Magnetically Targeted Drug Delivery Systems

Abstract

Magnetically targeted drug delivery systems (MTDDS) have emerged as an advanced strategy for improving drug localization while minimizing systemic toxicity. Despite substantial progress in magnetic nanoparticle engineering, current targeting approaches rely predominantly on static magnetic field configurations that inadequately account for patient-specific vascular complexity and physiological variability. This study identifies a critical research gap: the absence of intelligent adaptive optimization frameworks capable of dynamically adjusting magnetic targeting based on anatomical and hemodynamic characteristics. To address this challenge, the present manuscript proposes a conceptual Artificial Intelligence (AI)-driven decision framework integrating deep learning-based vascular prediction with reinforcement learning-assisted magnetic field optimization. The framework is designed to maximize nanoparticle accumulation at pathological sites while reducing off-target deposition. Recent developments in computational medicine, digital twins, and explainable AI are incorporated into the proposed architecture to improve clinical interpretability. The review demonstrates that combining intelligent prediction models with magnetically responsive nanocarriers has the potential to enhance precision medicine, improve therapeutic efficiency, and accelerate personalized drug delivery in oncology and inflammatory diseases