Towards an Artificial Pancreas: Software Architectural Model and Implementation for Personalized Insulin Administration

Research on an Artificial Pancreas has gained its momentum and focused on the processing of clinical data for continuous insulin administration. However, the overall research is rather sketchy, fragmented and not very well coordinated. In this paper, we propose an architectural model for creating software intensive environments, which address deficiencies of current solutions for insulin infusion. A new way of defining which data should be collected and which types of computations should be performed with the data is important if we wish to come close to the functioning of a natural pancreas. An excerpt of the proposed software architecture has been deployed using Watson Analytics and performed upon a selection of data collected from sensors, individual patient’s input and persistent patient records

Optimal Regulation of Blood Glucose Level in Type I Diabetes using Insulin and Glucagon

The Glucose-Insulin-Glucagon nonlinear model [1-4] accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.Comment: Accepted for publication in PLOS ON

Introducing carbohydrates suggestions and corrective boluses administrations in Artificial Pancreas by Model Predictive Control

Type 1 diabetes (T1D) is characterized by the absence of insulin production and thus by an impaired glycaemia control. T1D therapy consists in providing insulin to the patient, aiming to maintain the blood glucose level in euglycaemia (i.e., between 70 and 180 mg/dl), counteracting hyperglycaemia, but without incurring in hypoglycaemic events, which can lead to severe consequences in the short term. This is not trivial, due to disturbing factors and given the pharmacokinetics and pharmacodynamics of exogenous insulin. The Artificial Pancreas (AP) is a new technology for the automation and the optimization of basal insulin administration, which works by a closed-loop algorithm that controls the functioning of an insulin pump, basing on the measurements provided by a glucose sensor. The objective of this work is to improve the quality of glycaemia control, by adding in AP the possibility to suggest carbohydrates assumptions and the administration of corrective insulin boluses, by using the Model Predictive Control (MPC) algorithm. The idea is to strengthen the counteraction of hypo- and hyperglycaemia, respectively. To model the quantity of CHO to be suggested and the capability of the algorithm to choose whether to deliver a bolus or not, a series of Boolean support variables is needed and has to be included in the control problem. Therefore, our approach involves the resolution of a Mixed Integer Quadratic Programming (MIQP) problem, which the MPC’s control problem can be reformulated as. To evaluate the performances of the resultant system (the triple-action MPC AP), we resort to the UVa/Padova T1D Simulator®, an accurate model of a T1D patient's metabolism, which was accepted by the U.S. FDA (Food and Drug Administration) as a substitute of animal trials for preclinical testing of T1D therapies, and is integrated with a population of realistic virtual subjects to perform the trials on. We compare our approach with a state-of-the-art strategy (the single-action MPC AP), which only manages the basal insulin delivery, and an advanced technique (the dual-action MPC AP), which, in addition, can suggest carbohydrates intakes. The results show how the triple-action MPC AP outperforms both the single-action- and the dual-action MPC AP, with an increment of the average time in euglycaemia of more than 9% and almost 3%, respectively, with the optimal parametrization. Adopting a suboptimal tuning, inferred by using hyperparameters’ regression models, our approach still outperforms the single-action technique, with an increase of the time in euglycaemia of almost 5%, and shows slightly better performances with respect to the dual-action MPC AP, as well.Type 1 diabetes (T1D) is characterized by the absence of insulin production and thus by an impaired glycaemia control. T1D therapy consists in providing insulin to the patient, aiming to maintain the blood glucose level in euglycaemia (i.e., between 70 and 180 mg/dl), counteracting hyperglycaemia, but without incurring in hypoglycaemic events, which can lead to severe consequences in the short term. This is not trivial, due to disturbing factors and given the pharmacokinetics and pharmacodynamics of exogenous insulin. The Artificial Pancreas (AP) is a new technology for the automation and the optimization of basal insulin administration, which works by a closed-loop algorithm that controls the functioning of an insulin pump, basing on the measurements provided by a glucose sensor. The objective of this work is to improve the quality of glycaemia control, by adding in AP the possibility to suggest carbohydrates assumptions and the administration of corrective insulin boluses, by using the Model Predictive Control (MPC) algorithm. The idea is to strengthen the counteraction of hypo- and hyperglycaemia, respectively. To model the quantity of CHO to be suggested and the capability of the algorithm to choose whether to deliver a bolus or not, a series of Boolean support variables is needed and has to be included in the control problem. Therefore, our approach involves the resolution of a Mixed Integer Quadratic Programming (MIQP) problem, which the MPC’s control problem can be reformulated as. To evaluate the performances of the resultant system (the triple-action MPC AP), we resort to the UVa/Padova T1D Simulator®, an accurate model of a T1D patient's metabolism, which was accepted by the U.S. FDA (Food and Drug Administration) as a substitute of animal trials for preclinical testing of T1D therapies, and is integrated with a population of realistic virtual subjects to perform the trials on. We compare our approach with a state-of-the-art strategy (the single-action MPC AP), which only manages the basal insulin delivery, and an advanced technique (the dual-action MPC AP), which, in addition, can suggest carbohydrates intakes. The results show how the triple-action MPC AP outperforms both the single-action- and the dual-action MPC AP, with an increment of the average time in euglycaemia of more than 9% and almost 3%, respectively, with the optimal parametrization. Adopting a suboptimal tuning, inferred by using hyperparameters’ regression models, our approach still outperforms the single-action technique, with an increase of the time in euglycaemia of almost 5%, and shows slightly better performances with respect to the dual-action MPC AP, as well

Doctor of Philosophy

dissertationThe task of comparing and evaluating the performance of different computer-based clinical protocols is difficult and expensive to accomplish. This dissertation explores methods to compare and evaluate computer-based insulin infusion protocols based on an in silico analytical framework iteratively developed for this study, using data from the intensive care unit (ICU). In Methods for Aim 1, we used a pairwise comparative technique to evaluate two computer-based insulin infusion protocols. Our result showed that the pairwise method can rapidly identify a promising computer-based clinical protocol but with limitations. In Methods for Aim 2, we used a ranking strategy to evaluate six computer-based insulin infusion protocols. The ranking method enabled us to overcome a key limitation in Methods for Aim 1, making it possible to compare multiple computer-based clinical protocols simultaneously. In Methods for Aim 3, we developed a more comprehensive in silico method based on multiple-criteria decision analysis that included user-defined performance evaluation criteria examining different facets of the computer-based insulin infusion protocols. The in silico method appears to be an efficient way for identifying promising computer-based clinical protocols suitable for clinical evaluation. We discuss the advantages and disadvantages for each of the presented methods. We also discuss future research work and the generalizability of the framework to other potential clinical areas

Optimal Control Strategies for Complex Biological Systems

To better understand and to improve therapies for complex diseases such as cancer or diabetes, it is not sufficient to identify and characterize the interactions between molecules and pathways in complex biological systems, such as cells, tissues, and the human body. It also is necessary to characterize the response of a biological system to externally supplied agents (e.g., drugs, insulin), including a proper scheduling of these drugs, and drug combinations in multi drugs therapies. This obviously becomes important in applications which involve control of physiological processes, such as controlling the number of autophagosome vesicles in a cell, or regulating the blood glucose level in patients affected by diabetes. A critical consideration when controlling physiological processes in biological systems is to reduce the amount of drugs used, as in some therapies drugs may become toxic when they are overused. All of the above aspects can be addressed by using tools provided by the theory of optimal control, where the externally supplied drugs or hormones are the inputs to the system. Another important aspect of using optimal control theory in biological systems is to identify the drug or the combination of drugs that are effective in regulating a given therapeutic target, i.e., a biological target of the externally supplied stimuli. The dynamics of the key features of a biological system can be modeled and described as a set of nonlinear differential equations. For the implementation of optimal control theory in complex biological systems, in what follows we extract \textit{a network} from the dynamics. Namely, to each state variable $x_i$ we will assign a network node $v_i$ ($i=1,...,N$) and a network directed edge from node $v_i$ to another node $v_j$ will be assigned every time $x_j$ is present in the time derivative of $x_i$. The node which directly receives an external stimulus is called a \emph{driver nodes} in a network. The node which directly connected to an output sensor is called a \emph{target node}. %, and it has a prescribed final state that we wish to achieve in finite time. From the control point of view, the idea of controllability of a system describes the ability to steer the system in a certain time interval towards thea desired state with a suitable choice of control inputs. However, defining controllability of large complex networks is quite challenging, primarily because of the large size of the network, its complex structure, and poor knowledge of the precise network dynamics. A network can be controllable in theory but not in practice when a very large control effort is required to steer the system in the desired direction. This thesis considers several approaches to address some of these challenges. Our first approach is to reduce the control effort is to reduce the number of target nodes. We see that by controlling the states of a subset of the network nodes, rather than the state of every node, while holding the number of control signals constant, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs as long as the target set is appropriately sized. We call this strategy \emph{target control}. As our second approach is based on reducing the control efforts by allowing the prescribed final states are satisfied approximately rather than strictly. We introduce a new control strategy called \textit{balanced control} for which we set our objective function as a convex combination of two competitive terms: (i) the distance between the output final states at a given final time and given prescribed states and (ii) the total control efforts expenditure over the given time period. Based on the above two approaches, we propose an algorithm which provides a locally optimal control technique for a network with nonlinear dynamics. We also apply pseudo-spectral optimal control, together with the target and balance control strategies previously described, to complex networks with nonlinear dynamics. These optimal control techniques empower us to implement the theoretical control techniques to biological systems evolving with very large, complex and nonlinear dynamics. We use these techniques to derive the optimal amounts of several drugs in a combination and their optimal dosages. First, we provide a prediction of optimal drug schedules and combined drug therapies for controlling the cell signaling network that regulates autophagy in a cell. Second, we compute an optimal dual drug therapy based on administration of both insulin and glucagon to control the blood glucose level in type I diabetes. Finally, we also implement the combined control strategies to investigate the emergence of cascading failures in the power grid networks