Predicting safety of engineered nanomaterials (ENM) stands on understanding the ultimate nature of what makes a material toxic; this ranges from its sources, fate, exposure, dose and response. All of these are closely interconnected. There is a vast amount of data on physico-chemical, toxicological and ecotoxicological properties of ENM generated the last decades and new data coming from research; yet information is not fully available: a comprehensive insight is still lacking. NanoInformaTIX is developing a Sustainable Nanoinformatics Framework (SNF) platform for risk management of engineered nanomaterials (ENM) in industrial manufacturing. Our mission is to integrate these existing and emerging data into efficient user-friendly interfaces to enhance accessibility and usability of the Nanoinformatics models to industry, regulators, and civil society, thus supporting sustainable manufacturing of ENM-based products.
This mission can only be accomplished from fundamental knowledge on all stages of ENM lifecycle. Understanding materials fate in the environment and in our bodies, understanding how they interact among themselves in environment and our body, is achieved by integrating knowledge onto the SNF, and by knowing the rationale behind behaviour. This is the reason to have a WP devoted to describing the materials themselves, their structures and defects, and how these shapes how they interact with their environment (eco and body). During these months, the different groups in the different WP’s have interacted closely, meeting regularly, to address the key pillars on which NanoInformaTIX SNF stands.
Currently, NanoInformaTIX has
- Integrated data from existing databases of terminated/ongoing projects, established protocols to import data from NIOSH, a FAIRification workflow has been developed to facilitate data files import. It is also incorporating new descriptors generated by computational chemistry in the project and with validation data.
- We have developed a comprehensive description of nanomaterials through computational modeling, delivering parameters not readily available through experimentation an delivering new descriptors to better understand the fate of materials in the environment and biological media.
- We have modelled the fate and biodistribution in humans and in the environment, modelling uptake and biodistribution in mammals and in aquatic species and the fate and behaviour of nanomaterials in fresh and ocean water mesocosms. The models in human and environmental systems describe size-specific material flow models to predict releases to the environment, fate model of nanomaterials in oceans and in rivers, an integrated model on exposure to airborne nanomaterials in indoor species and a physiologically based pharmacokinetic model to predict biodistribution of nanomaterials in humans.
- The models above serve to develop representation of NM considering what determines their mobility in different media and to develop dose-response models for in vitro toxicity data. We also develop an in vivo dose-response modelling and its extrapolation to in vivo.
- Omics technologies (system biology) are being used to molecularly understand the nature of adverse outcomes and their pathways.
- Different experimental tests improve and validate models and integrate in a chain of models that engage into the ultimate goal, which is the sustainable nanoinformatics platform.
- The DSS is being developed and will enable the predictive knowledge to optimise engineered nanomaterials to be safer-by-design through the understanding of what makes a material toxic.
