The Artificial Pancreas: From Engineering Research to Patient Care

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease affecting approximately 35 million individuals world-wide, with associated annual healthcare costs in the US estimated to be approximately $15 billion. Current treatment requires either multiple daily insulin injections (MDI) or continuous subcutaneous insulin infusion (CSII) delivered via an insulin infusion pump. Both treatment modes necessitate frequent blood glucose measurements to determine the daily insulin requirements for maintaining near-normal blood glucose levels.  

More than 30 years ago, the idea of an artificial pancreas for patients with type 1 diabetes mellitus was envisioned. The closed-loop concept consisted of an insulin syringe, a blood glucose analyzer, and a transmitter. In the ensuing years, a number of theoretical research studies were performed with computer simulations to demonstrate the relevance of advanced process control design to the artificial pancreas, with delivery algorithms ranging from simple PID, to fuzzy logic, to model predictive control. As continuous glucose sensing technology matured, including the ability to measure interstitial glucose concentrations with sufficient accuracy every 5 minutes, and the development of hardware and algorithms to communicate with and control insulin pumps, the vision of closed-loop control of blood glucose has approached a reality.

In the last 20 years, our research group has been working with medical doctors on clinical demonstrations of feedback control algorithms for the artificial pancreas. In this talk, I will outline the difficulties inherent in controlling physiological variables, the challenges with regulatory approval of such devices, and will describe several process systems engineering algorithms we have tested in clinical and outpatient settings for the artificial pancreas. I will describe our latest work in creating an embedded version of our MPC algorithm to enable a portable implementation in a medical IoT framework.