caLIBRAte - establishment of the next generation nano-risk risk governance framework

摘要

Background: caLIBRAte is a new project addressing the EU Horizon NMP30 call for a next generation risk governance framework to support the European nanotechnology innovation. The general uncertainty about the potential human and environmental risks of manufactured nanomaterials (MN) and new nanoproducts may be significantly reduced if safety is taken into consideration during innovation and development. Due to lack of exposure data and incomplete documentation of MN hazards, such risk assessment need to strongly rely on precautionary or predictive model estimates. Before true benefits of such a paradigm can be reached, confidence must be established in the results generated by such prospective models. This can be achieved if predictive models are thoroughly tested, calibrated, and demonstrated in relevant use scenarios. Objective: The key objective of the caLIBRAte project is to establish a Systems-of-Systems (SoS) framework for nano-risk governance, which consists of calibrated qualitative to quantitative predictive models for assessment and management of both human and environmental risks of MN and MN-enabled products as well as scanning tools for identification of emerging risks. The SoS framework will specifically support safety in innovation by aligning suitable framework models to support the different decision steps in a "Cooper Stage-Gate?"-type of Idea-to-Launch innovation model as well as the conventional ISO 31000 risk governance framework. Methods: The caLIBRAte nano-risk governance framework will link different models for: 1) screening of apparent and perceived risks and trends in nanotechnology; 2) control banding, qualitative and fully integrated predictive quantitative risk assessment operational at different information levels; 3) safety-by-design and multi-criteria decision support methods; 4) risk surveillance, -management and -guidance. The SoS Nano-Risk governance framework: 1. shall enable systematic risk analysis with clear understanding of data gaps and uncertainties in the assessments for further risk mitigation and management. 2. models will all be refined, tested, calibrated and documented to the greatest extent possible. 3. enable high reliability and quality of risk assessment and management of MN and MN-enabled products to increase the trust and confidence in all steps of the industrial and regulatory risk governance and communication and risk transfer between stakeholders. Results: The establishment of a specific and scientifically documented risk governance system for MN and MN innovation is expected to be a major step forward. The outcome of this approach should be safer and state-of-the-art-assessed MN and MN-based products with the chance for faster implementation and better competitiveness and profit of MN as a key-enabling technology.