Oral Peptide Drug Delivery Innovations

Abstract

 Oral peptide therapeutics have emerged as an attractive alternative to injectable formulations, yet their clinical translation remains constrained by enzymatic degradation, poor epithelial permeability, and highly variable gastrointestinal physiology. Existing formulation strategies largely rely on empirical optimization, which is both time-intensive and resource demanding. This manuscript identifies a critical research gap: the absence of an integrated artificial intelligence framework capable of simultaneously predicting peptide stability, carrier performance, and intestinal absorption under patientspecific gastrointestinal conditions. To address this gap, a hybrid machine learning architecture is proposed that combines graph neural networks with explainable ensemble learning for intelligent formulation optimization. The framework incorporates physicochemical descriptors, carrier composition, and simulated gastrointestinal variables to estimate oral bioavailability. Recent advances in peptide engineering, nanocarriers, permeation enhancers, and AI-assisted pharmaceutical development (2021–2026) provide the scientific foundation. The proposed approach aims to accelerate formulation screening while reducing laboratory experimentation. This study establishes a conceptual pathway toward precision oral peptide delivery supported by digital pharmaceutical intelligence