(19)
(11)EP 2 203 112 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
11.03.2020 Bulletin 2020/11

(21)Application number: 08780896.0

(22)Date of filing:  20.06.2008
(51)International Patent Classification (IPC): 
G16H 50/50(2018.01)
(86)International application number:
PCT/US2008/067725
(87)International publication number:
WO 2008/157781 (24.12.2008 Gazette  2008/52)

(54)

METHOD, SYSTEM AND COMPUTER SIMULATION ENVIRONMENT FOR TESTING OF MONITORING AND CONTROL STRATEGIES IN DIABETES

VERFAHREN, SYSTEM UND COMPUTERSIMULATIONS-UMFELD ZUR ÜBERPRÜFUNG VON ÜBERWACHUNGS- UND KONTROLLSTRATEGIEN BEI DIABETES

Procédé, système et environnement de simulation par ordinateur pour tester des stratégies de surveillance et de contrôle du diabète


(84)Designated Contracting States:
AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MT NL NO PL PT RO SE SI SK TR

(30)Priority: 21.06.2007 US 936581 P
17.06.2008 US 73074

(43)Date of publication of application:
07.07.2010 Bulletin 2010/27

(73)Proprietor: University of Virginia Patent Foundation
Charlottesville, VA 22902 (US)

(72)Inventors:
  • KOVATCHEV, Boris, P.
    Charlottesville, Virginia 22901 (US)
  • BRETON, Marc, D.
    Charlottesville, Virginia 22901 (US)
  • COBELLI, Claudio
    I-35121 Padova (IT)
  • DALLA MAN, Chiara
    Venezia, Italy 30121 (IT)

(74)Representative: Appelt, Christian W. et al
Boehmert & Boehmert Anwaltspartnerschaft mbB Pettenkoferstrasse 22
80336 München
80336 München (DE)


(56)References cited: : 
WO-A2-02/087506
US-A1- 2007 038 475
US-B1- 6 835 175
WO-A2-03/054725
US-A1- 2007 071 681
  
  • Chiara Dalla Man ET AL: "GIM, Simulation Software of Meal Glucose-Insulin Model", Journal of diabetes science and technology, 1 May 2007 (2007-05-01), pages 323-330, XP055489042, United States DOI: 10.1177/193229680700100303 Retrieved from the Internet: URL:http://journals.sagepub.com/doi/pdf/10 .1177/193229680700100303 [retrieved on 2018-06-29] & Anonymous: "GIM, Simulation Software of Meal Glucose-Insulin Model - Chiara Dalla Man, Davide M. Raimondo, Robert A. Rizza, Claudio Cobelli, 2007", , 1 May 2007 (2007-05-01), XP055489192, Retrieved from the Internet: URL:http://journals.sagepub.com/doi/abs/10 .1177/193229680700100303?url_ver=Z39.88-20 03&rfr_id=ori:rid:crossref.org&rfr_dat=cr_ pub=pubmed [retrieved on 2018-06-29]
  • Hui C Kimko ET AL: "Introduction to Simulation for Designing Clinical Trials" In: "Simulation for Designing Clinical Trials: A Pharmacokinetic-pharmacodynamic Modeling Perspective", 12 December 2002 (2002-12-12), CRC Press, Baton Rouge, XP055529314, ISBN: 978-0-8247-0862-7 vol. 127
  • Diane R Mould: "Defining Covariate Distribution Models for Clinical Trial Simulation" In: "Simulation for Designing Clinical Trials: A Pharmacokinetic-pharmacodynamic Modeling Perspective", 12 December 2002 (2002-12-12), CRC Press, Baton Rouge, XP055529320, ISBN: 978-0-8247-0862-7 vol. 127, pages 31-54,
  • Christopher King ET AL: "Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp", Journal of Diabetes Science and Technology, vol. 1, no. 3, 1 May 2007 (2007-05-01), pages 317-322, XP055530555, US ISSN: 1932-2968, DOI: 10.1177/193229680700100302
  
Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


Description

RELATED APPLICATIONS



[0001] The present invention claims priority from U.S. Provisional Application Serial No. 60/936,581, filed June 21, 2007, entitled "Computer Simulation Environment in Silico Testing of Continuous Glucose Monitoring and Optimal Metabolic Control in Diabetes;" and U.S. Provisional Application Serial No. 61/073,074, filed June 17, 2008, entitled "Computer Simulation Environment in Silico Testing of Continuous Glucose Monitoring and Optimal Metabolic Control in Diabetes."

BACKGROUND OF THE INVENTION



[0002] Thirty years ago, the possibility for external closed-loop control of blood glucose (BG) levels in people with diabetes has been established with an instrument commercially known as the Biostator™, which used intravenous (i.v.) BG sampling and i.v. insulin and glucose delivery [1],[2],[3]. Recent studies of i.v. closed-loop control performed at the University of Virginia by Dr. Clarke (who has also been involved in the first Biostator™ studies) showed that i.v. control algorithms are capable of keeping BG levels within 10% from the preset targets during maintained euglycemia, descent into induced hypoglycemia, sustained hypoglycemia (at 50 mg/dl for 30 minutes), and controlled recovery [4]. However, i.v. closed-loop control is cumbersome and unsuited for outpatient use. Thus, increasing academic, industrial, and political effort has been focused on the development of minimally-invasive closed loop using subcutaneous (s.c.) systems using continuous glucose monitoring (CGM) and s.c. insulin delivery. Several s.c.-s.c. systems, generally using CGM coupled with insulin infusion pump and a control algorithm, have been tested [5],[6],[7],[8]. A recent United States Senate hearing emphasized the artificial pancreas initiative [9]. In September 2006 the Juvenile Diabetes Research Foundation (JDRF) initiated the Artificial Pancreas Project and funded six centers worldwide to carry closed-loop glucose control research [10]. These centers include the universities of Cambridge (England), Colorado, Santa Barbara, Stanford, Virginia, and Yale. So far, preliminary results have been reported from three closed-loop control studies conducted at Medtronic [8], Cambridge [6], and Yale using equipment provided by Medtronic MiniMed Inc.

[0003] The future development of the artificial pancreas will be greatly accelerated by employing mathematical modeling and computer simulation. Such in silico testing would provide direction for clinical studies, out-ruling ineffective control scenarios in a cost-effective manner. In the past two decades computer simulation and computer-aided design have made dramatic progress in all areas of design of complex engineering systems. A prime example is the Boeing 777 jetliner, which has been recognized as the first airplane to be 100% digitally designed and assembled in computer simulation environment. This virtual design has eliminated the need for many costly experiments and accelerated the development process. The final result has been impressive - the 777's flight deck and passenger cabin received the Design Excellence Award of the Industrial Designers Society - the first time any airplane was recognized by the society [11]. In the area of diabetes, accurate prediction of clinical trials has been done by the Archimedes diabetes model [12], [13]; a company - Entelos, Inc. - specializes in predictive biosimulation and in particular is working on diabetes simulator. These existing diabetes simulators, however, are based on population models. As a result, their capabilities are limited to prediction of population averages that would be observed during clinical trials.

[0004] The ability to simulate glucose-insulin system in normal life condition can be very useful in diabetes research. Several simulation models have been proposed in literature which proved to be useful in tackling various aspects of pathophysiology of diabetes [32-42]. Recently a new meal simulation model was proposed in [43]. The novelty and strength of this model is that it is based on virtually model-independent measurements of the various glucose and insulin fluxes occurring during a meal [44, 45]. In fact, the system is very complex and only the availability of glucose and insulin fluxes, in addition to their plasma concentrations, has allowed us to minimize structural uncertainties in modeling the various processes. The model may comprise of 12 nonlinear differential equations, 18 algebraic equations and 35 parameters. A user-friendly simulation software of this model would be of great help especially for investigators without a specific expertise in modeling. An aspect of the present invention is to present the interactive software GIM (Glucose Insulin Model), implemented in MATLAB version 7.0.1 which allows to simulate both normal and pathological conditions, e.g. type 2 diabetes and open-and closed-loop insulin infusion in type 1 diabetes. These case studies are only presented to illustrate the potential of the software and do not aim to address pathophysiological questions or to assess quality of glucose control by different strategies.
WO 02/087506 discloses a mathematical and computer model for simulation of a group of diabetes-related disorders and prediction of the likely effects of therapeutic interventions. Chiara Dalla Man ET AL: "GIM, Simulation Software of Meal Glucose-Insulin Model", Journal of diabetes science and technology, volume 1, issue 3, May 2007, pages 323-330, XP055489042 discloses a user-friendly software that implements a state-of-the-art physiological model of the glucose-insulin system during a meal. The GIM graphical interface makes its use extremely easy for investigators without specific expertise in modeling. Therefore, for the purposes of artificial pancreas development, a different type of computer simulator is needed - a system that is capable of simulating the glucose-insulin dynamics of a particular person.

Summary of the Invention



[0005] The present invention relates to a computer method according to claim 1, a computer simulations system according to claim 4 and a computer program product according to claim 7. Claims 2, 3 and 5, 6 respectively relate to specifically advantageous realizations of the subject matters of claims 1 and 4.

BRIEF DESCRIPTION OF THE DRAWINGS



[0006] The foregoing and other objects, features and advantages of the present invention, as well as the invention itself, will be more fully understood from the following description of preferred embodiments, when read together with the accompanying drawings in which:

Figure 1 provides

Figure 2 provides a schematic block diagram of the components of the computer simulation environment.

Figure 3(A) graphically presents the glycemic reaction of three simulated "subjects" 9 after a meal and pre-meal insulin bolus,

Figure 3(B) graphically presents the reaction of one simulated "subject" 9 to three meals with different carbohydrate content: 75, 85, and 95 grams.

Figure 4 graphically illustrates the errors of a simulated "sensor" which monitors the glucose fluctuations GF of a "subject" simulated by the GIM.

Figures 5(A)-(B) graphically present Poincaré plot of glucose dynamics of a person with Type 1 diabetes pre-islet transplantation and post islet-transplantation, respectively.

Figure 6 provides a schematic block diagram of the Simulink model silicon sensor module.

Figure 7 provides a schematic block diagram of the Simulink model of the subcutaneous insulin pump.

Figure 8 is a functional block diagram for a computer system for implementation of an exemplary embodiment or portion of an embodiment of present invention.


DETAILED DESCRIPTION OF THE INVENTION


Principal Components of Computer Simulation Environment



[0007] In an embodiment, a computer simulation environment (i.e., testing platform) for a system using s.c. CGM and s.c. insulin delivery via insulin pump would be a feasible step to validation of treatment strategies in type 1 diabetes (T1DM). Such a computer simulation environment (i.e., testing platform) may have three principal components: the simulated continuous monitoring sensor 5 providing frequent interstitial glucose determinations, the simulated insulin pump 7 delivering subcutaneous insulin and the simulated metabolic system of the person. In an embodiment the computer simulation environment (i.e., testing platform) may be comprised of the following three components as shown in Figure 2:
  1. a. The Meal Simulator Glucose-Insulin Model (GIM) 3, currently implemented in Simulink® or may be other simulation and Model-Based Design or software, and equipped with individual parameters for 300 subjects with T1DM, or any number as desired or required;
  2. b. The simulator of sensor errors 5, and
  3. c. The model of an insulin pump 7 ensuring discrete insulin delivery.


[0008] An aspect of the present invention method, system and computer program product provides means for testing of monitoring and/or treatment strategies for diabetes using a computer simulation environment, 101. The means for testing of the monitoring and/or treatment strategies includes representing the Human Metabolic System, 103. The representation module includes: applying a Mathematical Model of the Human Metabolic System, 105 and Providing a Plurality of Instances of a Simulated Subject, Creating a Simulated Population, 107. The means for testing includes representing of the Errors Of Continuous Glucose Monitoring Sensor, 109. The means for testing includes representing of Subcutaneous Insulin Delivery Via Insulin Pump, 111. Moreover, an interactive module is provided for allowing a user or device to interact with the testing means 113, accordingly as desired and required and as discussed throughout this disclosure.

"Silicon Subject 9" - the Glucose-Insulin Model of the Human Metabolic System



[0009] Dr. Claudio Cobelli pioneered, together with Richard Bergman, the mathematical modeling of glucose metabolism. Their, now classic, Minimal Model of Glucose Kinetics [14] served as the basis for numerous further developments (generating over 600 publications in the last 30 years), and as a gold-standard assessment of insulin sensitivity (Si) in humans. Dr. Cobelli's group has been at the forefront of these investigations, with more than 200 publications addressing aspects of glucose-insulin dynamics. Recently their studies have been extended to measure the same indices in the postprandial condition [15],[16],[17]. The minimal model and its generalizations allow therefore the estimation, for each individual, of his/her parameters of insulin sensitivity and insulin action.

[0010] Referring to an aspect of the present invention, the metabolic system of a particular person can be programmed into a computer simulator creating a "silicon subject 9" whose metabolism is closely related to its human original. In silico refers to, for example but not limited thereto, in computer simulation or in virtual reality.

[0011] A new generation of in silico model has very recently become possible thanks to a database collected by a study at the Division of Endocrinology, Diabetes, Metabolism & Nutrition, Mayo Clinic, Rochester, MN, directed by Dr. Robert A. Rizza. A unique meal data set of 204 nondiabetic individuals with various degrees of glucose tolerance has become available. The subjects underwent a triple tracer meal protocol, thus allowing us to obtain in a virtually model-independent fashion the time course of all the relevant glucose and insulin fluxes during a meal i.e. glucose rate of appearance in plasma, production, utilization and pancreatic insulin secretion [18],[19]. Thus, by using a "concentration and flux," it was possible to model the glucose-insulin system by resorting to a sub-system forcing function strategy, which minimizes structural uncertainties in modeling the various unit processes.

[0012] In order to simulate the metabolic system of a person with T1DM, the in silico model has been modified. First, the insulin secretion module has been eliminated. Then, in the model of glucose production the control of portal insulin has been removed due to the absence of insulin secretion. Glucose production was assumed to be higher on average (relative to non-diabetic subjects), e.g. 2.4 mg/kg/min. Finally, some steady state constrains have been altered to accommodate these model modifications.

[0013] An important aspect to realistic computer simulation is the availability of "silicon subjects 9," e.g. the availability of distributions of the model parameters across the population. Such distributions are difficult to obtain and are considered the "secret" allowing successful simulation. The identification of the simulation model in the database described above has provided estimates of all model parameters. This allowed the computer simulation of various metabolic scenarios on a "cohort" of "silicon subjects." For example, Figure 3(A) graphically presents the glycemic reaction of three "simulated or silicon subjects 9" after a meal and pre-meal insulin bolus, while Figure 3(B) graphically presents the reaction of one "subject" 9 to three meals with different carbohydrate content: 75, 85, and 95 grams. The current software implementation of the T1DM model is equipped with 300 "silicon subjects," which will allow the development of unified approach to the testing of closed-loop control algorithms 6.

[0014] The model glucose-insulin system or the glucose-insulin model (GIM) 3 puts in relation the measured plasma concentrations of glucose (G) and insulin (I) and the glucose fluxes (i.e. rate of appearance, Ra, endogenous glucose production, EGP, utilization, U, renal extraction, E, and insulin fluxes, i.e. secretion, S, and degradation D) in a person with Type 1 diabetes.

[0015] In particular, glucose kinetics is described by the two compartment model:

where Gp and Gt (mg/kg) are glucose masses in plasma and rapidly-equilibrating tissues, and in slowly-equilibrating tissues, respectively, G (mg/dl) plasma glucose concentration, suffix b denotes basal state, EGP endogenous glucose production (mg/kg/min), Ra glucose rate of appearance in plasma (mg/kg/min), E renal excretion (mg/kg/min), Uii and Uid insulin-independent and dependent glucose utilizations, respectively (mg/kg/min), VG distribution volume of glucose (dl/kg), and k1 and k2 (min-1) rate parameters.

[0016] At basal steady state the endogenous production EGPb equals glucose disappearance, i.e. the sum of glucose utilization and renal excretion (which is zero in health), Ub+Eb.

[0017] The functional description of EGP in terms of glucose and insulin signals comprises a direct glucose signal and both delayed and anticipated insulin signals:

where Ipo is the amount of insulin in the portal vein (pmol/kg), Id (pmol/l) is a delayed insulin signal realized with a chain of two compartments:

kp1 (mg/kg/min) is the extrapolated EGP at zero glucose and insulin, kp2 (min-1) liver glucose effectiveness, kp3 (mg/kg/min per pmol/l) parameter governing amplitude of insulin action on the liver, kp4 (mg/kg/min/(pmol/kg)) parameter governing amplitude of portal insulin action on the liver and ki (min-1) rate parameter accounting for delay between insulin signal and insulin action. EGP is also constrained to be non-negative.

[0018] At basal steady state one has:



[0019] The model of glucose intestinal absorption describes the glucose transit through the stomach and intestine by assuming the stomach to be represented by two compartments (one for solid and one for triturated phase), while a single compartment is used to describe the gut:

where Qsto (mg) is amount of glucose in the stomach (solid, Qsto1, and liquid phase, Qsto2), Qgut (mg) glucose mass in the intestine, kgri (min-1) rate of grinding, kempt(Qsto) (min-1) rate constant of gastric emptying which is a nonlinear function of Qsto:

and kabs (min-1) rate constant of intestinal absorption, f fraction of intestinal absorption which actually appears in plasma, D (mg) amount of ingested glucose, BW (kg) body weight and Ra (mg/kg/min) appearance rate of glucose in plasma.

[0020] The model of glucose utilization by body tissues during a meal (both insulin-independent and -dependent) assumes that glucose utilization is made up of two components. Insulin-independent utilization takes place in the first compartment, is constant and represents glucose uptake by the brain and erythrocytes (Fcns):



[0021] Insulin-dependent utilization takes place in the remote compartment and depends nonlinearly (Michaelis Menten) from glucose in the tissues:

where remote insulin, X(t), is given by:

where I is plasma insulin, suffix b denotes basal state, p2U (min-1) is rate constant of insulin action on the peripheral glucose utilization.

[0022] Total glucose utilization, U, is thus:



[0023] At basal steady state one has:

and:

from which:



[0024] Glucose excretion by the kidney occurs if plasma glucose exceeds a certain threshold and can be modeled by a linear relationship with plasma glucose:

where ke1 (min-1) is glomerular filtration rate and ke2 (mg/kg) renal threshold of glucose.

Population of simulated "subjects"



[0025] As noted above, the key to successful simulation is the availability of comprehensive population of simulated "subjects" that encompasses the distribution of key metabolic parameters observed in Type 1 diabetes in vivo. Next, the Biometric data (age, weight, insulin units per day, carbohydrate ratio, and maximal glucose decrease) for the population of 300 in silico subjects 9 are as follows : 100 adults, 100 adolescents, and 100 children. Carbohydrate ratio is computed from total daily insulin using standard 450 rule. The maximal glucose decrease (MGD) is computed as the maximum decrease in glucose following a simulated administration of one unit insulin.
Table 1:
ADULTSADOLESCENTS CHILDREN
IDAge [y]Weight [kg]Total daily insulin [U]CHO ratio [g/U]MGD [mg/dl] IDAge [y]Weight [kg]Total daily insulin [U]CHO ratio [g/U]MGD [mg/dl] IDAge [y]Weight [kg]Total daily insulin [U]CHO ratio [g/U]MGD [mg/dl]
51 24 76.4 38.9 11.6 13.2   31 16 54.3 51.6 8.7 10.5   11 11 58.9 26.0 17.3 32.9
52 27 102.6 34.3 13.1 31.0   32 15 60.8 59.5 7.6 10.5   12 7 24.8 21.9 20.5 73.8
53 70 74.6 61.4 7.3 3.0   33 13 44.7 37.3 12.1 23.8   13 10 46.9 16.6 27.0 45.2
54 62 57.3 28.7 15.7 59.9   34 16 60.4 38.8 11.6 9.8   14 10 51.6 46.8 9.6 20.4
55 40 59.1 40.6 11.1 15.1   35 12 45.3 37.3 12.1 21.4   15 7 43.5 18.7 24.1 74.4
56 77 68.7 43.0 10.5 19.0   36 16 50.6 50.8 8.9 8.0   16 7 38.2 45.3 9.9 13.8
57 23 67.3 27.5 16.4 22.0   37 15 46.0 63.3 7.1 7.8   17 9 34.6 48.4 9.3 42.8
58 47 68.3 33.5 13.4 37.4   38 12 51.2 53.2 8.5 5.6   18 7 38.2 32.3 13.9 58.9
59 44 64.0 52.1 8.6 17.6   39 12 50.8 57.2 7.9 5.7   19 10 36.8 59.8 7.5 13.9
60 66 66.6 41.2 10.9 17.0   40 15 48.8 70.7 6.4 5.7   20 11 58.1 45.8 9.8 30.2


[0026] Provided below is a reference list of the description of 26 in silico parameters defining each silicon subject 9.
kabs = rate constant of glucose absorption by the intestine
kmax = maximum rate constant of gastric emptying
kmin = minimum rate constant of gastric emptying
b = percentage of the dose for which kempt decreases at (kmax-kmin)/2
d = percentage of the dose for which kempt is back to (kmax-kmin)/2
ki = rate parameter accounting for delay between insulin signal and insulin action on the liver
kp2 = liver glucose effectiveness
kp3 = parameter governing amplitude of insulin action on the liver
Vg = distribution volume of glucose
Vmx= parameter governing amplitude of insulin action on glucose utilization
km0 = parameter governing glucose control on glucose utilization
K2 = rate parameter accounting for glucose transit from tissue to plasma
K1= rate parameter accounting for glucose transit from plasma to tissue
p2U= rate parameter accounting for delay between insulin signal and insulin action on glucose utilization
Vi = distribution volume of insulin
m1 = rate parameter of insulin kinetics
m5 = coefficient linking insulin hepatic extraction to insulin secretion rate
Gb = basal glucose concentration
EGPb = basal endogenous glucose production
BW = body weight
Ib = basal insulin concentration (resulting from a basal insulin infusion rate)
CL = insulin clearance
kd = rate constant of nonmonomeric insulin dissociation
ksc = rate constant taking into account the physiological delay of the sensor
ka1 = rate constants of nonmonomeric insulin absorption
ka2 = rate constants of monomeric insulin absorption

[0027] Provided below in tables 2 and 3, is a sample list of In Silico Model Parameters for 10 Test adults.
Table 2: ID numbers 51-60, Parameters kabs to K1
IDkabskmaxkminbdkikp2kp3VgVmxkm0K2K1
51 0.111 0.031 0.006 0.812 0.129 0.006 0.004 0.008 1.748 0.030 199.5 0.098 0.053
52 0.195 0.028 0.008 0.802 0.191 0.007 0.002 0.022 1.703 0.029 215.2 0.067 0.078
53 0.510 0.023 0.016 0.943 0.119 0.006 0.010 0.003 1.836 0.027 238.1 0.344 0.045
54 0.759 0.025 0.009 0.944 0.188 0.007 0.002 0.016 1.753 0.096 217.7 0.116 0.079
55 0.036 0.051 0.011 0.657 0.124 0.004 0.006 0.007 1.830 0.049 240.5 0.109 0.061
56 0.154 0.027 0.006 0.893 0.249 0.003 0.002 0.012 1.677 0.043 228.1 0.189 0.086
57 0.026 0.065 0.007 0.622 0.139 0.011 0.010 0.006 1.566 0.059 220.5 0.060 0.076
58 0.928 0.019 0.009 0.875 0.216 0.005 0.005 0.021 1.717 0.069 227.1 0.037 0.071
59 0.097 0.031 0.007 0.853 0.115 0.010 0.011 0.006 1.837 0.061 226.9 0.116 0.072
60 0.983 0.019 0.014 0.776 0.228 0.010 0.003 0.005 2.016 0.048 246.6 0.127 0.066
Table 3: Adults: ID numbers 51-60, Parameters p2U to ka2
IDp2UVim1m5GbEGPbBWIbCLkdkscka1ka2
51 0.015 0.059 0.156 0.043 151.3 3.001 76.37 90.56 1.033 0.017 0.108 0.001 0.007
52 0.030 0.062 0.326 0.083 157.5 2.351 102.62 88.51 1.098 0.017 0.091 0.002 0.016
53 0.012 0.049 0.104 0.021 152.3 2.864 74.61 69.24 1.005 0.017 0.071 0.001 0.012
54 0.047 0.060 0.186 0.041 154.4 2.475 57.32 103.75 0.925 0.016 0.045 0.002 0.012
55 0.033 0.043 0.085 0.030 139.7 2.953 59.06 93.76 1.162 0.015 0.065 0.002 0.020
56 0.013 0.058 0.205 0.029 154.9 2.602 68.71 96.16 1.224 0.016 0.128 0.002 0.014
57 0.027 0.075 0.550 0.030 128.4 2.616 67.32 70.25 1.160 0.019 0.176 0.001 0.014
58 0.034 0.063 0.190 0.042 149.5 2.872 68.28 94.61 1.145 0.013 0.129 0.001 0.012
59 0.017 0.107 0.172 0.024 145.3 3.132 64.00 138.98 1.136 0.017 0.112 0.002 0.016
60 0.018 0.070 0.135 0.017 140.8 3.415 66.63 81.65 1.141 0.014 0.108 0.002 0.026


[0028] Provided below in tables 4 and 5, is a sample list of In Silico Model Parameters for 10 Test adolescents.
Table 4: ID numbers 31-40, Parameters kabs to K1
IDkabskmaxkminbdkikp2kp3VgVmxkm0K2K1
31 0.101 0.050 0.009 0.623 0.116 0.004 0.012 0.004 1.860 0.030 231.3 0.047 0.086
32 0.140 0.040 0.008 0.824 0.118 0.003 0.004 0.002 1.793 0.020 214.3 0.084 0.072
33 0.209 0.042 0.019 0.786 0.056 0.004 0.008 0.003 1.964 0.052 187.0 0.056 0.075
34 0.194 0.028 0.009 0.698 0.237 0.004 0.005 0.007 1.819 0.018 218.2 0.054 0.058
35 0.235 0.036 0.007 0.739 0.168 0.006 0.004 0.009 1.979 0.037 228.5 0.069 0.064
36 0.021 0.092 0.007 0.664 0.091 0.007 0.004 0.004 1.830 0.020 244.6 0.093 0.052
37 1.555 0.022 0.015 1.011 0.170 0.003 0.005 0.003 2.057 0.022 239.7 0.074 0.079
38 0.079 0.037 0.010 0.615 0.122 0.007 0.012 0.002 1.710 0.022 242.3 0.124 0.068
39 1.730 0.016 0.003 0.766 0.371 0.003 0.006 0.019 1.892 0.012 307.4 0.117 0.058
40 0.142 0.047 0.019 0.699 0.187 0.003 0.012 0.004 1.685 0.018 269.7 0.103 0.090
Table 5: Adolescents: ID numbers 31-50, Parameters p2U to ka2
IDp2UVim1m5GbEGPbBWIbCLkdkscka1ka2
31 0.033 0.034 0.241 0.021 129.5 2.732 54.33 118.71 1.229 0.016 0.117 0.002 0.022
32 0.022 0.028 0.073 0.015 135.8 2.208 60.75 99.83 1.064 0.015 0.058 0.002 0.019
33 0.031 0.042 0.230 0.033 135.7 2.712 44.71 102.53 1.123 0.015 0.104 0.002 0.031
34 0.027 0.031 0.133 0.006 144.9 2.810 60.44 78.68 0.976 0.017 0.119 0.001 0.011
35 0.032 0.056 0.325 0.041 143.2 2.896 45.26 97.62 1.056 0.019 0.082 0.002 0.018
36 0.014 0.037 0.076 0.012 152.5 3.070 50.60 72.21 1.165 0.014 0.069 0.003 0.027
37 0.014 0.048 0.203 0.025 124.4 2.735 46.01 133.78 1.200 0.016 0.068 0.003 0.021
38 0.014 0.055 0.076 0.031 126.9 2.994 51.16 93.13 1.058 0.018 0.056 0.004 0.046
39 0.016 0.042 0.104 0.022 161.0 2.792 50.75 116.57 1.206 0.016 0.104 0.002 0.029
40 0.025 0.050 0.117 0.019 135.6 3.342 48.83 146.65 1.212 0.018 0.064 0.003 0.030


[0029] Provided below in tables 6 and 7, is a sample list of In Silico Model Parameters for 10 Test Children.
Table 6: ID numbers 11-20, Parameters kabs to K1
IDkabskmaxkminbdkikp2kp3VgVmxkm0K2K1
11 0.794 0.027 0.005 0.771 0.136 0.020 0.005 0.013 1.932 0.082 244.0 0.223 0.069
12 0.455 0.039 0.023 0.591 0.186 0.013 0.013 0.036 2.020 0.182 210.9 0.042 0.072
13 0.126 0.040 0.011 0.710 0.145 0.044 0.011 0.018 1.904 0.141 202.1 0.161 0.056
14 0.628 0.018 0.011 0.882 0.141 0.018 0.011 0.014 1.844 0.114 249.8 0.210 0.067
15 0.432 0.045 0.010 0.899 0.183 0.007 0.002 0.037 1.835 0.100 230.6 0.057 0.115
16 0.248 0.048 0.003 0.699 0.221 0.010 0.003 0.009 1.838 0.067 268.8 0.214 0.036
17 0.029 0.093 0.013 0.664 0.065 0.020 0.018 0.037 1.657 0.160 313.7 0.200 0.128
18 0.051 0.038 0.010 0.691 0.183 0.028 0.003 0.011 1.737 0.163 238.3 0.189 0.062
19 0.028 0.064 0.006 0.656 0.194 0.023 0.004 0.012 1.687 0.070 271.6 0.374 0.052
20 0.026 0.067 0.002 0.702 0.185 0.012 0.004 0.027 2.106 0.075 219.3 0.074 0.041
Table 7: Children: ID numbers 11-20, Parameters p2U to ka2
IDp2UVim1m5GbEGPbBWIbCLkdkscka1ka2
11 0.052 0.054 0.080 0.034 146.9 3.263 58.86 79.79 0.903 0.016 0.058 0.003 0.023
12 0.089 0.034 0.242 0.022 132.9 3.198 24.76 89.90 0.920 0.014 0.119 0.002 0.027
13 0.059 0.069 0.302 0.090 139.6 3.288 46.85 62.27 0.838 0.014 0.107 0.002 0.020
14 0.082 0.036 0.080 0.018 147.2 2.845 51.60 127.54 1.188 0.014 0.045 0.001 0.011
15 0.083 0.080 0.831 0.016 139.8 2.812 43.47 71.72 0.891 0.017 0.091 0.001 0.010
16 0.074 0.047 0.242 0.013 146.3 2.647 38.19 103.26 1.218 0.015 0.075 0.003 0.022
17 0.088 0.032 0.159 0.012 132.7 4.101 34.59 163.74 1.121 0.017 0.178 0.003 0.043
18 0.056 0.030 0.068 0.005 144.7 2.720 38.24 99.97 0.978 0.015 0.136 0.003 0.034
19 0.048 0.033 0.137 0.007 139.3 2.908 36.82 129.32 1.442 0.017 0.045 0.001 0.014
20 0.080 0.038 0.278 0.018 140.4 2.863 58.06 106.42 1.465 0.014 0.100 0.003 0.029

"Silicon Sensor 5"



[0030] Dr. Kovatchev's group has been involved in studies of CGM since the introduction of this technology. Many of these studies focused on accuracy of continuous monitoring sensors and on analysis of their errors. For example, in 2004 we introduced the Continuous Glucose Error-Grid Analysis (CG-EGA, [20]), which is still the only method for assessment of the dynamical accuracy of CGM. The CG-EGA has been designed with closed-loop control in mind - it assesses the accuracy of the clinical decisions taken on the basis of sensor data at short (e.g. 10-minute) time intervals. This initial development was followed by extensive studies of sensor accuracy, which allowed not only comparison of various sensors [21], but also the decomposition of sensor errors into errors due to calibration and errors dues to blood-to-interstitial glucose transfer [22]. These methods provide the base for realistic simulation of the errors that a CGM can make and include the resulting noise in the feed for the control algorithm 6, effectively creating a "silicon sensor."

[0031] Referring to Figure 4, Figure 4 graphically presents the main steps of adding a silicon sensor, which monitors the glucose fluctuations GF of a "silicon subject" simulated by the GIM. The "silicon sensor" SS is vulnerable to interstitial glucose IG delays, calibration and random errors. Figure 4 graphically provides information on monitoring a "silicon subject" via "silicon sensor".

[0032] It is worth noting that sensor errors are typically not random and are poorly represented by white noise. Thus, standard techniques based on adding independent identically distributed Gaussian noise to the output of the GIM simulation would not produce realistic sensor scenarios. Characteristics, such as degree of dependence between sequential readings, influence of calibration errors, and potential for loss of sensitivity need to be taken into account. We also acknowledge that in addition to common sensor error patterns, there exist device-specific errors. Thus, the "silicon sensor" needs to have the capability of representing most popular continuous monitoring devices. Such a capability would allow investigating the performance of control algorithms 6 with various sensors, and potentially optimal pairing between a sensor and a control algorithm 6. Currently, most extensively developed is the silicon image of the FreeStyle Navigator® (Abbott Diabetes Care). However, recently completed accuracy studies would provide data for the simulating of Guardian® RT (Medtronic Inc.) and Dexcom™ STS® (Dexcom, Inc.) sensors.

[0033] In summary, regarding an aspect of the present invention the addition of "silicon sensor" 5 allows for testing control algorithms 6 under realistic "noisy" conditions. Initial experiments show that such an approach is very beneficial, sometimes suggesting significant changes and rethinking of control strategies.

[0034] Regarding an aspect of an embodiment of the present invention, extensive analysis of sensor errors resulted in the following model equations defining in silico continuous glucose sensor (CGS) 5. The errors of the following devices can be simulated by this model: GuardianRT (Medtronic, Northridge, CA); Freestyle Navigator™ (Abbott Diabetes Care, Alameda, CA); and DexCom™ STS™ (DexCom, Inc. San Diego, CA) 7-day sensor. It may be noted that the simulator is not suitable for modeling the errors of the DexCom™ 3-day sensor.

[0035] The sensor model was initially derived via analysis of a large data set collected using the Freestyle Navigator™ (Abbott Diabetes Care, Alameda, CA). Further NIH-sponsored study at the University of Virgnia found that the errors of GuardianRT (Medtronic, Northridge, CA) have similar structure and can be modeled by the simulator. Recently, we analyzed accuracy data for the DexCom™ STS™ 7-day sensor (DexCom, Inc. San Diego, CA) provided by Decom Inc, and found that this sensor is compatible with the simulation procedure as well. Analysis of DexCom™ STS™ 3-day sensor found that the errors of this device have larger magnitude and therefore could not be simulated in this environment. The errors of these three differ from random noise by having substantial time-lag dependence and other non-i.i.d. characteristics. The components of sensor error were therefore modeled as:
  1. (i) Blood-to-interstitium glucose transport described by the equation:

    Here IG is the interstitial and BG is plasma glucose concentration; τ represents the time lag between the two fluids.
  2. (ii) Sensor lag - the time of glucose transport from interstitium to the sensor needle:

    Considering that these are two sequential first order diffusion models, we model them with one diffusion equation where the time lag is the resultant single diffusion process representing both the physiological lag and the sensor lag. Empirical estimation gives a time lag of 5 min (which produces a delay of approximately 15 minutes).
  3. (iii) The noise of the sensor is non-white (Gaussian).







[0036] An approach of an aspect of the present invention uses Autoregressive Moving Average (ARMA) process for its modeling. The sensor noise is εn, which is driven by the normally distributed time series en. The parameterṡ. ξ, λ, δ, and γ are the Johnson system (SU - unbounded system) parameters corresponding to the empirical noise distributions.

[0037] The sensor model was initially derived via analysis of a large data set collected using the Freestyle Navigator™ (Abbott Diabetes Care, Alameda, CA). Further, we analyzed accuracy data for two other sensors: GuardianRT (Medtronic, Northridge, CA) and DexCom™ STS™ - 3-Day sensor (DexCom, Inc. San Diego, CA), collected during NIH-sponsored study at the University of Virgnia. This study showed that the distribution and the range of the errors of the Navigator™ and the GuardianRT were generally equivalent, which allowed the errors of these two devices to be simulated by the same in silico routine. However, this study also showed that the errors of the DexCom™ STS™ - 3-Day sensor were approximately 30% larger, which prevented this device from inclusion in the simulation environment. The simulink model 43 of the sensor is provided in Figure 6. The input (IG) is added to the sensor noise coming from struttura.noise. The switch_noise block allows for testing with a simulated perfect sensor: by setting the struttura.noise_switch to a number greater than 0 struttura.noise is replaced by constant1 (i.e. 0). The zero order hold enables discrete sampling at rate equal to struttura.sensor_sampling, and the '30-600' saturation block enforces the hardwired limitation of the sensor (struttura.sensor_min and max). The noise is generated off-line prior to the simulation run as a vector of values with a 1 minute resolution. The create_noise.m script generates the noise vector and loads it into the Matlab workspace, from where it is accessible to the simulator.

"Silicon Insulin Pump"



[0038] Subcutaneous insulin delivery via insulin pump has two major specifics that need to be taken into account when testing control algorithms 6 in silico: (i) time and dynamics of insulin transport from subcutaneous compartment into blood, and (ii) discrete insulin infusion corresponding to stepwise basal pump rate and insulin boluses.

[0039] A subcutaneous insulin infusion module has been added to the model to account for the exogenous route of insulin delivery in T1DM. Several models of subcutaneous insulin kinetics have been published [23]. The model implemented by Dr. Cobelli assumes a two compartment description for insulin in the subcutaneous space: the first compartment represents the amount of the nonmonomeric insulin, which is then transformed into monomeric insulin (second compartment):

where Ip and Il (pmol/kg) are insulin masses in plasma and in liver, respectively, I (pmol/l) plasma insulin concentration, suffix b denotes basal state, Pump is the external insulin pump, m1, m2, m3, m4 (min-1) rate parameters; m3 depends on basal hepatic extraction, HEb:



[0040] Discrete insulin delivery is implemented in a pump-specific manner, currently simulation the functioning of Deltec Cozmo® insulin pump, Smiths Medical MD, Inc.

[0041] As with "silicon sensors," regarding an aspect of the present invention the parameters of various insulin pumps will be implemented into the simulation environment, which will create an array of insulin delivery options available to the control algorithms 6.

[0042] An aspect of the present invention models the subcutaneous insulin pump 7 as a discrete amount, continuous time, insulin delivery device. To reproduce as closely as possible real life pumps, we constraint possible injections, following manufacturers characteristics.

[0043] The simulink model 43 of the pump is provided in Figure 7. The input signal (SQ on the graph) is composed of 2 components: a basal rate and a bolus, therefore representing the two possible modes of classical SQ insulin pumps. Each component is controlled by a different set of rules (e.g. minimum and maximum injection) and these rules are enforced differentially (blue and pink blocks in Figure 7). Finally both regulated signals are combined to provide the final insulin injection rate. This is implemented as follows:



where JR is the regulated signal (basal or bolus), and min, max and inc are the rule parameters (different for boluses and basal). div() is the integer division operator (div(7,3)=2).

[0044] These model equations are suitable for in silico simulation of the following insulin pumps:
  • Deltec Cozmo® (Smiths Medical MD, Inc., St. Paul, MN), with parameters:

    Basal Increments 0.05 units/hour;Minimum Bolus Increment 0.05 units;

    Minimum basal rate 0 units/hour; Minimum Bolus Amount 0 units;

    Maximum Basal Rate 35 units/hour; Maximum Bolus Amount 75 units.

  • OmniPod Insulin Management System (Insulet Corporation, Bedford, MA) with parameters:

    Basal Increments 0.05 units/hour; Minimum Bolus Increment 0.05 units;

    Minimum basal rate 0 units/hour; Minimum Bolus Amount 0 units;

    Maximum Basal Rate 30 units/hour; Maximum Bolus Amount 30 units.



[0045] While the maximum bolus amounts of these two devices differ, the critical characteristics of the in silico pump model - basal and bolus increments - are identical. Because the simulation of closed-loop control uses basal and bolus increments proposed by the control algorithm 6 and is not dependent on maximal basal/bolus amounts (as long as they are sufficiently large as is the case with these two pumps), we concluded that the Deltec Cozmo ® and the OmniPod can be simulated by essentially the same simulation module, with a minor difference in allowable maximum bolus amount and basal rate.

Outcome Measures



[0046] To facilitate the interpretation of the results from various control strategies and to permit their direct comparison, a set of indices of glucose control will be implemented within the simulation environment. Emphasis will be placed on indices of temporal glucose variability and associated risks for hypoglycemia and hyperglycemia. This choice is directed by the basic premise of the artificial pancreas - beta cell replacement. In health, the beta cell reacts to temporal glucose fluctuations and aims the maintenance of equilibrium with minimum glucose excursion and particular attention to hypoglycemia, which is controlled by elaborate counterregulatory mechanisms. Average glycemia and clinically accepted standards, such as time spent within a preset target range, will be used as well. The suggested here criteria for testing the performance of control algorithms 6, as well as a set of figures visualizing the results have been previously published and shown to be quite sensitive to the effects of various treatments [24],[25]. In particular, we would suggest temporal glucose variability plots including traces of risks for hypoglycemia and hyperglycemia, as well as Poincaré plot of glucose dynamics. The latter is particularly representative of system stability - the principal property that should be achieved via closed loop control.

[0047] Referring to Figure 5, Figure 5 graphically presents Poincaré plot of glucose dynamics of a person with Type 1 diabetes pre-islet transplantation (Figure 5(A)), post islet-transplantation (Figure 5(B)). Although post-transplantation this person has not been insulin-independent, it is evident that the transplantation has restored partially the stability of this person's glucose-insulin system.

[0048] Suggested composite numerical measures of algorithm performance include average glucose for the duration of the experiment, as well as the following indices:
  1. (a) Low Blood Glucose Index (LBGI, ), which captures the propensity of the algorithm to overshoot the target and eventually trigger hypoglycemia;
  2. (b) High Blood glucose index (HBGI), which captures the propensity of the algorithm to stay above the target range and even more to "bounce out" of the target range due to oscillations;
  3. (c) Percent of time spent within a preset target range (e.g. 70-180 mg/dl);
  4. (d) Average Absolute Rate of Change, which captures the smoothness of the control algorithm - the more aggressive an algorithm and the less robust to noise in the signal, the bigger the absolute rate of change;
  5. (e) Numerical measures of system stability, such as the diameter of a Poincaré plot.


[0049] An embodiment of the present invention simulation environment may be modular, with "plug-and-play" capabilities for algorithm implementation, as well as for adding new silicon "subjects," "sensors," and insulin delivery systems. Unified software platform and outcome measures will allow the widespread implementation this simulation environment.

[0050] Further, the unified computer simulation environment should be accepted as a standard testing platform and as a prerequisite for insulin treatment strategy performance (i.e. a treatment needs to be able to control the simulator first, before attempting to control a person).

[0051] A simulation model of the glucose-insulin system in normal life condition can be very useful in diabetes research, e.g. testing insulin infusion algorithms and decision support systems, assessing glucose sensor performance, patient and student training.

[0052] An aspect of the present invention method, system and computer program product provides a new meal simulation model, which incorporates state of art quantitative knowledge on glucose metabolism and its control by insulin both at the organ/tissue and whole-body level. An aim of an embodiment of the present invention is to present the interactive simulation software, GIM (Glucose Insulin Model), which implements this model.

[0053] For instance, the model (and related method, system and computer program product) may be implemented in MATLAB, or any other software platform or program, and may be designed with a windows interface (or other operating system interface or communication) which allows the user to easily simulate 24 hours daily life (or other time period as desired or required) of a type 1 diabetic subject.

[0054] Turning to Figure 8, Figure 8 is a functional block diagram for a computer system 800 for implementation of an exemplary embodiment or portion of an embodiment of present invention. For example, a method or system of an embodiment of the present invention may be implemented using hardware, software or a combination thereof and may be implemented in one or more computer systems or other processing systems, such as personal digit assistants (PDAs) equipped with adequate memory and processing capabilities. In an example embodiment, the invention was implemented in software running on a general purpose computer 800 as illustrated in Figure 8. The computer system 800 may includes one or more processors, such as processor 804. The Processor 804 is connected to a communication infrastructure 806 (e.g., a communications bus, crossover bar, or network). The computer system 800 may include a display interface 802 that forwards graphics, text, and/or other data from the communication infrastructure 806 (or from a frame buffer not shown) for display on the display unit 830. Display unit 830 may be digital and/or analog.

[0055] The computer system 800 may also include a main memory 808, preferably random access memory (RAM), and may also include a secondary memory 810. The secondary memory 810 may include, for example, a hard disk drive 812 and/or a removable storage drive 814, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. The removable storage drive 814 reads from and/or writes to a removable storage unit 818 in a well known manner. Removable storage unit 818, represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 814. As will be appreciated, the removable storage unit 818 includes a computer usable storage medium having stored therein computer software and/or data.

[0056] In alternative embodiments, secondary memory 810 may include other means for allowing computer programs or other instructions to be loaded into computer system 800. Such means may include, for example, a removable storage unit 822 and an interface 820. Examples of such removable storage units/interfaces include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, and other removable storage units 822 and interfaces 820 which allow software and data to be transferred from the removable storage unit 822 to computer system 800.

[0057] The computer system 800 may also include a communications interface 824. Communications interface 824 allows software and data to be transferred between computer system 800 and external devices. Examples of communications interface 824 may include a modem, a network interface (such as an Ethernet card), a communications port (e.g., serial or parallel, etc.), a PCMCIA slot and card, a modem, etc. Software and data transferred via communications interface 824 are in the form of signals 828 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 824. Signals 828 are provided to communications interface 824 via a communications path (i.e., channel) 826. Channel 826 (or any other communication means or channel disclosed herein) carries signals 828 and may be implemented using wire or cable, fiber optics, blue tooth, a phone line, a cellular phone link, an RF link, an infrared link, wireless link or connection and other communications channels.

[0058] In this document, the terms "computer program medium" and "computer usable medium" are used to generally refer to media or medium such as various software, firmware, disks, drives, removable storage drive 814, a hard disk installed in hard disk drive 812, and signals 828. These computer program products ("computer program medium" and "computer usable medium") are means for providing software to computer system 800. The computer program product may comprise a computer useable medium having computer program logic thereon. The invention includes such computer program products. The "computer program product" and "computer useable medium" may be any computer readable medium having computer logic thereon.

[0059] Computer programs (also called computer control logic or computer program logic) are may be stored in main memory 808 and/or secondary memory 810. Computer programs may also be received via communications interface 824. Such computer programs, when executed, enable computer system 800 to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable processor 804 to perform the functions of the present invention. Accordingly, such computer programs represent controllers of computer system 800.

[0060] In an embodiment where the invention is implemented using software, the software may be stored in a computer program product and loaded into computer system 800 using removable storage drive 814, hard drive 812 or communications interface 824. The control logic (software or computer program logic), when executed by the processor 804, causes the processor 804 to perform the functions of the invention as described herein.

[0061] In another embodiment, the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

[0062] In yet another embodiment, the invention is implemented using a combination of both hardware and software.

[0063] In an example software embodiment of the invention, the methods described above may be implemented in SPSS control language or C + + programming language, but could be implemented in other various programs, computer simulation and computer-aided design, computer simulation environment, MATLAB, or any other software platform or program, windows interface or operating system (or other operating system) or other programs known or available to those skilled in the art.

[0064] It should be appreciated that various aspects of embodiments of the present method, system, devices and computer program product may be implemented with the following methods, systems, devices and computer program products disclosed in the following U.S.

[0065] Patent Applications, U.S. Patents, and PCT International Patent Applications that are co-owned with the assignee:

PCT/US2007/085588 not yet published filed November 27, 2007, entitled "Method, System, and Computer Program Product for the Detection of Physical Activity by Changes in Heart Rate, Assessment of Fast Changing Metabolic States, and Applications of Closed and Open Control Loop in Diabetes."

U.S. Serial No. 11/943,226, filed November 20, 2007, entitled "Systems, Methods and Computer Program Codes for Recognition of Patterns of Hyperglycemia and Hypoglycemia, Increased Glucose Variability, and Ineffective Self-Monitoring in Diabetes"

PCT International Application Serial No. PCT/US2005/013792, filed April 21, 2005, entitled "Method, System, and Computer Program Product for Evaluation of the Accuracy of Blood Glucose Monitoring Sensors/Devices,"

U.S. Patent Application No. 11/578,831, filed October 18, 2006 entitled "Method, System and Computer Program Product for Evaluating the Accuracy of Blood Glucose Monitoring Sensors/Devices;"

PCT International Application Serial No. PCT/US01/09884, filed March 29 2001, entitled "Method, System, and Computer Program Product for Evaluation of Glycemic Control in Diabetes Self-Monitoring Data;"

U.S. Patent No. 7,025,425 B2 issued April 11, 2006, entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-Monitoring Data;"

U.S. Patent Application No. 11/305,946 filed December 19, 2005 entitled "Method, System, and Computer Program Product for the Evaluation of Glycemic Control in Diabetes from Self-Monitoring Data" (Publication No. 20060094947);

PCT International Application Serial No. PCT/US2003/025053, filed August 8, 2003, entitled "Method, System, and Computer Program Product for the Processing of Self-Monitoring Blood Glucose (SMBG) Data to Enhance Diabetic Self-Management;"

U.S. Patent Application No. 10/524,094 filed February 9, 2005 entitled "Managing and Processing Self-Monitoring Blood Glucose"(Publication No. 2005214892);

PCT International Application Serial No PCT/US2006/033724, filed August 29, 2006, entitled "Method for Improvising Accuracy of Continuous Glucose Sensors and a Continuous Glucose Sensor Using the Same;"

PCT International Application No. PCT/US2007/000370, filed January 5, 2007, entitled "Method, System and Computer Program Product for Evaluation of Blood Glucose Variability in Diabetes from Self-Monitoring Data;"

U.S. Patent Application No. 11/925,689, filed October 26, 2007, entitled "For Method, System and Computer Program Product for Real-Time Detection of Sensitivity Decline in Analyte Sensors;"

PCT International Application No. PCT/US00/22886, filed August 21, 2000, entitled "Method and Apparatus for Predicting the Risk of Hypoglycemia;"

U.S. Patent No. 6,923,763 B1, issued August 2, 2005, entitled "Method and Apparatus for Predicting the Risk of Hypoglycemia;" and

PCT International Patent Application No. PCT/US2007/082744, filed October 26, 2007, entitled "For Method, System and Computer Program Product for Real-Time Detection of Sensitivity Decline in Analyte Sensors."

U.S. Patent Application Publication No. US2007/0287144, December 13, 2007, "Biological Response Prediction System, Method for Predicting Biological Response and Computer Program Product", Kouchi,Y., et al.

U.S. Patent Application Publication No. US2007/0179771, August 2, 2007, "Medical Simulation System, Computer System and Computer Program Product", Kouchi, Y., et al.

U.S. Patent Application Publication No. US2007/0118347, May 24, 2007, "Medical Simulation System and Computer Program Product, Kouchi, Y., et al.

U.S. Patent Application Publication No. US2007/0071681, March 29, 2007, "Apparatus and Method for Computer Modeling Type 1 Diabetes", Gadkar, K., et al.

U.S. Patent Application Publication No. US2006/0277015, December 7, 2006, "Simulation System for Functions of Biological Organs and Recording Medium in which Program Therefore is Recorded", Kouchi, Y., et al.

U.S. Patent Application Publication No. US2005/0288910, December 29, 2005, "Generation of Continuous Mathematical Model for Common Features of a Subject Group", Schlessinger, L., et al.

U.S. Patent Application Publication No. 2002/0026110, February 28, 2002, "Methods for Improving Performance and Reliability of Biosensors", Parris, N., et al.


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[0067] Unless clearly specified to the contrary, there is no requirement for any particular described or illustrated activity or element, any particular sequence or such activities, any particular size, speed, material, duration, contour, dimension or frequency, or any particularly interrelationship of such elements. Moreover, any activity can be repeated, any activity can be performed by multiple entities, and/or any element can be duplicated. Further, any activity or element can be excluded, the sequence of activities can vary, and/or the interrelationship of elements can vary. It should be appreciated that aspects of the present invention may have a variety of sizes, contours, shapes, compositions and materials as desired or required.

[0068] In summary, while the present invention has been described with respect to specific embodiments, many modifications, variations, alterations, substitutions, and equivalents will be apparent to those skilled in the art. The present invention is not to be limited in scope by the specific embodiment described herein. Indeed, various modifications of the present invention, in addition to those described herein, will be apparent to those of skill in the art from the foregoing description and accompanying drawings. Accordingly, the invention is to be considered as limited only by the scope of the following claims, including all modifications and equivalents.

[0069] Still other embodiments will become readily apparent to those skilled in this art from reading the above-recited detailed description and drawings of certain exemplary embodiments. It should be understood that numerous variations, modifications, and additional embodiments are possible, and accordingly, all such variations, modifications, and embodiments are to be regarded as being within the spirit and scope of this application. For example, regardless of the content of any portion (e.g., title, field, background, summary, abstract, drawing figure, etc.) of this application, unless clearly specified to the contrary, there is no requirement for the inclusion in any claim herein or of any application claiming priority hereto of any particular described or illustrated activity or element, any particular sequence of such activities, or any particular interrelationship of such elements. Moreover, any activity can be repeated, any activity can be performed by multiple entities, and/or any element can be duplicated. Further, any activity or element can be excluded, the sequence of activities can vary, and/or the interrelationship of elements can vary. Unless clearly specified to the contrary, there is no requirement for any particular described or illustrated activity or element, any particular sequence or such activities, any particular size, speed, material, dimension or frequency, or any particularly interrelationship of such elements. Accordingly, the descriptions and drawings are to be regarded as illustrative in nature, and not as restrictive. Moreover, when any number or range is described herein, unless clearly stated otherwise, that number or range is approximate. When any range is described herein, unless clearly stated otherwise, that range includes all values therein and all sub ranges therein.


Claims

1. A computer method for testing of closed-loop_treatment strategies for Type 1 diabetes provided by an insulin pump applying a control algorithm using a computer simulation environment including a mathematical model of the human glucose-insulin metabolic system, a simulated continuous glucose sensor comprising a model of the errors of continuous glucose monitoring using non-white noise, based on a degree of dependence between sequential readings, influence of calibration errors, and potential for loss of sensitivity, and a simulated insulin pump delivering insulin according to said control algorithm, executing a treatment strategy for Type 1 diabetes in said computer simulation environment using said mathematical model, said simulated continuous glucose sensor, and said simulated insulin pump on a population of simulated subjects in a computer processor, and measuring the outcome of such treatment strategy as a result of said execution;
wherein said computer simulation environment includes a database of said population of simulated human subjects, said population having a set of metabolic parameters encompassing a distribution of metabolic parameters observed in Type 1 diabetic subjects in vivo.
 
2. The method of claim 1, wherein said population is representative of the general diabetic population.
 
3. The method of claim 1, providing an interactive module for allowing a user or device to interact with said testing method to implement said testing method.
 
4. A computer simulation system environment for testing of closed loop_treatment strategies for Type 1 diabetes provided by an insulin pump applying a control algorithm, comprising a computer module including a mathematical model of the human glucose-insulin metabolic system, a simulated continuous glucose sensor comprising a model of the errors of continuous glucose monitoring using non-white noise, based on a degree of dependence between sequential readings, influence of calibration errors, and potential for loss of sensitivity, and a simulated insulin pump delivering insulin according to said control algorithm;
wherein said computer simulation system environment includes a database of a population of simulated human subjects, said population having a set of metabolic parameters encompassing a distribution of metabolic parameters observed in Type 1 diabetic subjects in vivo, and;
a computer processor for executing a treatment strategy for Type 1 diabetes using said mathematical model module, said simulated glucose sensor, and said simulated insulin pump in conjunction with said database.
 
5. The system of claim 4, wherein said population is representative of the general diabetic population.
 
6. The system of claim 4, further comprises:
an interactive module, said interactive module for allowing a user or device to interact with said computer simulation system environment for testing for implementing said testing.
 
7. A computer program product comprising a computer readable medium having computer readable data and executable instructions stored therein for enabling at least one processor in a computer system for testing of closed loop treatment strategies for Type 1 diabetes provided by an insulin pump applying a control algorithm, said computer readable data and executable instructions comprising:

instructions representing a mathematical model of the human glucose-insulin metabolic system;

instructions representing a simulated continuous glucose sensor comprising a model of the errors of continuous glucose monitoring using non-white noise, based on a degree of dependence between sequential readings, influence of calibration errors, and potential for loss of sensitivity; and

instructions representing a simulated insulin pump delivering insulin according to said control algorithm;

wherein a computer simulation system environment includes a database of a population of simulated human subjects, said population having a set of metabolic parameters encompassing a distribution of metabolic parameters observed in Type 1 diabetic subjects in vivo, and;

instructions for executing a treatment strategy for Type 1 diabetes using said mathematical model, said simulated glucose sensor, and said simulated insulin pump in conjunction with said database and measuring the outcome of such monitoring and/or treatment strategy as a result of said execution.


 


Ansprüche

1. Computerverfahren zur Überprüfung von Behandlungsstrategien im geschlossenen Regelkreis für Typ-1-Diabetes, die von einer Insulinpumpe bereitgestellt werden, die einen Regelalgorithmus unter Verwendung einer Computersimulationsumgebung anwendet, welche ein mathematisches Modell des menschlichen Glukose-Insulin-Stoffwechselsystems, einen simulierten kontinuierlichen Glukosesensor, der ein Modell der Fehler der kontinuierlichen Glukoseüberwachung unter Verwendung von nicht-weißem Rauschen umfasst, basierend auf einem Grad der Abhängigkeit zwischen sequentiellen Messungen, dem Einfluss von Kalibrierungsfehlern, und Potenzial für einen Verlust der Empfindlichkeit, und eine simulierte Insulinpumpe beinhaltet, die Insulin gemäß dem Regelalgorithmus abgibt, wobei es eine Behandlungsstrategie für Typ-1-Diabetes in der Computersimulationsumgebung ausführt unter Verwendung des mathematischen Modells, des simulierten kontinuierlichen Glukosesensors und der simulierten Insulinpumpe an einer Population von simulierten Subjekten in einem Computerprozessor und das Messen des Ergebnisses einer solchen Behandlungsstrategie als Ergebnis der Ausführung; wobei die Computersimulationsumgebung eine Datenbank der Population von simulierten menschlichen Subjekten beinhaltet, wobei die Population einen Satz von metabolischen Parametern aufweist, die eine Verteilung von metabolischen Parametern umfassen, die bei diabetischen Subjekten vom Typ 1 in vivo beobachtet wurden.
 
2. Verfahren nach Anspruch 1, wobei die Population repräsentativ für die allgemeine diabetische Population ist.
 
3. Verfahren nach Anspruch 1, das ein interaktives Modul vorsieht, welches es einem Benutzer oder einer Vorrichtung ermöglicht, mit dem Prüfungsverfahren zu interagieren, um das Prüfungsverfahren zu implementieren.
 
4. Computersimulations-Systemumgebung zum Überprüfen von Behandlungsstrategien für Typ-1-Diabetes im geschlossenen Regelkreis, die von einer Insulinpumpe unter Anwendung eines Regelalgorithmus bereitgestellt wird, umfassend ein Computermodul, das ein mathematisches Modell des menschlichen Glukose-Insulin-Stoffwechselsystems, einen simulierten kontinuierlichen Glukosesensor, der ein Modell der Fehler der kontinuierlichen Glukoseüberwachung unter Verwendung von nicht-weißem Rauschen umfasst, basierend auf einem Grad der Abhängigkeit zwischen sequentiellen Messungen, dem Einfluss von Kalibrierungsfehlern und dem Potenzial für einen Verlust der Empfindlichkeit, und eine simulierte Insulinpumpe, die Insulin gemäß dem Regelalgorithmus abgibt;
wobei die Computersimulations-Systemumgebung eine Datenbank einer Population von simulierten menschlichen Subjekten beinhaltet, wobei die Population einen Satz von metabolischen Parametern aufweist, die eine Verteilung von metabolischen Parametern umfassen, die bei diabetischen Subjekten vom Typ 1 in vivo beobachtet wurden, und;
einen Computerprozessor zum Ausführen einer Behandlungsstrategie für Typ-1-Diabetes unter Verwendung des mathematischen Modellmoduls, des simulierten Glukosesensors und der simulierten Insulinpumpe in Verbindung mit der Datenbank.
 
5. System nach Anspruch 4, wobei die Population repräsentativ für die allgemeine diabetische Population ist.
 
6. System nach Anspruch 4, ferner umfassend:
ein interaktives Modul, wobei das interaktive Modul es einem Benutzer oder einer Vorrichtung ermöglicht, mit der Computersimulations-Systemumgebung zur Überprüfung der Implementierung der Überprüfung zu interagieren.
 
7. Computerprogrammprodukt, umfassend ein computerlesbares Medium, das computerlesbare Daten und darin gespeicherte ausführbare Anweisungen aufweist, um mindestens einen Prozessor in einem Computersystem zur Überprüfung von Behandlungsstrategien für Typ-1-Diabetes im geschlossenen Regelkreis zu ermöglichen, die von einer Insulinpumpe unter Anwendung eines Regelalgorithmus bereitgestellt werden, wobei die computerlesbaren Daten und ausführbaren Anweisungen umfassen:

Anweisungen, die ein mathematisches Modell des menschlichen Glukose-Insulin-Stoffwechselsystems darstellen;

Anweisungen, die einen simulierten kontinuierlichen Glukosesensor darstellen, der ein Modell der Fehler der kontinuierlichen Glukoseüberwachung unter Verwendung von nicht-weißem Rauschen umfasst, basierend auf einem Grad der Abhängigkeit zwischen sequentiellen Messungen, dem Einfluss von Kalibrierungsfehlern und dem Potenzial für einen Verlust der Empfindlichkeit; und

Anweisungen, die eine simulierte Insulinpumpe darstellen, die Insulin gemäß dem Regelalgorithmus abgibt;

wobei die Computersimulations-Systemumgebung eine Datenbank einer Population von simulierten menschlichen Subjekten beinhaltet, wobei die Population einen Satz von metabolischen Parametern aufweist, die eine Verteilung von metabolischen Parametern umfassen, die bei diabetischen Subjekten vom Typ 1 in vivo beobachtet wurden, und;

Anweisungen zum Ausführen einer Behandlungsstrategie für Typ-1-Diabetes unter Verwendung des mathematischen Modells, des simulierten Glukosesensors und der simulierten Insulinpumpe in Verbindung mit der Datenbank und zum Messen des Ergebnisses einer solchen Überwachung und/oder Behandlungsstrategie als Ergebnis der Ausführung.


 


Revendications

1. Procédé informatique pour tester des stratégies de traitement en boucle fermée pour le diabète de type 1 générées au moyen d'une pompe à insuline appliquant un algorithme de commande utilisant un environnement de simulation par ordinateur incluant un modèle mathématique du système métabolique glucose-insuline humain, un capteur de glucose en continu simulé comprenant un modèle des erreurs de surveillance du glucose en continu utilisant un bruit non-blanc, basé sur un degré de dépendance entre valeurs séquentielles, influence d'erreurs d'étalonnage et potentiel de perte de sensibilité, et une pompe à insuline simulée administrant de l'insuline en fonction dudit algorithme de commande, exécutant une stratégie de traitement pour le diabète de type 1 dans ledit environnement de simulation par ordinateur utilisant ledit modèle mathématique, ledit capteur de glucose en continu simulé, et ladite pompe à insuline simulée sur une population de sujets simulés dans un processeur d'ordinateur, et mesurant l'effet d'une telle stratégie de traitement en tant que résultat de ladite exécution ;
dans lequel ledit environnement de simulation par ordinateur inclut une base de données de ladite population de sujets humains simulés, ladite population ayant un ensemble de paramètres métaboliques englobant une distribution de paramètres métaboliques observés chez les sujets diabétiques de type 1 in vivo.
 
2. Procédé selon la revendication 1, dans lequel ladite population est représentative de la population diabétique générale.
 
3. Procédé selon la revendication 1, prévoyant un module interactif pour permettre à un utilisateur ou dispositif d'interagir avec ledit procédé de test pour mettre en œuvre ledit procédé de test.
 
4. Environnement de système de simulation par ordinateur pour tester des stratégies de traitement en boucle fermée pour le diabète de type 1 générées par une pompe à insuline appliquant un algorithme de commande, comprenant un module informatique incluant un modèle mathématique du système métabolique glucose-insuline humain, un capteur de glucose en continu simulé comprenant un modèle des erreurs de surveillance du glucose en continu utilisant un bruit non-blanc, basé sur un degré de dépendance entre valeurs séquentielles, influence d'erreurs d'étalonnage et potentiel de perte de sensibilité, et une pompe à insuline simulée administrant de l'insuline en fonction dudit algorithme de commande ;
dans lequel ledit environnement de système de simulation par ordinateur inclut une base de données d'une population de sujets humains simulés, ladite population comportant un ensemble de paramètres métaboliques englobant une distribution de paramètres métaboliques observés chez les sujets diabétiques de type 1 in vivo, et ;
un processeur d'ordinateur pour exécuter une stratégie de traitement pour le diabète de type 1 utilisant ledit module de modèle mathématique, ledit capteur de glucose simulé et ladite pompe à insuline simulée en conjugaison avec ladite base de données.
 
5. Système selon la revendication 4, dans lequel ladite population est représentative de la population diabétique générale.
 
6. Système selon la revendication 4, comprenant en outre :
un module interactif, ledit module interactif étant conçu pour permettre à un utilisateur ou dispositif d'interagir avec ledit environnement de système de simulation par ordinateur pour tester la mise en œuvre dudit test.
 
7. Produit de programme informatique comprenant un support lisible par ordinateur dans lequel sont stockées des données lisibles par ordinateur et des instructions exécutables pour permettre à au moins un processeur dans un système informatique de tester des stratégies de traitement en boucle fermée pour le diabète de type 1 fournies par une pompe à insuline appliquant un algorithme de commande, lesdites données lisibles par ordinateur et instructions exécutables comprenant :

des instructions représentant un modèle mathématique du système métabolique glucose-insuline humain ;

des instructions représentant un capteur de glucose en continu simulé comprenant un modèle des erreurs de surveillance de glucose en continu utilisant un bruit non-blanc, basé sur un degré de dépendance entre valeurs séquentielles, influence d'erreurs d'étalonnage et potentiel de perte de sensibilité ; et

des instructions représentant une pompe à insuline simulée administrant de l'insuline en fonction dudit algorithme de commande ;

dans lequel un environnement de système de simulation par ordinateur inclut une base de données d'une population de sujets humains simulés, ladite population ayant un ensemble de paramètres métaboliques englobant une distribution de paramètres métaboliques observés chez des sujets diabétiques de type 1 in vivo ; et ;

des instructions pour exécuter une stratégie de traitement pour le diabète de type 1 utilisant ledit modèle mathématique, ledit capteur de glucose simulé et ladite pompe à insuline simulée en conjugaison avec ladite base de données et mesurer l'effet d'une telle surveillance et/ou stratégie de traitement en tant que résultat de ladite exécution.


 




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Cited references

REFERENCES CITED IN THE DESCRIPTION



This list of references cited by the applicant is for the reader's convenience only. It does not form part of the European patent document. Even though great care has been taken in compiling the references, errors or omissions cannot be excluded and the EPO disclaims all liability in this regard.

Patent documents cited in the description




Non-patent literature cited in the description