Fourches Denis, Pu Dongqiuye, Tropsha Alexander
Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, 27599, USA.
Comb Chem High Throughput Screen. 2011 Mar 1;14(3):217-25. doi: 10.2174/138620711794728743.
Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structure and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure - Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle's descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.
评估人造纳米粒子(MNPs)的预期和非预期生物学效应对于纳米技术的未来至关重要。实验研究,尤其是毒理学研究,既耗时又昂贵,因此需要开发高效的计算工具,能够根据MNPs的结构和物理化学性质预测其引起的生物学事件。本综述评估了现代化学信息学方法(如定量构效关系建模)的潜力,以开发具有统计学意义和外部预测能力的模型,这些模型可以根据MNPs的物理、化学和几何性质准确预测其生物学效应。我们讨论了使用纳米材料的实验性质和计算性质进行模型构建和验证的主要方法。我们考虑了两类不同的MNP数据集:(i)那些包含具有不同金属核心和有机修饰的MNPs的数据集,其实验测量的性质可作为粒子的描述符;(ii)那些涉及具有相同核心(如碳纳米管)但不同表面修饰有机分子的MNPs的数据集,对于这些数据集,可以为装饰分子的单个代表计算计算描述符。我们通过三个案例研究来说明这些概念,在这些案例研究中我们成功构建并验证了预测模型。总之,本综述表明,类似于定量构效关系建模在生物活性有机分子数据集分析中的传统应用,其在MNPs建模中的应用(我们称之为定量纳米结构活性关系(QNAR)建模)可用于(i)仅根据其代表性描述符预测新型MNPs的活性谱,以及(ii)设计和制造具有所需性质的更安全的纳米材料。