A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform

Area of Science:

• Nanomaterials Science
• Computational Toxicology
• cheminformatics

Background:

• Multi-walled carbon nanotubes (MWCNTs) are widely used, necessitating rapid assessment of their biological and toxicological impacts.
• Traditional experimental methods for evaluating nanomaterial safety are time-consuming and costly.
• Computational approaches, like those in cheminformatics, offer a promising alternative for predicting chemical hazards.

Purpose of the Study:

• To develop and validate a predictive nanoinformatics model for assessing the biological and toxicological profiles of decorated MWCNTs.
• To provide a user-friendly, web-based tool for researchers and developers.
• To support a safe-by-design framework for novel carbon nanotube development.

Main Methods:

• Development of a nanoinformatics workflow leveraging computational methods.
• Validation of the model according to OECD principles.
• Deployment of the validated model as an online web-service on the Enalos Cloud platform.

Main Results:

• A validated and predictive nanoinformatics model for decorated MWCNTs was successfully developed.
• The model accurately predicts the biological and toxicological profile of these nanomaterials.
• The Enalos Cloud platform now hosts a user-friendly web-service for easy access.

Conclusions:

• Computational nanoinformatics models can effectively predict the safety profiles of nanomaterials.
• The developed web-service facilitates informed decision-making in the safe design of carbon nanotubes.
• This approach reduces the need for extensive experimental testing, accelerating innovation.