An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project.

Area of Science:

• Nanotechnology
• Computational Toxicology
• Risk Assessment

Background:

• Safe-by-design strategies are crucial for nanotechnology development.
• Traditional nanomaterial safety testing is often infeasible due to cost and time.
• Computational modeling provides an efficient alternative for safety evaluation.

Purpose of the Study:

• To describe the components of a computational framework for evaluating nanoparticle environmental and health impacts.
• To outline methods for data generation, nanodescriptor development, and nanostructure-activity relationship establishment.
• To present approaches for nanoparticle categorization, hazard ranking, and risk assessment.

Main Methods:

• Data generation, management, and integration for nanotoxicology models.
• Development of nanodescriptors to characterize nanomaterials.
• Establishment of nanostructure-activity relationships (NARs).
• Identification of nanoparticle categories for targeted assessment.
• Hazard ranking and risk assessment methodologies.

Main Results:

• The FP7 Project MODERN successfully developed and evaluated key components of a computational framework.
• The framework integrates data, descriptors, and NARs for predictive nanotoxicology.
• It enables hazard ranking and risk assessment of nanoparticles.

Conclusions:

• The developed computational framework supports the implementation of safe-by-design strategies.
• It offers a time and cost-effective alternative to traditional safety testing for nanomaterials.
• This approach is essential for the responsible development of future nanotechnology-enabled products.