Innovations in technology for the treatment of diabetes: clinical development of the artificial pancreas (an autonomous system)

A Digital-First Model of Diabetes Care

If we were to create the diabetes care experience anew, there is little doubt that it would not resemble the current bricks-and-mortar way we do things currently. For however a future model of care is designed, it would assume a digital-first approach, whereby the modern conveniences of digitally-mediated services we have experienced in other industries would be reflected in our diabetes care. To this end, our diabetes data would be liberated, transparent to those that need it, but safe and secure otherwise. We would have access to new tools that create insights that lower the burden, not add to it. And access to care would be just in time, convenient, and from a distance when needed. What is stopping a digital-first model is complex and deeply seated, but not insurmountable with engagement from industry, regulators, and care providers that are all willing to modernize the way care is delivered. Personal human interaction will continue to play an important part in the care for millions of people living with diabetes, no matter the sophistication of these digital services. What these technologies will provide is the human capacity to deal with the higher need, vulnerable people for whom access to timely care is an issue. Moreover, it will provide choice for an increasingly diverse population that seeks options for the form, and the delivery, of their personalized care.

Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes

PURPOSE OF REVIEW

The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems.

RECENT FINDINGS

A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.

Design of the health monitoring system for the artificial pancreas: low glucose prediction module

BACKGROUND

The purpose of this study was to design and evaluate a safety system for the artificial pancreas device system (APDS). Safe operation of the APDS is a critical task, where the safety system is engaged only as needed to ensure reliable operation without positive feedback to the controller.

METHODS

The Health Monitoring System (HMS) was designed as a modular system to ensure the safety of the APDS and the user. It was designed using a large set of ambulatory data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS alerting. The HMS was validated in-clinic with a real-life challenge of a subject who overdosed insulin prior to admission.

RESULTS

The HMS was evaluated for clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes were detected with 2.9 false alarms per day. During in silico evaluation, the HMS reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested in-clinic, the subject overdosed ~3 U of insulin prior to her arrival to a closed-loop session (against protocol). The controller reduced insulin delivery, and the HMS gave four alerts that were successfully received via clinical software and text and multimedia messages. Even with insulin reduction and CHO supplements, hypoglycemia was unavoidable but manageable due to the HMS, confirming that a safety system to detect adverse events is an essential part of the APDS.

CONCLUSIONS

The ability of the HMS to be an effective alert system that provides a safety layer to the APDS controller has been demonstrated in a clinical setting.

Wearable and implantable pancreas substitutes

A lifelong-implanted and completely automated artificial or bioartificial pancreas (BAP) is the holy grail for type 1 diabetes treatment, and could be a definitive solution even for other severe pathologies, such as pancreatitis and pancreas cancer. Technology has made several important steps forward in the last years, providing new hope for the realization of such devices, whose feasibility is strictly connected to advances in glucose sensor technology, subcutaneous and intraperitoneal insulin pump development, the design of closed-loop control algorithms for mechatronic pancreases, as well as cell and tissue engineering and cell encapsulation for biohybrid pancreases. Furthermore, smart integration of the mentioned components and biocompatibility issues must be addressed, bearing in mind that, for mechatronic pancreases, it is most important to consider how to recharge implanted batteries and refill implanted insulin reservoirs without requiring periodic surgical interventions. This review describes recent advancements in technologies and concepts related to artificial and bioartificial pancreases, and assesses how far we are from a lifelong-implanted and self-working pancreas substitute that can fully restore the quality of life of a diabetic (or other type of) patient.