Thermoresponsive Injectable Drug Delivery Systems

Abstract

Thermoresponsive injectable drug delivery systems have emerged as minimally invasive platforms capable of transforming from liquid formulations to semisolid depots after administration. Despite substantial advances in polymer chemistry, predicting gelation behavior, release kinetics, and patient-specific therapeutic performance remains largely dependent on empirical experimentation. This study identifies a critical research gap in integrating thermoresponsive artificial injectable intelligence formulations for personalized optimization before laboratory validation. A novel AI-assisted predictive framework is proposed to estimate gelation temperature, injectability, depot stability, and sustained drug release using physicochemical and formulation descriptors. Unlike conventional optimization methods, the proposed framework combines machine learning with formulation engineering to reduce development time while improving prediction accuracy. Recent advances between 2021 and 2026 indicate increasing availability of formulation datasets suitable for data-driven modeling, yet standardized predictive systems remain limited. This manuscript reviews current progress in thermoresponsive injectable drug delivery while establishing the conceptual foundation for AI-enabled formulation optimization. The proposed approach supports faster pharmaceutical development and advances precision medicine through intelligent formulation design