Center for the Environmental Implications of Nanotechnology, California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA.
Small. 2011 Apr 18;7(8):1118-26. doi: 10.1002/smll.201002366. Epub 2011 Mar 24.
A classification-based cytotoxicity nanostructure-activity relationship (nanoSAR) is presented based on a set of nine metal oxide nanoparticles to which transformed bronchial epithelial cells (BEAS-2B) were exposed over a range of concentrations (0.375-200 mg L(-1) ) and exposure times up to 24 h. The nanoSAR is developed using cytotoxicity data from a high-throughput screening assay that was processed to identify and label toxic (in terms of the propidium iodide uptake of BEAS-2B cells) versus nontoxic events relative to an unexposed control cell population. Starting with a set of fourteen intuitive but fundamental physicochemical nanoSAR input parameters, a number of models were identified which had a classification accuracy above 95%. The best-performing model had a 100% classification accuracy in both internal and external validations. This model is based on three descriptors: atomization energy of the metal oxide, period of the nanoparticle metal, and nanoparticle primary size, in addition to nanoparticle volume fraction (in solution). Notwithstanding the success of the present modeling approach with a relatively small nanoparticle library, it is important to recognize that a significantly larger data set would be needed in order to expand the applicability domain and increase the confidence and reliability of data-driven nanoSARs.
提出了一种基于分类的细胞毒性纳米结构-活性关系（nanoSAR），该关系基于一组九种金属氧化物纳米粒子，将转化的支气管上皮细胞（BEAS-2B）暴露于一系列浓度（0.375-200 mg/L）和暴露时间长达 24 小时。该 nanoSAR 是使用来自高通量筛选测定的细胞毒性数据开发的，该测定经过处理以识别和标记相对于未暴露的对照细胞群体的有毒（根据 BEAS-2B 细胞的碘化丙啶摄取）与无毒事件。从一组十四个直观但基本的物理化学 nanoSAR 输入参数开始，确定了许多具有超过 95％分类准确性的模型。表现最佳的模型在内部和外部验证中均具有 100％的分类准确性。该模型基于三个描述符：金属氧化物的原子化能，纳米粒子金属的周期和纳米粒子的原始尺寸，以及纳米粒子的体积分数（在溶液中）。尽管目前的建模方法在相对较小的纳米粒子库中取得了成功，但重要的是要认识到，需要一个更大的数据集才能扩大适用范围，并提高数据驱动型 nanoSAR 的置信度和可靠性。
