Grouping, Read-Across, CharacterIsation and classificatiOn framework and Strategies for risk analysis of engineered nanomaterials.

The H2020 GRACIOUS project started in January 2018. In 3.5 years, 23 partners will collaborate to develop the existing concepts of grouping and read-across for nanomaterials into practically feasible approaches and methods. This way, optimal use can be made of test data for the risk assessment of nanomaterials. This is very important for nanomaterials because, in addition to chemical properties, form, size and surface area also determine how a nanomaterial behaves. Stakeholders as for example ECHA, EFSA, SCCS and industry are consulted throughout the project in order to develop a framework that is applicable and aligns to the needs of the stakeholders. 


The array of nanomaterials on the market and under development is increasing. GRACIOUS will develop an innovative science-based Framework to enable practical application of grouping, leading to read-across and classification of nanomaterials and nanoforms.


GRACIOUS involves first class scientists from large companies, government, small and medium enterprises and academia. These partners have been actively involved in developing the existing grouping, read-across and hazard identification schemes for NMs published by industry (BASF, Akzo-Nobel), by policy makers (RIVM, JRC, NRCWE, BfR) and EC funded research projects (e.g. MARINA, ENPRA).

RIVM role

RIVM has a leading role in the development of the draft framework during the first half year of the project and will be involved in stakeholder consultation and adaptation of the framework during the rest of the project. RIVM is also involved in developing the scientific basis, for example as lead of WP4 on ‘Where they go: Human toxicokinetics and environmental fate’.  Dr Doctor (Doctor ) Eric Bleeker is work package leader and Agnes Oomen is task leader. She also coordinates the project at RIVM, as more RIVM staff members from the Centre for Safety of Substances and Products are involved. 


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 760840.