Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, Union, NJ, USA.
Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS, USA.
Nanotoxicology. 2022 Jun;16(5):566-579. doi: 10.1080/17435390.2022.2123750. Epub 2022 Sep 23.
Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using as a test model, cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the 'independent action' generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through 'independent action' to induce cytotoxicity to instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.
金属氧化物纳米粒子（MONPs）作为化学混合物普遍存在于水和陆地系统中。评估与单一和组合的 MONPs 相关的细胞毒性可以真正识别出相关的环境风险。因此，以 作为测试模型，根据标准生物测定方案评估了 6 种单一 MONPs、15 种二元混合物和 20 种三元混合物的细胞毒性，这些混合物的毒性比为等毒性比。氧化应激评估表明，活性氧（ROS）的产生可忽略不计，金属锌离子的释放对 MONP 混合物的毒性起着重要作用。根据我们的实验数据点，基于我们创建的基于元素周期表的描述符和实验分析的 Zeta 电位，开发了七个定量结构-活性关系（QSAR）模型来模拟这些 MONPs 的细胞毒性。为了建模目的，考虑了两种策略性方法，即药理学和数学假设，以识别混合物描述符池。严格的验证标准表明，使用平方根摩尔贡献生成的混合物描述符开发的模型（模型 M4）在考虑验证标准时优于其他六个模型。在考虑药理学方法时，“独立作用”生成的描述符池提供了最佳模型（模型 M2），这有力地证实了混合物中的每个 MONP 通过“独立作用”来诱导 产生细胞毒性，而不是促进 MONPs 之间的加性、拮抗或协同作用。特定金属氧化物中金属的总电负性相对于氧原子的数量和金属价与细胞毒性呈正相关。同时，核心数反映了分子的体积和 Zeta 电位，与细胞毒性呈负相关。
