Evaluation of Microbial Efficacy of Silver-Containing Wound Dressings: Standardized Approach for Addressing Emerging...

Effective wound management is crucial, especially during outbreaks of new microorganisms or antibiotic-resistant strains. Developing new wound dressings can be impractical due to time and cost constraints, and redesigning existing products poses significant challenges. Therefore, evaluating the effectiveness of current wound dressings is essential. This study aims to assess the antibacterial properties of existing wound dressings without altering their design. Its objective is to present a systematic, design-agnostic approach for quantifying antimicrobial activity against a range of pathogens, including Gram-positive and Gram-negative bacteria, yeasts, and fungi, providing a framework for organizations to validate their wound dressings against emerging microbial threats. Unlike prior studies, this work integrates AI/ML-based predictive modeling validated with experimental data, offering a forward-compatible tool for evaluating dressing efficacy in a rapidly evolving microbial landscape.

A comprehensive methodology was employed to evaluate the antibacterial properties of silvercontaining wound dressings in both elemental and ionic forms. Drug release was quantified over 96 hours using a Franz diffusion cell, microwave-assisted digestion, and cloud point extraction, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) quantified silver content. Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) were determined for pathogens like Candida albicans, Methicillin-Resistant Staphylococcus aureus (MRSA), Vancomycin-Resistant Enterococci (VRE), Klebsiella pneumoniae, and Mucor racemosus, according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Additional metrics included colony count and absorption-based antimicrobial tests, barrier penetration assays, and biofilm disruption. Physiological performance was assessed through biocompatibility testing, Water Vapor Transmission Rate (WVTR) testing, visual degradation studies, and ISO-based shelf-life stability testing. A prototype AI/ML model based on Random Forest regression was trained and validated using fivefold cross-validation to predict microbial log reduction from MIC, MBC, and pathogen species, supporting future data-driven screening strategies.

Results showed sustained silver release, high biocompatibility, significant biofilm disruption, and barrier function against microbial infiltration. The AI/ML model achieved high predictive accuracy (R² = 0.8177), validating its potential as a decision-support tool.

This integrated methodology demonstrated the feasibility of evaluating antimicrobial efficacy across multiple pathogens without altering existing product design. The integration of AI/ML modeling provides predictive insights that closely align with experimental outcomes, suggesting a powerful tool for rapid screening.

This study provides a robust, scalable framework for evaluating wound dressings. It enhances preparedness for microbial threats, reduces reliance on new product development, and enables evidence-based product validation using predictive and psychologically relevant testing approaches.