QNAR modeling of cytotoxicity of mixing nano-TiO2 and heavy metals.
QNAR modeling of cytotoxicity of mixing nano-TiO2 and heavy metals.
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DOI：
10.1016/j.ecoenv.2020.111634
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发表时间：
2021-01
影响因子：
6.8
通讯作者：
Beilei Yuan;Pengfei Wang;Leqi Sang;Junhui Gong;Yong Pan;Yanhui Hu
中科院分区：
文献类型：
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作者：
Beilei Yuan;Pengfei Wang;Leqi Sang;Junhui Gong;Yong Pan;Yanhui Hu
摘要定量构效关系（QSAR）已被用于有机混合物的研究，但在纳米材料领域的QSAR（QNAR）仍然是新的。毒性是许多物质相互作用的结果。QNAR的研究重点是长期的单一纳米材料。由于混合物的复杂性，很难找到合适的描述符来建立模型。在这里，我们试图建立一个QNAR模型来预测HK-2细胞暴露于含有纳米TiO 2和重金属的混合物的细胞活力。将HK-2细胞暴露于四组含有重金属和纳米材料的混合物中，并加入CCK 8以获得活细胞数。同时，研究了活性氧对这一机制的影响。分别使用公式D mix=∑ i= 1 n Di x i获得组分和混合物的每个描述符。我们使用了多重偏最小二乘回归（PLS）和随机森林回归（RF）来建立QNAR模型。两种模型都可靠地预测和评估暴露于混合物的HK-2细胞的活力。RF模型具有较好的稳定性和较高的预测精度，可应用于环境纳米毒理学研究。
Abstract 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 2 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 mix=∑ i= 1 n D i x i 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.