Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening.
Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening.
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DOI：
10.1021/ar300022h
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发表时间：
2013-03-19
影响因子：
18.3
通讯作者：
Zhang, Haiyuan
中科院分区：
文献类型：
--
作者：
Nel, Andre;Xia, Tian;Meng, Huan;Wang, Xiang;Lin, Sijie;Ji, Zhaoxia;Zhang, Haiyuan
工程纳米材料（ENMs）的生产是材料设计和新消费产品开发的科学突破。虽然纳米技术的成功实施对全球经济的增长很重要，但我们也需要考虑可能产生危险生物后果的新型物理化学特性对环境健康和安全（EHS）的影响。为了评估ENM的危害，需要可靠和可重复的筛选方法来测试基本材料以及纳米产品。我们需要一个平台来研究纳米/生物界面上潜在的无穷无尽的生物物理化学相互作用，为此我们开发了一种预测毒理学方法。我们将预测毒理学方法定义为在体外使用基于机制的高通量筛选来预测可能导致体内病理或疾病结果的enm的物理化学特性。体内结果用于验证和改进体外高通量筛选（HTS），并建立结构-活性关系（SARs），从而通过适当的体外和体内测试组合进行危险排序和建模。这一概念与美国国家科学院2007年发布的具有里程碑意义的报告《21世纪的毒性测试：愿景与战略》（http://www.nap.edu/catalog.php?record_id=11970）一致，该报告主张通过使用高通量方法在人类细胞或细胞系中进行定量、机制和基于途径的毒性测试，从定性、描述性动物测试过渡到提高毒性测试的效率。因此，我们已经实施了HTS方法来筛选组合和组合ENM文库，以建立可用于预测体内损伤结果的危害排名和结构-活性关系。这种预测方法允许在体外进行大量筛选分析和大量数据生成，随后在动物或整个生物体中进行有限但关键的验证研究。因此，减少暴露的人类或环境人群的风险可以侧重于限制或避免引发这些毒理学反应的暴露，以及实施更安全的潜在危险环境物质设计。在本文中，我们回顾了建立预测毒理学范式所需的工具，通过使用组合和组合ENM文库、基于机制的HTS分析、危害排序和纳米sars的发展来评估吸入和环境毒理学情景。我们将讨论基于特定ENM性质出现的主要损伤范式，以及基于溶解化学表征作为毒性主要预测因子的ZnO纳米颗粒的更安全设计。
The production of engineered nanomaterials (ENMs) is a scientific breakthrough in material design and the development of new consumer products. While the successful implementation of nanotechnology is important for the growth of the global economy, we also need to consider the possible environmental health and safety (EHS) impact as a result of the novel physicochemical properties that could generate hazardous biological outcomes. In order to assess ENM hazard, reliable and reproducible screening approaches are needed to test the basic materials as well as nano-enabled products. A platform is required to investigate the potentially endless number of bio-physicochemical interactions at the nano/bio interface, in response to which we have developed a predictive toxicological approach. We define a predictive toxicological approach as the use of mechanisms-based high throughput screening in vitro to make predictions about the physicochemical properties of ENMs that may lead to the generation of pathology or disease outcomes in vivo. The in vivo results are used to validate and improve the in vitro high throughput screening (HTS) and to establish structure-activity relationships (SARs) that allow hazard ranking and modeling by an appropriate combination of in vitro and in vivo testing. This notion is in agreement with the landmark 2007 report from the US National Academy of Sciences, “Toxicity Testing in the 21st Century: A Vision and a Strategy” (http://www.nap.edu/catalog.php?record_id=11970), which advocates increased efficiency of toxicity testing by transitioning from qualitative, descriptive animal testing to quantitative, mechanistic and pathway-based toxicity testing in human cells or cell lines using high throughput approaches. Accordingly, we have implemented HTS approaches to screen compositional and combinatorial ENM libraries to develop hazard ranking and structure-activity relationships that can be used for predicting in vivo injury outcomes. This predictive approach allows the bulk of the screening analysis and high volume data generation to be carried out in vitro, following which limited, but critical, validation studies are carried out in animals or whole organisms. Risk reduction in the exposed human or environmental populations can then focus on limiting or avoiding exposures that trigger these toxicological responses as well as implementing safer design of potentially hazardous ENMs. In this communication, we review the tools required for establishing predictive toxicology paradigms to assess inhalation and environmental toxicological scenarios through the use of compositional and combinatorial ENM libraries, mechanism-based HTS assays, hazard ranking and development of nano-SARs. We will discuss the major injury paradigms that have emerged based on specific ENM properties, as well as describing the safer design of ZnO nanoparticles based on characterization of dissolution chemistry as a major predictor of toxicity.
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影响因子：
11.4
作者：
Rallo, Robert;France, Bryan;Liu, Rong;Nair, Sumitra;George, Saji;Damoiseaux, Robert;Giralt, Francesc;Nel, Andre;Bradley, Kenneth;Cohen, Yoram
通讯作者：
Cohen, Yoram
影响因子：
17.1
作者：
Xia, Tian;Kovochich, Michael;Liong, Monty;Maedler, Lutz;Gilbert, Benjamin;Shi, Haibin;Yeh, Joanne I.;Zink, Jeffrey I.;Nel, Andre E.
通讯作者：
Nel, Andre E.
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：
17.1
作者：
Wang X;Xia T;Ntim SA;Ji Z;George S;Meng H;Zhang H;Castranova V;Mitra S;Nel AE
通讯作者：
Nel AE
影响因子：
17.1
作者：
George S;Lin S;Ji Z;Thomas CR;Li L;Mecklenburg M;Meng H;Wang X;Zhang H;Xia T;Hohman JN;Lin S;Zink JI;Weiss PS;Nel AE
通讯作者：
Nel AE