Generation of Human iPSC–Derived Intestinal Epithelial Cell Monolayers by CDX2 Transduction

Cellular and Molecular Gastroenterology and Hepatology(2019)

引用 37|浏览22
暂无评分
摘要
Background & aims To develop an effective and safe orally administered drug, it is important to predict its intestinal absorption rate, intestinal first-pass effect, and drug-drug interactions of orally administered drugs. However, there is no existing model to comprehensively predict the intestinal pharmacokinetics and drug-response of orally administered drugs. In this study, we attempted to generate homogenous and functional intestinal epithelial cells from human induced pluripotent stem (iPS) cells for pharmaceutical research. Methods We generated almost-homogenous Villin- and zonula occludens-1 (ZO1)-positive intestinal epithelial cells by caudal-related homeobox transcription factor 2 (CDX2) transduction into human iPS cell-derived intestinal progenitor cells. Results The drug absorption rates in human iPS cell-derived intestinal epithelial cell monolayers (iPS-IECM) were highly correlated with those in humans (R2=0.91). The expression levels of cytochrome P450 (CYP) 3A4, a dominant drug-metabolizing enzyme in the small intestine, in human iPS-IECM were similar to those in human small intestine in vivo. In addition, intestinal availability in human iPS-IECM (the fraction passing the gut wall: Fg=0.73) was more similar to that in the human small intestine in vivo (Fg=0.57) than to that in Caco-2 cells (Fg=0.99), a human colorectal adenocarcinoma cell line. Moreover, the drug-drug interaction and drug-food interaction could be observed by using our human iPS-IECM in the presence of an inducer and inhibitor of CYP3A4, i.e., rifampicin and grape fruit juice, respectively. Conclusion Taking these results together, we succeeded in generating the human iPS-IECM that can be applied to various intestinal pharmacokinetics and drug-response tests of orally administered drugs.
更多
查看译文
关键词
CYP3A4,Intestinal First-Pass Effect,Differentiation,Adenovirus
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要