Ky Young Cho
The incidence of pediatric inflammatory bowel disease (PIBD) has risen worldwide, creating significant challenges for healthcare professionals. Compared with adult-onset inflammatory bowel disease, PIBD often presents with more extensive intestinal involvement, a more aggressive course, and complications affecting growth and puberty, making timely disease control critical for PIBD. However, treatment selection remains largely empirical, and many patients fail to respond to initial therapy. Additionally, conventional experimental models, including 2-dimensional cell culture and animal systems, often fail to reproduce the complexities of human intestinal tissues. These limitations highlight the need for human-relevant experimental platforms capable of capturing patient-specific disease biology and supporting precise therapeutic decision-making in PIBD. In this context, patient-derived intestinal organoids have emerged as valuable tools for studying disease mechanisms and evaluating therapeutic responsiveness in patients with PIBD. Organoid-based systems enable direct investigations of epithelial barrier dysfunction, inflammatory signaling, metabolic alterations, and genotype-associated molecular features that provide insight into tissue-level disease heterogeneity. Emerging data suggest that ex vivo drug responses observed in patient-derived intestinal organoids may parallel clinical treatment outcomes, indicating their potential to support more informed therapy selection in PIBD. Despite these advances, current organoid models have important limitations including the absence of immune, stromal, and vascular components as well as challenges related to standardization, scalability, and cost-effectiveness. To overcome these limitations, next-generation platforms incorporating immune cell coculture systems, microbiota, and microfluidic technologies are being developed to better replicate the complexity of the host intestinal microenvironment. As these integrated systems evolve, intestinalorganoids are expected to become increasingly powerful tools for disease modeling, biomarker discovery, and therapeutic response prediction, ultimately supporting the development of personalized and effective treatment strategies for PIBD.