Pharmacokinetics of insulin aspart and glucagon in type 1 diabetes during closed-loop operation

Ultra-stable insulin-glucagon fusion protein exploits an endogenous hepatic switch to mitigate hypoglycemic risk

The risk of hypoglycemia and its serious medical sequelae restrict insulin replacement therapy for diabetes mellitus. Such adverse clinical impact has motivated development of diverse glucose-responsive technologies, including algorithm-controlled insulin pumps linked to continuous glucose monitors ("closed-loop systems") and glucose-sensing ("smart") insulins. These technologies seek to optimize glycemic control while minimizing hypoglycemic risk. Here, we describe an alternative approach that exploits an endogenous glucose-dependent switch in hepatic physiology: preferential insulin signaling (under hyperglycemic conditions) versus preferential counter-regulatory glucagon signaling (during hypoglycemia). Motivated by prior reports of glucagon-insulin co-infusion, we designed and tested an ultra-stable glucagon-insulin fusion protein whose relative hormonal activities were calibrated by respective modifications; physical stability was concurrently augmented to facilitate formulation, enhance shelf life and expand access. An N-terminal glucagon moiety was stabilized by an α-helix-compatible Lys 13 -Glu 17 lactam bridge; A C-terminal insulin moiety was stabilized as a single chain with foreshortened C domain. Studies in vitro demonstrated (a) resistance to fibrillation on prolonged agitation at 37 °C and (b) dual hormonal signaling activities with appropriate balance. Glucodynamic responses were monitored in rats relative to control fusion proteins lacking one or the other hormonal activity, and continuous intravenous infusion emulated basal subcutaneous therapy. Whereas efficacy in mitigating hyperglycemia was unaffected by the glucagon moiety, the fusion protein enhanced endogenous glucose production under hypoglycemic conditions. Together, these findings provide proof of principle toward a basal glucose-responsive insulin biotechnology of striking simplicity. The fusion protein's augmented stability promises to circumvent the costly cold chain presently constraining global insulin access.

Significance Statement

The therapeutic goal of insulin replacement therapy in diabetes is normalization of blood-glucose concentration, which prevents or delays long-term complications. A critical barrier is posed by recurrent hypoglycemic events that results in short- and long-term morbidities. An innovative approach envisions co-injection of glucagon (a counter-regulatory hormone) to exploit a glycemia-dependent hepatic switch in relative hormone responsiveness. To provide an enabling technology, we describe an ultra-stable fusion protein containing insulin- and glucagon moieties. Proof of principle was obtained in rats. A single-chain insulin moiety provides glycemic control whereas a lactam-stabilized glucagon extension mitigates hypoglycemia. This dual-hormone fusion protein promises to provide a basal formulation with reduced risk of hypoglycemia. Resistance to fibrillation may circumvent the cold chain required for global access.

Nonlinear Mixed Effects Modeling of Glucagon Kinetics in Healthy Subjects

OBJECTIVE

To date, the lack of a model of glucagon kinetics precluded the possibility of estimating and studying glucagon secretion in vivo, e.g., using deconvolution, as done for other hormones like insulin and C-peptide. Here, we used a nonlinear mixed effects technique to develop a robust population model of glucagon kinetics, able to describe both the typical population kinetics (TPK) and the between-subject variability (BSV), and relate this last to easily measurable subject characteristics.

METHODS

Thirty-four models of increasing complexity (variably including covariates and correlations among random effects) were identified on glucagon profiles obtained from 53 healthy subjects, who received a constant infusion of somatostatin to suppress endogenous glucagon production, followed by a continuous infusion of glucagon (65 ng/kg/min). Model selection was performed based on its ability to fit the data, provide precise parameter estimates, and parsimony criteria.

RESULTS

A two-compartment model was the most parsimonious. The model was able to accurately describe both the TPK and the BSV of model parameters as function of body mass and body surface area. Parameters were precisely estimated, with central volume of distribution V1 = 5.46 L and peripheral volume of distribution V2 = 5.51 L. The introduction of covariates resulted in a significant shrinkage of the unexplained BSV and considerably improved the model fit.

CONCLUSION

We developed a robust population model of glucagon kinetics.

SIGNIFICANCE

This model provides a deeper understanding of glucagon kinetics and is usable to estimate glucagon secretion in vivo by deconvolution of plasma glucagon concentration data.

Utilization of Pharmacokinetic/Pharmacodynamic Modeling in Pharmacoepidemiological Studies: A Systematic Review on Antiarrhythmic and Glucose-Lowering Medicines

Introduction: In human pharmacology, there are two important scientific branches: clinical pharmacology and pharmacoepidemiology. Pharmacokinetic/pharmacodynamic (PK/PD) modeling is important in preclinical studies and randomized control trials. However, it is rarely used in pharmacoepidemiological studies on the effectiveness and medication safety where the target population is heterogeneous and followed for longer periods. The objective of this literature review was to investigate how far PK/PD modeling is utilized in observational studies on glucose-lowering and antiarrhythmic drugs.

Method: A systematic literature search of MEDLINE, Embase, and Web of Science was conducted from January 2010 to 21 February 2020. To calculate the utilization of PK/PD modeling in observational studies, we followed two search strategies. In the first strategy, we screened a 1% random set from 95,672 studies on glucose-lowering and antiarrhythmic drugs on inclusion criteria. In the second strategy, we evaluated the percentage of studies in which PK/PD modeling techniques were utilized. Subsequently, we divided the total number of included studies in the second search strategy by the total number of eligible studies in the first search strategy.

Results: The comprehensive search of databases and the manual search of included references yielded a total of 29 studies included in the qualitative synthesis of our systematic review. Nearly all 29 studies had utilized a PK model, whereas only two studies developed a PD model to evaluate the effectiveness of medications. In total, 16 out of 29 studies (55.1%) used a PK/PD model in the observational setting to study effect modification. The utilization of PK/PD modeling in observational studies was calculated as 0.42%.

Conclusion: PK/PD modeling techniques were substantially underutilized in observational studies of antiarrhythmic and glucose-lowering drugs during the past decade.

Empagliflozin add-on therapy to closed-loop insulin delivery in type 1 diabetes: a 2 × 2 factorial randomized crossover trial

There is a need to optimize closed-loop automated insulin delivery in type 1 diabetes. We assessed the glycemic efficacy and safety of empagliflozin 25 mg d^-1 as add-on therapy to insulin delivery with a closed-loop system. We performed a 2 × 2 factorial randomized, placebo-controlled, crossover two-center trial in adults, assessing 4 weeks of closed-loop delivery versus sensor-augmented pump (SAP) therapy and empagliflozin versus placebo. The primary outcome was time spent in the glucose target range (3.9-10.0 mmol l^-1). Primary comparisons were empagliflozin versus placebo in each of closed-loop or SAP therapy; the remaining comparisons were conditional on its significance. Twenty-four of 27 randomized participants were included in the final analysis. Compared to placebo, empagliflozin improved time in target range with closed-loop therapy by 7.2% and in SAP therapy by 11.4%. Closed-loop therapy plus empagliflozin improved time in target range compared to SAP therapy plus empagliflozin by 6.1% but by 17.5% for the combination of closed-loop therapy and empagliflozin compared to SAP therapy plus placebo. While no diabetic ketoacidosis or severe hypoglycemia occurred during any intervention, uncomplicated ketosis events were more common on empagliflozin. Empagliflozin 25 mg d^-1 added to automated insulin delivery improves glycemic control but increases ketone concentration and ketosis compared to placebo.

Dual-hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

Over the last few years, technological advances have led to tremendous improvement in the management of type 1 diabetes (T1D). Artificial pancreas systems have been shown to improve glucose control compared with conventional insulin pump therapy. However, clinically significant hypoglycemic and hyperglycemic episodes still occur with the artificial pancreas. Postprandial glucose excursions and exercise-induced hypoglycemia represent major hurdles in improving glucose control and glucose variability in many patients with T1D. In this regard, dual-hormone artificial pancreas systems delivering other hormones in addition to insulin (glucagon or amylin) may better reproduce the physiology of the endocrine pancreas and have been suggested as an alternative tool to overcome these limitations in clinical practice. In addition, novel ultra-rapid-acting insulin analogs with a more physiological time-action profile are currently under investigation for use in artificial pancreas devices, aiming to address the unmet need for further improvements in postprandial glucose control. This review article aims to discuss the current progress and future outlook in the development of novel ultra-rapid insulin analogs and dual-hormone closed-loop systems, which offer the next steps to fully closing the loop in the artificial pancreas.

Ultra Rapid Lispro (URLi) Accelerates Insulin Lispro Absorption and Insulin Action vs Humalog® Consistently Across Study Populations: A Pooled Analysis of Pharmacokinetic and Glucodynamic Data

BACKGROUND AND OBJECTIVE

Ultra rapid lispro (URLi) is a novel insulin lispro formulation developed to more closely match physiological insulin secretion and improve postprandial glucose control. This pooled analysis compared the pharmacokinetics and glucodynamics between URLi and Humalog^® in healthy subjects and patients with type 1 or type 2 diabetes mellitus.

METHODS

The analysis included four randomized, double-blind, crossover, single-dose studies (healthy subjects [n = 74], patients with type 1 diabetes [n = 78], and type 2 diabetes [n = 38]) evaluating subcutaneous doses of 7, 15, or 30 U of URLi and Humalog during an 8- to 10-h euglycemic clamp procedure.

RESULTS

The pooled analysis showed an ~ 5-min faster onset of appearance, an ~8-fold greater exposure in the first 15 min, a 43% reduction in exposure beyond 3 h, and a 68-min shorter exposure duration with URLi vs Humalog across all study populations and dose range. Compared with Humalog, URLi had a 10-min faster onset of action, a 3-fold greater insulin action in the first 30 min, a 35% reduction in insulin action beyond 4 h, and a 44-min shorter duration of action across all populations and dose range. Overall exposure and insulin action were similar between URLi and Humalog for each dose level and study population.

CONCLUSIONS

Across the studied populations and dose range, URLi consistently demonstrated a faster absorption, reduced late exposure, and overall shorter exposure duration compared with Humalog. Similarly, URLi demonstrated earlier insulin action while reducing late insulin action and shorter insulin action compared with Humalog across the study populations and dose range.

CLINICAL TRIAL REGISTRATION

NCT02942654 (registered: 21 October, 2016), NCT03286751 (registered: 15 September, 2017), NCT03166124 (registered: 23 May, 2017), and NCT03305822 (registered: 5 October, 2017).

Development platform for artificial pancreas algorithms

BACKGROUND AND AIMS

Assessing algorithms of artificial pancreas systems is critical in developing automated and fault-tolerant solutions that work outside clinical settings. The development and evaluation of algorithms can be facilitated with a platform that conducts virtual clinical trials. We present in this paper a clinically validated cloud-based distributed platform that supports the development and comprehensive testing of single and dual-hormone algorithms for type 1 diabetes mellitus (T1DM).

METHODS

The platform is built on principles of object-oriented design and runs user algorithms in real-time virtual clinical trials utilizing a multi-threaded environment enabled by concurrent execution over a cloud infrastructure. The platform architecture isolates user algorithms located on personal machines from proprietary patient data running on the cloud. Users import a plugin into their algorithms (Matlab, Python, or Java) to connect to the platform. Once connected, users interact with a graphical interface to design experimental protocols for their trials. Protocols include trial duration in days, mealtimes and amounts, variability in mealtimes and amounts, carbohydrate counting errors, snacks, and onboard insulin levels.

RESULTS

The platform facilitates development by solving the ODE model in the cloud on large CPU-optimized machines, providing a 62% improvement in memory, speed and CPU utilization. Users can easily debug & modify code, test multiple strategies, and generate detailed clinical performance reports. We validated and integrated into the platform a glucoregulatory system of ordinary differential equations (ODEs) parameterized with clinical data to mimic the inter and intra-day variability of glucose responses of 15 T1DM patients.

CONCLUSION

The platform utilizes the validated patient model to conduct virtual clinical trials for the rapid development and testing of closed-loop algorithms for T1DM.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy

OBJECTIVE

Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data.

METHODS

Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed.

RESULTS

The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm.

CONCLUSION

Our algorithm has the potential to improve glycemic control in people with T1D using MDI.

SIGNIFICANCE

This work is a step forward towards a decision support system that improves their quality of life.

Postprandial hyperglycaemia following insulin suspensions by the artificial pancreas: Implications for bolus calculators

Conventional bolus calculators apply negative prandial corrections when premeal glucose levels are low. However, no study has evaluated the need for this negative correction with closed-loop systems. We analysed data retrospectively from a cohort study evaluating a closed-loop artificial pancreas system conducted in a diabetes camp over a period of 11 days. Meal boluses with negative correction (n = 98) of 47 participants aged 8 to 22 years were examined. If there was no insulin-on-board from previous boluses at mealtime, the postprandial hyperglycaemia rate increased with increased duration of insulin suspension (P = .03), with odds ratios being exaggerated by 17% per 10 minutes of suspension. However, if there was insulin-on-board from previous boluses, the hyperglycaemia rate did not change with increased duration of insulin suspension (P = .24). When there was no insulin-on-board, the rate of hyperglycaemia after meals preceded by no suspension was 21% (3/14), compared with 52% (12/23) and 64% (9/14) after meals preceded by suspensions of ≥50 and ≥70 minutes, respectively. Meal size did not influence these results. We conclude that, in the absence of insulin-on-board, negative prandial corrections may not be necessary following long insulin suspensions.

Insulin-and-Glucagon Artificial Pancreas Versus Insulin-Alone Artificial Pancreas: A Short Review

IN BRIEF The advantage of the insulin-and-glucagon artificial pancreas is based on the rapid effect of subcutaneous glucagon delivery in preventing hypoglycemia compared to suspension of insulin delivery. In short-term studies, the dual-hormone artificial pancreas reduced daytime hypoglycemia, especially during exercise, compared to the insulin-alone artificial pancreas, but the insulin-alone system seemed sufficient in eliminating nocturnal hypoglycemia. The comparative benefits of the single- and dual-hormone systems for improving A1C and preventing severe hypoglycemia remain unknown.

The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes

For patients with type 1 diabetes, closed-loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal-time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post-meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate-matched bolus in the post-meal period. The latter strategy has been efficient in controlling post-meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal-priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag-time. New and upcoming faster acting insulins could provide important benefits. Multi-hormone CLS (eg, dual-hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP-1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

BACKGROUND

Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon.

METHODS

Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE).

RESULTS

Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients.

CONCLUSIONS

The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.

Glucagon in artificial pancreas systems: Potential benefits and safety profile of future chronic use

The role of glucagon in the pathophysiology of diabetes has long been recognized, although its approved clinical use has so far been limited to the emergency treatment of severe hypoglycaemia. A novel use of glucagon as intermittent mini-boluses is proposed in the dual-hormone version (insulin and glucagon) of the external artificial pancreas. Short-term studies suggest that the incorporation of glucagon into artificial pancreas systems has the potential to further decrease hypoglycaemic risk and improve overall glucose control; however, the potential long-term safety and benefits also need to be investigated given the recognized systemic effects of glucagon. In the present report, we review the available animal and human data on the physiological functions of glucagon, as well as its pharmacological use, according to dosing and duration (acute and chronic). Along with its main role in hepatic glucose metabolism, glucagon affects the cardiovascular, renal, pulmonary and gastrointestinal systems. It has a potential role in weight reduction through its central satiety function and its role in increasing energy expenditure. Most of the pharmacological studies investigating the effects of glucagon have used doses exceeding 1 mg, in contrast to the mini-boluses used in the artificial pancreas. The available data are reassuring but comprehensive human studies using small but chronic glucagon doses that are close to the physiological ranges are lacking. We propose a list of variables that could be monitored during long-term trials of the artificial pancreas. Such trials should address the questions about the risk-benefit ratio of chronic glucagon use.

Effects of subcutaneous, low-dose glucagon on insulin-induced mild hypoglycaemia in patients with insulin pump treated type 1 diabetes

AIM

To investigate the dose-response relationship of subcutaneous (s.c.) glucagon administration on plasma glucose and on counter-regulatory hormone responses during s.c. insulin-induced mild hypoglycaemia in patients with type 1 diabetes treated with insulin pumps.

METHODS

Eight insulin pump-treated patients completed a blinded, randomized, placebo-controlled study. Hypoglycaemia was induced in the fasting state by an s.c. insulin bolus and, when plasma glucose reached 3.4 mmol/l [95% confidence interval (CI) 3.2-3.5], an s.c. bolus of either 100, 200, 300 µg glucagon or saline was administered. Plasma glucose, counter-regulatory hormones, haemodynamic variables and side effects were measured throughout each study day. Peak plasma glucose level was the primary endpoint.

RESULTS

Plasma glucose level increased significantly by a mean (95% CI) of 2.3 (1.7-3.0), 4.2 (3.5-4.8) and 5.0 (4.3-5.6) mmol/l to 6.1 (4.9-7.4), 7.9 (6.4-9.3) and 8.7 (7.8-9.5) vs 3.6 (3.4-3.9) mmol/l (p < 0.001) after the three different glucagon doses as compared with saline, and the increase was neither correlated with weight nor insulin levels. Area under the plasma glucose curve, peak plasma glucose, time to peak plasma glucose and duration of plasma glucose level above baseline were significantly enhanced with increasing glucagon doses; however, these were not significantly different between 200 and 300 µg glucagon. Free fatty acids and heart rates were significantly lower initially after glucagon than after saline injection. Other haemodynamic variables, counter-regulatory hormones and side effects did not differ between interventions.

CONCLUSIONS

An s.c. low-dose glucagon bolus effectively restores plasma glucose after insulin overdosing. Further research is needed to investigate whether low-dose glucagon may be an alternative treatment to oral carbohydrate intake for mild hypoglycaemia in patients with type 1 diabetes.

Pharmacokinetics and pharmacodynamics of various glucagon dosages at different blood glucose levels

AIMS

To evaluate the pharmacokinetics and pharmacodynamics of different doses of glucagon administered subcutaneously (s.c.) at different blood glucose levels.

METHODS

This study was an open-label, randomized, three-period, cross-over experiment in 6 patients with type 1 diabetes. During each of the three periods, different blood glucose levels were established in four consecutive steps (8, 6, 4 and 2.8 mmol/l) and glucagon was given at each blood glucose level in doses from 0.11 to 0.44 mg and 0.33, 0.66 and 1 mg at the lowest glucose concentration.

RESULTS

Maximum glucagon concentration and area under the curve increased with increasing glucagon dose. Maximum glucagon concentration was reached after 10-20 min. Glucagon raised blood glucose in a dose-dependent manner at different baseline blood glucose levels. The median glucose excursion ranged from 2.6 to 6.2 mmol/l. Time to maximum glucose concentration was dose-dependent for the glucagon doses at 2.8 mmol/l, with median values from 40 to 80 min.

CONCLUSIONS

Glucagon administered s.c. produces a stable pharmacokinetic and pharmacodynamic response at lower doses than the usual rescue dose and across a range of hypo- to hyperglycaemic blood glucose levels. This supports the use of small glucagon doses in the artificial pancreas to correct and prevent hypoglycaemia.

Glucagon - the new 'insulin' in the pathophysiology of diabetes

PURPOSE OF REVIEW

Autoimmune destruction of the β cells is considered the key abnormality in type 1 diabetes mellitus and insulin replacement the primary therapeutic strategy. However, a lack of insulin is accompanied by disturbances in glucagon release, which is excessive postprandially, but insufficient during hypoglycaemia. In addition, replacing insulin alone appears insufficient for adequate glucose control. This review focuses on the growing body of evidence that glucagon abnormalities contribute significantly to the pathophysiology of diabetes and on recent efforts to target the glucagon axis as adjunctive therapy to insulin replacement.

RECENT FINDINGS

This review discusses recent (since 2013) advances in abnormalities of glucagon regulation and their link to the pathophysiology of diabetes; new mechanisms of glucagon action and regulation; manipulation of glucagon in diabetes treatment; and analytical and systems biology tools to study glucagon regulation.

SUMMARY

Recent efforts 'resurrected' glucagon as a key hormone in the pathophysiology of diabetes. New studies target its abnormal regulation and action that is key for improving diabetes treatment. The progress is promising, but major questions remain, including unravelling the mechanism of loss of glucagon counterregulation in type 1 diabetes mellitus and how best to manipulate glucagon to achieve more efficient and safer glycaemic control.

Efficacy of dual-hormone artificial pancreas to alleviate the carbohydrate-counting burden of type 1 diabetes: A randomized crossover trial

AIM

Carbohydrate-counting is a complex task for many patients with type 1 diabetes. This study examined whether an artificial pancreas, delivering insulin and glucagon based on glucose sensor readings, could alleviate the burden of carbohydrate-counting without degrading glucose control.

METHODS

Twelve adults were recruited into a randomized, three-way, crossover trial (ClinicalTrials.gov identifier No. NCT01930097). Participants were admitted on three occasions from 7AM to 9PM and consumed a low-carbohydrate breakfast (women: 30g; men: 50g), a medium-carbohydrate dinner (women: 50g; men: 70g) and a high-carbohydrate lunch (women: 90g; men: 120g). At each visit, glucose levels were randomly regulated by: (1) conventional pump therapy; (2) an artificial pancreas (AP) accompanied by prandial boluses, matching the meal's carbohydrate content based on insulin-to-carbohydrate ratios (AP with carbohydrate-counting); or (3) an AP accompanied by prandial boluses based on qualitative categorization (regular or large) of meal size (AP without carbohydrate-counting).

RESULTS

The AP without carbohydrate-counting achieved similar incremental AUC values compared with carbohydrate-counting after the low- (P=0.54) and medium- (P=0.38) carbohydrate meals, but yielded higher post-meal excursions after the high-carbohydrate meal (P=0.004). The AP with and without carbohydrate-counting yielded similar mean glucose levels (8.2±2.1mmol/L vs. 8.4±1.7mmol/L; P=0.52), and both strategies resulted in lower mean glucose compared with conventional pump therapy (9.6±2.0mmol/L; P=0.02 and P=0.03, respectively).

CONCLUSION

The AP with qualitative categorization of meal size could alleviate the burden of carbohydrate-counting without compromising glucose control, although more categories of meal sizes are probably needed to effectively control higher-carbohydrate meals.

Pharmacokinetics of diluted (U20) insulin aspart compared with standard (U100) in children aged 3-6 years with type 1 diabetes during closed-loop insulin delivery: a randomised clinical trial

AIMS/HYPOTHESIS

The aim of this study was to compare the pharmacokinetics of two different concentrations of insulin aspart (B28Asp human insulin) in children aged 3-6 years with type 1 diabetes.

METHODS

Young children with type 1 diabetes underwent an open-label, randomised, two-period crossover study in a clinical research facility, 2-6 weeks apart. In random order, diluted (1:5 dilution with saline [154 mmol/l NaCl]; 20 U/ml) or standard strength (100 U/ml) insulin aspart was administered via an insulin pump as a meal bolus and then overnight by closed-loop insulin delivery as determined by a model predictive algorithm. Plasma insulin was measured every 30-60 min from 17:00 hours on day 1 to 8:00 hours on day 2. We measured the time-to-peak insulin concentration (tmax), insulin metabolic clearance rate (MCR(I)) and background insulin concentration (ins(c)) using compartmental modelling.

RESULTS

Eleven children (six male; age range 3.75-6.96 years, HbA1c 7.6% ± 1.3% [60 ± 14 mmol/mol], BMI standard deviation score 1.0 ± 0.8, duration of diabetes 2.2 ± 1.0 years, total daily dose 12.9 [10.6-16.5] U, fasting C-peptide concentration 5 [5-17.1] pmol/l; mean ± SD or median [interquartile range]) participated in the study. No differences between standard and diluted insulin were observed in terms of t max (59.2 ± 14.4 vs 61.6 ± 8.7) min for standard vs diluted, p = 0.59; MCR I (1.98 × 10(-2) ± 0.99 × 10(-2) vs 1.89 × 10(-2) ± 0.82 × 10(-2) 1/kg/min, p = 0.47), and ins c (34 [1-72] vs 23 [3-65] pmol/l, p = 0.66). However, t max showed less intersubject variability following administration of diluted aspart (SD 14.4 vs 8.7 min, p = 0.047).

CONCLUSIONS/INTERPRETATION

Diluting insulin aspart does not change its pharmacokinetics. However, it may result in less variable absorption and could be used in young children with type 1 diabetes undergoing closed-loop insulin delivery.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01557634.

FUNDING

FUNDING was provided by the JDRF, 7th Framework Programme of the European Union, Wellcome Trust Strategic Award and the National Institute for Health Research Cambridge Biomedical Research Centre.

Quantification of the glycemic response to microdoses of subcutaneous glucagon at varying insulin levels

OBJECTIVE

Glucagon delivery in closed-loop control of type 1 diabetes is effective in minimizing hypoglycemia. However, high insulin concentration lowers the hyperglycemic effect of glucagon, and small doses of glucagon in this setting are ineffective. There are no studies clearly defining the relationship between insulin levels, subcutaneous glucagon, and blood glucose.

RESEARCH DESIGN AND METHODS

Using a euglycemic clamp technique in 11 subjects with type 1 diabetes, we examined endogenous glucose production (EGP) of glucagon (25, 75, 125, and 175 μg) at three insulin infusion rates (0.016, 0.032, and 0.05 units/kg/h) in a randomized, crossover study. Infused 6,6-dideuterated glucose was measured every 10 min, and EGP was determined using a validated glucoregulatory model. Area under the curve (AUC) for glucose production was the primary outcome, estimated over 60 min.

RESULTS

At low insulin levels, EGP rose proportionately with glucagon dose, from 5 ± 68 to 112 ± 152 mg/kg (P = 0.038 linear trend), whereas at high levels, there was no increase in glucose output (19 ± 53 to 26 ± 38 mg/kg, P = NS). Peak glucagon serum levels and AUC correlated well with dose (r2 = 0.63, P < 0.001), as did insulin levels with insulin infusion rates (r2 = 0.59, P < 0.001).

CONCLUSIONS

EGP increases steeply with glucagon doses between 25 and 175 μg at lower insulin infusion rates. However, high insulin infusion rates prevent these doses of glucagon from significantly increasing glucose output and may reduce glucagon effectiveness in preventing hypoglycemia when used in the artificial pancreas.

Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Insulin pharmacokinetics is not well understood during continuous subcutaneous insulin infusion in type 2 diabetes (T2D). We analyzed data collected in 11 subjects with T2D [6 male, 9 white European and two of Indian ethnicity; age 59.7(12.1) years, BMI 30.1(3.9) kg/m(2), fasting C-peptide 1002.2(365.8) pmol/l, fasting plasma glucose 9.6(2.2) mmol/l, diabetes duration 8.0(6.2) years and HbA1c 8.3(0.8)%; mean(SD)] who underwent a 24-h study investigating closed-loop insulin delivery at the Wellcome Trust Clinical Research Facility, Cambridge, UK. Subcutaneous delivery of insulin lispro was modulated every 15 min according to a model predictive control algorithm. Two complementary insulin assays facilitated discrimination between exogenous (lispro) and endogenous plasma insulin concentrations measured every 15-60 min. Lispro pharmacokinetics was represented by a linear two-compartment model whilst parameters were estimated using a Bayesian approach applying a closed-form model solution. The time-to-peak of lispro absorption (t(max)) was 109.6 (75.5-120.5) min [median (interquartile range)] and the metabolic clearance rate (MCR(I)) 1.26 (0.87-1.56)×10(-2) l/kg/min. MCR(I) was negatively correlated with fasting C-peptide (r(s)=-0.84; P=.001) and with fasting plasma insulin concentration (r(s)=-0.79; P=.004). In conclusion, compartmental modelling adequately represents lispro kinetics during continuous subcutaneous insulin infusion in T2D. Fasting plasma C-peptide or fasting insulin may be predictive of lispro metabolic clearance rate in T2D but further investigations are warranted.