Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.
Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.
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DOI：
10.1002/smll.201002366
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发表时间：
2011-04-18
期刊：
影响因子：
13.3
通讯作者：
Cohen, Yoram
中科院分区：
文献类型：
--
作者：
Liu, Rong;Rallo, Robert;George, Saji;Ji, Zhaoxia;Nair, Sumitra;Nel, Andre E.;Cohen, Yoram
基于一组9种金属氧化物纳米颗粒，将转化的支气管上皮细胞（BEAS-2B）暴露于0.375-200 mg·L-1的浓度范围内，暴露时间长达24 h，提出了基于分类的细胞毒性纳米结构活性关系（nano-SAR）。使用来自高通量筛选（HTS）试验的细胞毒性数据开发了nano-SAR，该试验经处理以鉴别和标记相对于未暴露对照细胞群的毒性（根据BEAS-2B细胞的碘化丙啶摄取）与无毒事件。从一组14个直观但基本的物理化学nano-SAR输入参数开始，确定了一些模型，其分类准确度超过95%。表现最好的模型在内部和外部验证中都具有100%的分类准确度。该模型基于四个描述符，包括金属氧化物的原子化能量、纳米颗粒金属的周期、纳米颗粒初级尺寸以及纳米颗粒体积分数（在溶液中）。尽管本建模方法成功地使用了相对较小的纳米颗粒库，但重要的是要认识到，为了扩展适用性域并增加数据驱动的纳米SAR的置信度和可靠性，需要显著更大的数据集。
A classification based cytotoxicity nano-structure-activity-realtionship (nano-SAR) 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 of 0.375–200 mg·L−1 and exposure times up to 24 h. The nano-SAR is developed using cytotoxicity data from high throughput screening (HTS) assay that was processed to identify and label toxic (in terms of the Propidium Iodide uptake of BEAS-2B cells) versus non-toxic events relative to unexposed control cell population. Starting with a set of fourteen intuitive but fundamental physicochemical nano-SAR input parameters, a number of models were identified which had classification accuracy above 95%. The best performing model had a 100% classification accuracy in both internal and external validation. This model is based on four descriptors including the atomization energy of the metal oxide, period of the nanoparticle metal, 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 nano-SARs.
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影响因子：
17.1
作者：
Fourches D;Pu D;Tassa C;Weissleder R;Shaw SY;Mumper RJ;Tropsha A
通讯作者：
Tropsha A
影响因子：
17.1
作者：
George, Saji;Pokhrel, Suman;Xia, Tian;Gilbert, Benjamin;Ji, Zhaoxia;Schowalter, Marco;Rosenauer, Andreas;Damoiseaux, Robert;Bradley, Kenneth A.;Maedler, Lutz;Nel, Andre E.
通讯作者：
Nel, Andre E.
影响因子：
5
作者：
Grassian, Vicki H.;Adamcakova-Dodd, Andrea;Thorne, Peter S.
通讯作者：
Thorne, Peter S.
影响因子：
46.9
作者：
Colvin, VL
通讯作者：
Colvin, VL
影响因子：
3.8
作者：
Li, Yi;Pan, Dahua;Tseng, Yufeng J.
通讯作者：
Tseng, Yufeng J.