pH-Responsive Smart Oral Drug Delivery Systems

Abstract

Oral drug delivery remains the most preferred route of administration because of its convenience and high patient acceptance. However, conventional oral formulations often exhibit poor site specificity, premature drug degradation, and inconsistent bioavailability due to variations in gastrointestinal pH. Although pH-responsive nanocarriers have demonstrated promising controlled-release behavior, limited research has explored the integration of artificial intelligence (AI) for predicting polymer behavior and optimizing formulation performance before laboratory development. This study addresses this gap by proposing an AI-assisted framework for designing pH-responsive smart oral drug delivery systems capable of adapting drug release according to gastrointestinal pH conditions. The proposed framework combines machine learning-based polymer selection with predictive release modeling to minimize experimental iterations while improving formulation efficiency. Recent advances in intelligent pharmaceutical formulation design are incorporated to develop a datadriven optimization strategy. The proposed concept has the potential to enhance therapeutic precision, reduce formulation costs, and accelerate pharmaceutical development. The findings provide a foundation for future AI-enabled intelligent oral drug delivery platforms