Self-Emulsifying Drug Delivery Innovations

Abstract

Self-emulsifying drug delivery systems (SEDDS) have emerged as an effective strategy for improving the oral delivery of poorly water-soluble drugs. Despite significant advances in lipid-based formulations, conventional formulation development remains dependent on extensive laboratory experimentation, leading to increased cost and prolonged optimization cycles. This study identifies a research gap in the integration of explainable artificial intelligence with formulation design for predicting physicochemical stability and in vitro performance before experimental validation. A hybrid machine learning framework is proposed to estimate formulation quality using compositional and processing variables derived from experimentally reported SEDDS studies. The framework combines feature engineering with ensemble learning to predict formulation success while maintaining interpretability through feature importance analysis. Recent developments in digital pharmaceutics and computational formulation science provide the foundation for the proposed approach. The study demonstrates how AI-assisted formulation screening can reduce experimental burden while improving decisionmaking during early-stage pharmaceutical development. The findings establish a roadmap for intelligent SEDDS optimization within future digital drug delivery platforms.