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Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case
Irini Furxhi1,2, Rossella Bengalli3, Giulia Motta3
1Transgero Ltd, Limerick V42V384, Ireland.
ACS Applied Nano Materials
|March 20, 2023
Summary
This study develops quantitative hazard criteria for nanoforms (NFs) using machine learning and Bayesian networks. The findings guide engineers in designing safer NFs early in innovation, improving chemical safety.
Area of Science:
- Nanomaterial safety assessment
- Computational toxicology
- Sustainable chemistry
Background:
- European Union policies promote safe and sustainable practices for chemicals, including nanoforms (NFs).
- A Safe and Sustainable by Design (SSbD) framework requires quantitative criteria for safety and sustainability dimensions.
- This research focuses on developing quantitative intrinsic hazard criteria for the safety dimension of SSbD.
Purpose of the Study:
- To demonstrate the development of quantitative intrinsic hazard criteria for nanoforms (NFs).
- To utilize findable, accessible, interoperable, and reusable data for creating New Approach Methodologies (NAMs).
- To guide material engineers in synthesizing inherently safer NFs at the earliest stages of innovation.
Main Methods:
- Curated and merged data for developing quantitative structure-activity relationship (QSAR) models using machine learning.
- Employed regression and classification algorithms to predict NF hazard classes.
- Utilized system-dependent and independent nanoscale features, in vitro attributes, and experimental conditions.
- Developed interpretable rules and certainty factors using a Bayesian network structure.
Main Results:
- Achieved a predictive capability of approximately 78% average accuracy across all hazard classes.
- Demonstrated the transition from the conceptual SSbD framework to practical implementation.
- Identified quantitative intrinsic hazard criteria for the synthesis stage of NFs.
Conclusions:
- The study provides quantitative criteria to enhance the safety aspects of NF synthesis.
- Highlights challenges and future directions for generating and refining criteria for SSbD paradigms.
- Enables cost-efficient in silico toxicological screening of existing and novel NFs.
