The chronic wound challenge, often stemming from inadequate initial management, necessitates personalized approaches to promote healing. Microbial burden control remains paramount, and while existing antimicrobials have limitations, nanotechnology offers promising solutions. Our research group developed a novel green chemistry approach for silver nanoparticle (AgNP) production, demonstrating broad-spectrum antimicrobial activity against diverse bacteria (Gupta et al., 2020).  However, personalized medicine demands tailoring treatment based on individual patient factors. We propose leveraging artificial intelligence (AI) and machine learning for personalized chronic wound management with AgNPs.

Specifically, AI will predict the optimal antimicrobial efficacy of AgNPs against patient-specific microbes by considering immune responses through machine learning models. This will enable personalized dosage determination, ensuring effective infection control while minimizing potential side effects. Furthermore, AgNPs will be incorporated into advanced wound dressings utilizing bacterial cellulose (BC), a biocompatible and customizable material.

To ensure successful clinical translation, multidisciplinary data integration and AI-driven analysis are crucial. This includes physicochemical characterization of AgNPs, mechanistic studies of their action against various microbes including biofilms, and cytotoxicity evaluation. By harnessing the power of AI to extract valuable insights from these diverse datasets, we aim to bridge the gap between laboratory research and effective clinical applications of AgNP-based wound dressings, paving the way for personalized and efficient treatment of chronic wounds.

Reference:

Gupta A, et al., 2020. Synthesis of Silver Nanoparticles Using Curcumin-Cyclodextrins Loaded into Bacterial Cellulose-Based Hydrogels for Wound Dressing Applications. Biomacromolecules. 21: 1802-1811.