Development of structure–activity relationship for metal oxide nanoparticles

Liu, Rong; Zhang, Hai Yuan; Zhao, Jimin; Ralló, Robert; Xia, Tian; Chang, Chong Hyun; Nel, André E.; Cohen, Yoram

doi:10.1039/c3nr01533e

Cited by 122 publications

(156 citation statements)

References 48 publications

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“…Quantitative Nano-structure Activity Relationships (QNAR) (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, Puzyn et al, 2009b, Toropov et al, 2007, Toropov and Leszczynski, 2006, Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013 In silico tools for hazard assessment (Liu et al, 2014, Liu et al, 2013a, Liu et al, 2013b In silico tools for hazard assessment Some of these tools are capable of assessing uncertainties. The Precautionary Matrix for Synthetic Nanomaterials uses a "specific framework conditions" criterion that represents uncertainties resulting from knowledge gaps with respect to the origin of the MNs, their characteristics and uses.…”

Section: Control Banding and Risk Screening Toolsmentioning

confidence: 99%

“…principal component analysis, neural networks) have also been applied to model the properties and effects of MNs (Puzyn et al, 2009b, Puzyn et al, 2009a, Toropov et al, 2007, Toropov and Leszczynski, 2006, Sayes et al, 2013, Lynch et al, 2014. Such methods were used for example in the EU-funded MODERN project with the aim to establish in silico modelling of the effects of metal and metal oxide MNs (Liu et al, 2013b, Liu et al, 2014, Liu et al, 2013a, Gómez et al, 2013.…”

Section: Hazard Assessment Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

106

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Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screeninglevel assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.

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Section: Control Banding and Risk Screening Toolsmentioning

confidence: 99%

Section: Hazard Assessment Toolsmentioning

confidence: 99%

Frameworks and tools for risk assessment of manufactured nanomaterials

Hristozov

Gottardo

Semenzin

et al. 2016

Environment International

106

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“…In another study, (R. Liu, Zhang, et al, 2013) attempted to relate the physicochemical properties of MO-NPs to their toxicity by developing a structure-activity relationship. A number of classification nano-(Q)SAR models were developed on a large toxicity dataset of 24 MO-NPs.…”

Section: Nano-(q)sar Researchmentioning

confidence: 99%

“…Despite all the challenges and open questions, there are some pioneering studies investigating the use of (Q)SAR models to predict the toxicity of ENMs Fourches, et al, 2010; R. R. Liu, Zhang, et al, 2013;T.…”

Section: Introductionmentioning

confidence: 99%

(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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“…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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Development of structure–activity relationship for metal oxide nanoparticles

Cited by 122 publications

References 48 publications

Frameworks and tools for risk assessment of manufactured nanomaterials

Frameworks and tools for risk assessment of manufactured nanomaterials

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

Current situation on the availability of nanostructure–biological activity data

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