Winkler David A
CSIRO Manufacturing Flagship, Bag 10 Clayton South MDC 3169 Australia; Monash Institute of Pharmaceutical Sciences, 392 Royal Parade, Parkville 3052, Australia; Latrobe Institute for Molecular Science, Bundoora 3083, Australia; School of Chemical and Physical Sciences, Flinders University, Bedford Park 5042, Australia.
Toxicol Appl Pharmacol. 2016 May 15;299:96-100. doi: 10.1016/j.taap.2015.12.016. Epub 2015 Dec 23.
Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based, have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods.
纳米材料研究是当代发展最快的研究领域之一。这些材料前所未有的特性意味着它们正迅速被纳入产品中。监管机构担心他们无法充分评估这些材料的潜在危害,因为关于纳米材料生物学特性的数据仍然相对有限且获取成本高昂。计算建模方法在帮助理解毒性可能发生的机制以及预测尚未经过实验测试的材料产生不良生物影响的可能性方面有很大作用。本文综述了这些方法,特别是基于定量构效关系(QSAR)的方法,在理解和预测纳米材料潜在不良生物效应方面取得的进展,以及这些方法的局限性和缺陷。