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AI/ML Modelling and Standardization of Stability Assessment for Silver Nanoparticle Wound Dressings Aligned with
Apoorva Agarwal1, Avijit Mazumder1, Salahuddin1
1Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India.
Background:
Antimicrobial wound dressings are widely used for infection control in acute and chronic wounds; however, unlike pharmaceuticals, there is no harmonized framework defining stability testing requirements (e.g., real-time and accelerated aging conditions or performance parameters). This gap weakens quality assurance amid increasing antimicrobial resistance (AMR).
Objective:
To develop a standardized framework for evaluating the physicochemical stability and sustained antimicrobial performance of antimicrobial wound dressings over their labelled shelf life. This was derived from established pharmaceutical principles. Long-term and accelerated stability studies, functional testing such as absorbency, WVTR, OTR, sterility, and biofilm inhibition, silver release, and antimicrobial activity via MIC and MBC were evaluated. An AI/ML model was used as a tool for trend analysis.
Method:
NSD01, NSD02, and NSD03 were three batches that were tested for long-term [at 25°C (±2°C) and 60% (±5%) RH] for two years and accelerated [40°C (±2°C) and 75% (±5%) RH] for six months. The parameters that were assessed included color, size, GSM, absorbency, sterility, WVTR, OTR, UV Visible spectroscopy, silver release (Franz diffusion, ICP MS), MIC/MBC assays (CLSI M07, A11), and Random Forest AI/ML modelling.
Results:
Physical, chemical, and microbiological integrity was found as per specifications in both studies. Sustained antimicrobial activity was observed via MIC/MBC assays. Biofilm inhibition was found to be significant and long-term. AI/ML models provided insights about performance characteristics and trends.
Discussion:
The findings suggest that a device-specific framework incorporating parameters of pharmaceutical stability testing and additional tests, such as sustained antimicrobial activity, can be used to assess commercially available dressings. The suggested AI/ML Model was used for explorative trend analysis.
Conclusion:
The standardized framework suggested here can be used as a tool for quality assurance. It can be used for data-based antimicrobial dressing assessment after the product has been marketed.
