Abstract: An electronic system and method simulates a glucose-insulin metabolic system of a T2DM or prediabetic subject, wherein the system includes a subsystem that models dynamic glucose concentration in a T2DM or prediabetic subject, including an electronic module that models endogenous glucose production (EGP(t)), or meal glucose rate of appearance (Ra(t), or glucose utilization (U(t)), or renal excretion of glucose (B(t)), a subsystem that models dynamic insulin concentration in the T2DM or prediabetic subject, including an electronic module that models insulin secretion (S(t)), an electronic database containing a population of virtual T2DM or prediabetic subjects, each virtual subject having a plurality of metabolic parameters, and a processing module that calculates an effect of variation of at least one metabolic parameter value on the glucose insulin metabolic system of a virtual subject by inputting the plurality of metabolic parameter values.

Abstract: An electronic system is provided that simulates a glucose-insulin metabolic system of a T2DM or prediabetic subject, wherein the system includes a subsystem that models dynamic glucose concentration in a T2DM or prediabetic subject, including an electronic module that models endogenous glucose production (EGP(t)), or meal glucose rate of appearance (Ra(t>>, or glucose utilization (U(t)), or renal excretion of glucose (B(t)), a subsystem that models dynamic insulin concentration in said T2DM or prediabetic subject, including an electronic module that models insulin secretion (S(t)), an electronic database containing a population of virtual T2DM or prediabetic subjects, each virtual subject having a plurality of metabolic parameters, and a processing module that calculates an effect of variation of at least one metabolic parameter value on the glucose insulin metabolic system of a virtual subject by inputting the plurality of metabolic parameter values.

Abstract: A simulation environment for in silico testing of monitoring methods, open-loop and closed-loop treatment strategies in type 1 diabetes. Some exemplary principal components of the simulation environment comprise, but not limited thereto, the following: 1) a “population” of in silico “subjects” with type 1 diabetes in three age groups; 2) a simulator of CGM sensor errors; 3) a simulator of insulin pumps and discrete insulin delivery; 4) an interface allowing the input of user-specified treatment scenarios; and 5) a set of standardized outcome measures and graphs evaluating the quality of the tested treatment strategies. These components can be used separately or in combination for the preclinical evaluation of open-loop or closed-loop control treatments of diabetes.

Abstract: A simulator for in-silico testing of Type 1 diabetes patients uses a model that puts in relation plasma concentrations, i.e., glucose G and insulin /, with glucose fluxes, i.e. endogenous glucose production (EGP), glucose rate of appearance (Ra), glucose utilization by the tissues (U), renal extraction (E), and insulin fluxes, i.e., rate of insulin appearance from the subcutaneous tissues (SC) and insulin degradation (D). A module is also included to describe counter-regulation, i.e. glucagon kinetics, secretion and action. A glucagon subcutaneous absorption model enables simulation of dual hormone control.

Abstract: A method (400) controls the delivery of insulin in a diabetic patient (P) based on data (d) representative of at least a fraction of a meal (m(k+i)) that the patient (P) will consume. The method provides from a block (R) representative of conventional therapy or open loop rule that the patient (P) is subject to, based on the data (d) representative of at least a fraction of the meal (m(k+i)), a reference insulin value (u0). The method is also based on data representative of the difference between input data (?), a reference glycemic level, and feedback data (yCGM) representative of the glycemic level detected in the patient (P). A control module (301; 401) provides a value of insulin (i) to be delivered to the patient (P) based on the various representative data.

Abstract: In a method of determining insulin sensitivity in a patient, glucose level is sensed continuously. A first area under the curve representing the glucose level over time is calculated. An amount of insulin that has been administered to the patient is sensed. An estimation of insulin on board the patient is calculated based on the glucose level and the amount of insulin administered to the patient. A second area under the curve representing the insulin on board over time is calculated. Patient data indicative of at least one patient physical parameter is received. Information indicative of amount of glucose ingested by the patient during a meal is received. An insulin sensitivity output indicative of ability of insulin to stimulate glucose utilization and inhibit glucose production in the patient based on the first and second area under the curve, the patient data and the meal information is generated.

Abstract: A simulator for in-silico testing of Type 1 diabetes patients uses a model that puts in relation plasma concentrations, i.e., glucose G and insulin /, with glucose fluxes, i.e. endogenous glucose production (EGP), glucose rate of appearance (Ra), glucose utilization by the tissues (U), renal extraction (E), and insulin fluxes, i.e., rate of insulin appearance from the subcutaneous tissues (SC) and insulin degradation (D). A module is also included to describe counter-regulation, i.e. glucagon kinetics, secretion and action. A glucagon subcutaneous absorption model enables simulation of dual hormone control.

Abstract: A method (400) controls the delivery of insulin in a diabetic patient (P) based on data (d) representative of at least a fraction of a meal (m(k+i)) that the patient (P) will consume. The method provides from a block (R) representative of conventional therapy or open loop rule that the patient (P) is subject to, based on the data (d) representative of at least a fraction of the meal (m(k+i)), a reference insulin value (u0). The method is also based on data representative of the difference between input data (?), a reference glycemic level, and feedback data (yCGM) representative of the glycemic level detected in the patient (P). A control module (301; 401) provides a value of insulin (i) to be delivered to the patient (P) based on the various representative data.

Abstract: A method, system and computer program product for correcting a nominal treatment strategy of a subject with diabetes. The method, system and computer program product may be configured for providing input whereby the input may include: open-loop therapy settings for the subject, data about glycemic state of the subject; and (optionally) data about meals and/or exercise of the subject. The method, system and computer program product may be configured for providing output, whereby the out-put may include an adjustment (correction) to the open-loop therapy settings for the subject for insulin delivery to the subject.

Abstract: An electronic system is provided that simulates a glucose-insulin metabolic system of a T2DM or prediabetic subject, wherein the system includes a subsystem that models dynamic glucose concentration in a T2DM or prediabetic subject, including an electronic module that models endogenous glucose production (EGP(t)), or meal glucose rate of appearance (Ra(t>>, or glucose utilization (U(t)), or renal excretion of glucose (B(t)), a subsystem that models dynamic insulin concentration in said T2DM or prediabetic subject, including an electronic module that models insulin secretion (S(t)), an electronic database containing a population of virtual T2DM or prediabetic subjects, each virtual subject having a plurality of metabolic parameters, and a processing module that calculates an effect of variation of at least one metabolic parameter value on the glucose insulin metabolic system of a virtual subject by inputting the plurality of metabolic parameter values.

Abstract: A system and method for providing optimal insulin injections to a subject, using a controller, a continuous glucose monitor, and an insulin delivery unit is disclosed. The controller possesses a discrete-time, linear model predictive control law, means for sending information to the insulin delivery unit, and means for receiving information from the CGM. The control law implemented is derived from a discrete-time model of glucose insulin dynamics and an aggressiveness parameter. The result is that using only glucose measurements obtained from sensor readings and, prior values of external insulin infusion and meal and exercise announcement the optimal insulin injection necessary to safely regulate blood glucose can be calculated.

Abstract: A simulation environment for in silico testing of monitoring methods, open-loop and closed-loop treatment strategies in type 1 diabetes. Some exemplary principal components of the simulation environment comprise, but not limited thereto, the following: 1) a “population” of in silico “subjects” with type 1 diabetes in three age groups; 2) a simulator of CGM sensor errors; 3) a simulator of insulin pumps and discrete insulin delivery; 4) an interface allowing the input of user-specified treatment scenarios; and 5) a set of standardized outcome measures and graphs evaluating the quality of the tested treatment strategies. These components can be used separately or in combination for the preclinical evaluation of open-loop or closed-loop control treatments of diabetes.