Implantable Controlled Drug Release Systems

Abstract

Implantable controlled drug release systems have transformed long-term therapeutic management by enabling localized and sustained drug administration while reducing dosing frequency and systemic toxicity. Despite rapid advances in biomaterials and microfabrication, current implant design remains largely dependent on iterative laboratory experimentation, making optimization time-consuming and resource intensive. This study identifies a critical research gap: the absence of integrated artificial intelligence frameworks capable of simultaneously optimizing implant material characteristics, release kinetics, and patient-specific physiological conditions. To address this limitation, a hybrid machine learning framework is proposed for predicting controlled drug release performance using multimodal implant descriptors and physiological variables. The proposed approach combines explainable ensemble learning with temporal prediction models to estimate release efficiency, implant stability, and therapeutic consistency. Recent developments in digital health and intelligent biomedical implants are incorporated to establish a clinically relevant decision support architecture. The framework aims to accelerate implant development while improving personalization and reducing experimental burden. The study contributes toward the emerging convergence of AI-assisted biomaterials engineering and precision drug delivery