Analytical and Characterisation Excellence in nanomaterial risk assessment: A tiered approach

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SBFI

Projektnummer

17.00012

Projekttitel

Analytical and Characterisation Excellence in nanomaterial risk assessment: A tiered approach

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Abstract (Englisch)	An increasing number of nanomaterials (NMs) are entering the market in every day products spanning from health care and leisure to electronics, cosmetics and foodstuff. Nanotechnology is a truly enabling technology, with unlimited potential for innovation. However, the novelty in properties and forms of NMs makes the development of a well-founded and robust legislative framework to ensure safe development of nano-enabled products particularly challenging. At the heart of the challenge lies the difficulty in the reliable and reproducible characterisation of NMs given their extreme diversity and dynamic nature, particularly in complex environments, such as within different biological, environmental and technological compartments. Two key steps can resolve this: 1) the development of a holistic framework for reproducible NM characterisation, spanning from initial needs assessment through method selection to data interpretation and storage; and 2) the embedding of this framework in an operational, linked-up ontological regime to allow identification of causal relationships between NMs properties, be they intrinsic, extrinsic or calculated, and biological, (eco)toxicological and health impacts fully embedded in a mechanistic risk assessment framework. ACEnano was conceived in response to the NMBP 26 call with the aim to comprehensively address these two steps. More specifically ACEnano will introduce confidence, adaptability and clarity into NM risk assessment by developing a widely implementable and robust tiered approach to NM physico-chemical characterisation that will simplify and facilitate contextual (hazard or exposure) description and its transcription into a reliable NMs grouping framework. This will be achieved by the creation of a conceptual “toolbox?? that will facilitate decision-making in choice of techniques and SOPs, linked to a characterisation ontology framework for grouping and risk assessment and a supporting data management system.

Kategorie

Text

Abstract
(Englisch)

An increasing number of nanomaterials (NMs) are entering the market in every day products spanning from health care and leisure to electronics, cosmetics and foodstuff. Nanotechnology is a truly enabling technology, with unlimited potential for innovation. However, the novelty in properties and forms of NMs makes the development of a well-founded and robust legislative framework to ensure safe development of nano-enabled products particularly challenging. At the heart of the challenge lies the difficulty in the reliable and reproducible characterisation of NMs given their extreme diversity and dynamic nature, particularly in complex environments, such as within different biological, environmental and technological compartments. Two key steps can resolve this: 1) the development of a holistic framework for reproducible NM characterisation, spanning from initial needs assessment through method selection to data interpretation and storage; and 2) the embedding of this framework in an operational, linked-up ontological regime to allow identification of causal relationships between NMs properties, be they intrinsic, extrinsic or calculated, and biological, (eco)toxicological and health impacts fully embedded in a mechanistic risk assessment framework. ACEnano was conceived in response to the NMBP 26 call with the aim to comprehensively address these two steps. More specifically ACEnano will introduce confidence, adaptability and clarity into NM risk assessment by developing a widely implementable and robust tiered approach to NM physico-chemical characterisation that will simplify and facilitate contextual (hazard or exposure) description and its transcription into a reliable NMs grouping framework. This will be achieved by the creation of a conceptual “toolbox?? that will facilitate decision-making in choice of techniques and SOPs, linked to a characterisation ontology framework for grouping and risk assessment and a supporting data management system.