Stimuli-Responsive Polymeric Drug Carriers

Abstract

Stimuli-responsive polymeric drug carriers have emerged as a promising platform for precision medicine because they enable controlled drug release in response to physiological or external triggers. Despite rapid advances in polymer engineering, selecting optimal carrier compositions for multiple biological environments remains a significant challenge due to the complex interactions among polymer chemistry, drug characteristics, and pathological conditions. This study identifies a critical research gap in the absence of intelligent computational systems capable of simultaneously predicting multi-stimuli responsiveness before laboratory formulation. To address this limitation, an Artificial Intelligence-driven predictive framework is proposed for optimizing polymer selection under pH, temperature, enzymatic, oxidative, and redox conditions. The framework integrates multimodal pharmaceutical datasets with machine learning-based feature extraction to estimate release efficiency, stability, and targeting performance. The proposed research aims to reduce experimental iterations while improving formulation accuracy. This work establishes a digital decision-support paradigm that bridges pharmaceutical formulation science and explainable artificial intelligence. The proposed framework has potential applications in personalized medicine, smart nanotherapeutics, and digital pharmaceutical manufacturing