Microneedle Systems for Vaccine Administration

Abstract

Vaccination remains one of the most effective public health interventions, yet conventional needle-based delivery continues to encounter challenges related to coldchain dependence, healthcare workforce availability, patient anxiety, and inconsistent immune responses across diverse populations. Microneedle-based vaccine delivery has emerged as a minimally invasive alternative capable of improving accessibility and patient compliance. Despite substantial progress in microneedle fabrication, relatively little attention has been devoted to intelligent optimization of vaccine delivery using artificial intelligence (AI). This manuscript addresses this research gap by proposing an adaptive AI-assisted framework that predicts vaccine release efficiency and immunogenic performance from microneedle structural characteristics and formulation variables. The study synthesizes recent developments in dissolving polymeric manufacturing, and microneedles, machine digital learning–guided optimization. Existing literature is critically examined to identify limitations in current design methodologies and opportunities for predictive modeling. The proposed research establishes a foundation for integrating computational intelligence with transdermal vaccine technologies to support personalized immunization strategies and improve vaccine deployment in resourcelimited settings