# Development of a miniaturized single-port automated insulin delivery system utilizing a glucose sensing catheter, ultra-concentrated insulin, and an optimized control algorithm

> **NIH NIH R44** · PACIFIC DIABETES TECHNOLOGIES · 2022 · $552,430

## Abstract

ABSTRACT
Significance: There are over 5 million people with insulin-treated diabetes in the United States who represent
a disproportionately large share of the $237B in direct medical costs attributable to diabetes. The use of
continuous glucose monitoring (CGM) has been shown to reduce HbA1c levels, a proven predictor of health
outcomes within this population, with the greatest improvement achieved when CGM is coupled with
continuous subcutaneous insulin infusion (CSII). The recent convergence of CGM and insulin pumps has
enabled the first generation of automated insulin delivery (AID) systems, promising even better glycemic
control for insulin-treated diabetes. However, current AID systems are complex, cumbersome, and expensive
for the patient because they require multiple devices to be worn on the body: a glucose sensor, an insulin
pump, and an insulin delivery catheter. We have developed a glucose sensing catheter that reduces the
number of subcutaneous components from two to one, significantly reducing the size and complexity of these
systems. The PDT interoperable sensing cannula assembly that we are proposing to commercialize in this
phase 2 SBIR will allow any insulin patch pump manufacturer to rapidly integrate CGM directly on the insulin
delivery cannula, thereby enabling people with T1D who are patch pump users to effortlessly utilize CGM
through a single subcutaneous injection site. Importantly, this platform will also improve AID system reliability
and security by replacing the wireless communication from CGM to pump controller with a direct wired
connection. Resulting reductions in system size, complexity, and cost will increase adoption rates for pump
user and people using AID, helping improve compliance, lower HbA1c levels, and improve health outcomes
among people with type 1 diabetes. Preliminary Data: PDT has recently demonstrated that delivering insulin
at the site of glucose sensing is possible using a patented redox mediator-based sensing cannula. However,
we have also shown that there is a dilution artifact that occurs immediately after a dose of insulin is delivered
through the cannula. We have shown that this artifact is independent of whether insulin or saline is delivered.
In Phase 1 of this SBIR, we demonstrated in a swine study that this artifact is related to the size of the bolus.
We further demonstrated that the artifact can be significantly reduced by using higher concentration insulin and
ultimately eliminated by using sophisticated predictive signal processing methods. Specific Aims: In Phase 2
of this project, we will use the products of Phase 1 to take the next logical steps in integration of our sensing
cannula into a dual function patch pump platform. In Specific Aim 1, we will further characterize and evaluate
the accuracy of the PDT sensing cannula in a human study. In Specific Aim 2, we will work with a commercial
pump partner (EOFlow) to develop and evaluate an interoperable sensing cannula assembly (ISCA...

## Key facts

- **NIH application ID:** 10452613
- **Project number:** 5R44DK123766-03
- **Recipient organization:** PACIFIC DIABETES TECHNOLOGIES
- **Principal Investigator:** Thomas Ludwig Seidl
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $552,430
- **Award type:** 5
- **Project period:** 2019-09-20 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10452613

## Citation

> US National Institutes of Health, RePORTER application 10452613, Development of a miniaturized single-port automated insulin delivery system utilizing a glucose sensing catheter, ultra-concentrated insulin, and an optimized control algorithm (5R44DK123766-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10452613. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*