In the current paper, a new strategy for risk assessment of nanomaterials is described, which builds upon previous project outcomes and is developed within the FP7 NANoREG project. NANoREG has the aim to develop, for the long term, new testing strategies adapted to a high number of nanomaterials where many factors can affect their environmental and health impact. In the proposed risk assessment strategy, approaches for (Quantitative) Structure Activity Relationships ((Q)SARs), grouping and read-across are integrated and expanded to guide the user how to prioritise those nanomaterial applications that may lead to high risks for human health. Furthermore, those aspects of exposure, kinetics and hazard assessment that are most likely to be influenced by the nanospecific properties of the material under assessment are identified. These aspects are summarised in six elements, which play a key role in the strategy: exposure potential, dissolution, nanomaterial transformation, accumulation, genotoxicity and immunotoxicity. With the current approach it is possible to identify those situations where the use of nanospecific grouping, read-across and (Q)SAR tools is likely to become feasible in the future, and to point towards the generation of the type of data that is needed for scientific justification, which may lead to regulatory acceptance of nanospecific applications of these tools.

The GUIDEnano quality tool establishes objective criteria to score the quality of eco/toxicity studies performed with nanomaterials.

Within the EU FP-7 GUIDEnano project, a methodology was developed to systematically quantify the similarity between a nanomaterial (NM) that has been tested in toxicity studies and the NM for which risk needs to be evaluated, for the purpose of extrapolating toxicity data between the two materials. The methodology is a first attempt to use current knowledge on NM property-hazard relationships to develop a series of pragmatic and systematic rules for assessing NM similarity. Moreover, the methodology takes into account the practical feasibility, in that it is based on generally available NM characterization information. In addition to presenting this methodology, the lessons learnt and the challenges faced during its development are reported here. We conclude that there is a large gap between the information that is ideally needed and its application to real cases. The current database on property-hazard relationships is still very limited, which hinders the agreement on the key NM properties constituting the basis of the similarity assessment and the development of associated science-based and unequivocal rules. Currently, one of the most challenging NM properties to systematically assess in terms of similarity between two NMs is surface coating and functionalization, which lacks standardized parameters for description and characterization methodology. Standardization of characterization methods that lead to quantitative, unambiguous, and measurable parameters describing NM properties are necessary in order to build a sufficiently robust property-hazard database that allows for evidence-based refinement of our methodology, or any other attempt to systematically assess the similarity of NMs.

Highlights The transparent and systematic reporting of computational models facilitates their regulatory acceptance and use. A reporting format for physiologically based kinetic, toxicodynamic and environmental fate models was developed. The QSAR Model Reporting Format (QMRF) was adapted to describe QSARs for nanomaterials. The model documentation is stored in the publicly accessible JRC Data Catalogue. The model documentation was used to give an overview of the model landscape for nanomaterials.