Deliverable 3.5 Predictive meta- models of MCNMs and HARNs

The work within WP3 is aimed at developing and implementing new specific multiscale modelling methodologies and Structure-Activity Prediction Networks (SAPNet) for assessing and characterizing nano-specific properties and toxicity and AOPs of MCNMs and HARNs in real life-environment; developing predictive models of MCNMs and HARNs exposure and hazard to establish structure–property–function (SPF) and structure–property–hazard (SPH) relationship, including different environmental conditions, specific behavior and its interactions with biological and environmental systems, rates of degradation and biological activity of the separate and interacted individual components in MCNMs; apply the developed models for filling the gaps in the understanding of exposure, toxicity, and hazard of NMs and HARNs; integrate developed in silico methodologies for SbD approach of complex MCNMs and HARNs nanostructures.
Thus, to fulfill the above described aim, as a part of Deliverable 3.5 Predictive meta-models of MCNMs and HARNs, the QSAR Lab Team proposed a multi-scale modelling framework, meta-models, and Quantitative-Structure Activity Relationship (QSAR) models combined with AOP crucial for MCNMs. In addition, QSAR Lab, in collaboration with other NMBP-16 projects (SUNSHINE and HARMLES), proposed for the first time to adapt and transfer concepts of similarity, such as grouping, categorization, and read-across, enable for a fast comparative screening of hazards of more complex MCNs based on core-shell QDs. Through these actions, a pivotal role has been played in realizing DIAGONAL's vision of integrating specific MCNM characteristics into risk assessment and SbD strategies. Finally, we summarized the outcomes from ongoing research for enhancing the human and environmental safety of complex NMs (nanoEHS). We defined the challenges of a reliable representation of MCNMs in the context of predictive nanoinformatics. The chainages must be fulfilled before computational models and tools will be fully adjusted for the development of safe and sustainable by design of MCNMs.