.1016/j.ecoenv.2020.111634. Epub 2020 Nov 18.
The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula D= [Formula: see text] respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology.
定量构效关系（QSAR）已被用于研究有机混合物，但纳米材料领域的定量构效关系（QNAR）仍属新兴领域。毒性是多种物质相互作用的结果。QNAR 研究侧重于长期暴露于单一纳米材料的情况。由于混合物的复杂性，很难找到合适的描述符来构建模型。在这里，我们尝试构建一个 QNAR 模型，以预测暴露于含有纳米 TiO2 和重金属混合物的 HK-2 细胞的细胞活力。将 HK-2 细胞暴露于含有重金属和纳米材料的四组混合物中，并添加 CCK8 以获得活细胞数量。同时，研究 ROS 以研究这种机制。分别使用公式 D=[Formula: see text]获得各成分和混合物的描述符。我们使用多元偏最小二乘回归（PLS）和随机森林回归（RF）来构建 QNAR 模型。两个模型都能可靠地预测和评估混合暴露对 HK-2 细胞活力的影响。RF 模型在毒性预测方面表现出更高的稳定性和精度，可应用于环境纳米毒理学。
