Abstract

BackgroundAn 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.MethodsThe 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.ResultsA 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.ConclusionThe 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.

Highlights

  • An increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation

  • We present a case study of grouping and read-across of TiO2 nanoforms where we apply a simplified version of the grouping framework proposed by European Chemicals Agency (ECHA) to predict the in vitro comet assay results of the target substances

  • Step 1: Identification of theforms of the substance According to ECHA's guidance [4], and following the workflow presented in Fig. 1, analogues were identified through the following physicochemical parameters (“what they are”): chemical composition, crystalline structure, impurities, surface chemistry, particle size, shape, surface area, and porosity

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Summary

Introduction

An increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation. 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. Chemicals safety assessment is addressed in Europe by the Regulation (EC) No 1907/2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) [1] which requires companies to assess the risks posed by marketed chemicals This implies the generation of toxicological data as it is required in risk assessment to address any identified hazard. This guidance proposes a revised version of a strategy presented earlier [5] and considers properties beyond chemical composition (e.g. aspect ratio, particle size, shape, or solubility), and reaffirms the similarity rules from REACH Annex XI for NMs

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