Buccal Films for Personalized Drug Administration

Abstract

Personalized medicine demands dosage systems capable of adapting to individual physiological and therapeutic requirements. Buccal films have emerged as attractive alternatives to conventional oral dosage forms because they provide rapid drug absorption, avoid first-pass metabolism, and improve patient compliance. However, current buccal film development largely relies on standardized formulations rather than individualized therapeutic needs. This study identifies a critical research gap in the integration of artificial intelligence with buccal film formulation for patient-specific dose customization. A novel AI-driven optimization framework is proposed that combines patient characteristics, formulation variables, and drug-release behavior to generate personalized buccal film recommendations. The proposed approach utilizes machine learning to predict formulation performance while simultaneously optimizing dosage according to clinical requirements. The framework aims to reduce formulation development time, improve therapeutic precision, and minimize adverse drug reactions. This work establishes a conceptual foundation for intelligent pharmaceutical manufacturing within future digital healthcare ecosystems