Journal of Nanoparticle Research ( IF 2.1 ) Pub Date : 2021-08-04 , DOI: 10.1007/s11051-021-05288-0 Eliecer Peláez Sifonte 1 , Fidel Antonio Castro-Smirnov 2 , Héctor Raúl González Diez 2 , Argenis Adrian Soutelo Jimenez 3 , Fernando Guzmán Martínez 4
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The production of nanomaterials for biomedical research and applications increases exponentially. Interestingly, there is an increase in the use of nanoparticles in pharmaceutical sciences for diagnosis and treatment purposes, and therefore, nano-toxicity becomes one of the major role aspects in the future of pharmaceutical nanotechnology. This study focused on discerning and identifying the main variables that govern a group of metal oxide nanoparticles’ toxicity in human keratinous cells (HaCaT), combining computational simulation and semiempirical calculations with the available experimental data allowed revealing and explaining the nanoparticle toxicity for the corresponding cell line, through the development and validation of an interpretive nano-QSAR model with acceptable statistical quality by applying a multivariate linear regression with a coupled genetic algorithm. This function included only two descriptors, orthogonal to each other: the enthalpy of a standard formation of metal oxide nanocluster\( {\Delta \mathrm{H}}_{\mathrm{f}}^{\mathrm{c}} \) and the absolute value of Fermi energy from the cluster\( {\upepsilon}_{\mathrm{Fermi}}^{\mathrm{c}} \).The values of statistical indices obtained for this model showed its quality and robustness, for example, R2 = 0.90; \( {\mathrm{Q}}_{\mathrm{cv}}^2 \) = 0.86 and F = 37.15. This study demonstrated the need to use quantum-mechanical descriptors to explain the toxicity of metal oxide nanoparticles, capable of characterizing the electronic state of nanostructures. Regularization methods based on LASSO and Ridge regression have been employed in the model selection and validation. Furthermore, we propose a mechanism for toxicological effects applicable to a relevant group of nanoparticles, as well as their generalization to other toxicity studies not available in the literature, with potential nanopharmaceutical applications.
Graphical abstract
中文翻译：
纳米 QSAR 模型中的量子力学描述符可预测人类角质细胞中金属氧化物纳米粒子的毒性
用于生物医学研究和应用的纳米材料的生产呈指数增长。有趣的是，纳米粒子在药物科学中用于诊断和治疗目的的使用有所增加，因此，纳米毒性成为未来药物纳米技术的主要作用方面之一。这项研究的重点是辨别和确定控制一组金属氧化物纳米粒子在人类角质细胞 (HaCaT) 中毒性的主要变量，将计算模拟和半经验计算与可用的实验数据相结合，从而揭示和解释纳米粒子对相应细胞的毒性线，通过应用带有耦合遗传算法的多元线性回归，开发和验证具有可接受统计质量的解释性纳米 QSAR 模型。该函数仅包括两个相互正交的描述符：金属氧化物纳米簇标准形成的焓\( {\Delta \mathrm{H}}_{\mathrm{f}}^{\mathrm{c}} \)和来自簇的费米能量的绝对值\( {\upepsilon}_{\mathrm{ Fermi}}^{\mathrm{c}} \) . 该模型获得的统计指标值显示了其质量和稳健性，例如，R 2 = 0.90；\( {\mathrm{Q}}_{\mathrm{cv}}^2 \) = 0.86 和F= 37.15。这项研究表明需要使用量子力学描述符来解释金属氧化物纳米粒子的毒性，能够表征纳米结构的电子状态。在模型选择和验证中采用了基于 LASSO 和 Ridge 回归的正则化方法。此外，我们提出了一种适用于相关纳米粒子组的毒理学效应机制，以及它们对文献中未提供的其他毒性研究的推广，具有潜在的纳米药物应用。
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