Research Proposal. 1. Background and Rationale

Transcription

1 1. Background and Rationale Insulin is secreted by the pancreatic beta cells and reduces blood glucose concentration (1). Glucagon is secreted by the pancreatic alpha cells and increases glucose concentration. In healthy individuals, blood glucose is tightly controlled by insulin and glucagon. In type 1 diabetes, insulin secretion is lost due to the autoimmune destruction of the beta cells (2). Type 1 diabetes accounts for 5-15% of 366 million people with diabetes worldwide (3). Over 300,000 Canadians live nowadays with type 1 diabetes (4). Type 1 diabetes is currently treated with life-long insulin-replacement therapy implemented using multiple daily injections or continuous subcutaneous insulin infusion via a portable pump. Tight glucose control is recommended as sustained elevation of glucose levels leads to long-term complications such as heart diseases, blindness, kidney failure, and lower extremity amputations (5, 6). Glucose control is assessed using HbA1c levels, a biomarker correlated with the mean blood glucose level over the last three months. A target of HbA1c below 7.0% is recommended for most patients with type 1 diabetes (7). Despite advances in insulin analogs, insulin pumps and continuous glucose sensors, most patients do not achieve glucose targets (8, 9). The EDIC study reported an average HbA1c of 8.1% in 1,349 patients (8). HbA1c higher than 7% is associated with a significant increase of the risk of complications (7). For example, 10% relative increase in HbA1c is associated with 40% increase in the progression rate of diabetic retinopathy (10). Around only 27% of patients achieve HbA1c levels less than 7% (11). Hypoglycemia (low blood glucose) is the major barrier to achieve glucose targets (12). Treatment intensification is associated with an increased risk of hypoglycemia (13). Non-severe hypoglycemia may lead to confusion, blurred vision, difficulty speaking and dizziness while severe hypoglycemia leads to coma or seizure. Hypoglycemia is currently a fact of life for patients with type 1 diabetes with an incidence rate of around 2.7 episodes of non-severe hypoglycemia per patient per week (14). Moreover, the incidence of severe hypoglycemia is around 1.3 episodes per patient per year (15). Recent advances in continuous glucose sensors have motivated the research towards closed-loop delivery systems, termed the artificial pancreas, to automatically regulate glucose levels in patients with type 1 diabetes (16). In the artificial pancreas, the pump insulin infusion rate is repeatedly altered based on a control algorithm that relies on continuous glucose sensor readings (Figure 1). The novelty of this approach resides in the realtime feedback between glucose levels and insulin delivery. The development of the artificial pancreas is the current focus of my research. Figure 1. Artificial Pancreas System. A sensor measures glucose levels and transmits them to a mobile-phone-sized controller, which runs a control algorithm. An insulin pump delivers insulin subcutaneously. The communication is wireless. Reprinted from (16). 2. Literature Review Research is under steady progress to develop artificial pancreas systems for type 1 diabetes. Several groups around the world are developing different control algorithms and system configurations, adopting various 1

2 strategies related to meals and exercises (where glucose control is challenging), and conducting randomized and non-randomized clinical trials that differ in their purpose, length, and protocol. The University of Cambridge Artificial Pancreas Group has developed a dosing algorithm based on model predictive control (MPC) combined with an interacting multiple-model estimation algorithm (17). This artificial pancreas algorithm was compared to conventional pump therapy in regulating overnight (8pm-8am) glucose in 19 patients aged 5-18 years (18). The artificial pancreas increased the percentage of time for which glucose levels are in the target range (60% vs. 40%, p = 0.002) while halving the time spent in hypoglycemia (2.1% vs. 4.1% p = 0.03). The Cambridge algorithm has also been tested overnight in the adult population and has shown similar promising results (19, 20). Medtronic, a world leader in medical device technology, developed a proportional-integral-derivative (PID) algorithm and tested it in several clinical studies (21, 22). Despite promising results, nocturnal hypoglycemic events were initially observed (21). The algorithm was subsequently modified by feeding back a model-based estimate of plasma insulin concentration to prevent insulin overdosing (23). The new algorithm resulted in an improved performance and was tested in a serious of clinical trials (23-25). The International Artificial Pancreas Study Group developed an MPC control algorithm and tested it in 20 adult patients (26). In that multinational study, the artificial pancreas increased the time for which overnight plasma glucose is in target range compared to conventional pump therapy (78% vs. 64%, p=0.03). However, nocturnal hypoglycemic events were still observed during on artificial pancreas nights. Other limitations of that study are that a randomised design was not adopted, and the algorithm s recommendations were sometimes overridden by an attending physician. This algorithm was modified and recently tested in a study for 22-hour period (27). Fuzzy systems are a class of algorithms that emulate the decision-making process of an expert (28). An artificial pancreas fuzzy controller was recently proposed (29) and tested in 56 young patients at a diabetes camp (overnight control; 12 hours) (30). It was observed that nocturnal hypoglycemia was reduced and glucose control was improved with the fuzzy-logic artificial pancreas system compared to sensor-augmented pump therapy. However, seven hypoglycemic events were still observed during the 56 nights of artificial pancreas operation. The aforementioned studies used insulin as the sole hormone to regulate glucose levels. Dual-hormone systems were also proposed where insulin delivery is accompanied by glucagon to further reduce hypoglycemic risk. In a trial studying 13 patients for 22 hours and utilizing a PID-like algorithm (31), dual-hormone delivery reduced hypoglycemia from 40 min/day to 15 min/day compared to single-hormone delivery. El-Khatib et al. (32) also developed a dual-hormone algorithm where insulin delivery is based on a generalized predictive controller and glucagon on a PD controller. This algorithm was tested on six patients for 51 hours (33). A limitation of these studies (31, 33) is that treatments of hypoglycemic events were still needed during dual-hormone closed-loop operation (including overnight) due to excessive delivered insulin as commanded by the dosing algorithm. During my PhD, I assessed the efficacy of my independently-developed dual-hormone algorithm as compared to conventional pump treatment in 15 adult patients during an evening exercise, dinner and an overnight period (34). The results were very promising; the dual-hormone system significantly reduced hypoglycemic risk by 8- fold (20-fold during the night period) while increasing the percentage of time for which plasma glucose levels were in target range from 57% to 71% (P=0.003). Mini-doses of glucagon have safely prevented hypoglycemia and reverted glucose trend without inducing hyperglycemia. My work was the first to assess the efficacy of a dual-hormone artificial pancreas system compared to conventional pump therapy using a randomized design. Most of the conducted studies had a small sample size (usually < 20) and short duration (often less than a day) and were conducted in an inpatient setting under standardized conditions. There is a need to move gradually toward larger and longer outpatient studies. Moreover, randomized trials assessing the glucagon worth in artificial pancreas performance are still lacking, and more work is also needed to understand the limitations and the advantages of the artificial pancreas around exercises. 3. Research Design and Methods The development of the artificial pancreas is the current focus of my research. During my postdoctoral, I plan to work on three important aspects that would accelerate the availability of the artificial pancreas in clinical practice: 3.1 Development of Advanced Algorithms for Artificial Pancreas Systems 2

3 From a theoretical standpoint, developing the artificial pancreas is a control engineering problem. The artificial pancreas delivers insulin and glucagon into the body as guided by a control algorithm that relies on continuous glucose sensor readings. However, the control problem is challenged by the large intra- and inter-patient variability, sensor inaccuracies, and the time-lag in subcutaneous insulin absorption. I have developed a first version of the artificial pancreas based on model predictive control, extended Kalman filtering and fuzzy-logic and tested it in a clinical trial in 15 adults for a 15-hour period (34). During my postdoctoral, I aim to develop a dosing algorithm for a single-hormone artificial pancreas. The algorithm will be based on model-predictive control and will utilize a physiologically-based model. Different model-based adaptation techniques will be exploited such as a linear and extended Kalman filters and multimodel least-squares hybrid filters. An artificial pancreas system utilizing this algorithm will be compared clinically (see Section 3.2) with the conventional pump treatment and the dual-hormone system (developed during my doctoral studies, and will be refined during postdoctoral). Currently, patients need to calculate the carbohydrate content of every meal they eat and give a matching insulin dose. This is a challenging task for most patients and errors in calculations might lead to post-meal low or high glucose levels. I aim to develop a dosing algorithm for the artificial pancreas that would alleviate the burden of carbohydrate counting from the patients. This would be achieved by giving a small carbohydrate-independent pre-meal insulin bolus (e.g. function of weight or the individualized insulin-to-carbohydrate ratios), and then giving the remaining needed insulin following sensor reading excursions as guided by the control algorithm. Due to delays in insulin absorption (insulin appears in the blood stream minutes after delivery), the algorithm should predict reliably future meal-glucose appearance in the blood to be able to deliver matching insulin. This prediction by the algorithm is equivalent to what the patients do now in calculating carbohydrate content of the meal before eating, but the algorithm will estimate the carbohydrate content using post-meal glucose excursions as measured by the sensor. This could be done using advanced estimation techniques, for example, by characterizing the meal effect by a parametric model and estimate its parameters in real time using Kalman filter or least square methods. This algorithm will be tested in a clinical trial during my postdoctoral (see Section 3.2). The algorithms will be implemented in JAVA and C programming languages according to health regulatory standards for medical devices. The JAVA version of the algorithm will be ported in a Samsung device whereas the C language version will be ported in a windows-based phone (Motorola). These developments are needed before moving to longer in-patient or out-patient studies of the artificial pancreas systems by the end of my postdoctoral. I received the McGill University William and Rhea Seath Award in Engineering Innovation ($20,000) that I will use to pay BFS Legal, a leading law firm in medical device and intellectual property regulation. The company will provide us with a development plan (e.g., standards, data required, type of tests, and risk analysis plan) that lines up with Health Canada standards and requirements. 3.2 Clinical Testing of Artificial Pancreas Systems During my postdoctoral, I aim to test the artificial pancreas system across different populations and scenarios. I have already established collaboration between different institutes and we have a solid team with the required expertise. The clinical team consists of Laurent Legault (Pediatrician; Montreal Children Hospital), Remi Rabasa- Lhoret (Diabetologist; IRCM) and Bruce Perkins (Diabetologist; University of Toronto). We currently plan the following studies: CLASS03 ( ): To compare single-hormone artificial pancreas, dual-hormone artificial pancreas, and conventional pump treatment in regulating glucose levels for 24 hours under standardized conditions (including 60-min exercise) in 30 adult and adolescent patients. This study will allow, for the first time, multiple comparisons and evaluating the benefits of each component separately (i.e. the benefits of merely closing the loop with insulin alone vs. adding glucagon to the closed-loop strategy). CLASS04 ( ): A multi-centre randomized trial comparing single-hormone system, dual-hormone system and pump therapy in regulating overnight glucose in 30 adult and adolescent patients. For each intervention, each subject will be tested for two nights preceded by 1) exercise to increase the risk of nocturnal hypoglycemia; and 2) carbohydrate-rich evening meal to increase the risk of hyperglycemia. The testing will be done in an outpatient setting (the subject s home) with the presence of medical personnel. CLASS05 (2014): A randomized, three-way, cross-over study to compare 1) artificial pancreas combined with meal-and-carbohydrate-announcement; 2) artificial pancreas combined with meal-announcement; and 3

4 3) conventional pump therapy in 12 adult patients for 14 hours including three meals. The aim is to assess if, with the artificial pancreas, we could alleviate the burden of carbohydrate counting from the patients. CLASS06 (2014): To compare the efficacy of single-hormone system, dual-hormone system and pump treatment in regulating glucose levels during exercises of different intensities and durations. We will also infuse isotope tracers which will allow us to probe exercise physiology and to develop mathematical models of exercise s effect on glucose levels, to be used in metabolic simulators (See Section 3.3). CLASS07 ( ): The objective of this project is to serve as a bridge toward out-patient studies. We aim to test the single-hormone and dual-hormone artificial pancreas systems during free-living conditions for a long period (60 hours) in 20 patients. A randomized crossover study design will be adopted allowing a comparison of the two systems. To simulate real-life conditions, study participants will be staying in an apartment located in our clinical research centre, will have access to the cafeteria and the training room and will have the freedom to decide on their day schedule, food intake and exercise. My role will be to 1) design studies; 2) coordinate study team; 3) maintain and update the algorithm and 4) process and interpret data. Note that all the studies mentioned above are currently funded. 3.3 Building a Computer-Simulation Environment Clinical trials are an integral part of the development process but are time-consuming, resource demanding, and costly. Pre-clinical testing in a computer-simulation environment accelerates development and facilitates optimization of control algorithms. During my PhD, I developed novel a system identification method to generate virtual patients from experimental data to be used in metabolic simulators for the assessment of control algorithms. The method adopts a non-linear physiologically-motivated time-varying model of glucose regulation and uses Bayesian inference and Markov chain Monte Carlo methods to estimate individual parameters. During my postdoctoral, using the data of our clinical studies, I aim to generate a more sophisticated computersimulation environment for the assessment of artificial pancreas systems by one-to-one mapping of data of individual patients into a set of parameters of a mathematical model. This work differs from the work of my doctoral studies. I aim here to further model the effect of exercise on glucose levels, the effect of glucagon on glucose levels (how fast it is absorbed through the subcutaneous tissue and how much it increases glucose levels), and the effect of meals with different sizes and glycemic loads. These models have not yet fully developed and I will explore several physiologically-based models and non-physiological non-parametric models. These developments will be done in a stochastic framework using Bayesian estimation techniques with the adoption of Markov chain Monte Carlo methods to sample from the posterior distributions. 4. Technology Transfer I envision this technology to lead to commercial products within the next 5-7 years. Most of the hardware that incorporate into the system are now commercially available. Developing and clinically testing the control algorithms is the fundamental component that requires further development. In our clinical trials, we are using standard off-the-shelf commercially available glucose sensors and infusion pumps to facilitate technology transfer. For any commercialization purposes, partnership with pump and sensor manufacturers is essential. Links between our group and the industry is being established through IRCM Office of Technology Transfer. 4. Knowledge Dissemination Knowledge dissemination is largely integrated in my research activities through 1) registering the trials in public database (ClinicalTrial.gov); 2) conference presentations and publishing in peer-reviewed journals along with press releases to highlight key findings; 3) creating a web page for the project 1 ; and 4) approaching the media through our communication team. My work has been highlighted and I was interviewed on provincial and national TV channels and several general and diabetic magazines 2. The artificial pancreas has the potential to revolutionize diabetes care and patient safety. I hope that my work in this field would accelerate the availability of the artificial pancreas in clinical practice

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