CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials

Area of Science:

• Nanomaterial Safety and Toxicology
• Computational Nanoscience
• Nanoinformatics and Data Science

Background:

• Nanomaterials (NMs) offer innovation but pose safety challenges.
• Existing frameworks like NanoSolveIT and NanoCommons provide a foundation.
• Regulatory acceptance of New Approach Methodologies (NAMs) for nanosafety is limited.

Purpose of the Study:

• To integrate nanoinformatics, computational modeling, and predictive toxicology for safer NM design.
• To apply Safe-by-Design (SbD) principles for inherently safer nanomaterials.
• To enhance regulatory compliance and international collaboration in nanosafety.

Main Methods:

• Utilized advanced in vitro models and in silico approaches.
• Employed machine learning (ML), artificial intelligence (AI), and 1st-principles computational modeling.
• Generated atomistic/quantum-mechanical descriptors and evaluated NM-biological system interactions.
• Standardized data reporting and management adhering to FAIR principles.

Main Results:

• Developed predictive models for NM risk assessment.
• Successfully generated NM descriptors and evaluated biological interactions.
• Created tools and models for NM safety evaluation and redesign.
• Predicted biomolecule coronas to determine NM biological/environmental identity.

Conclusions:

• CompSafeNano successfully developed integrated tools and models for computational nanosafety.
• The project enables the redesign of NMs for inherent safety and sustainable use.
• Further refinement of models, database expansion, and stakeholder engagement are planned to promote SbD adoption.