Patient-specific, combinatorial NAMs for gastrointestinal diseases and drug response prediction

About This Grant

ABSTRACT Millions of people in the US are impacted by gastrointestinal diseases including Inflammatory Bowel Disease (IBD), Metabolic Disfunction Associated Steatotic Liver Disease (MASLD) and Pancreatitis. There are only a small number of drugs for IBD and MASLD, and none for Pancreatitis, making this a critically significant clinical question. Animal models have proven inadequate surrogates for these diseases and reliance on current preclinical evaluations are considered to be among the most problematic steps in drug discovery. The goal of Cincinnati Advanced NAM Development and Operational Research center (CANDOR) is to develop combinatorial New Approach Methodologies (NAMs) that more accurately model the pathophysiologic complexity and drug responses in patients with these gastrointestinal (GI) diseases. We have established an interdisciplinary team of collaborators of clinicians, scientists, experimental and computational biologists with a history of developing in vitro organoid and in silico NAMs with a focus on inflammatory diseases of the GI tract. CANDOR will provide a collaborative pipeline starting with existing cohorts of deeply phenotyped patients with IBD, MASLD, and Pancreatitis. Clinical data and patient samples will be used to build in silico NAMs, based on molecular pathways and cell-cell interactions that corelate with patient outcome and drug response. Each disease will have a corresponding in vitro NAM comprising intestinal, liver, and pancreatic organoids each with immune cells. Pluripotent stem cell banks have been generated from patients with each of these diseases, and healthy controls, and all organoid platforms are established and benchmarked to human samples. The aims of CANDOR are to establish in vitro NAMs that accurately model clinical features of IBD, MASLD, and Pancreatitis; to build disease-focused in silico NAMs that are based on gene regulatory, cell-cell interactions, and pharmacometric models from patients and combine these with data from in vitro NAMs; and to validate and disseminate combinatorial NAM technologies through training, outreach, and distribution.