Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology

Abstract

The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation. Roadmaps for the future of safe nanotechnology were defined more than a decade ago, but many roadblocks identified in these studies remain. Here, we discuss the roadblocks that are still hindering the effective application of informatics and predictive computational nanotoxicology methods from providing more effective guidance to nanomaterials regulatory agencies and safe-by-design rationale for industry. We describe how developments in high throughput synthesis, characterization, and biological assessment of nanomaterials will overcome many of these roadblocks, allowing a clearly defined roadmap for computational design of effective but safe-by-design nanomaterials to be realized.

Machine-learning Assisted Insights into Cytotoxicity of Zinc Oxide Nanoparticles

“…Having a complete understanding of nanotechnology-related environmental, health, and safety (nano-EHS) issues is critical for bringing nano-enabled materials, products, and technologies to the mass market and ensuring their sustainable commercial use. Combining the results of multiple studies is increasingly applied in nano-EHS to pre-screen different NPs based on their toxicity potential and to look for early signs of harmful effects (10,27,28). By statistically integrating findings of independent nanotoxicity-screening studies, it is possible to get a more accurate representation of complex behavior of nano-systems in cellular systems.…”

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

“…However, these packages were mostly designed for organic small molecules rather than NMs. In fact, the lack of nanospecific molecular descriptors is generally considered one of the major roadblocks to the application of ML in nanotoxicology …”

“…Moreover, the labor-intensive data collection, annotation, curation, and integration create additional challenges . Although the construction of model-friendly databases will improve the availability of nanotoxicity data, the lack of NM annotation, standardization, and ontologies is a critical technical challenge. , The European Union has funded several Horizon 2020 projects to address the challenges of faster synthesis and characterization and annotation enhancements such as NInChI (International Chemical Identifier for NMs). − …”

“…They should ideally reflect the influence of the external environment (e.g., the protein coronathe layer of proteins that naturally bind to NMs in biological fluids that modulates their biological effects) . Unfortunately, most available nanodescriptors were originally developed for small molecules and are not specific to NMs. , Thus, complex NM structures and their interactions with the environment and biological systems are not sufficiently represented . Moreover, machine learning (ML, a subset of AI algorithms) deployment in nanotoxicology has to deal with feature selection, model optimization, and interpretation. ,, …”

Representing and describing nanomaterials in predictive nanoinformatics

“…One important feature of nanodescriptors, especially in investigating the toxicity of more complex structures, is interpretability. It is defined as the ability to elucidate and rationalize the underlying nanodescriptors responsible for biological behaviour 13 , which needs to be translated to ease of use for the rational design of improved ENMs and understanding of the mechanisms of the biological properties 14 . Slow progress in developing interpretable descriptors is one of the limiting factors in progress towards earlier-identified ENM milestones 14 , and more recent milestones highlighted in the EU-US nanoinformatics roadmap 2030 15 .…”