Closed-loop insulin delivery – what lies between where we are and where we are going?

A comparison among three maximal mathematical models of the glucose-insulin system

The most well-known and widely used mathematical representations of the physiology of a diabetic individual are the Sorensen and Hovorka models as well as the UVAPadova Simulator. While the Hovorka model and the UVAPadova Simulator only describe the glucose metabolism of a subject with type 1 diabetes, the Sorensen model was formulated to simulate the behaviour of both normal and diabetic individuals. The UVAPadova model is the most known model, accepted by the FDA, with a high level of complexity. The Hovorka model is the simplest of the three models, well documented and used primarily for the development of control algorithms. The Sorensen model is the most complete, even though some modifications were required both to the model equations (adding useful compartments for modelling subcutaneous insulin delivery) and to the parameter values. In the present work several simulated experiments, such as IVGTTs and OGTTs, were used as tools to compare the three formulations in order to establish to what extent increasing complexity translates into richer and more correct physiological behaviour. All the equations and parameters used for carrying out the simulations are provided.

A revised Sorensen model: Simulating glycemic and insulinemic response to oral and intra-venous glucose load

In 1978, Thomas J. Sorensen defended a thesis in chemical engineering at the University of California, Berkeley, where he proposed an extensive model of glucose-insulin control, model which was thereafter widely employed for virtual patient simulation. The original model, and even more so its subsequent implementations by other Authors, presented however a few imprecisions in reporting the correct model equations and parameter values. The goal of the present work is to revise the original Sorensen's model, to clearly summarize its defining equations, to supplement it with a missing gastrio-intestinal glucose absorption and to make an implementation of the revised model available on-line to the scientific community.

A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages

BACKGROUND

Computer clinical decision support (CDS) systems are uncommon in the pediatric intensive care unit (PICU), despite evidence suggesting they improve outcomes in adult ICUs. We reasoned that a bedside CDS protocol for intravenous insulin titration, eProtocol-insulin, would be feasible and safe in critically ill children.

METHODS

We retrospectively reviewed data from non-diabetic children admitted to the PICU with blood glucose (BG) ≥140 mg/dL who were managed with intravenous insulin by either unaided clinician titration or eProtocol-insulin. Primary outcomes were BG measurements in target range (80-110 mg/dL) and severe hypoglycemia (BG ≤40 mg/dL); secondary outcomes were 60-day mortality and PICU length of stay. We assessed bedside nurse satisfaction with the eProtocol-insulin protocol by using a 5-point Likert scale and measured clinician compliance with eProtocol-insulin recommendations.

RESULTS

Over 5 years, 69 children were titrated with eProtocol-insulin versus 104 by unaided clinicians. eProtocol-insulin achieved target range more frequently than clinician titration (41% vs. 32%, P < 0.001). Severe hypoglycemia was uncommon in both groups (4.3% of patients in eProtocol-insulin, 8.7% in clinician titration, P = 0.37). There were no differences in mean time to BG target or median BG between the groups. Mortality was 23% in both groups. Clinician compliance with eProtocol-insulin recommendations was 89%. Nurses believed that eProtocol-insulin was easy to understand and safer than clinician titration.

CONCLUSIONS

eProtocol-insulin is safe for titration of intravenous insulin in critically ill children. Clinical research protocols and quality improvement initiatives aimed at optimizing BG control should utilize detailed computer protocols that enable replicable clinician decisions.

Stimuli sensitive polymers and self regulated drug delivery systems: a very partial review

Since the early days of the Journal of Controlled Release, there has been considerable interest in materials that can release drug on an "on-demand" basis. So called "stimuli-responsive" and "intelligent" systems have been designed to deliver drug at various times or at various sites in the body, according to a stimulus that is either endogenous or externally applied. In the past three decades, research along these lines has taken numerous directions, and each new generation of investigators has discovered new physicochemical principles and chemical schemes by which the release properties of materials can be altered. No single review could possibly do justice to all of these approaches. In this article, some general observations are made, and a partial history of the field is presented. Both open loop and closed loop systems are discussed. Special emphasis is placed on stimuli-responsive hydrogels, and on systems that can respond repeatedly. It is argued that the most success at present and in the foreseeable future is with systems in which biosensing and actuation (i.e. drug delivery) are separated, with a human and/or cybernetic operator linking the two.

A model of glucose-insulin-pramlintide pharmacokinetics and pharmacodynamics in type I diabetes

Type 1 diabetes mellitus (T1DM) complications are significantly reduced when normoglycemic levels are maintained via intensive therapy. The artificial pancreas is designed for intensive glycemic control; however, large postprandial excursions after a meal result in poor glucose regulation. Pramlintide, a synthetic analog of the hormone amylin, reduces the severity of postprandial excursions by reducing appetite, suppressing glucagon release, and slowing the rate of gastric emptying. The goal of this study is to create a glucose-insulin-pramlintide physiological model that can be employed into a controller to improve current control approaches used in the artificial pancreas. A model of subcutaneous (SC) pramlintide pharmacokinetics (PK) was developed by revising an intravenous (IV) pramlintide PK model and adapting SC insulin PK from a glucose-insulin model. Gray-box modeling and least squares optimization were used to obtain parameter estimates. Pharmacodynamics (PD) were obtained by choosing parameters most applicable to pramlintide mechanisms and then testing using a proportional PD effect using least squares optimization. The model was fit and validated using 27 data sets, which included placebo, PK, and PD data. SC pramlintide PK root mean square error values range from 1.98 to 10.66 pmol/L. Pramlintide PD RMSE values range from 10.48 to 42.76 mg/dL. A new in silico model of the glucose-insulin-pramlintide regulatory system is presented. This model can be used as a platform to optimize dosing of both pramlintide and insulin as a combined therapy for glycemic regulation, and in the development of an artificial pancreas as the kernel for a model-based controller.

Day and night closed-loop control in adults with type 1 diabetes: a comparison of two closed-loop algorithms driving continuous subcutaneous insulin infusion versus patient self-management

OBJECTIVE

To compare two validated closed-loop (CL) algorithms versus patient self-control with CSII in terms of glycemic control.

RESEARCH DESIGN AND METHODS

This study was a multicenter, randomized, three-way crossover, open-label trial in 48 patients with type 1 diabetes mellitus for at least 6 months, treated with continuous subcutaneous insulin infusion. Blood glucose was controlled for 23 h by the algorithm of the Universities of Pavia and Padova with a Safety Supervision Module developed at the Universities of Virginia and California at Santa Barbara (international artificial pancreas [iAP]), by the algorithm of University of Cambridge (CAM), or by patients themselves in open loop (OL) during three hospital admissions including meals and exercise. The main analysis was on an intention-to-treat basis. Main outcome measures included time spent in target (glucose levels between 3.9 and 8.0 mmol/L or between 3.9 and 10.0 mmol/L after meals).

RESULTS

Time spent in the target range was similar in CL and OL: 62.6% for OL, 59.2% for iAP, and 58.3% for CAM. While mean glucose level was significantly lower in OL (7.19, 8.15, and 8.26 mmol/L, respectively) (overall P = 0.001), percentage of time spent in hypoglycemia (<3.9 mmol/L) was almost threefold reduced during CL (6.4%, 2.1%, and 2.0%) (overall P = 0.001) with less time ≤2.8 mmol/L (overall P = 0.038). There were no significant differences in outcomes between algorithms.

CONCLUSIONS

Both CAM and iAP algorithms provide safe glycemic control.

Closed-loop insulin delivery in type 1 diabetes

Advances in diabetes technology have led to significant improvements in the quality of life and care received by individuals with diabetes. Despite this, achieving tight glycemic control through intensive insulin therapy and modern insulin regimens is challenging because of the barrier of hypoglycemia, the most feared complication of insulin therapy as reported by patients, caregivers, and physicians. This article outlines the individual components of the closed-loop system together with the existing clinical evidence. The artificial pancreas prototypes currently used in clinical studies are reviewed as well as obstacles and limitations facing the technology.

Closed-loop insulin delivery for treatment of type 1 diabetes

Type 1 diabetes is one of the most common endocrine problems in childhood and adolescence, and remains a serious chronic disorder with increased morbidity and mortality, and reduced quality of life. Technological innovations positively affect the management of type 1 diabetes. Closed-loop insulin delivery (artificial pancreas) is a recent medical innovation, aiming to reduce the risk of hypoglycemia while achieving tight control of glucose. Characterized by real-time glucose-responsive insulin administration, closed-loop systems combine glucose-sensing and insulin-delivery components. In the most viable and researched configuration, a disposable sensor measures interstitial glucose levels, which are fed into a control algorithm controlling delivery of a rapid-acting insulin analog into the subcutaneous tissue by an insulin pump. Research progress builds on an increasing use of insulin pumps and availability of glucose monitors. We review the current status of insulin delivery, focusing on clinical evaluations of closed-loop systems. Future goals are outlined, and benefits and limitations of closed-loop therapy contrasted. The clinical utility of these systems is constrained by inaccuracies in glucose sensing, inter- and intra-patient variability, and delays due to absorption of insulin from the subcutaneous tissue, all of which are being gradually addressed.

Effect of varying molecular weight of dextran on acrylic-derivatized dextran and concanavalin A glucose-responsive materials for closed-loop insulin delivery

AIM

Dextran methacrylate (dex-MA) and concanavalin A (con A)-methacrylamide were photopolymerized to produce covalently cross-linked glucose-sensitive gels for the basis of an implantable closed-loop insulin delivery device.

METHODS

The viscoelastic properties of these polymerized gels were tested rheologically in the non-destructive oscillatory mode within the linear viscoelastic range at glucose concentrations between 0 and 5% (w/w).

RESULTS

For each cross-linked gel, as the glucose concentration was raised, a decrease in storage modulus, loss modulus and complex viscosity (compared at 1 Hz) was observed, indicating that these materials were glucose responsive. The higher molecular weight acrylic-derivatized dextrans [degree of substitution (DS) 3 and 8%] produced higher complex viscosities across the glucose concentration range.

CONCLUSIONS

These studies coupled with in vitro diffusion experiments show that dex-MA of 70 kDa and DS (3%) was the optimum mass average molar mass to produce gels that show reduced component leach, glucose responsiveness, and insulin transport useful as part of a self-regulating insulin delivery device.

The effect of insulin feedback on closed loop glucose control

CONTEXT

Initial studies of closed-loop proportional integral derivative control in individuals with type 1 diabetes showed good overnight performance, but with breakfast meal being the hardest to control and requiring supplemental carbohydrate to prevent hypoglycemia.

OBJECTIVE

The aim of this study was to assess the ability of insulin feedback to improve the breakfast-meal profile.

DESIGN AND SETTING

We performed a single center study with closed-loop control over approximately 30 h at an inpatient clinical research facility.

PATIENTS

Eight adult subjects with previously diagnosed type 1 diabetes participated.

INTERVENTION

Subjects received closed-loop insulin delivery with supplemental carbohydrate as needed.

MAIN OUTCOME MEASURES

Outcome measures were plasma insulin concentration, model-predicted plasma insulin concentration, 2-h postprandial and 3- to 4-h glucose rate-of-change following breakfast after 1 d of closed-loop control, and the need for supplemental carbohydrate in response to nadir hypoglycemia.

RESULTS

Plasma insulin levels during closed loop were well correlated with model predictions (R = 0.86). Fasting glucose after 1 d of closed loop was not different from nighttime target (118 ± 9 vs. 110 mg/dl; P = 0.38). Two-hour postbreakfast glucose was 132 ± 16 mg/dl with stable values 3-4 h after the meal (0.03792 ± 0.0884 mg/dl · min, not different from 0; P = 0.68) and at target (97 ± 6 mg/dl, not different from 90; P = 0.28). Three subjects required supplemental carbohydrates after breakfast on d 2 of closed loop.

CONCLUSIONS/INTERPRETATION

Insulin feedback can be implemented using a model estimate of concentration. Proportional integral derivative control with insulin feedback can achieve a desired breakfast response but still requires supplemental carbohydrate to be delivered in some instances. Studies assessing more optimal control configurations and safeguards need to be conducted.

Physiologic insulin delivery with insulin feedback: a control systems perspective

Individuals with type 1 diabetes mellitus must effectively manage glycemia to avoid acute and chronic complications related to aberrations of glucose levels. Because optimal diabetes management can be difficult to achieve and burdensome, research into a closed-loop insulin delivery system has been of interest for several decades. This paper provides an overview, from a control systems perspective, of the research and development effort of a particular algorithm--the external physiologic insulin delivery system. In particular the introduction of insulin feedback, based on β-cell physiology, is covered in detail. A summary of human clinical trials is provided in the context of the evolution of this algorithm, and this paper outlines some of the research avenues that show particular promise.

Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomised controlled studies

OBJECTIVE

To compare the safety and efficacy of overnight closed loop delivery of insulin (artificial pancreas) with conventional insulin pump therapy in adults with type 1 diabetes.

DESIGN

Two sequential, open label, randomised controlled crossover, single centre studies.

SETTING

Clinical research facility.

PARTICIPANTS

24 adults (10 men, 14 women) with type 1 diabetes, aged 18-65, who had used insulin pump therapy for at least three months: 12 were tested after consuming a medium sized meal and the other 12 after consuming a larger meal accompanied by alcohol.

INTERVENTION

During overnight closed loop delivery, sensor measurements of glucose were fed into a computer algorithm, which advised on insulin pump infusion rates at 15 minute intervals. During control nights, conventional insulin pump settings were applied. One study compared closed loop delivery of insulin with conventional pump therapy after a medium sized evening meal (60 g of carbohydrates) at 1900, depicting the scenario of "eating in." The other study was carried out after a later large evening meal (100 g of carbohydrates) at 2030, accompanied by white wine (0.75 g/kg ethanol) and depicted the scenario of "eating out."

MAIN OUTCOME MEASURES

The primary outcome was the time plasma glucose levels were in target (3.91-8.0 mmol/L) during closed loop delivery and a comparable control period. Secondary outcomes included pooled data analysis and time plasma glucose levels were below target (≤ 3.9 mmol/L).

RESULTS

For the eating in scenario, overnight closed loop delivery of insulin increased the time plasma glucose levels were in target by a median 15% (interquartile range 3-35%), P = 0.002. For the eating out scenario, closed loop delivery increased the time plasma glucose levels were in target by a median 28% (2-39%), P = 0.01. Analysis of pooled data showed that the overall time plasma glucose was in target increased by a median 22% (3-37%) with closed loop delivery (P < 0.001). Closed loop delivery reduced overnight time spent hypoglycaemic (plasma glucose ≤ 3.9 mmol/L) by a median 3% (0-20%), P=0.04, and eliminated plasma glucose concentrations below 3.0 mmol/L after midnight.

CONCLUSION

These two small crossover trials suggest that closed loop delivery of insulin may improve overnight control of glucose levels and reduce the risk of nocturnal hypoglycaemia in adults with type 1 diabetes. Trial registration ClinicalTrials.gov NCT00910767 and NCT00944619.

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.

Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial

BACKGROUND

Closed-loop systems link continuous glucose measurements to insulin delivery. We aimed to establish whether closed-loop insulin delivery could control overnight blood glucose in young people.

METHODS

We undertook three randomised crossover studies in 19 patients aged 5-18 years with type 1 diabetes of duration 6.4 years (SD 4.0). We compared standard continuous subcutaneous insulin infusion and closed-loop delivery (n=13; APCam01); closed-loop delivery after rapidly and slowly absorbed meals (n=7; APCam02); and closed-loop delivery and standard treatment after exercise (n=10; APCam03). Allocation was by computer-generated random code. Participants were masked to plasma and sensor glucose. In APCam01, investigators were masked to plasma glucose. During closed-loop nights, glucose measurements were fed every 15 min into a control algorithm calculating rate of insulin infusion, and a nurse adjusted the insulin pump. During control nights, patients' standard pump settings were applied. Primary outcomes were time for which plasma glucose concentration was 3.91-8.00 mmol/L or 3.90 mmol/L or lower. Analysis was per protocol. This trial is registered, number ISRCTN18155883.

FINDINGS

17 patients were studied for 33 closed-loop and 21 continuous infusion nights. Primary outcomes did not differ significantly between treatment groups in APCam01 (12 analysed; target range, median 52% [IQR 43-83] closed loop vs 39% [15-51] standard treatment, p=0.06;

INTERPRETATION

Closed-loop systems could reduce risk of nocturnal hypoglycaemia in children and adolescents with type 1 diabetes.

FUNDING

Juvenile Diabetes Research Foundation; European Foundation for Study of Diabetes; Medical Research Council Centre for Obesity and Related Metabolic Diseases; National Institute for Health Research Cambridge Biomedical Research Centre.

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Key Words Collapse

MESH Headings

• Adolescent
• Algorithms
• Biosensing Techniques
• Blood Glucose/metabolism
• Child
• Child, Preschool
• Cross-Over Studies
• Diabetes Mellitus, Type 1/blood
• Diabetes Mellitus, Type 1/drug therapy
• Female
• Humans
• Hypoglycemic Agents/administration & dosage
• Infusions, Subcutaneous
• Insulin/administration & dosage
• Insulin/blood
• Insulin Infusion Systems
• Male
• Treatment Outcome

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Grants

• G0600717 Medical Research Council

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Affiliation(s)

Roman Hovorka Department of Paediatrics, University of Cambridge, Cambridge, UK.
Janet M Allen
Daniela Elleri
Ludovic J Chassin
Julie Harris
Dongyuan Xing
Craig Kollman
Tomas Hovorka
Anne Mette F Larsen
Marianna Nodale
Alessandra De Palma
Malgorzata E Wilinska
Carlo L Acerini
David B Dunger

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Overnight closed-loop insulin delivery with model predictive control: assessment of hypoglycemia and hyperglycemia risk using simulation studies

BACKGROUND

Hypoglycemia and hyperglycemia during closed-loop insulin delivery based on subcutaneous (SC) glucose sensing may arise due to (1) overdosing and underdosing of insulin by control algorithm and (2) difference between plasma glucose (PG) and sensor glucose, which may be transient (kinetics origin and sensor artifacts) or persistent (calibration error [CE]). Using in silico testing, we assessed hypoglycemia and hyperglycemia incidence during over-night closed loop. Additionally, a comparison was made against incidence observed experimentally during open-loop single-night in-clinic studies in young people with type 1 diabetes mellitus (T1DM) treated by continuous SC insulin infusion.

METHODS

Simulation environment comprising 18 virtual subjects with T1DM was used to simulate overnight closed-loop study with a model predictive control (MPC) algorithm. A 15 h experiment started at 17:00 and ended at 08:00 the next day. Closed loop commenced at 21:00 and continued for 11 h. At 18:00, protocol included meal (50 g carbohydrates) accompanied by prandial insulin. The MPC algorithm advised on insulin infusion every 15 min. Sensor glucose was obtained by combining model-calculated noise-free interstitial glucose with experimentally derived transient and persistent sensor artifacts associated with FreeStyle Navigator (FSN). Transient artifacts were obtained from FSN sensor pairs worn by 58 subjects with T1DM over 194 nighttime periods. Persistent difference due to FSN CE was quantified from 585 FSN sensor insertions, yielding 1421 calibration sessions from 248 subjects with diabetes.

RESULTS

Episodes of severe (PG < or = 36 mg/dl) and significant (PG < or = 45 mg/dl) hypoglycemia and significant hyperglycemia (PG > or = 300 mg/dl) were extracted from 18,000 simulated closed-loop nights. Severe hypoglycemia was not observed when FSN CE was less than 45%. Hypoglycemia and hyperglycemia incidence during open loop was assessed from 21 overnight studies in 17 young subjects with T1DM (8 males; 13.5 +/- 3.6 years of age; body mass index 21.0 +/- 4.0 kg/m2; duration diabetes 6.4 +/- 4.1 years; hemoglobin A1c 8.5% +/- 1.8%; mean +/- standard deviation) participating in the Artificial Pancreas Project at Cambridge. Severe and significant hypoglycemia during simulated closed loop occurred 0.75 and 17.11 times per 100 person years compared to 1739 and 3479 times per 100 person years during experimental open loop, respectively. Significant hyperglycemia during closed loop and open loop occurred 75 and 15,654 times per 100 person years, respectively.

CONCLUSIONS

The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during stimulated overnight closed loop with MPC compared to that observed during open-loop overnight clinical studies in young subjects with T1DM. Hyperglycemia was 200 times less likely. Overnight closed loop with the FSN and the MPC algorithm is expected to reduce substantially the risk of hypoglycemia and hyperglycemia.

Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM)

Continuous glucose monitoring (CGM) is an evolving technology poised to redefine current concepts of glycemic control and optimal diabetes management. To date, there are few randomized studies examining how to most effectively use this new tool. Therefore, a group of eight diabetes specialists heard presentations on continuous glucose sensor technology and then discussed their experience with CGM in order to identify fundamental considerations, objectives, and methods for applying this technology in clinical practice. The group concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia. The need for such progress is indicated by the growing prevalence of inadequately treated hyperglycemia. Coordinating financial and educational resources and developing clear protocols for using glucose sensor technology are urgent priorities in promoting wide adoption of CGM by patients and health care providers. Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices.

A Feedforward-Feedback Glucose Control Strategy for Type 1 Diabetes Mellitus

As the "artificial pancreas" becomes closer to reality, automated insulin delivery based on real-time glucose measurements becomes feasible for people with diabetes. This paper is concerned with the development of novel feedforward-feedback control strategies for real-time glucose control and type 1 diabetes. Improved post-meal responses can be achieved by a pre-prandial snack or bolus, or by reducing the glucose setpoint prior to the meal. Several feedforward-feedback control strategies provide attractive alternatives to the standard meal insulin bolus and are evaluated in simulations using a physiological model.

Islet encapsulation: strategies to enhance islet cell functions

Diabetes is one of the most prevalent, costly, and debilitating diseases in the world. Although traditional insulin therapy has alleviated the short-term effects, long-term complications are ubiquitous and harmful. For these reasons, alternative treatment options are being developed. This review investigates one appealing area: cell replacement using encapsulated islets. Encapsulation materials, encapsulation methods, and cell sources are presented and discussed. In addition, the major factors that currently limit cell viability and functionality are reviewed, and strategies to overcome these limitations are examined. This review is designed to introduce the reader to cell replacement therapy and cell and tissue encapsulation, especially as it applies to diabetes.

Evaluation of the effect of gain on the meal response of an automated closed-loop insulin delivery system

A continuous closed-loop insulin delivery system using subcutaneous insulin delivery was evaluated in eight diabetic canines. Continuous glucose profiles were obtained by extrapolation of blood glucose measurements. Insulin delivery rate was calculated, using a model of beta-cell insulin secretion, and delivered with a Medtronic MiniMed subcutaneous infusion pump. The model acts like a classic proportional-integral-derivative controller, delivering insulin in proportion to glucose above target, history of past glucose values, and glucose rate of change. For each dog, a proportional gain was set relative to the open-loop total daily dose (TDD) of insulin. Additional gains based on 0.5 x TDD and 1.5 x TDD were also evaluated (gain dose response). Control was initiated 4 h before the meal with a target of 6.7 mmol/l. At the time of the meal, glucose was similar for all three gains (6.0 +/- 0.3, 5.2 +/- 0.3, and 4.9 +/- 0.5 mmol/l for 0.5 x TDD, TDD, and 1.5 x TDD, respectively; P > 0.05) with near-target values restored at the end of experiments (8.2 +/- 0.9, 6.0 +/- 0.6, and 6.0 +/- 0.5, respectively). The peak postprandial glucose level decreased significantly with increasing gain (12.1 +/- 0.6, 9.6 +/- 1.0, and 8.5 +/- 0.6 mmol/l, respectively; P < 0.05). The data demonstrate that closed-loop insulin delivery using the subcutaneous site can provide stable glycemic control within a range of gain.