Advances in Artificial Pancreas Systems

A brief history of diabetes and artificial pancreas systems is presented. Diabetes is a metabolic disease where production and/or utilization of insulin is impaired, leading to elevated blood glucose levels. Blood glucose is the main source of energy for the human body. Insulin, a hormone made by the body, enables the glucose in the bloodstream to get into cells. Over time, elevated glucose levels leads to problems related to heart disease, stroke, kidney, eye, nerve, dental and foot. Diabetes has no cure yet, but advances in technology have improved treatment to manage the disease and keep blood glucose levels in the desired range. People with type 1 diabetes (no insulin production by the body) administer three to five insulin injections daily or infuse insulin with an insulin pump to regulate their blood glucose concentration. This manual regulation is laborious and many people still experience dangerous low and high glucose levels during their daily lives. An artificial pancreas automates insulin pumps by using a closed-loop controller that receives information from glucose sensors, and manipulates the infusion rate of the pump. This new technology has been under development over the last three decades to develop artificial pancreas systems that are safe and effective in glucose level management in people with diabetes under free living conditions.

This chapter provides an extensive review of the components of artificial pancreas (AP) systems. Continuous glucose monitoring (CGM) sensors, insulin pumps and control algorithms are the three basic components of all AP systems. Information about currently available CGM sensors and insulin pumps is provided. Multivariable AP systems use information from wearable devices in addition to CGM information. Various wearable devices that can be used in an AP system are described. A brief description of control algorithms is also introduced. A more comprehensive discussion on control algorithms is provided in later chapters.

An AP system is challenged by several factors such as meals, exercise, sleep and stress that may have significant effects on glucose dynamics in the body. In this chapter, the relationship between these factors and the glucose dynamics are discussed. Most AP systems are based only on glucose measurements. These systems usually require manual inputs or adjustments by the users about the occurrences of some of these factors such as meals and exercise. Alternatively, multivariable AP systems have been proposed that use biometric variables in addition to glucose measurements to indicate the presence of these factors without a need for manual user input. The effects of different types of insulin as well as use of glucagon in AP systems is also discussed. The chapter includes a discussion of time delays in glucose sensors that affect the performance of predictive hypoglycemia alarm systems and APs.

Modeling of glucose and insulin concentration dynamics are discussed, and two different modeling approaches are presented. Physiological models describe the glucose and insulin dynamics with differential equations and physiological rules based on detailed understanding of the glucose and insulin dynamics, mass transfer and reaction kinetics. The chapter outlines progress in physiological modeling over the years. Alternatively, data-driven empirical modeling techniques have also been used to relate the effects of various measured inputs on glucose and insulin dynamics. The advantage of the latter is ease of model development with less information about glucose and insulin dynamics, and rapid model development and updating. Several empirical model types are presented and the technical steps to develop and evaluate them are outlined.

Alarm systems warn people with T1D when hypoglycemia occurs or can be predicted to occur in the near future if the current glucose concentration trends continue. Various alarm system development strategies are outlined in this chapter. Severe hypoglycemia has significant effects ranging from dizziness to diabetic coma and death while long periods of hyperglycemia cause damage to the vascular system. Fear of hypoglycemia is a major concern for many people with T1D. High doses of exogenous insulin relative to food, activity and low blood glucose levels can precipitate hypoglycemia. Hypoglycemia and hyperglycemia early alarm systems would be very beneficial for people with T1D to warn them or their caregivers about the potential hypoglycemia and hyperglycemia episode before it happens and empowers them to take measures to prevent these events.

In AP systems, the control algorithm is responsible for calculating the best estimate of basal and bolus insulin and/or glucagon dose based on the patient’s glucose concentration estimates and physiological properties, to reach the target glucose range. Various types of control algorithms that have been investigated for AP systems with simulations and clinical experiments over the last three decades are presented. Mathematical details are given for each type of control algorithm and their advantages and limitations are discussed.

The complexity of glucose homeostasis presents a challenge for tight control of blood glucose concentrations (BGC) in response to major disturbances. The nonlinearities and time-varying changes of the BGC dynamics, the occurrence of nonstationary disturbances, time-varying delays on measurements and insulin infusion, and noisy data from sensors provide a challenging system for the AP. In this chapter, a multimodule, multivariate, adaptive AP system is described to deal with several of these challenges simultaneously. Adaptive control systems can tolerate unpredictable changes in a system, and external disturbances by quickly adjusting the controller parameters without any need for knowledge of the initial parameters or conditions of the system. Physiological variables provide additional information that enable feedforward action for measurable disturbances such as exercise. Integration of control algorithms with hypoglycemia alarm module reduces the probability of hypoglycemic events.

AP systems that use both insulin and glucagon to regulate BCG can provide control that mimics the performance of the pancreas more realistically. The added complexity of control algorithms and hardware, the current stage of development of long-term stable glucagons provide challenges to the dual-hormone AP system. The progress made by various research groups is reported.

The performance of an AP system depends on successful operation of its components. Faults in sensors other hardware and software affect the performance and may force the system to manual operation. Many AP systems use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. Their performance depends on the accuracy of the models and data used for predictions. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. Techniques for fault detection and diagnosis and reconciliation of erroneous data with reliable estimates are presented. Since the models used in the controller may become less accurate with changes in the operating conditions, controller performance assessment is also conducted to evaluate the performance and determine if it can be improved by adjusting the model, parameters or constraints of the controller.

Clinical studies ranging from investigational experiments to clinical trials that compare alternate treatments and control techniques are listed.

Various possible future directions in the development of AP systems are discussed. Further development of faster and more stable hormones will make the AP systems to react as fast as the human body. More accurate glucose and wearable sensors with faster response times will enhance the ability of AP systems for better glucose regulation. Integration of biometric variables into AP system will reduce manual information needed from users. A hybrid AP system with manual meal entries is already available. Several companies and research teams are developing more advanced AP systems that are being tested in clinical trials and are expected to become commercially available in the near future.