Statistical chain-based model for predicting drug release from degradable hydrogels

Abstract

Hydrogel-based drug delivery systems have emerged as promising platforms for controlled cargo release due to their tunable degradation and swelling properties. These systems can trap drug particles within their polymeric network, releasing them gradually as the network degrades and swells. This study presents a statistical chain-based multiphysics model that integrates degradation, swelling, and diffusion to predict drug release profiles from degradable hydrogels. The model, calibrated using a recently developed degradable hydrogel, incorporates the interplay between chain-level degradation, dynamic mesh size changes, and evolving cargo diffusivity. Experimental validation demonstrates the model's accuracy, and parametric studies highlight the impact of various design parameters such as cargo size, gel geometry, and crosslinker functionality on release profiles. This framework provides insights into hydrogel design for tailored drug release, addressing limitations in current phenomenological approaches.