National Center for Computational Toxicology (NCCT), Office of Research and Development, US Environmental Protection Agency (US EPA), Research Triangle Park, NC, USA.
Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2013 Sep-Oct;5(5):430-48. doi: 10.1002/wnan.1229. Epub 2013 May 9.
Thousands of nanomaterials (NMs) are in commerce and few have toxicity data. To prioritize NMs for toxicity testing, high-throughput screening (HTS) of biological activity may be the only practical and timely approach to provide the necessary information. As in all nanotoxicologic studies, characterization of physicochemical properties of NMs and their immediate environments in HTS is critical to understanding how these properties affect NM bioactivity and to allow extrapolation to NMs not screened. The purpose of the study, the expert-groups-recommended minimal characterization, and NM physicochemical properties likely to affect measured bioactivity all help determine the scope of characterization. A major obstacle in reaping the full benefits of HTS for NMs is the low throughput of NM physicochemical characterization, which may require more sample quantity than HTS assays. Increasing the throughput and speed, and decreasing the amount of NMs needed for characterization are crucial. Finding characterization techniques and biological activity assays compatible with diverse classes of NMs is a challenge and multiple approaches for the same endpoints may be necessary. Use of computational tools and nanoinformatics for organizing and analyzing data are important to fully utilize the power of HTS. Other desired advances include the ability to more fully characterize: pristine NM without prior knowledge of NM physicochemical properties; non-pristine NMs (e.g., after use); NM in not-perfectly-dispersed suspension; and NM in biological samples at exposure-relevant conditions. Through combining HTS and physicochemical characterization results, we will better understand NM bioactivities, prioritize NMs for further testing, and build computational models to predict NM toxicity.
数以千计的纳米材料(NMs)已经在商业中使用,但只有少数具有毒性数据。为了优先对毒性进行测试,对生物活性进行高通量筛选(HTS)可能是提供必要信息的唯一实用且及时的方法。在所有纳米毒理学研究中,对 NMs 的物理化学特性及其在 HTS 中的即时环境进行特性描述对于理解这些特性如何影响 NM 生物活性以及允许对未筛选的 NMs 进行推断至关重要。研究的目的、专家组推荐的最小特性描述以及可能影响测量生物活性的 NM 物理化学特性有助于确定特性描述的范围。在充分利用 HTS 对 NMs 的优势方面,一个主要障碍是 NM 物理化学特性的低吞吐量,这可能需要比 HTS 分析更多的样品量。提高吞吐量和速度,减少特性描述所需的 NMs 数量至关重要。找到与各种 NM 类兼容的特性描述技术和生物活性分析方法是一个挑战,并且可能需要针对相同终点的多种方法。使用计算工具和纳米信息学来组织和分析数据对于充分利用 HTS 的功能非常重要。其他期望的进展包括能够更全面地描述:没有 NM 物理化学特性先验知识的原始 NM;非原始 NM(例如,使用后);在不完全分散的悬浮液中的 NM;以及在暴露相关条件下的生物样品中的 NM。通过将 HTS 和物理化学特性描述结果相结合,我们将更好地了解 NM 的生物活性,优先对 NMs 进行进一步测试,并建立计算模型来预测 NM 毒性。
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