The bio-inspired artificial pancreas for type 1 diabetes control in the home: System architecture and preliminary results

BACKGROUND: Artificial pancreas (AP) technology has been proven to improve glucose and patient-centered outcomes for people with type 1 diabetes (T1D). Several approaches to implement the AP have been described, clinically evaluated, and in one case, commercialized. However, none of these approaches has shown a clear superiority with respect to others. In addition, several challenges still need to be solved before achieving a fully automated AP that fulfills the users' expectations. We have introduced the Bio-inspired Artificial Pancreas (BiAP), a hybrid adaptive closed-loop control system based on beta-cell physiology and implemented directly in hardware to provide an embedded low-power solution in a dedicated handheld device. In coordination with the closed-loop controller, the BiAP system incorporates a novel adaptive bolus calculator which aims at improving postprandial glycemic control. This paper focuses on the latest developments of the BiAP system for its utilization in the home environment. METHODS: The hardware and software architectures of the BiAP system designed to be used in the home environment are described. Then, the clinical trial design proposed to evaluate the BiAP system in an ambulatory setting is introduced. Finally, preliminary results corresponding to two participants enrolled in the trial are presented. RESULTS: Apart from minor technical issues, mainly due to wireless communications between devices, the BiAP system performed well (~88% of the time in closed-loop) during the clinical trials conducted so far. Preliminary results show that the BiAP system might achieve comparable glycemic outcomes to the existing AP systems (~73% time in target range 70-180 mg/dL). CONCLUSION: The BiAP system is a viable platform to conduct ambulatory clinical trials and a potential solution for people with T1D to control their glucose control in a home environment

Coming of age: the artificial pancreas for type 1 diabetes.

The artificial pancreas (closed-loop system) addresses the unmet clinical need for improved glucose control whilst reducing the burden of diabetes self-care in type 1 diabetes. Glucose-responsive insulin delivery above and below a preset insulin amount informed by sensor glucose readings differentiates closed-loop systems from conventional, threshold-suspend and predictive-suspend insulin pump therapy. Insulin requirements in type 1 diabetes can vary between one-third-threefold on a daily basis. Closed-loop systems accommodate these variations and mitigate the risk of hypoglycaemia associated with tight glucose control. In this review we focus on the progress being made in the development and evaluation of closed-loop systems in outpatient settings. Randomised transitional studies have shown feasibility and efficacy of closed-loop systems under supervision or remote monitoring. Closed-loop application during free-living, unsupervised conditions by children, adolescents and adults compared with sensor-augmented pumps have shown improved glucose outcomes, reduced hypoglycaemia and positive user acceptance. Innovative approaches to enhance closed-loop performance are discussed and we also present the outlook and strategies used to ease clinical adoption of closed-loop systems.Supported by National Institute of Health Research Cambridge Biomedical Research Centre, Efficacy and Mechanism Evaluation National Institute for Health Research (#14/23/09), The Leona M. & Harry B. Helmsley Charitable Trust (#2016PG-T1D045), JDRF (#2-SRA-2014-256-M-R), National Institute of Diabetes and Digestive and Kidney Diseases (1UC4DK108520-01), and Diabetes UK (#14/0004878).This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00125-016-4022-

Development of Artificial Pancreas

Diabetic patients who have an abnormal blood glucose level suffer from acute effects of hypoglycemia and long term effects of hyperglycemia which include disorders of eyes, kidneys and nerves. This paper introduces the concept of an artificial pancreas which is a revolutionary device for the diabetic population. The arti?cial pancreas (AP), known as closed-loop control of blood glucose in diabetes, is a system combining a glucose sensor, a control algorithm, and an insulin infusion device. The development of artificial pancreas can be sketched back 50 years to when the possibility for external regulation of a human’s blood glucose was established by studies of type 1 diabetes using intravenous glucose measurement and infusion of insulin and glucose.The goal of the artificial pancreas is to relieve the person with diabetes from some of the burden of daily doses and medicines and insulin shots

Challenges and Opportunities in Design of Control Algorithm for Artificial Pancreas

With discovery of the insulin, Type-1 diabetes converted from a fatal and acute to a chronic disease which includes micro-vascular complications which range from Kidney disease to stroke and micro-vascular complications such as retinopathy, nephropathy and neuropathy. Artificial pancreas is a solution to improve the quality of life for people with this very fast growing disease in the world and to reduce the costs. Despite technological advances e.g., in subcutaneous sensors and actuators for insulin injection, modeling of blood glucose dynamics and control algorithms still need significant improvement. In this paper, we investigate challenges and opportunities for development of efficient algorithm for designing robust artificial pancreas. We discuss the state of the art and summarize clinical and in silico assessment results. We contrast conventional integer order system approach with a newly proposed fractal control and summarize its benefits

Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial

The safety and effectiveness of a continuous, day-and-night automated glycaemic control system using insulin and glucagon has not been shown in a free-living, home-use setting. We aimed to assess whether bihormonal bionic pancreas initialised only with body mass can safely reduce mean glycaemia and hypoglycaemia in adults with type 1 diabetes who were living at home and participating in their normal daily routines without restrictions on diet or physical activity

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to $85.6\%$ with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D

Day and night home closed-loop insulin delivery in adults with type 1 diabetes: three-center randomized crossover study.

OBJECTIVE: To evaluate the feasibility of day and night closed-loop insulin delivery in adults with type 1 diabetes under free-living conditions. RESEARCH DESIGN AND METHODS: Seventeen adults with type 1 diabetes on insulin pump therapy (means ± SD age 34 ± 9 years, HbA1c 7.6 ± 0.8%, and duration of diabetes 19 ± 9 years) participated in an open-label multinational three-center crossover study. In a random order, participants underwent two 8-day periods (first day at the clinical research facility followed by 7 days at home) of sensor-augmented insulin pump therapy (SAP) or automated closed-loop insulin delivery. The primary end point was the time when sensor glucose was in target range between 3.9 and 10.0 mmol/L during the 7-day home phase. RESULTS: During the home phase, the percentage of time when glucose was in target range was significantly higher during closed-loop compared with SAP (median 75% [interquartile range 61-79] vs. 62% [53-70], P = 0.005). Mean glucose (8.1 vs. 8.8 mmol/L, P = 0.027) and time spent above target (P = 0.013) were lower during closed loop, while time spent below target was comparable (P = 0.339). Increased time in target was observed during both daytime (P = 0.017) and nighttime (P = 0.013). CONCLUSIONS: Compared with SAP, 1 week of closed-loop insulin delivery at home reduces mean glucose and increases time in target without increasing the risk of hypoglycemia in adults with relatively well-controlled type 1 diabetes.This is the author accepted manuscript. The final version can be found published here: http://care.diabetesjournals.org/content/37/7/1931.abstract

The changing landscape of automated insulin delivery in the management of type 1 diabetes

Automated insulin delivery systems, also known as closed-loop o r ‘artificial pancreas’ systems, are transforming the management of type 1 diabetes. These systems consist of an algorithm which responds to real-time glucose sensor levels by automatically modulating insulin delivery through an insulin pump. We review the rapidly changing landscape of automated insulin-delivery systems over recent decades, from initial prototypes to the different hybrid closed-loop systems commercially available today. We discuss the growing body of clinical trials and real-world evidence demonst rating their glycaemic and psychosocial benefits. We also address future directions in auto mated insulin delivery such as dual-hormone systems and adjunct therapy as well as the chal lenges around ensuring equitable access to closed-loop technology

Optimisation du contrôle glycémique des patients atteints de diabète de type 1 : traitement efficace des hypoglycémies, calcul des glucides et pancréas artificiel

L’optimisation du contrôle glycémique est primordiale pour réduire le risque de complications associées au diabète. Toutefois, une proportion importante des patients atteints de diabète de type 1 (DT1) n’atteint pas les cibles d’hémoglobine glyquée (HbA1c) et la fréquence des hypoglycémies demeure élevée. Nous pensons qu’une simplification du contrôle glycémique prandial ainsi qu’un traitement efficace de l’hypoglycémie permettraient d’améliorer le contrôle glycémique global. Nous avons montré qu’une majorité de patients se disent confiants relativement à l’utilisation du calcul des glucides pour la détermination du bolus d’insuline prandial, mais un nombre élevé de difficultés spécifiques sont rapportées. De plus, les outils technologiques pour faciliter le calcul des glucides sont peu utilisés par les patients malgré une perception favorable. Le système en boucle-fermée de contrôle de la glycémie communément appelé « pancréas artificiel (PA) » est une technologie par laquelle les taux d’infusion d’insuline par un système de perfusion sous-cutanée continue d'insuline (pompe à insuline) sont modifiés périodiquement selon les recommandations générées par un ordinateur (algorithme) et découlant des lectures du système de surveillance en continu du glucose. Le PA améliore considérablement le contrôle glycémique chez les patients atteints de DT1, mais le contrôle glycémique prandial demeure un défi important. Nous avons développé une stratégie simplifiée de bolus prandial combinée au PA. Avec cette stratégie, les patients choisissent une catégorie de repas basée sur une évaluation semi-quantitative du contenu en glucides (ex. repas régulier = 30 à 60 g de glucides). Dans des conditions contrôlées avec un PA à double-hormone (insuline et glucagon), cette stratégie s’est avérée efficace pour le contrôle glycémique postprandial, sauf dans le cas des repas à teneur très élevée en glucides (> 90 g). Après avoir ajouté une catégorie de repas, nous avons testé la stratégie simplifiée dans des conditions non contrôlées. Le contrôle glycémique avec la stratégie simplifiée était similaire à celui obtenu avec le calcul des glucides classique, avec toutefois une fréquence élevée d’hypoglycémies, ce qui a mené à des modifications de l’algorithme. Nous avons finalement démontré la sécurité d’une telle approche simplifiée de bolus prandial dans un contexte de catégorisation erronée du repas. Nous avons également observé une faible adhésion aux recommandations pour le traitement de l’hypoglycémie légère à modérée. Les patients atteints de DT1 consomment en moyenne le double de glucides pour le traitement de l’hypoglycémie comparativement à la recommandation actuelle. D’un autre côté, nous avons démontré chez des patients avec DT1 traités par pompe à insuline que 16 à 21 g de glucides, tel que recommandé, était insuffisant pour traiter une majorité des épisodes d’hypoglycémie dans un délai de 15 minutes. En résumé, les patients rapportent plusieurs difficultés associées au calcul des glucides et le PA représente une thérapie prometteuse pour amoindrir ce fardeau. Une stratégie de bolus prandial basée sur une catégorisation du repas permettrait de simplifier le calcul des glucides tout en préservant un contrôle glycémique adéquat. Par ailleurs, une majorité de patients atteints de DT1 n’adhère pas aux recommandations pour le traitement de l’hypoglycémie, ce qui pourrait en partie s’expliquer par le manque d’efficacité de cette recommandation chez un large sous-groupe de patients.Optimal glucose control is essential to reduce the risk of diabetes-related complications. Yet, a large proportion of patients with type 1 diabetes (T1D) struggles to achieve glycated hemoglobin (A1c) targets and a high frequency of hypoglycemia events is still observed. We believe simplification of meal glucose control as well as efficient treatment of hypoglycemia have the potential to improve glucose control in T1D. We showed that a large proportion of patients report being confident in applying carbohydrate counting for meal bolus calculation; yet, many specific difficulties were encountered by patients. The use of available technologies for carbohydrate counting by participants appears to be uncommon despite a favorable perception and interest towards such technology. Closed-loop automated insulin delivery systems, also called “artificial pancreas (AP)”, is a technology by which insulin infusion rates from the continuous subcutaneous insulin infusion (CSII; insulin pump) are regulated based on algorithm-generated recommendations relying on continuous glucose monitoring systems readings. The AP improves glucose control considerably in patients with T1D, but prandial glucose control remains a significant challenge. We have developed a simplified meal bolus strategy combined with the AP. In this strategy, patients choose a meal category based on a semi-quantitative carbohydrate assessment (e.g. a regular meal = 30 to 60 g of carbohydrates). In controlled conditions using a dual-hormone AP (insulin and glucagon), this strategy yielded adequate postprandial glucose control, except for very large carbohydrate content meals (> 90 g). After adding a meal category, this strategy was tested in uncontrolled conditions. Glucose control with the simplified strategy was comparable to what was seen with precise carbohydrate counting. However, a higher frequency of hypoglycemia events was observed which led to modifications to the algorithm. We finally showed the safety of this simplified strategy in the context of misclassification of the meals. We also found a low adherence to guidelines for self-treatment of mild to moderate hypoglycemia. The average treatment observed for hypoglycemia was twice the recommended quantity of carbohydrates. On the other hand, we showed that 16 to 21 grams of carbohydrates is insufficient to treat, in a 15-min delay, a significant proportion of hypoglycemia episodes in T1D patients treated with insulin pump. In conclusion, patients encounter several difficulties associated with carbohydrate counting and the AP represents a promising therapy to alleviate some of the carbohydrate counting burden. A simplified prandial bolus based on meal categorization could simplify carbohydrate counting while preserving adequate glycemic control. In addition, a large proportion of patients with T1D do not follow recommendations for treatment of hypoglycemia episodes, which could be partly explained by the lack of efficacy of this treatment in a large subgroup of patients

New closed-loop insulin systems.

Advances in diabetes technologies have enabled the development of automated closed-loop insulin delivery systems. Several hybrid closed-loop systems have been commercialised, reflecting rapid transition of this evolving technology from research into clinical practice, where it is gradually transforming the management of type 1 diabetes in children and adults. In this review we consider the supporting evidence in terms of glucose control and quality of life for presently available closed-loop systems and those in development, including dual-hormone closed-loop systems. We also comment on alternative 'do-it-yourself' closed-loop systems. We remark on issues associated with clinical adoption of these approaches, including training provision, and consider limitations of presently available closed-loop systems and areas for future enhancements to further improve outcomes and reduce the burden of diabetes management