Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling

Lubinski, Lukasz; Urbaszek, Piotr; Gajewicz, Agnieszka; Cronin, Mark T.D.; Enoch, Steven J.; Madden, Judith C.; Leszczyńska, Danuta; Leszczyński, Jerzy; Puzyn, Tomasz

doi:10.1080/1062936x.2013.840679

Cited by 52 publications

(42 citation statements)

References 11 publications

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“…In a recent study, (Lubinski, et al, 2013) developed a framework to help modellers evaluate the quality of existing data for modelling (e.g. nano-(Q)SAR) purposes.…”

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

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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“…In a recent study, (Lubinski, et al, 2013) developed a framework to help modellers evaluate the quality of existing data for modelling (e.g. nano-(Q)SAR) purposes.…”

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

confidence: 99%

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

et al. 2015

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“…Predefined "Comment […]" fields were added to the Investigation file template for recording additional important metadata, e.g., "Comment [GLP]" for recording whether or not the corresponding studies were carried out according to Good Laboratory Practice [27,48].…”

Section: General Overview Of Templatesmentioning

confidence: 99%

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

Robinson¹,

Cronin²,

Richarz³

et al. 2016

Nano Online

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Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed.These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.

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“…The sufficiency of the data for modelling and the feasibility of developing nano-(Q)SAR models should be properly evaluated, with careful attention being given to (1) the reliability of the data source, (2) the quality and quantity of the dataset and (3) the suitability of the data for computational analysis. One of the unique studies addressing the quality and suitability of the existing research data for nano-(Q)SAR purposes has been conducted by Lubinski et al [2]. These authors presented a data evaluation framework, that places a strong focus on the source, quality and quantity of the data, for assessing not only the quality of the data but also its suitability for modelling purposes.…”

Section: 1input Data For Nano-(q)sar Analysismentioning

confidence: 99%

“…On the one hand, the complex nature of the nano-systems and the lack of regulatory frameworks specific to the applications that use nanotechnology make the assessment of the potential risks of ENMs to human health and the environment challenging [1]. On the other hand, there is now a large number ENMs with unknown health risks and it will soon be impossible to individually evaluate their toxicities [2]. Hence, it has been highlighted by many researchers [3][4][5] that alternative methods and approaches are needed in order to help close the research gap in nanotoxicology, before it widens any further.…”

Section: Introductionmentioning

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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Evaluation criteria for the quality of published experimental data on nanomaterials and their usefulness for QSAR modelling

Cited by 52 publications

References 11 publications

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

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

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

Current situation on the availability of nanostructure–biological activity data

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