Development of computational models for the prediction of the toxicity of nanomaterials

Richarz, Andrea-Nicole; Madden, Judith C.; Robinson, Richard L. Marchese; Lubinski, Lukasz; Mokshina, Elena; Urbaszek, Piotr; Кузьмин, В. Е.; Puzyn, Tomasz; Cronin, Mark T.D.

doi:10.1016/j.pisc.2014.11.015

Cited by 25 publications

(18 citation statements)

References 6 publications

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“…Such ITS would combine testing and non-testing methods to generate data for RA. In this context the development and application of in silico tools has become prominent and a number of such tools have been proposed (Burello and Worth, 2011a, Burello and Worth, 2011b, Puzyn et al, 2011, Puzyn et al, 2009a, KAR et al, 2014, Richarz et al, 2015, Toropova and Toropov, 2015. Statistical analysis and machine learning methods (e.g.…”

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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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: 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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“…A number of nano-QSAR models have been developed for cytotoxicity [13,85,86,87,88,89,90,91] and some models have also been developed for nanomaterial genotoxicity [9,92,93].…”

Section: Toxicity Data Captured By the Templatesmentioning

confidence: 99%

“…Experimental data are critical to advancing understanding of the properties of nanomaterials and the ability to design nanomaterials with desirable technological properties and acceptable safety profiles [2,9,10,11]. In order to enable "safety by design", data from toxicity studies need to be related to relevant structural/physicochemical data [10], where the latter may include information about chemical composition as well as a range of other measured properties such as size distribution statistics and zeta potential, to name but two [12].…”

Section: Introductionmentioning

confidence: 99%

“…In order to enable "safety by design", data from toxicity studies need to be related to relevant structural/physicochemical data [10], where the latter may include information about chemical composition as well as a range of other measured properties such as size distribution statistics and zeta potential, to name but two [12]. Being able to relate these data allows for the development of predictive models based on quantitative structure-activity relationships (QSARs) for nanomaterials -so-called quantitative nanostructure-activity relationships ("QNARs") [10] or "nano-QSARs" [ 13] -as well as "category formation" and "readacross" predictions [9,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…In order to make most effective use of these data, experimental datasets should be made available via a standardised, electronic format that facilitates meaningful exchange of information between different researchers, submission to (web-based) searchable databases, integration with other electronic data resources and analysis via appropriate (modelling) software [9,16,17,18]. This could entail directly populating files based on a standardised format or direct entry of data into searchable databases using a (web-based) data entry tool [19], followed via data export/exchange in a standardised format.…”

Section: Introductionmentioning

confidence: 99%

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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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