(Q)SAR modelling of nanomaterial toxicity: A critical review--颗粒学报PARTICUOLOGY

Oksel, C., Ma, C. Y., Liu, J. J., Wilkins, T., & Wang, X. Z. (2015). (Q)SAR modelling of nanomaterial toxicity: A critical review. Particuology, 21, 1-19. https://doi.org/10.1016/j.partic.2014.12.001

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

Ceyda Oksel ^a, Cai Y. Ma ^a, Jing J. Liu ^{a b}, Terry Wilkins ^a, Xue Z. Wang ^{a b *}

a Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK
b School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510641, China

10.1016/j.partic.2014.12.001

Volume 21, August 2015, Pages 1-19

Received 17 August 2014, Revised 24 November 2014, Accepted 15 December 2014, Available online 5 March 2015, Version of Record 6 June 2015.

E-mail: x.z.wang@leeds.ac.uk; xuezhongwang@scut.edu.cn

Highlights

• State of the art review was made for nano-(Q)SAR in silico prediction to toxicity of NMs.

• A critique of measurement methods for physicochemical properties of NMs was provided.

• Computational methods for estimation of physicochemical properties were reviewed.

• Available data in literature was summarized and commented for its usefulness in nano-(Q)SAR.

• Modelling tools that can give source of toxicity and make use of limited data are preferable.

Abstract

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 alternative 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.

Graphical abstract

Keywords

Nanomaterial toxicity; Nanotoxicology; QSAR; NanoSAR; In silico toxicity prediction

文章导读