A feasibility study of bihormonal closed-loop blood glucose control using dual subcutaneous infusion of insulin and glucagon in ambulatory diabetic swine

Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence

Since Banting and Best isolated insulin in the 1920s, dramatic progress has been made in the treatment of type 1 diabetes mellitus (T1DM). However, dose titration and timely injection to maintain optimal glycemic control are often challenging for T1DM patients and their families because they require frequent blood glucose checks. In recent years, technological advances in insulin pumps and continuous glucose monitoring systems have created paradigm shifts in T1DM care that are being extended to develop artificial pancreas systems (APSs). Numerous studies that demonstrate the superiority of glycemic control offered by APSs over those offered by conventional treatment are still being published, and rapid commercialization and use in actual practice have already begun. Given this rapid development, keeping up with the latest knowledge in an organized way is confusing for both patients and medical staff. Herein, we explore the history, clinical evidence, and current state of APSs, focusing on various development groups and the commercialization status. We also discuss APS development in groups outside the usual T1DM patients and the administration of adjunct agents, such as amylin analogues, in APSs.

Chemical Synthesis and Characterization of a Nonfibrillating Glycoglucagon

The current commercially available glucagon formulations for the treatment of severe hypoglycemia must be reconstituted immediately prior to use, owing to the susceptibility of glucagon to fibrillation and aggregation in an aqueous solution. This results in the inconvenience of handling, misuse, and wastage of this drug. To address these issues, we synthesized a glycosylated glucagon analogue in which the 25th residue (Trp) was replaced with a cysteine (Cys) and a Br-disialyloligosaccharide was conjugated at the Cys thiol moiety. The resulting analogue, glycoglucagon, is a highly potent full agonist at the glucagon receptor. Importantly, glycoglucagon exhibits markedly reduced propensity for fibrillation and enhanced thermal and metabolic stability. This novel analogue is thus a valuable lead for producing stable liquid glucagon formulations that will improve patient compliance and minimize wastage.

Phenolic Preservative Removal from Commercial Insulin Formulations Reduces Tissue Inflammation while Maintaining Euglycemia

Background: Exogenous insulin therapy requires stabilization of the insulin molecule, which is achieved through the use of excipients (e.g., phenolic preservatives (PP)) that provide protein stability, sterility and prolong insulin shelf life. However, our laboratory recently reported that PP, (e.g., m-creosol and phenol) are also cytotoxic, inducing inflammation and fibrosis. Optimizing PP levels through filtration would balance the need for insulin preservation with PP-induced inflammation. Method: Zeolite Y (Z-Y), a size-exclusion-based resin, was employed to remove PP from commercial insulin formulations (Humalog) before infusion. Results: PP removal significantly decreased cell toxicity in vitro and inflammation in vivo. Infusion site histological analysis after a 3 day study demonstrated that leukocyte accumulation increased with nonfiltered preparations but decreased after filtration. Additional studies demonstrated that a Z-Y fabricated filter effectively removed excess PP such that the filtered insulin solution achieved equivalent glycemic control in diabetic mice when compared to nonfiltered insulin. Conclusion: This approach represents the proof of concept that using Z-Y for in-line PP removal assists in lowering inflammation at the site of insulin infusion and thus could lead to extending the functional lifespan of insulin infusion sets in vivo.

Feasibility of large experimental animal models in testing novel therapeutic strategies for diabetes

Diabetes is among the top 10 causes of death in adults and caused approximately four million deaths worldwide in 2017. The incidence and prevalence of diabetes is predicted to increase. To alleviate this potentially severe situation, safer and more effective therapeutics are urgently required. Mice have long been the mainstay as preclinical models for basic research on diabetes, although they are not ideally suited for translating basic knowledge into clinical applications. To validate and optimize novel therapeutics for safe application in humans, an appropriate large animal model is needed. Large animals, especially pigs, are well suited for biomedical research and share many similarities with humans, including body size, anatomical features, physiology, and pathophysiology. Moreover, pigs already play an important role in translational studies, including clinical trials for xenotransplantation. Progress in genetic engineering over the past few decades has facilitated the development of transgenic animals, including porcine models of diabetes. This article discusses features that attest to the attractiveness of genetically modified porcine models of diabetes for testing novel treatment strategies using recent technical advances.

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

BACKGROUND

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.

METHODS

We did a random-order crossover study in volunteers at least 18 years old who had type 1 diabetes and lived within a 30 min drive of four sites in the USA. Participants were randomly assigned (1:1) in blocks of two using sequentially numbered sealed envelopes to glycaemic regulation with a bihormonal bionic pancreas or usual care (conventional or sensor-augmented insulin pump therapy) first, followed by the opposite intervention. Both study periods were 11 days in length, during which time participants continued all normal activities, including athletics and driving. The bionic pancreas was initialised with only the participant's body mass. Autonomously adaptive dosing algorithms used data from a continuous glucose monitor to control subcutaneous delivery of insulin and glucagon. The coprimary outcomes were the mean glucose concentration and time with continuous glucose monitoring (CGM) glucose concentration less than 3·3 mmol/L, analysed over days 2-11 in participants who completed both periods of the study. This trial is registered with ClinicalTrials.gov, number NCT02092220.

FINDINGS

We randomly assigned 43 participants between May 6, 2014, and July 3, 2015, 39 of whom completed the study: 20 who were assigned to bionic pancreas first and 19 who were assigned to the comparator first. The mean CGM glucose concentration was 7·8 mmol/L (SD 0·6) in the bionic pancreas period versus 9·0 mmol/L (1·6) in the comparator period (difference 1·1 mmol/L, 95% CI 0·7-1·6; p<0·0001), and the mean time with CGM glucose concentration less than 3·3 mmol/L was 0·6% (0·6) in the bionic pancreas period versus 1·9% (1·7) in the comparator period (difference 1·3%, 95% CI 0·8-1·8; p<0·0001). The mean nausea score on the Visual Analogue Scale (score 0-10) was greater during the bionic pancreas period (0·52 [SD 0·83]) than in the comparator period (0·05 [0·17]; difference 0·47, 95% CI 0·21-0·73; p=0·0024). Body mass and laboratory parameters did not differ between periods. There were no serious or unexpected adverse events in the bionic pancreas period of the study.

INTERPRETATION

Relative to conventional and sensor-augmented insulin pump therapy, the bihormonal bionic pancreas, initialised only with participant weight, was able to achieve superior glycaemic regulation without the need for carbohydrate counting. Larger and longer studies are needed to establish the long-term benefits and risks of automated glycaemic management with a bihormonal bionic pancreas.

FUNDING

National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health, and National Center for Advancing Translational Sciences.

Glucagon sensitivity and clearance in type 1 diabetes: insights from in vivo and in silico experiments

Glucagon use in artificial pancreas for type 1 diabetes (T1D) is being explored for prevention and rescue from hypoglycemia. However, the relationship between glucagon stimulation of endogenous glucose production (EGP) viz., hepatic glucagon sensitivity, and prevailing glucose concentrations has not been examined. To test the hypothesis that glucagon sensitivity is increased at hypoglycemia vs. euglycemia, we studied 29 subjects with T1D randomized to a hypoglycemia or euglycemia clamp. Each subject was studied at three glucagon doses at euglycemia or hypoglycemia, with EGP measured by isotope dilution technique. The peak EGP increments and the integrated EGP response increased with increasing glucagon dose during euglycemia and hypoglycemia. However, the difference in dose response based on glycemia was not significant despite higher catecholamine concentrations in the hypoglycemia group. Knowledge of glucagon's effects on EGP was used to develop an in silico glucagon action model. The model-derived output fitted the obtained data at both euglycemia and hypoglycemia for all glucagon doses tested. Glucagon clearance did not differ between glucagon doses studied in both groups. Therefore, the glucagon controller of a dual hormone control system may not need to adjust glucagon sensitivity, and hence glucagon dosing, based on glucose concentrations during euglycemia and hypoglycemia.

Use of microdialysis-based continuous glucose monitoring to drive real-time semi-closed-loop insulin infusion

Continuous glucose monitoring (CGM) and automated insulin delivery may make diabetes management substantially easier, if the quality of the resulting therapy remains adequate. In this study, a semi-closed-loop control algorithm was used to drive insulin therapy and its quality was compared to that of subject-directed therapy. Twelve subjects stayed at the study site for approximately 70 hours and were provided with the investigational Automated Pancreas System Test Stand (APS-TS), which was used to calculate insulin dosage recommendations automatically. These recommendations were based on microdialysis CGM values and common diabetes therapy parameters. For the first half of their stay, the subjects directed their diabetes therapy themselves, whereas for the second half, the insulin recommendations were delivered by the APS-TS (so-called algorithm-driven therapy). During subject-directed therapy, the mean glucose was 114 mg/dl compared to 125 mg/dl during algorithm-driven therapy. Time in target (90 to 150 mg/dl) was approximately 46% during subject-directed therapy and approximately 58% during algorithm-driven therapy. When subjects directed their therapy, approximately 2 times more hypoglycemia interventions (oral administration of carbohydrates) were required than during algorithm-driven therapy. No hyperglycemia interventions (delivery of addition insulin) were necessary during subject-directed therapy, while during algorithm-driven therapy, 2 hyperglycemia interventions were necessary. The APS-TS was able to adequately control glucose concentrations in the subjects. Time in target was at least comparable or moderately higher during closed-loop control and markedly fewer hypoglycemia interventions were required, thus increasing patient safety.

Reducing risks in type 1 diabetes using H∞ control

A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an H∞ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified. The identification procedure is based on the distribution version of the UVA/Padova metabolic simulator using the simulation adult cohort. The SM prevents dangerous scenarios by acting upon a prediction of future glucose levels, and the IFL modifies the loop gain in order to reduce postprandial hypoglycemia risks. The procedure is tested on the complete alic>in silico adult cohort of the UVA/Padova metabolic simulator, which has been accepted by the Food and Drug Administration (FDA) in lieu of animal trials.

Clinical evaluation of an automated artificial pancreas using zone-model predictive control and health monitoring system

BACKGROUND

This study was performed to evaluate the safety and efficacy of a fully automated artificial pancreas using zone-model predictive control (zone-MPC) with the health monitoring system (HMS) during unannounced meals and overnight and exercise periods.

SUBJECTS AND METHODS

A fully automated closed-loop artificial pancreas was evaluated in 12 subjects (eight women, four men) with type 1 diabetes (mean±SD age, 49.4±10.4 years; diabetes duration, 32.7±16.0 years; glycosylated hemoglobin, 7.3±1.2%). The zone-MPC controller used an a priori model that was initialized using the subject's total daily insulin. The controller was designed to keep glucose levels between 80 and 140 mg/dL. A hypoglycemia prediction algorithm, a module of the HMS, was used in conjunction with the zone controller to alert the user to consume carbohydrates if the glucose level was predicted to fall below 70 mg/dL in the next 15 min.

RESULTS

The average time spent in the 70-180 mg/dL range, measured by the YSI glucose and lactate analyzer (Yellow Springs Instruments, Yellow Springs, OH), was 80% for the entire session, 92% overnight from 12 a.m. to 7 a.m., and 69% and 61% for the 5-h period after dinner and breakfast, respectively. The time spent < 60 mg/dL for the entire session by YSI was 0%, with no safety events. The HMS sent appropriate warnings to prevent hypoglycemia via short and multimedia message services, at an average of 3.8 treatments per subject.

CONCLUSIONS

The combination of the zone-MPC controller and the HMS hypoglycemia prevention algorithm was able to safely regulate glucose in a tight range with no adverse events despite the challenges of unannounced meals and moderate exercise.

Systematically in silico comparison of unihormonal and bihormonal artificial pancreas systems

Automated closed-loop control of blood glucose concentration is a daily challenge for type 1 diabetes mellitus, where insulin and glucagon are two critical hormones for glucose regulation. According to whether glucagon is included, all artificial pancreas (AP) systems can be divided into two types: unihormonal AP (infuse only insulin) and bihormonal AP (infuse both insulin and glucagon). Even though the bihormonal AP is widely considered a promising direction, related studies are very scarce due to this system's short research history. More importantly, there are few studies to compare these two kinds of AP systems fairly and systematically. In this paper, two switching rules, P-type and PD-type, were proposed to design the logic of orchestrates switching between insulin and glucagon subsystems, where the delivery rates of both insulin and glucagon were designed by using IMC-PID method. These proposed algorithms have been compared with an optimal unihormonal system on virtual type 1 diabetic subjects. The in silico results demonstrate that the proposed bihormonal AP systems have outstanding superiorities in reducing the risk of hypoglycemia, smoothing the glucose level, and robustness with respect to insulin/glucagon sensitivity variations, compared with the optimal unihormonal AP system.

Stable liquid glucagon formulations for rescue treatment and bi-hormonal closed-loop pancreas

Small doses of glucagon given subcutaneously in the research setting by an automated system prevent most cases of hypoglycemia in persons with diabetes. However, glucagon is very unstable and cannot be kept in a portable pump. Glucagon rapidly forms amyloid fibrils, even within the first day after reconstitution. Aggregation eventually leads to insoluble gels, which occlude pump catheters. Fibrillation occurs rapidly at acid pH, but is absent or minimal at alkaline pH values of ~10. Glucagon also degrades over time; this problem is greater at alkaline pH. Several studies suggest that its primary degradative pathway is deamidation, which results in a conversion of asparagine to aspartic acid. A cell-based assay for glucagon bioactivity that assesses glucagon receptor (GluR) activation can screen promising glucagon formulations. However, mammalian hepatocytes are usually problematic as they can lose GluR expression during culture. Assays for cyclic AMP (cAMP) or its downstream effector, protein kinase A (PKA), in engineered cell systems, are more reliable and suitable for inexpensive, high-throughput assessment of bioactivity.

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.

Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels

BACKGROUND

With continuous glucose sensors (CGSs), it is possible to obtain a dynamical signal of the patient's subcutaneous glucose concentration in real time. How could that information be exploited? We suggest a model-based diagnosis system with a twofold objective: real-time state estimation and long-term model parameter identification.

METHODS

To obtain a dynamical model, Bergman's nonlinear minimal model (considering plasma glucose G, insulin I, and interstitial insulin X) is extended by two states describing first and second insulin response. Furthermore, compartments for oral glucose and subcutaneous insulin inputs as well as for subcutaneous glucose measurement are added. The observability of states and external inputs as well as the identifiability of model parameters are assessed using the empirical observability Gramian. Signals are estimated for different nondiabetic and diabetic scenarios by unscented Kalman filter.

RESULTS

(1) Observability of different state subsets is evaluated, e.g., from CGSs, {G, I} or {G, X} can be observed and the set {G, I, X} cannot. (2) Model parameters are included, e.g., it is possible to estimate the second-phase insulin response gain k(G2) additionally. This can be used for model adaptation and as a diagnostic parameter that is almost zero for diabetes patients. (3) External inputs are considered, e.g., oral glucose is theoretically observable for nondiabetic patients, but estimation scenarios show that the time delay of 1 h limits application.

CONCLUSIONS

A real-time estimation of states (such as plasma insulin I) and parameters (such as k(G2)) is possible, which allows an improved real-time state prediction and a personalized model.

A genetic algorithm tuned optimal controller for glucose regulation in type 1 diabetic subjects

An optimal state feedback controller is designed with the objective of minimizing the elevated glucose levels caused by meal intake in Type 1 diabetic subjects, by the minimal infusion of insulin. The states for the controller based on linear quadratic regulator theory are estimated from noisy data using Kalman filter. The controller designed for a physiological relevant mathematical model is coupled with another model for simulating meal dynamics, which converts meal intake into glucose appearance rate in the plasma. The tuning parameters (weighting matrices) of the controller and the design parameters (noise covariance matrices) of the Kalman filter are optimized using genetic algorithm. The controller based on the combined framework of evolutionary computing and state estimated linear quadratic regulator is found to maintain normoglycemia for meal intakes of varying carbohydrate content. The proposed approach addresses noisy output measurement, modeling error and delay in sensor measurement.

Closed-loop artificial pancreas: current studies and promise for the future

PURPOSE OF REVIEW

The availability of glucose sensors and insulin pumps has enabled the development of devices and software to partially or completely automate insulin delivery. Over the last 2 years, the number of centers developing and evaluating such systems, as well as the number of reports of these studies in the literature, have expanded dramatically. The purpose of this review is to highlight the progress along multiple fronts to develop automated systems to improve control of type 1 diabetes.

RECENT FINDINGS

Multiple approaches, including automated suspension for actual or impending hypoglycemia, automated augmentation for hyperglycemia, as well as hybrid and full closed-loop control, are in parallel development. So far, early hypoglycemia prevention studies and small inpatient feasibility studies have demonstrated the potential for reducing hypoglycemia and improving overall diabetes control.

SUMMARY

Current sensors, pumps, and control algorithms show promise for use in a closed-loop system but have been limited to inpatient studies. The next phase of development should focus on their evaluation in controlled short-term outpatient safety and efficacy trials.

U-100, pH-Neutral formulation of VIAject(®) : faster onset of action than insulin lispro in patients with type 1 diabetes

AIMS

VIAject® is a formulation of human insulin with a very fast onset of action. Previous studies used VIAject in a concentration of 25 U/ml and a pH of 4 [VIAject 25 (VJ25)]. Objective of this double blind, three-way crossover study was to compare the pharmacodynamic/pharmacokinetic properties of a novel formulation of VIAject with a concentration of 100 U/ml and a neutral pH [VIAject 7 (VJ7)] with VJ25 and insulin lispro (LIS).

METHODS

Forty-three patients with type 1 diabetes [aged 43 (21-65) years, BMI 24.1 (20-28) kg/m(2) and HbA1c 7.5 (5.7-9.5) %] participated in this study. They received subcutaneous injections of 12 U of each insulin formulation under euglycaemic glucose clamp conditions.

RESULTS

VJ7 was bioequivalent to VJ25 [90% confidence interval (CI) of the ratios for total insulin AUCs and maximum insulin concentration (C(INS max) ) was within 0.80-1.25]. VJ7 showed a faster absorption compared to LIS [time to C(INS max) 23 vs. 60 min; difference (CI) -30 (-35 to -23)] and faster onset of action [time to early half-maximal glucose infusion rate (GIR) 25 vs. 44 min; -18 (-26 to -10)], and a higher AUC of glucose infusion rate (AUC(GIR) ) in the first 60 min after injection [176 vs. 107 mg/kg; ratio 1.65 (1.27 to 2.14)], contributing to a slightly higher value for AUC(GIR 0-480) [1263 vs. 1095 mg/kg; 1.15 (1.06 to 1.26)]. Maximum GIR was similar between VJ7 and LIS [6.1 vs.6.6 mg/kg/min; ratio 0.93 (0.86 to 1.01)], whereas the duration of action (t(GIR50%-late) ) was longer with VJ7 [274 vs. 228 min; 50 (25 to 73)].

CONCLUSIONS

This formulation of VIAject is bioequivalent to the previously used formulation and has a faster absorption/onset of action than LIS.

Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?

Closed-loop (automated) controllers are encountered in all aspects of modern life in applications ranging from air-conditioning to spaceflight. Although these systems are virtually ubiquitous, they are infrequently used in anesthesiology because of the complexity of physiologic systems and the difficulty in obtaining reliable and valid feedback data from the patient. Despite these challenges, closed-loop systems are being increasingly studied and improved for medical use. Two recent developments have made fluid administration a candidate for closed-loop control. First, the further description and development of dynamic predictors of fluid responsiveness provides a strong parameter for use as a control variable to guide fluid administration. Second, rapid advances in noninvasive monitoring of cardiac output and other hemodynamic variables make goal-directed therapy applicable for a wide range of patients in a variety of clinical care settings. In this article, we review the history of closed-loop controllers in clinical care, discuss the current understanding and limitations of the dynamic predictors of fluid responsiveness, and examine how these variables might be incorporated into a closed-loop fluid administration system.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.

Efficacy determinants of subcutaneous microdose glucagon during closed-loop control

BACKGROUND

During a previous clinical trial of a closed-loop blood glucose (BG) control system that administered insulin and microdose glucagon subcutaneously, glucagon was not uniformly effective in preventing hypoglycemia (BG<70 mg/dl). After a global adjustment of control algorithm parameters used to model insulin absorption and clearance to more closely match insulin pharmacokinetic (PK) parameters observed in the study cohort, administration of glucagon by the control system was more effective in preventing hypoglycemia. We evaluated the role of plasma insulin and plasma glucagon levels in determining whether glucagon was effective in preventing hypoglycemia.

METHODS

We identified and analyzed 36 episodes during which glucagon was given and categorized them as either successful or unsuccessful in preventing hypoglycemia.

RESULTS

In 20 of the 36 episodes, glucagon administration prevented hypoglycemia. In the remaining 16, BG fell below 70 mg/dl (12 of the 16 occurred during experiments performed before PK parameters were adjusted). The (dimensionless) levels of plasma insulin (normalized relative to each subject's baseline insulin level) were significantly higher during episodes ending in hypoglycemia (5.2 versus 3.7 times the baseline insulin level, p=.01). The relative error in the control algorithm's online estimate of the instantaneous plasma insulin level was also higher during episodes ending in hypoglycemia (50 versus 30%, p=.003), as were the peak plasma glucagon levels (183 versus 116 pg/ml, p=.007, normal range 50-150 pg/ml) and mean plasma glucagon levels (142 versus 75 pg/ml, p=.02). Relative to mean plasma insulin levels, mean plasma glucagon levels tended to be 59% higher during episodes ending in hypoglycemia, although this result was not found to be statistically significant (p=.14). The rate of BG descent was also significantly greater during episodes ending in hypoglycemia (1.5 versus 1.0 mg/dl/min, p=.02).

CONCLUSIONS

Microdose glucagon administration was relatively ineffective in preventing hypoglycemia when plasma insulin levels exceeded the controller's online estimate by >60%. After the algorithm PK parameters were globally adjusted, insulin dosing was more conservative and microdose glucagon administration was very effective in reducing hypoglycemia while maintaining normal plasma glucagon levels. Improvements in the accuracy of the controller's online estimate of plasma insulin levels could be achieved if ultrarapid-acting insulin formulations could be developed with faster absorption and less intra- and intersubject variability than the current insulin analogs available today.

A bihormonal closed-loop artificial pancreas for type 1 diabetes

Automated control of blood glucose (BG) concentration is a long-sought goal for type 1 diabetes therapy. We have developed a closed-loop control system that uses frequent measurements of BG concentration along with subcutaneous delivery of both the fast-acting insulin analog lispro and glucagon (to imitate normal physiology) as directed by a computer algorithm. The algorithm responded only to BG concentrations and incorporated a pharmacokinetic model for lispro. Eleven subjects with type 1 diabetes and no endogenous insulin secretion were studied in 27-hour experiments, which included three carbohydrate-rich meals. In six subjects, the closed-loop system achieved a mean BG concentration of 140 mg/dl, which is below the mean BG concentration target of < or =154 mg/dl recommended by the American Diabetes Association. There were no instances of treatment-requiring hypoglycemia. Five other subjects exhibited hypoglycemia that required treatment; however, these individuals had slower lispro absorption kinetics than the six subjects that did not become hypoglycemic. The time-to-peak plasma lispro concentrations of subjects that exhibited hypoglycemia ranged from 71 to 191 min (mean, 117 +/- 48 min) versus 56 to 72 min (mean, 64 +/- 6 min) in the group that did not become hypoglycemic (aggregate mean of 84 min versus 31 min longer than the algorithm's assumption of 33 min, P = 0.07). In an additional set of experiments, adjustment of the algorithm's pharmacokinetic parameters (time-to-peak plasma lispro concentration set to 65 min) prevented hypoglycemia in both groups while achieving an aggregate mean BG concentration of 164 mg/dl. These results demonstrate the feasibility of safe BG control by a bihormonal artificial endocrine pancreas.

Can we really close the loop and how soon? Accelerating the availability of an artificial pancreas: a roadmap to better diabetes outcomes

Development of a closed-loop artificial pancreas has been a long-time goal that could transform diabetes management. The primary limitation until recent years was the lack of a robust and portable continuous glucose sensor. There has been significant progress over the past 5 years in the development and commercialization of continuous glucose monitoring (CGM) devices. Used adjunctively, CGM has been demonstrated to add significant value in improving diabetes management by increasing time spent in glycemic targets and improving overall glycemic control. However, these benefits are limited by the human user's finite capacity to respond to the data provided by the device. By automating even a portion of the insulin delivery functionality of combined sensor/pump systems via computer algorithm, impending excursions could be handled more quickly and effectively. This review will describe very promising preliminary closed-loop studies, describe a potential roadmap to an artificial pancreas that will be safe and effective, and propose a solution-a hypo- and hyperglycemia minimizing control-to-range approach-that may allow for near-term delivery of a semiautomated system to people with diabetes.

Mathematical modeling research to support the development of automated insulin-delivery systems

The world leaders in glycemia modeling convened during the Eighth Annual Diabetes Technology Meeting in Bethesda, Maryland, on 14 November 2008, to discuss the current practices in mathematical modeling and make recommendations for its use in developing automated insulin-delivery systems. This report summarizes the collective views of the 25 participating experts in addressing the following four topics: current practices in modeling efforts for closed-loop control; framework for exchange of information and collaboration among research centers; major barriers for the development of accurate models; and key tasks for developing algorithms to build closed-loop control systems. Among the participants, the following main conclusions and recommendations were widely supported: 1. Physiologic variance represents the single largest technical challenge to creating accurate simulation models. 2. A Web site describing different models and the data supporting them should be made publically available, with funding agencies and journals requiring investigators to provide open access to both models and data. 3. Existing simulation models should be compared and contrasted, using the same evaluation and validation criteria, to better assess the state of the art, understand any inherent limitations in the models, and identify gaps in data and/or model capability.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

Continuous glucose monitoring awaits its "killer app"

Continuous glucose monitoring (CGM) could drive a paradigm shift in diabetes care, but realization of this promise awaits a complementary shift in the way CGM data is used. The most exciting use for CGM is as the input for automated, closed-loop glucose control. Although first generation CGM devices leave much room for improvement, closed-loop control does not have to wait. Algorithms should target blood glucose levels above the normal range for safety in the setting of imperfect CGM measurements. If the mean glucose under closed-loop control is sufficiently close to the chosen target, hemoglobin A1c goals could be met while minimizing risk of hypoglycemia. CGM may also improve the care of intensive care unit patients treated with intensive insulin therapy and the large numbers of diabetic patients in general hospital wards.