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

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) that is designed for rapid integration into a patch pump. The ISCA will include the required electronics, mechanical components, and a software development kit that will enable rapid integration into commercial patch pumps. Working with our academic partners at OHSU, we will transfer the artifact elimination predictive signal processing algorithm and port this algorithm to the ISCA for use in real-time operation. In Specific Aim 3, we will integrate the sensor assembly into our commercial partner’s patch pump and validate the performance and accuracy of the design in a swine study. At the conclusion of Phase 2, we will have a dual-function glucose- sensing patch pump validated in a swine study and poised to enter clinical study. In Phase 2B, we will conduct those studies, and work with our academic collaborators and commercialization partners to incorporate a model predictive controller into the patch pump to yield an all-in-one automated insulin delivery solution.