(Q)SAR modelling of nanomaterial toxicity:A critical review

作     者：Ceyda Oksel Cai Y.Ma Jing J.Liu Terry Wilkins Xue Z.Wang 

作者机构：Institute of Particle Science and EngineeringSchool of Chemical and Process EngineeringUniversity of Leeds School of Chemistry and Chemical EngineeringSouth China University of Technology 

出 版 物：《Particuology》 (颗粒学报（英文版）)

年 卷 期：2015年第13卷第4期

页      面：1-19页

核心收录：

学科分类：07[理学] 070205[理学-凝聚态物理] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：financial support from EU FP7(Project:236215,-Managing Risks of Nanomaterials(MARINA)) the UK Department for Environment,Food & Rural Affairs(Project:17857,Development and Evaluation of QSAR Tools for Hazard Assessment and Risk Management of Manufactured Nanoparticles) in support of the EU FP7 project entitled NANoREG:A common European approach to the regulatory testing of nanomaterials(FP7-NMP-2012-LARGE) 

主　　题：Nanomaterial toxicity Nanotoxicology QSAR NanoSAR In silico toxicity prediction 

摘      要：There is increasing recognition that some nanomaterials may pose a risk to human health and the environment. Moreover, the industrial use of the novel engineered nanomaterials （ENMs） increases at a higher rate than data generation for hazard assessment; consequently, many of them remain untested. The large number of nanomaterials and their variants （e.g., different sizes and coatings） requiring testing and the ethical pressure towards nonanimal testing means that in a first instance, expensive animal bioassays are precluded, and the use of（quantitative） structure-activity relationships （（Q）SARs） models as an alter- native source of （screening） hazard information should be explored. （Q）SAR modelling can be applied to contribute towards filling important knowledge gaps by making best use of existing data, prioritizing the physicochemical parameters driving toxicity, and providing practical solutions for the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of （Q）SAR methods to ENM toxicity prediction, summarizes the published nano-（Q）SAR studies, and outlines the challenges ahead for nano-（Q）SAR modelling. It provides a critical review of （1） the present availability of ENM characterization/toxicity data, （2） the characterization of nanostructures that meet the requirements for （Q）SAR analysis, （3） published nano-（Q）SAR studies and their limitations, （4） in silico tools for （Q）SAR screening of nanotoxicity, and （5） prospective directions for the development of nano-（Q）SAR models.