Mucoadhesive Nanoparticles for Localized Therapy

Abstract

Localized drug delivery using mucoadhesive nanoparticles has emerged as a promising strategy for improving therapeutic efficacy while minimizing systemic toxicity. Despite rapid progress in nanotechnology, selecting optimal nanoparticle formulations for different mucosal tissues remains largely dependent on trial-anderror experimentation. This study identifies the lack of intelligent prediction of nanoparticle–mucus interactions as a major research gap. We propose an Artificial Intelligence-assisted framework that integrates physicochemical descriptors with machine learning to estimate mucoadhesion strength and localized drug retention before laboratory validation. The approach combines material characteristics, formulation variables, and biological parameters to support rational formulation design. Recent advances in explainable AI are incorporated to improve transparency of prediction models. The proposed framework aims to reduce formulation development time while improving reproducibility across oral, nasal, ocular, pulmonary, and vaginal drug delivery systems. This research establishes a foundation for AI-guided personalized localized therapy using next-generation mucoadhesive nanomedicine