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二十一世纪的纳米材料毒性测试:应用预测性毒理学方法和高通量筛选。

Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening.

机构信息

Department of Medicine, Division of NanoMedicine, University of California, Los Angeles, CA 90095, USA.

出版信息

Acc Chem Res. 2013 Mar 19;46(3):607-21. doi: 10.1021/ar300022h. Epub 2012 Jun 7.

Abstract

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 nanoenabled products. A platform is required to investigate the potentially endless number of biophysicochemical 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 Account, 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.

摘要

工程纳米材料(ENM)的生产是材料设计和新型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,但我们也需要考虑由于可能产生有害生物结果的新颖物理化学特性而对环境健康和安全(EHS)产生的潜在影响。为了评估 ENM 的危害,需要可靠且可重复的筛选方法来测试基本材料以及纳米增强产品。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使用基于机制的高通量筛选体外来预测 ENM 的物理化学性质,这些性质可能导致体内病理学或疾病结果的产生。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(SAR),通过体外和体内测试的适当组合进行危害分级和建模。这一概念与美国国家科学院 2007 年的里程碑式报告《21 世纪的毒性测试:愿景与战略》(http://www.nap.edu/catalog.php?record_id=11970)一致,该报告提倡通过从定性、描述性动物测试向基于高通量的人类细胞或细胞系的定量、机制和途径毒性测试过渡,提高毒性测试的效率。因此,我们已经实施了 HTS 方法来筛选成分和组合的 ENM 库,以开发危害分级和结构-活性关系,可用于预测体内损伤结果。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,然后在动物或整个生物体中进行有限但关键的验证研究。然后,可以将暴露人群的风险降低重点放在限制或避免触发这些毒性反应的暴露上,以及实施潜在危险的 ENM 的更安全设计上。在本说明中,我们审查了建立预测毒理学范例所需的工具,以通过使用成分和组合的 ENM 库、基于机制的 HTS 测定、危害分级和纳米 SAR 的开发来评估吸入和环境毒理学情况。我们将讨论基于特定 ENM 属性出现的主要损伤范例,并描述基于 ZnO 纳米粒子溶解化学特性作为毒性主要预测因子的更安全设计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8e7/4034475/bff05ea02a33/nihms-586006-f0002.jpg

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