The gastrointestinal (GI) epithelium is the body’s first line of defense to luminal contents such as food, microbiota, pharmaceuticals, and toxins. A small pool of proliferative stem and progenitor cells is responsible for maintaining and repairing the entire GI epithelium. Oncology chemotherapeutics target these rapidly dividing cells, contributing to adverse event risks. As a result, 50-80% of patients develop diarrhea. Historically, these compounds have the lowest overall success rate for regulatory approval. A preclinical model for rapid screening of a candidate compound’s GI toxicity profile would be beneficial. For example, an in vitro intestinal model for GI safety could provide a better safety profile prior to entering clinical trials. Although animal models, immortalized cell lines, and other complex in vitro systems exist for GI tox testing, RepliGut Planar offers a simpler approach. It utilizes a monolayer of primary human epithelial cells, which can more accurately predict GI toxicity.
Utilizing human colonic monolayers isolated from multiple donors, we investigated how chemotherapeutics affect the epithelium during proliferation and differentiation. We used an industry-backed test set of compounds, which included known positive and negative compounds for GI toxicity. Each compound was tested to provide a dose-response curve. For proliferative cell testing, compounds were given to actively dividing cells. Read-outs included resistance measurements indicative of barrier formation potential. Additionally, total cell counts were taken to assess cell viability, and total EdU-positive cell counts indicated monolayer proliferative capacity. For differentiated cell testing, compounds were given to non-dividing cells. The read-outs included barrier resistance measurements. Overall, the toxic concentrations in the epithelial cell model were well-aligned with clinically relevant concentrations that produce toxicity, as measured by Cmax.
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Predicting Clinical GI Safety Outcomes Using an in vitro Human Intestinal Epithelial Model
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