Advancing risk assessment of engineered nanomaterials: Application of computational approaches

Gajewicz, Agnieszka; Rasulev, Bakhtiyor; Dinadayalane, T. C.; Urbaszek, Piotr; Puzyn, Tomasz; Leszczyńska, Danuta; Leszczyński, Jerzy

doi:10.1016/j.addr.2012.05.014

Cited by 200 publications

(134 citation statements)

References 244 publications

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“…In nano-(Q)SAR models, the importance of high-quality and well-described dataset is even more pronounced since the novel properties of ENMs are mostly associated with particular size and conditions (Gajewicz, 2012). Ideally, the input data required to build a reliable (Q)SAR model should be (1) obtained from a preferably single and standardized protocol, (2) examined in terms of accuracy and suitability for (Q)SAR analysis and (3) large enough to allow rational division of the data into training and test sets.…”

Section: Input Data For Nano-(q)sar and Its Current Availabilitymentioning

confidence: 99%

“…This data-driven approach brings many advantages in terms of cost, time-effectiveness and ethical concerns. Although it has been satisfactorily used to predict the physicochemical properties of NMs, such as solubility (Gajewicz, 2012;Sivaraman, Srinivasan, Vasudeva Rao, & Natarajan, 2001;Toropov, Leszczynska, & Leszczynski, 2007;Toropov, Toropova, Benfenati, Leszczynska, & Leszczynski, 2009) and elasticity (Mohammadpour, Awang, & Abdullah, 2011;Toropov & Leszczynski, 2006), development of reliable (Q)SAR models becomes more complicated when the actual processes and the endpoints of interest are biologically complex.…”

Section: Introductionmentioning

confidence: 99%

“…Cohen et al (2012) the use of in silico models for hazard assessment of ENMs. Gajewicz (2012) computational methods/tools to support risk assessment of ENMs Nel et al (2012) development of predictive toxicological paradigms for ENMs. summary of the current status and known gaps of nano-QSAR modelling …”

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confidence: 99%

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

et al. 2015

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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.

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Section: Input Data For Nano-(q)sar and Its Current Availabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…These methods provide valuable NMs hazard and toxicity data. Moreover, in addition to the regulation of NMs use, in vivo methods, and reducing, refining, and replacing animal experiment principle (3Rs), in vitro and in silico standards have been proposed for nanotoxicity investigations (Burello 2015; Gajewicz et al 2012;Gong et al 2012;He et al 2013;Ivask et al 2010;Kaweeteerawat et al 2015;Kleandrova et al 2014;McCracken et al 2016;Mortimer et al 2010;Qin et al 2008). For further information about the safety of manufactured nanomaterials, readers are referred to OECD regulations (http://www.oecd.org/science/nanosafety/).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of nano-specific toxicity of zinc oxide, copper oxide, and silver nanoparticles through toxic ratio

Zhang

Bao

et al. 2016

J Nanopart Res

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For safety and environmental risk assessments of nanomaterials (NMs) and to provide essential toxicity data, nano-specific toxicities, or excess toxicities, of ZnO, CuO, and Ag nanoparticles (NPs) (20, 20, and 30 nm, respectively) to Escherichia coli and Saccharomyces cerevisiae in short-term (6 h) and long-term (48 h) bioassays were quantified based on a toxic ratio. ZnO NPs exhibited no nano-specific toxicities, reflecting similar toxicities as ZnO bulk particles (BPs) (as well as zinc salt). However, CuO and Ag NPs yielded distinctly nano-specific toxicities when compared with their BPs. According to their nano-specific toxicities, the capability of these NPs in eliciting hazardous effects on humans and the environment was as follows: CuO > Ag > ZnO NPs. Moreover, long-term bioassays were more sensitive to nano-specific toxicity than short-term bioassays. Overall, nano-specific toxicity is a meaningful measurement to evaluate the environmental risk of NPs. The log T e particle value is a useful parameter for quantifying NP nano-specific toxicity and enabling comparisons of international toxicological data. Furthermore, this value could be used to determine the environmental risk of NPs.

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“…Despite these limitations, there is a growing literature on the use of (Q)SARlike models in nanotoxicology studies. There are a great number of reviews [3,5,10,[12][13][14] and research articles [15][16][17][18][19][20][21][22][23][24][25][26][27][28] devoted to the investigations of in silico modelling of ENM toxicity in peer-reviewed scientific journals.…”

Section: "[Insert Figure 1 About Here]"mentioning

confidence: 99%

Current situation on the availability of nanostructure–biological activity data

Oksel

Wang

2015

SAR and QSAR in Environmental Research

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The recent developments in nanotechnology have not only increased the number of nanoproducts on the market, but also raised concerns about the safety of engineered nanomaterials (ENMs) for human health and the environment. As the production and use of ENMs are increasing, we are approaching the point at which it is impossible to individually assess the toxicity of a vast number of ENMs. Therefore, it is desirable to use time-effective computational methods, such as the quantitative structure-activity relationship (QSAR) models, in order to predict the toxicity of ENMs. However, the accuracy of the nano-(Q)SARs is directly tied to the quality of the data from which the model is estimated.Although the amount of available nanotoxicity data is insufficient for generating robust nano-(Q)SAR models in most cases, there are a handful of studies that provide appropriate experimental data for (Q)SAR-like modelling investigations. The aim of this study is to review the available literature data that are particularly suitable for nano-(Q)SAR modelling.We hope that this paper can serve as a starting point for those who would like to know more about the current availability of experimental data on the health effects of ENMs for future modelling purposes.

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Advancing risk assessment of engineered nanomaterials: Application of computational approaches

Cited by 200 publications

References 244 publications

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

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

Evaluation of nano-specific toxicity of zinc oxide, copper oxide, and silver nanoparticles through toxic ratio

Current situation on the availability of nanostructure–biological activity data

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