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Framework and strategies for nanomaterial characterisation, classification, grouping and read-across for risk analysis - NMBP-28-2017
Deadline: May 4, 2017  

 International Cooperation
 Industrial Manufacturing
 Nanotechnology and Nanosciences
 Horizon Europe


Specific Challenge:

The number of available nanomaterials is growing rapidly and testing each material thoroughly is virtually impossible. For convincingly managing eventual risk, precise quantification of hazards and exposure would be necessary for all cases and engineering-out or reducing risk must follow in cases of non-acceptable risks. All engineered nanomaterials (ENMs) would need characterising along all value chains and all used media and physiological chemistries. It is therefore essential to set the basis for an appropriate and sustainable framework and define strategies towards ENMs classification, grouping (categorisation for further purposes) and read-across for risk analysis in a regulatory perspective.


The existing and rapidly progressing knowledge in this domain, in terms of characterisation of material properties and of possible adverse effects from their applications, is expected to allow for classification of ENMs based on morphology, composition, complexity/functionality, and by bio or eco-interactions. The classification approaches should aim to support grouping of ENMs for further risk analysis, to help in developing intelligent testing strategies and identifying "ENMs properties of concern" that need to be tested more thoroughly. Methods for grouping and for read-across within or between groups, should be defined to reduce unnecessary efforts in testing. Grouping can take into account quantification of possible adverse effects depending on the use on ENMs in specific applications. Results from these studies should be collected and combined in a consistent and progressive system enabling both the integration of newer data and the use of raw and aggregated data for regulatory purposes. Particular attention should be paid to supporting safer-by-design practices, so that novel products containing ENMs provide the benefits originally claimed by maintaining fullest possible intended functionality and at the same time pose the least possibly risks to humans, the environment and ecosystem services. The proposed projects should include appropriate data curation expertise, modelling (including development of theoretical models if appropriate) and input into the possible development of Q(n)SP/AR approaches in order to develop user friendly interfaces to enable data driven predictions from other ENMs with similar properties or behaviour, and predictive risk assessment tools.

Activities are expected to focus on Technology Readiness Levels 5 to 7

This topic is part of the open data pilot.

This topic is particularly suitable for international cooperation.

The Commission considers that proposals requesting a contribution from the EU between EUR 5 and 7 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.

No more than one action will be funded.

Expected Impact:

  • The research approach should be innovative and represent a significant advance beyond the current state-of-the-art in the whole area of nanomaterials hazard and exposure assessment;
  • Sustainable solutions to the long-term challenge of nanosafety at a level that will allow both consistent integration of newer data and regulatory application of scientifically sound concepts;
  • Cutting-edge progress towards a framework and methods for groupings and read-across useable in a regulatory environment;
  • Demonstration of consistent, applicable and scientifically sound grouping and read-across strategies in specific value chains, ready for use by industry and regulators, enabling predictive hazard and exposure modelling for risk analysis, and including the input towards safer-by-design guidance;
  • Outputs should be tailored to address the needs of each of the stakeholder communities, including the modelling community. Delivered predictive models and tools should be disseminated through publically available, ready-to-use applications.

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