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Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques

Overview of attention for article published in Particle and Fibre Toxicology, September 2018
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Title
Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques
Published in
Particle and Fibre Toxicology, September 2018
DOI 10.1186/s12989-018-0273-1
Pubmed ID
Authors

L. Lamon, D. Asturiol, A. Richarz, E. Joossens, R. Graepel, K. Aschberger, A. Worth

Abstract

An increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation. The hazard characterisation of all these forms is not only technically challenging but resource and time demanding. The use of non-testing strategies like read-across is deemed essential to assure the assessment of all NMs in due time and at lower cost. The fact that read-across is based on the structural similarity of substances represents an additional difficulty for NMs as in general their structure is not unequivocally defined. In such a scenario, the identification of physicochemical properties affecting the hazard potential of NMs is crucial to define a grouping hypothesis and predict the toxicological hazards of similar NMs. In order to promote the read-across of NMs, ECHA has recently published "Recommendations for nanomaterials applicable to the guidance on QSARs and Grouping", but no practical examples were provided in the document. Due to the lack of publicly available data and the inherent difficulties of reading-across NMs, only a few examples of read-across of NMs can be found in the literature. This manuscript presents the first case study of the practical process of grouping and read-across of NMs following the workflow proposed by ECHA. The workflow proposed by ECHA was used and slightly modified to present the read-across case study. The Read-Across Assessment Framework (RAAF) was used to evaluate the uncertainties of a read-across within NMs. Chemoinformatic techniques were used to support the grouping hypothesis and identify key physicochemical properties. A dataset of 6 nanoforms of TiO2 with more than 100 physicochemical properties each was collected. In vitro comet assay result was selected as the endpoint to read-across due to data availability. A correlation between the presence of coating or large amounts of impurities and negative comet assay results was observed. The workflow proposed by ECHA to read-across NMs was applied successfully. Chemoinformatic techniques were shown to provide key evidence for the assessment of the grouping hypothesis and the definition of similar NMs. The RAAF was found to be applicable to NMs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 24%
Student > Master 10 17%
Student > Ph. D. Student 9 15%
Other 6 10%
Student > Doctoral Student 4 7%
Other 7 12%
Unknown 9 15%
Readers by discipline Count As %
Chemistry 10 17%
Environmental Science 7 12%
Biochemistry, Genetics and Molecular Biology 4 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Computer Science 3 5%
Other 13 22%
Unknown 18 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2018.
All research outputs
#20,533,782
of 23,103,903 outputs
Outputs from Particle and Fibre Toxicology
#466
of 564 outputs
Outputs of similar age
#296,205
of 340,828 outputs
Outputs of similar age from Particle and Fibre Toxicology
#6
of 9 outputs
Altmetric has tracked 23,103,903 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 564 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,828 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.