Colon-Specific Drug Delivery Platforms

Abstract

Colon-specific drug delivery has emerged as an effective therapeutic strategy for inflammatory bowel disease, colorectal cancer, Crohn's disease, ulcerative colitis, and microbiome-targeted therapies. However, current delivery systems remain constrained by interpatient variability in gastrointestinal transit time, microbiota composition, luminal pH, and enzymatic activity, resulting in inconsistent drug release profiles. This study identifies a previously underexplored research gap: the absence of an integrated artificial intelligence framework capable of simultaneously modeling physiological heterogeneity and nanoparticle formulation characteristics to optimize colon-targeted release. An explainable hybrid machine learning architecture is proposed to predict spatiotemporal drug release efficiency using physicochemical formulation parameters together with patient-specific gastrointestinal variables. The framework combines ensemble learning with interpretable feature attribution to guide formulation optimization while maintaining clinical transparency. Unlike conventional optimization methods that rely primarily on laboratory experimentation, the proposed digital approach enables rapid virtual screening of formulation candidates. The study establishes a conceptual pathway toward personalized colon-specific drug delivery within emerging digital health ecosystems. The framework demonstrates how AI-assisted pharmaceutical design may substantially reduce development time while improving therapeutic precision