Mu Yunsong, Wu Fengchang, Zhao Qing, Ji Rong, Qie Yu, Zhou Yue, Hu Yan, Pang Chengfang, Hristozov Danail, Giesy John P, Xing Baoshan
a State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences , Beijing , China .
b Institute of Applied Ecology, Chinese Academy of Sciences , Shenyang , China .
Nanotoxicology. 2016 Nov;10(9):1207-14. doi: 10.1080/17435390.2016.1202352. Epub 2016 Jul 11.
The enormous physicochemical and structural diversity of metal oxide nanoparticles (MeONPs) poses significant challenges to the testing of their biological uptake, biodistribution, and effects that can be used to develop understanding of key nano-bio modes of action. This has generated considerable uncertainties in the assessment of their human health and environmental risks and has raised concerns about the adequacy of their regulation. In order to surpass the extremely resource intensive case-by-case testing, intelligent strategies combining testing methods and non-testing predictive modeling should be developed.
The quantitative structure-activity relationship (QSARs) in silico tools can be instrumental in understanding properties that affect the potencies of MeONPs and in predicting toxic responses and thresholds of effects.
The present study proposes a predictive nano-QSAR model for predicting the cytotoxicity of MeONPs. The model was applied to test the relationships between 26 physicochemical properties of 51 MeONPs and their cytotoxic effects in Escherichia coli. The two parameters, enthalpy of formation of a gaseous cation (▵Hme+) and polarization force (Z/r), were elucidated to make a significant contribution for the toxic effect of these MeONPs. The study also proposed the mechanisms of toxic potency in E. coli through the model, which indicated that the MeONPs as well as their released metal ions could collectively induce DNA damage and cell apoptosis.
These findings may provide an alternative method for prioritizing current and future MeONPs for potential in vivo testing, virtual prescreening and for designing environmentally benign nanomaterials.
金属氧化物纳米颗粒（MeONPs）在物理化学和结构上具有巨大的多样性，这给测试它们的生物摄取、生物分布及其效应带来了重大挑战，而这些信息可用于深入了解关键的纳米-生物作用模式。这在评估其对人类健康和环境的风险时产生了相当大的不确定性，并引发了对其监管充分性的担忧。为了超越逐个案例进行的极其耗费资源的测试，应开发结合测试方法和非测试预测模型的智能策略。
定量构效关系（QSARs）计算机工具有助于理解影响MeONPs效能的性质，并预测毒性反应和效应阈值。
本研究提出了一种预测MeONPs细胞毒性的纳米QSAR模型。该模型用于测试51种MeONPs的26种物理化学性质与其在大肠杆菌中的细胞毒性效应之间的关系。研究阐明了气态阳离子形成焓（▵Hme+）和极化力（Z/r）这两个参数对这些MeONPs的毒性效应有显著贡献。该研究还通过该模型提出了大肠杆菌中毒性效能的机制，表明MeONPs及其释放的金属离子可共同诱导DNA损伤和细胞凋亡。
这些发现可能为对当前和未来的MeONPs进行体内测试潜力的优先排序、虚拟预筛选以及设计环境友好型纳米材料提供一种替代方法。
