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

Abstract

There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and 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 status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

Decision tree models to classify nanomaterials according to theDF4nanoGroupingscheme

“…CT have proven especially useful with 'noisy' data, e.g. nanoparticle toxicity (Thomas et al 2014;Oksel et al 2015;Oksel et al 2016), where they can be used for pre-screening and reducing redundant variables. Besides key descriptors a classification tree also gives a set of property value thresholds, which also may be useful for designing nanomaterials.…”

Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches

“…The number and quality of in silico studies in the nanotechnology field is increasing, as is the interest in the further development and optimization of traditional computational tools in this new field of application [1][2][3][4][5][6]. Machine learning (ML) approaches, such as those based on the use of support vectors, neural networks and forest-like classifiers, have found large application in the field of computational chemistry [7][8][9][10].…”

“…Computational nanotoxicology seems to stand apart from the more traditional fields of application of ML, generally due to more limited sample populations, restricted availability of suitable theoretical descriptors and a wider diversity of physico-structural characteristics. However, several successful models have recently been published [1][2][3][4][5][6][13][14][15][16][17][18].…”

A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles

“…Recently, review papers have focused on several challenges that face the development of nano-QSARs and other predictive models, including the lack of high-quality experimental data, lack of knowledge regarding interactions between nanoparticles like aggregation, high polydispersity in nanoparticles, etc. (72, 73). These are definitely significant challenges that the field of nanoinformatics faces and should definitely be focuses for future research.…”