Bioadhesive Ocular Drug Delivery Technologies

Abstract

Ocular drug delivery remains one of the most challenging areas in pharmaceutical science because rapid tear turnover, blinking, and nasolacrimal drainage substantially reduce drug residence time and ocular bioavailability. Recent bioadhesive ocular delivery systems have improved retention; however, formulation development continues to rely heavily on empirical experimentation, resulting in prolonged optimization cycles and increased development costs. A significant research gap exists in the absence of explainable artificial intelligence (XAI) models capable of simultaneously predicting bioadhesion, formulation stability, and therapeutic efficiency using physicochemical descriptors. This study proposes an AI-assisted framework integrating ensemble machine learning with explainable prediction mechanisms to optimize bioadhesive ocular formulations before laboratory validation. The framework emphasizes transparent feature importance rather than black-box prediction, enabling rational formulation design. Recent advances in polymer science, ophthalmic biomaterials, and digital pharmaceutical development are synthesized to establish the conceptual foundation. The proposed approach demonstrates how interpretable AI can accelerate ophthalmic formulation development while supporting regulatory confidence. The manuscript contributes toward intelligent pharmaceutical manufacturing and precision ophthalmic therapeutics