A definitive conclusion about the dangers associated with human or animal exposure to a particular nanomaterial can currently be made upon complex and costly procedures including complete NM characterisation with consequent careful and well-controlled in vivo experiments. A significant progress in the ability of the robust nanotoxicity prediction can be achieved using modern approaches based on one hand on systems biology, on another hand on statistical and other computational methods of analysis. In this project, using a comprehensive self-consistent study, which includes in-vivo, in-vitro and in-silico research, we address main respiratory toxicity pathways for representative set of nanomaterials, identify the mechanistic key events of the pathways, and relate them to interactions at bionano interface via careful post-uptake nanoparticle characterisation and molecular modelling. This approach will allow us to formulate novel set of toxicological mechanism-aware end-points that can be assessed in by means of economic and straightforward tests. Using the exhaustive list of end-points and pathways for the selected nanomaterials and exposure routs, we will enable clear discrimination between different pathways and relate the toxicity pathway to the properties of the material via intelligent QSARs. If successful, this approach will allow grouping of materials based on their ability to produce the pathway-relevant key events, identification of properties of concern for new materials, and will help to reduce the need for blanket toxicity testing and animal testing in the future.

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.