69 research outputs found

    Towards an alternative testing strategy for nanomaterials used in nanomedicine: lessons from NanoTEST.

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    In spite of recent advances in describing the health outcomes of exposure to nanoparticles (NPs), it still remains unclear how exactly NPs interact with their cellular targets. Size, surface, mass, geometry, and composition may all play a beneficial role as well as causing toxicity. Concerns of scientists, politicians and the public about potential health hazards associated with NPs need to be answered. With the variety of exposure routes available, there is potential for NPs to reach every organ in the body but we know little about the impact this might have. The main objective of the FP7 NanoTEST project ( www.nanotest-fp7.eu ) was a better understanding of mechanisms of interactions of NPs employed in nanomedicine with cells, tissues and organs and to address critical issues relating to toxicity testing especially with respect to alternatives to tests on animals. Here we describe an approach towards alternative testing strategies for hazard and risk assessment of nanomaterials, highlighting the adaptation of standard methods demanded by the special physicochemical features of nanomaterials and bioavailability studies. The work has assessed a broad range of toxicity tests, cell models and NP types and concentrations taking into account the inherent impact of NP properties and the effects of changes in experimental conditions using well-characterized NPs. The results of the studies have been used to generate recommendations for a suitable and robust testing strategy which can be applied to new medical NPs as they are developed

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

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    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

    QSAR modeling of nanomaterials

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