Ludovic J. Chassin

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.

Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin

We investigated insulin lispro kinetics with bolus and continuous subcutaneous insulin infusion (CSII) modes of insulin delivery. Seven subjects with type-1 diabetes treated by CSII with insulin lispro have been studied during prandial and postprandial conditions over 12 hours. Eleven alternative models of insulin kinetics have been proposed implementing a number of putative characteristics. We assessed 1) the effect of insulin delivery mode, i.e., bolus or basal, on the insulin absorption rate, the effects of 2) insulin association state and 3) insulin dose on the rate of insulin absorption, 4) the remote insulin effect on its volume of distribution, 5) the effect of insulin dose on insulin disappearance, 6) the presence of insulin degradation at the injection site, and finally 7) the existence of two pathways, fast and slow, of insulin absorption. An iterative two-stage parameter estimation technique was used. Models were validated through assessing physiological feasibility of parameter estimates, posterior identifiability, and distribution of residuals. Based on the principle of parsimony, best model to fit our data combined the slow and fast absorption channels and included local insulin degradation. The model estimated that 67(53-82)% [mean (interquartile range)] of delivered insulin passed through the slow absorption channel [absorption rate 0.011(0.004-0.029) min/sup -1/] with the remaining 33% passed through the fast channel [absorption rate 0.021(0.011-0.040) min/sup -1/]. Local degradation rate was described as a saturable process with Michaelis-Menten characteristics [V/sub MAX/=1.93(0.62-6.03) mU min/sup -1/, K/sub M/=62.6(62.6-62.6) mU]. Models representing the dependence of insulin absorption rate on insulin disappearance and the remote insulin effect on its volume of distribution could not be validated suggesting that these effects are not present or cannot be detected during physiological conditions.

Closing the Loop: The Adicol Experience

The objective of the project Advanced Insulin Infusion using a Control Loop (ADICOL) was to develop a treatment system that continuously measures and controls the glucose concentration in subjects with type 1 diabetes. The modular concept of the ADICOLs extracorporeal artificial pancreas consisted of a minimally invasive subcutaneous glucose system, a handheld PocketPC computer, and an insulin pump (D-Tron, Disetronic, Burgdorf, Switzerland) delivering subcutaneously insulin lispro. The present paper describes a subset of ADICOL activities focusing on the development of a glucose controller for semi-closed-loop control, an in silico testing environment, clinical testing, and system integration. An incremental approach was adopted to evaluate experimentally a model predictive glucose controller. A feasibility study was followed by efficacy studies of increasing complexity. The ADICOL project demonstrated feasibility of a semi-closed-loop glucose control during fasting and fed conditions with a wearable, modular extracorporeal artificial pancreas.

Simulation Environment to Evaluate Closed-Loop Insulin Delivery Systems in Type 1 Diabetes

Background: Closed-loop insulin delivery systems linking subcutaneous insulin infusion to real-time continuous glucose monitoring need to be evaluated in humans, but progress can be accelerated with the use of in silico testing. We present a simulation environment designed to support the development and testing of closed-loop insulin delivery systems in type 1 diabetes mellitus (T1DM). Methods: The principal components of the simulation environment include a mathematical model of glucose regulation representing a virtual population with T1DM, the glucose measurement model, and the insulin delivery model. The simulation environment is highly flexible. The user can specify an experimental protocol, define a population of virtual subjects, choose glucose measurement and insulin delivery models, and specify outcome measures. The environment provides graphical as well as numerical outputs to enable a comprehensive analysis of in silico study results. The simulation environment is validated by comparing its predictions against a clinical study evaluating overnight closed-loop insulin delivery in young people with T1DM using a model predictive controller. Results: The simulation model of glucose regulation is described, and population values of 18 synthetic subjects are provided. The validation study demonstrated that the simulation environment was able to reproduce the population results of the clinical study conducted in young people with T1DM. Conclusions: Closed-loop trials in humans should be preceded and concurrently guided by highly efficient and resource-saving computer-based simulations. We demonstrate validity of population-based predictions obtained with our simulation environment.

A simulation model of glucose regulation in the critically ill

Focused research is underway to improve the delivery of tight glycaemic control at the intensive care unit. A major component is the development of safe, efficacious and effective insulin titration algorithms, which are normally evaluated in time-consuming resource-demanding clinical studies. Simulation studies with virtual critically ill patients can substantially accelerate the development process. For this purpose, we created a model of glucoregulation in the critically ill. The model includes five submodels: a submodel of endogenous insulin secretion, a submodel of insulin kinetics, a submodel of enteral glucose absorption, a submodel of insulin action and a submodel of glucose kinetics. Model parameters are estimated utilizing prior knowledge and data collected routinely at the intensive care unit to represent the high intersubject and temporal variation in insulin needs in the critically ill. Bayesian estimation combined with the regularization method is used to estimate (i) time-invariant model parameters and (ii) a time-varying parameter, the basal insulin concentration, which represents the temporal variation in insulin sensitivity. We propose a validation process to validate virtual patients developed for the purpose of testing glucose controllers. The parameter estimation and the validation are exemplified using data collected in six critically ill patients treated at a medical intensive care unit. In conclusion, a novel glucoregulatory model has been developed to create a virtual population of critically ill facilitating in silico testing of glucose controllers at the intensive care unit.

Evaluation of glucose controllers in virtual environment: methodology and sample application

OBJECTIVE Adaptive systems to deliver medical treatment in humans are safety-critical systems and require particular care in both the testing and the evaluation phase, which are time-consuming, costly, and confounded by ethical issues. The objective of the present work is to develop a methodology to test glucose controllers of an artificial pancreas in a simulated (virtual) environment. MATERIAL AND METHODS A virtual environment comprising a model of the carbohydrate metabolism and models of the insulin pump and the glucose sensor is employed to simulate individual glucose excursions in subjects with type 1 diabetes. The performance of the control algorithm within the virtual environment is evaluated by considering treatment and operational scenarios. RESULTS The developed methodology includes two dimensions: testing in relation to specific life style conditions, i.e. fasting, post-prandial, and life style (metabolic) disturbances; and testing in relation to various operating conditions, i.e. expected operating conditions, adverse operating conditions, and system failure. We define safety and efficacy criteria and describe the measures to be taken prior to clinical testing. The use of the methodology is exemplified by tuning and evaluating a model predictive glucose controller being developed for a wearable artificial pancreas focused on fasting conditions. CONCLUSION Our methodology to test glucose controllers in a virtual environment is instrumental in anticipating the results of real clinical tests for different physiological conditions and for different operating conditions. The thorough testing in the virtual environment reduces costs and speeds up the development process.

On‐line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes

Aims  To evaluate an algorithm with glucose prediction capacity and continuous adaptation of patient parameters—a model predictive control (MPC) algorithm—to control blood glucose concentration during fasting conditions in patients with Type 1 diabetes. In the subcutaneous (sc) route within a closed loop system.

Roadmap to the artificial pancreas

Abstract Objective: To outline a roadmap to the artificial pancreas (AP) comprising a subcutaneous (sc) glucose monitor, a control algorithm, and an insulin pump delivering sc insulin. Research design and methods: A literature review, personal views, and material from the Juvenile Diabetes Research Foundation have been used to prepare the roadmap. Results: The roadmap identifies sensor reliability but not sensor accuracy as the critical roadblock, which can be addressed by advanced control algorithms combined with safety critical features. It is argued that incremental rather than perfect glucose control should be the objective of the first generation of the AP. Overnight glucose control is more easily achieved than postmeal control and could be the first aim for a commercial AP. Glycated haemoglobin and the risk of hypoglycaemia will remain the primary efficacy and safety measures being accompanied by complementary measures of variability of glucose excursions. Research priorities include the development of AP prototypes incorporating existing glucose monitors with focus on evaluation at home over short- and long-term periods. Conclusions: The AP promises to revolutionise insulin treatment. Building on technological progress in glucose sensing, the true potential of the AP needs to be assessed by building prototypes and evaluations in home settings.

In silico testing--impact on the progress of the closed loop insulin infusion for critically ill patients project.

Background: In silico testing was used extensively in the European Commission-funded Closed Loop Insulin Infusion for Critically Ill Patients (Clinicip) project, which aimed to develop prototype systems for closed loop glucose control in the critically ill. This article presents two examples of how the simulation environment was utilized in this project. Methods: The in silico simulation environment was used to simulate a 48-hour clinical trial in a surgical intensive care unit to achieve tight glycemic control. A set of 10 critically ill synthetic subjects was selected for two different studies. In the first study, two sets of clinical trials were simulated using two versions of a model predictive control (MPC)-based glucose control algorithm: MPC Version 0.1.5 with hourly glucose measurements and updated MPC Version 1.4.3 with variable 1- to 4-hour glucose sampling. In the second study, four sets of clinical trials were simulated with four levels of measurement error at 2, 5, 7, and 15% coefficient of variation corresponding to the measurement error of commercially available glucose measuring devices. Results: In the first study, more frequent glucose measurements associated with MPC Version 0.1.5 facilitated more efficacious and safer glucose control compared to that obtained with the prolonged and variable glucose sampling rate associated with MPC Version 1.4.3. In the second study, a marked deterioration in safety measures was observed in studies performed with a measurement error of 15%. Conclusions: The presented simulation studies highlighted two important uses of in silico simulation environment in the Clinicip project. The impressive progress and successful completion of the Clinicip project would not be possible without computer-based simulations.

Effects of prolonged fasting and sustained lipolysis on insulin secretion and insulin sensitivity in normal subjects

Normal beta-cells adjust their function to compensate for any decrease in insulin sensitivity. Our aim was to explore whether a prolonged fast would allow a study of the effects of changes in circulating free fatty acid (FFA) levels on insulin secretion and insulin sensitivity and whether any potential effects could be reversed by the antilipolytic agent acipimox. Fourteen (8 female, 6 male) healthy young adults (aged 22.8-26.9 yr) without a family history of diabetes and a body mass index of 22.6 +/- 3.2 kg/m(2) were studied on three occasions in random order. Growth hormone and FFA levels were regularly measured overnight (2200-0759), and subjects underwent an intravenous glucose tolerance test in the morning (0800-1100) on each visit. Treatment A was an overnight fast, treatment B was a 24-h fast with regular administrations of a placebo, and treatment C was a 24-h fast with regular ingestions of 250 mg of acipimox. The 24-h fast increased overnight FFA levels (as measured by the area under the curve) 2.8-fold [51.3 (45.6-56.9) vs. 18.4 (14.4-22.5) *10(4) micromol/l*min, P < 0.0001], and it led to decreases in insulin sensitivity [5.7 (3.6-8.9) vs. 2.6 (1.3-4.7) *10(-4) min(-1) per mU/l, P < 0.0001] and the acute insulin response [16.3 (10.9-21.6) vs. 12.7 (8.7-16.6) *10(2) pmol/l*min, P = 0.02], and therefore a reduction in the disposition index [93.1 (64.8-121.4) vs. 35.5 (21.6-49.4) *10(2) pmol/mU, P < 0.0001]. Administration of acipimox during the 24-h fast lowered FFA levels by an average of 20% (range: -62 to +49%; P = 0.03), resulting in a mean increase in the disposition index of 31% (P = 0.03). In conclusion, the 24-h fast was accompanied by substantial increases in fasting FFA levels and induced reductions in the acute glucose-simulated insulin response and insulin sensitivity. The use of acipimox during the prolonged fast increased the disposition index, suggesting a partial reversal of the effects of fasting on the acute insulin response and insulin sensitivity.