Hydrogel-Based Controlled Release Drug Platforms

Abstract

Hydrogel-based drug delivery systems have transformed controlled drug release by providing biocompatible matrices capable of sustained therapeutic delivery. Despite substantial advances in hydrogel engineering, current platforms rarely incorporate adaptive artificial intelligence (AI) models capable of predicting release behavior under dynamic physiological conditions before experimental validation. This study addresses this gap by proposing an AI-assisted predictive framework that integrates physicochemical descriptors of hydrogel formulations with machine learning to estimate controlled drug release performance. The research introduces a data-driven strategy for optimizing hydrogel composition while minimizing laboratory iterations. Recent developments in polymer science, explainable AI, and digital pharmaceutical manufacturing provide the foundation for the proposed framework. The study emphasizes prediction accuracy, formulation optimization, and computational efficiency for nextgeneration hydrogel design. The proposed approach aims to support precision drug delivery while reducing experimental costs and accelerating formulation development. The findings contribute to the growing intersection of biomaterials, artificial intelligence, and digital health