Tantra Ratna, Oksel Ceyda, Puzyn Tomasz, Wang Jian, Robinson Kenneth N, Wang Xue Z, Ma Cai Y, Wilkins Terry
National Physical Laboratory , Teddington, Middlesex , UK .
Nanotoxicology. 2015;9(5):636-42. doi: 10.3109/17435390.2014.952698. Epub 2014 Sep 11.
Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.
对纳米材料进行监管迫在眉睫，采用智能测试策略的趋势也很明显。这样的策略不仅能带来经济效益，还能减少动物实验引发的道德和伦理问题。出于监管目的，REACH推动了这种方法，特别是使用定量构效关系[(Q)SAR]作为根据化合物的物理化学和毒理学性质对其进行分类的工具。除了化合物，(Q)SAR还以纳米(Q)SAR的形式应用于纳米材料。虽然化学品中的(Q)SAR已经成熟，但纳米(Q)SAR仍处于发展初期，其成功应用还远未实现。本文旨在识别与纳米(Q)SAR在纳米材料分类方面相关的一些陷阱和挑战。我们的研究结果表明，如果要从这些模型获得可靠的预测，研究框架中存在必须解决的明显差距。确定了三个主要障碍：需要提高用于开发模型的实验数据质量，需要为纳米(Q)SAR模型的开发制定实用指南，以及需要为监管目的对活动进行标准化和协调。在这三个障碍中，第一个，即提高数据质量的需求需要立即关注，因为它是与后两个障碍相关活动的基础。应该注意的是，在纳米(Q)SAR建模背景下数据的有用性不仅关乎数据的数量，还关乎这些数据的质量、一致性和可获取性。
