Authors : D. Umamaheswari; R. Senthil Prabhu; K. Gayathri; A. Gowtham; M. Keerthana; M. Priyadharshini

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/msr7ce2t

Scribd : https://tinyurl.com/4k9s2xka

DOI : https://doi.org/10.38124/ijisrt/26jun343

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Colon-targeted drug delivery has emerged as a promising strategy for the management of lower gastrointestinal diseases, including inflammatory bowel disease (IBD) and colorectal cancer, as well as for improving the bioavailability of drugs susceptible to gastric degradation or hepatic first-pass metabolism [1–4]. Conventional colonic delivery systems — relying on time-controlled release, pH-sensitive polymer coatings, or microbiota-triggered polysaccharide degradation — are limited by significant inter-individual variability in gastrointestinal physiology, restricting their therapeutic precision and scalability. To overcome these issues, pharmaceutical research is increasingly incorporating machine learning (ML) and artificial intelligence (AI) . The relevance of AI-enhanced physiologically based pharmacokinetic (PBPK) modeling for patient-specific digital twin dosing and reactive oxygen species (ROS)-responsive smart nanoplatforms for site-selective IBD therapy is thoroughly examined in this review. Additionally examined are the implications for customized colonic therapy design and the incorporation of AI into diagnostic techniques. AI-driven optimization is examined in relation to advanced formulation technologies, such as hot-melt extrusion (HME), three-dimensional (3D) printing, computer-aided molecular simulation, and electronic drug delivery devices like IntelliCap®. Despite notable progress, challenges remain regarding model interpretability, data scarcity, lack of standardized protocols, and incomplete integration between AI-based diagnostics and therapeutic formulation pipelines [33–35]. This review concludes that the convergence of AI with colonic drug delivery science offers a transformative path toward precision, efficiency, and patient-individualized therapeutic outcomes.

Keywords : Artificial Intelligence; Machine Learning; Colon-Targeted Drug Delivery; Inflammatory Bowel Disease; Colorectal Cancer; Physiologically Based Pharmacokinetic Modeling; Graph Neural Networks; Hot-Melt Extrusion; 3D Printing; DrugMicrobiota Interactions.

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