NanoInformatics Approaches for Safe-by-Design NanoMaterials

The unique properties of nanomaterials (NMs), relative to their bulk form, has seen them used in a rapidly increasing number of commercial applications. However, with these useful new properties of NMs come potential health and environmental hazards.

Thus, as part of a responsible innovation approach, NMs potential risks must be assessed in parallel to exploitation of their benefits.

Due to their enormous variability, NM risk assessment urgently needs advanced in silico methodologies capable of machine learning from limited experimental datasets.

These in silico tools for NMs characterisation, exposure, hazard and risk assessment and sustainability and life cycle assessment, need to support implementation of existing regulatory guidelines and extend regulatory risk assessment to integrate the extensive new knowledge generated computationally.

CompSafeNano’s overarching objective is thus to drive the development of integrated and universally applicable nanoinformatics models, with broad domains of applicability across NMs compositions and forms, that are directly usable by industry, especially SMEs, and regulators for NMs risk assessment and decision making.

CompSafeNano will establish an extended safe-by-design paradigm that includes environmental sustainability (life cycle assessment) based on in silico predictions with experimental testing to validate the results.

CompSafeNano has a clear set of objectives to deliver this vision of an in silico safe-by-design computational platform and will be in close communication with other EU projects to access existing data on NM hazard and integrate existing nanoinformatics and NMs risk governance platforms (i.e. within NanoCommons, NanoSolveIT & RiskGONE).

Training activities will benefit both ESRs and ERs from participating organizations, with a strong focus on inter-sectoral exchange (SME-academia) and international collaboration, filling the well-recognised current skills gap in nanoinformatics and big data analytics.