# Analysis and engineering of receptor-mediated transcytosis across the intestinal epithelium

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $326,409

## Abstract

PROJECT SUMMARY
The process of receptor-mediated transcytosis is a critical, yet highly selective, mechanism for the transport of
proteins from the intestinal lumen into systemic circulation. Despite the fundamental importance of this
transport mechanism and the potential to exploit it to revolutionize oral delivery of protein therapeutics, our
understanding of intestinal transcytosis has been limited by both the lack of a more accurate, scalable in vitro
model of the human intestinal epithelium and the difficulty in identifying the full repertoire of receptors that can
actively and selectively transport cargo from the luminal side to the basolateral side of this physical barrier. In
this R01 proposal, we will synergistically integrate stem cell engineering, directed protein evolution, and
synthetic biology approaches to first uncover new mechanisms of receptor-mediated transcytosis and to then
apply these findings to create a novel platform for oral delivery of insulin. More generally, our approaches and
results should have broad utility in understanding the formation of epithelial barriers and in efficiently delivering
drugs across them in a highly selective manner.

## Key facts

- **NIH application ID:** 10145663
- **Project number:** 5R01DK114453-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Casim Sarkar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $326,409
- **Award type:** 5
- **Project period:** 2018-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10145663

## Citation

> US National Institutes of Health, RePORTER application 10145663, Analysis and engineering of receptor-mediated transcytosis across the intestinal epithelium (5R01DK114453-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10145663. Licensed CC0.

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