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Ex-vivo Confocal Imaging and Proteomic Profiling to Determine Treatment Response in Children With IBD
Sponsor: Cook Children's Health Care System
Summary
This study aims to test the overall hypothesis that the membrane tissue binding capacity of cytokines in the biopsied tissue of patients with Inflammatory Bowel Disease (IBD) is predictive of/strongly correlated to clinical response/outcomes observed. The key questions under investigation are: Aim 1: To assess the fluorescent signal intensity at baseline (control antibody with control biopsy and control antibody with IBD biopsy). Aim 2: To characterize the cellular landscape by surveying surface markers using bar-coded antibodies and performing gene expression profiling on every cell within inflamed tissue of patients with IBD. Aim 3: Develop algorithm using artificial intelligence to predict responders versus non-responders and to further subclassify IBD patients using phenotype data.
Official title: A Prospective Study Evaluating Ex-vivo Confocal Imaging of Fluorescent Tagged Monoclonal Antibodies and Proteomic Profiles of Biopsied Tissue to Predict Therapeutic Response Among Children and Adolescents With Inflammatory Bowel Disease
Key Details
Gender
All
Age Range
2 Years - 21 Years
Study Type
INTERVENTIONAL
Enrollment
40
Start Date
2025-09-01
Completion Date
2028-07-01
Last Updated
2025-08-14
Healthy Volunteers
No
Interventions
Confocal Laser Endomicroscopy
Patients will undergo Esophagogastroduodenoscopy (EGD) and/or Ileocolonoscopy (IC) EGD with CLE as per standard of care. Each participant will have 3-4 mucosal biopsies taken from the terminal ileum, rectosigmoid and cecum, ideally from the most affected areas of accessible segment. Ex vivo staining of biopsied tissue will be expanded to include FITC-labeled antibodies to cytokines IL12 and IL12/IL23 and to cytokine receptors IL12R and IL23R and possibly other cytokines, receptors and adhesion molecules. All biopsies tested for membrane bound antibodies will be done using CLE technology with artificial intelligence (AI). The cellular landscape will be characterized by surveying surface markers using bar-coded antibodies and performing gene expression profiling on every cell within inflamed tissue of patients with IBD. We will develop algorithm using AI to predict responders versus non-responders and to further subclassify IBD patients using phenotype data.
Locations (2)
University of Texas at Arlington
Arlington, Texas, United States
Cook Children's Health Care System
Fort Worth, Texas, United States