Artificial Pancreas Systems: Progress in Development of an Artificial Pancreas

This issue of Journal of Diabetes Science and Technology contains a collection of 12 original articles describing the latest advances in the development of algorithms for controlling insulin delivery in an artificial pancreas. Algorithms presented in this issue are affected by numerous quantifiable factors, including insulin pharmaco-kinetics, timing of meal carbohydrate appearance, meal size, amount of exercise, presence of stress, day-to-day variations in insulin sensitivity, insulin time-activity profiles, accuracy of glucose monitor calibration, metabolic profiles of both adults and neonates, and risks of hypoglycemia/hyperglycemia. These articles present theoretical advances in insulin delivery algorithms from modeled in silico patients, as well as clinical data from actual patients who have used closed loop systems. The novel approaches described in these articles are expected to bring us much closer to realization of a commercially available closed loop system for controlling glucose levels in patients with diabetes.     

Advancement of the Artificial Pancreas through the Development of Interoperability Standards

Despite advancements in the development of the artificial pancreas, barriers in the form of proprietary data and communication protocols of diabetes devices have made the integration of these components challenging. The Artificial Pancreas Standards and Technical Platform Project is an initiative funded by the JDRF Canadian Clinical Trial Network with the goal of developing device communication standards for the interoperability of diabetes devices. Stakeholders from academia, industry, regulatory agencies, and medical and patient communities have been engaged in advancing this effort. In this article, we describe this initiative along with the process involved in working with the standards organizations and stakeholders that are key to ensuring effective standards are developed and adopted. Discussion from a special session of the 12th Annual Diabetes Technology Meeting is also provided.     

Insulin Delivery Route for the Artificial Pancreas: Subcutaneous,Intraperitoneal, or Intravenous? Pros and Cons

Insulin delivery is a crucial component of a closed-loop system aiming at the development of an artificial pancreas. The intravenous route, which has been used in the bedside artificial pancreas model for 30 years, has clear advantages in terms of pharmacokinetics and pharmacodynamics, but cannot be used in any ambulatory system so far. Subcutaneous (SC) insulin infusion benefits from the broad expansion of insulin pump therapy that promoted the availability of constantly improving technology and fast-acting insulin analog use. However, persistent delays of insulin absorption and action, variability and shortterm stability of insulin infusion from SC-inserted catheters generate effectiveness and safety issues in view of an ambulatory, automated, glucose-controlled, artificial beta cell. Intraperitoneal insulin delivery, although still marginally used in diabetes care, may offer an interesting alternative because of its more-physiological plasma insulin profiles and sustained stability and reliability of insulin delivery.     

Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise.     

Algorithms for a Closed-Loop Artificial Pancreas: The Case for Proportional-Integral-Derivative Control

Closed-loop insulin delivery continues to be one of most promising strategies for achieving near-normal control of blood glucose levels in individuals with diabetes. Of the many components that need to work well for the artificial pancreas to be advanced into routine use, the algorithm used to calculate insulin delivery has received a substantial amount of attention. Most of that attention has focused on the relative merits of proportional-integral-derivative versus model-predictive control. A meta-analysis of the clinical data obtained in studies performed to date with these approaches is conducted here, with the objective of determining if there is a trend for one approach to be performing better than the other approach. Challenges associated with implementing each approach are reviewed with the objective of determining how these approaches might be improved. Results of the meta-analysis, which focused predominantly on the breakfast meal response, suggest that to date, the two approaches have performed similarly. However, uncontrolled variables among the various studies, and the possibility that future improvements could still be effected in either approach, limit the validity of this conclusion. It is suggested that a more detailed examination of the challenges associated with implementing each approach be conducted.     

A Computational Proof of Concept of a Machine-Intelligent Artificial Pancreas Using Lyapunov Stability and Differential Game Theory

Background:This study demonstrated the novel application of a “machine-intelligent” mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties.Methods:Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of “black box” simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a “dynamic envelope” of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states.Results:This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target.Conclusions:These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies.     

Artificial Pancreas Systems: Automatic Data Processing to Achieve a Safe Telemedical Artificial Pancreas

Background

The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient''s safety.

Methods

The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis.

Results

The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump.

Conclusions

Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.     

Using a Do-It-Yourself Artificial Pancreas: Perspectives from Patients and Diabetes Providers

Background:A growing number of people with diabetes are choosing to adopt do-it-yourself artificial pancreas system (DIYAPS) despite a lack of approval from the US Food and Drug Administration.We describe patients’ experiences using DIYAPS, and patient and diabetes providers’ perspectives on the use of such technology.Methods:We distributed surveys to patients and diabetes providers to assess each group’s perspectives on the use of DIYAPS. The patient survey also assessed glycemic control and impact on sleep. The patient survey was distributed in February 2019 via Facebook and Twitter (n = 101). The provider survey was distributed via the American Association of Diabetes Educators’ e-mail newsletter in April 2019 and the Pediatric Endocrine Society membership e-mail list in May 2019 (n = 152).Results:Patients overwhelmingly described improvements in glycemic control and sleep quality: 94% reported improvement in time in range, and 64% reported improvement in all five areas assessed. Eighty-nine percent of patients described DIYAPS as “Safe” or “Very Safe,” compared to only 27% of providers. Most felt encouraged by their diabetes provider to continue using DIYAPS, but few described providers as knowledgeable regarding its use. Providers cited a lack of experience with such systems and an inability to troubleshoot them as their most significant challenges.Conclusions:Despite evidence that DIYAPS usage is increasing, our surveys suggest that patients’ adoption of this technology and trust in it is outpacing that of diabetes providers. Providers must be aware of this growing population of patients and familiarize themselves with DIYAPS to support patients using this technology.     

Glucagon in the Artificial Pancreas: Supply and Marketing Challenges

The use of glucagon, in conjunction with insulin, in a dual chamber pump (artificial pancreas, AP) is a working goal for multiple companies and researchers. However, capital investment to create, operate, and maintain facilities with sufficient scale to produce enough glucagon to treat millions of patients, at a level of profit that makes it feasible, will be substantial. It can be assumed that the marketplace will expect the daily cost of glucagon (to the consumer) to be similar to the daily cost of insulin. After one subtracts wholesaler and pharmacy markup, there may be very few dollars remaining for the drug company to cover profit, capital expenditures, marketing, burden, and other costs. Without the potential for adequate margins, manufacturers may not be willing to take the risk. Assuming that the projections discussed in this article are in the right ballpark, advance planning for the supply for glucagon needs to start today and not wait for the AP to come to market.     

Glucose Sensing Issues for the Artificial Pancreas

The first retrospective continuous glucose monitor entered the market in 1999. Now that this tool gives online data, the question arises whether it is ready to be incorporated into a closed-loop system. The author discusses the following questions: (1) Is the accuracy of current continuous glucose monitoring (CGM) systems good enough for use in a prototype artificial pancreas system?; (2) How do we assess CGM accuracy?; (3) What is the minimal distance between a continuous glucose monitor and an insulin delivery site in which a CGM can function accurately?; and (4) Does any physiological and instrumental delay associated with continuous glucose monitoring hamper the development of an artificial pancreas?     

Development of Stable Liquid Glucagon Formulations for Use in Artificial Pancreas

Background:A promising approach to treat diabetes is the development of fully automated artificial/bionic pancreas systems that use both insulin and glucagon to maintain euglycemia. A physically and chemically stable liquid formulation of glucagon does not currently exist. Our goal is to develop a glucagon formulation that is stable as a clear and gel-free solution, free of fibrils and that has the requisite long-term shelf life for storage in the supply chain, short-term stability for at least 7 days at 37°C, and pump compatibility for use in a bihormonal pump.Methods:We report the development of two distinct families of stable liquid glucagon formulations which utilize surfactant or surfactant-like excipients (LMPC and DDM) to “immobilize” the glucagon in solution potentially through the formation of micelles and prevention of interaction between glucagon molecules.Results:Data are presented that demonstrate long-term physical and chemical stability (~2 years) at 5°C, short-term stability (up to 1 month) under accelerated 37°C testing conditions, pump compatibility for up to 9 days, and adequate glucose responses in dogs and diabetic swine.Conclusions:These stable glucagon formulations show utility and promise for further development in artificial pancreas systems.     

The Artificial Pancreas on the Threshold of Ambulatory Use: Setting the Stage for a Critical Transition: Periodic-Zone Model Predictive Control for Diurnal Closed-Loop Operation of an Artificial Pancreas

Background

The objective of this research is an artificial pancreas (AP) that performs automatic regulation of blood glucose levels in people with type 1 diabetes mellitus. This article describes a control strategy that performs algorithmic insulin dosing for maintaining safe blood glucose levels over prolonged, overnight periods of time and furthermore was designed with outpatient, multiday deployment in mind. Of particular concern is the prevention of nocturnal hypoglycemia, because during sleep, subjects cannot monitor themselves and may not respond to alarms. An AP intended for prolonged and unsupervised outpatient deployment must strategically reduce the risk of hypoglycemia during times of sleep, without requiring user interaction.

Methods

A diurnal insulin delivery strategy based on predictive control methods is proposed. The so-called “periodic-zone model predictive control” (PZMPC) strategy employs periodically time-dependent blood glucose output target zones and furthermore enforces periodically time-dependent insulin input constraints to modulate its behavior based on the time of day.

Results

The proposed strategy was evaluated through an extensive simulation-based study and a preliminary clinical trial. Results indicate that the proposed method delivers insulin more conservatively during nighttime than during daytime while maintaining safe blood glucose levels at all times. In clinical trials, the proposed strategy delivered 77% of the amount of insulin delivered by a time-invariant control strategy; specifically, it delivered on average 1.23 U below, compared with 0.31 U above, the nominal basal rate overnight while maintaining comparable, and safe, blood glucose values.

Conclusions

The proposed PZMPC algorithm strategically prevents nocturnal hypoglycemia and is considered a significant step toward deploying APs into outpatient environments for extended periods of time in full closed-loop operation.     

User and Healthcare Professional Perspectives on Do-It-Yourself Artificial Pancreas Systems: A Need for Guidelines

A growing number of individuals with type 1 diabetes are choosing to use “do-it-yourself” artificial pancreas systems (DIY APS) to support their diabetes self-management. Observational and self-report data of glycemic benefits of DIY APS are promising; however, without rigorous clinical trials or regulation from governing bodies, liability and user safety continue to be central concerns for stakeholders. Despite DIY APS having been used for several years now, there are no guidelines to assist users and healthcare professionals in addressing DIY APS use in routine clinical care. This commentary reports key stakeholders’ perspectives presented at the annual Advanced Technologies and Treatments in Diabetes conference in February 2020. Important considerations to inform the development of clinical care guidelines are also presented to generate further debate.     

Artificial Pancreas Systems: A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator

The objective of this article is to present a comprehensive strategy for a closed-loop artificial pancreas. A meal detection and meal size estimation algorithm is developed for situations in which the subject forgets to provide a meal insulin bolus. A pharmacodynamic model of insulin action is used to provide insulin-on-board constraints to explicitly include the future effect of past and currently delivered insulin boluses. In addition, a supervisory pump shut-off feature is presented to avoid hypoglycemia. All of these components are used in conjunction with a feedback control algorithm using model predictive control (MPC). A model for MPC is developed based on a study of 20 subjects and is tested in a hypothetical clinical trial of 100 adolescent and 100 adult subjects using a Food and Drug Administration-approved diabetic subject simulator. In addition, a performance comparison of previously and newly proposed meal size estimation algorithms using 200 in silico subjects is presented. Using the new meal size estimation algorithm, the integrated artificial pancreas system yielded a daily mean glucose of 138 and 132 mg/dl for adolescents and adults, respectively, which is a substantial improvement over the MPC-only case, which yielded 159 and 145 mg/dl.     

A New Meal Absorption Model for Artificial Pancreas Systems

Background:Artificial pancreas (AP) systems reduce the treatment burden of Type 1 Diabetes by automatically regulating blood glucose (BG) levels. While many disturbances stand in the way of fully closed-loop (automated) control, unannounced meals remain the greatest challenge. Furthermore, different types of meals can have significantly different glucose responses, further increasing the uncertainty surrounding the meal.Methods:Effective attenuation of a meal requires quick and accurate insulin delivery because of slow insulin action relative to meal effects on BG. The proposed Variable Hump (VH) model adapts to meals of varying compositions by inferring both meal size and shape. To appropriately address the uncertainty of meal size, the model divides meal absorption into two disjoint regions: a region with coarse meal size predictions followed by a fine-grain region where predictions are fine-tuned by adapting to the meal shape.Results:Using gold-standard triple tracer meal data, the proposed VH model is compared to three simpler second-order response models. The proposed VH model increased model fit capacity by 22% and prediction accuracy by 12% relative to the next best models. A 47% increase in the accuracy of uncertainty predictions was also found. In a simple control scenario, the controller governed by the proposed VH model provided insulin just as fast or faster than the controller governed by the other models in four out of the six meals. While the controllers governed by the other models all delivered at least a 25% excess of insulin at their worst, the VH model controller only delivered 9% excess at its worst.Conclusions:The VH Model performed best in accuracy metrics and succeeded over the other models in providing insulin quickly and accurately in a simple implementation. Use in an AP system may improve prediction accuracy and lead to better control around mealtimes.     

Patients' Perception and Future Acceptance of an Artificial Pancreas

Background

Little is known of patient acceptance of an artificial pancreas (AP). The purpose of this study was to investigate future acceptance of an AP and its determinants.

Methods

Patients with type 1 diabetes treated with insulin pump therapy were interviewed using questions based on the technology acceptance model and completed the diabetes treatment and satisfaction questionnaire (DTSQ).

Results

Twenty-two adults with type 1 diabetes participated. Half of the patients were followed in a university hospital, and the others were under treatment in an affiliated teaching hospital. Half of the patients were male. The mean DTSQ score was 29 (range 23–33). The AP was perceived as likely to be useful. Perceived advantages were a stable glucose regulation, less need for self-monitoring of blood glucose, relief of daily concerns, and time saving. Participants were confident in their capability to use the system. Although many participants (58%) had been reluctant to start continuous subcutaneous insulin infusion, the majority (79%) felt they would have no barriers to start using the AP. Trust in the AP was related to the quality of glucose control it would provide. Almost everyone expressed the intention to use the new system when available, even if it would initially not cover 24/24 hours.

Conclusion

The overall attitude on the AP was positive. Intention to use was dependent on trust in the AP, which was related to the quality of glucose control provided by the AP.     

Artificial Pancreas Systems: Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience

Background

Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented.

Methods

Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70–140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared.

Results

With closed-loop control, once BG levels achieved the target range (70–140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced (p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control

Conclusion

Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This “artificial pancreas” controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.     

In Silico Evaluation of an Artificial Pancreas Combining Exogenous Ultrafast-Acting Technosphere Insulin with Zone Model Predictive Control

Background

Because of the slow pharmacokinetics of subcutaneous (SC) insulin, avoiding postprandial hyperglycemia has been a major challenge for an artificial pancreas (AP) using SC insulin without a meal announcement.

Methods

A semiautomated AP with Technosphere® Insulin (TI; MannKind Corporation, Valencia, CA) was designed to combine pulmonary and SC insulin. Manual inhalation of 10 U ultrafast-absorbing TI at mealtime delivers the first, or cephalic, phase of insulin, and an SC insulin pump controlled by zone model predictive controller delivers second-phase and basal insulin. This AP design was evaluated on 100 in silico subjects from the University of Virginia/Padova metabolic simulator using a protocol of two 50 g carbohydrate (CHO) meals and two 15 g CHO snacks.

Results

Simulation analysis shows that the semiautomated AP with TI provides 32% and 16% more time in the controller target zone (80–140 mg/dl) during the 4 h postprandial period, with 39 and 20 mg/dl lower postprandial blood glucose peak on average than the pure feedback AP and the AP with manual feed-forward SC bolus, respectively. No severe hypoglycemia (<50 mg/dl) was observed in any cases.

Conclusions

The semiautomated AP with TI provides maximum time in the clinically accepted region when compared with pure feedback AP and AP with manual feed-forward SC bolus. Furthermore, the semiautomated AP with TI provides a flexible operation (optional TI inhalation) with minimal user interaction, where the controller design can be tailored to specific user needs and abilities to interact with the device.