System, Method and Computer Simulation Environment For In Silico Trials in Pre-Diabetes and Type 2 Diabetes

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.

Description

BACKGROUND OF THE INVENTION

Over 20 million people in the United States alone have Type 2 Diabetes Mellitus (T2DM)—a complex derangement of the glucose-insulin metabolic system, which results in an increased insulin resistance and inappropriate insulin secretion. However, this pathological state does not appear suddenly, but usually subjects move from a healthy state to a diabetic state passing through an intermediate phase, called prediabetes: e.g. it is well known that individuals with impaired fasting glucose (IFG) have a 20-30% chance of developing diabetes over the following 5-10 years [1-3]. The risk is even greater if they have combined IFG and impaired glucose tolerance (IGT). Furthermore, IFG and IGT are associated with increased risk of cardiovascular events [4,5]. Therefore, in addition to studies on T2DM, the pathogenesis of IFG alone or in combination with IGT has engendered considerable interest. For instance, recently, it has been shown that postprandial hyperglycemia in individuals with early diabetes is due to lower rates of glucose disappearance rather than increased meal appearance or impaired suppression of endogenous glucose production (EGP), regardless of their fasting glucose. In contrast, insulin secretion, action, and the pattern of postprandial turnover are essentially normal in individuals with isolated IFG [6]. These results suggest that for treatment and prevention of T2 DM it is very important to provide drugs with a specific target, e.g. the ability to stimulate secretion instead of increasing insulin secretion, if this is the case.

To this purpose it is essential to investigate the mechanisms of glucose-insulin system derangement and drug pharmacodynamics, with properly designed experimental trials. However, it may not be possible, appropriate, convenient or desirable to perform such evaluation experiments on the diabetic subject in vivo, because some experiments cannot be done at all or are too difficult too dangerous, too expensive or not ethical. An in silico simulation environment could offer an alternative tool to test different treatment strategies, e.g. drug, exercise, diet, in prediabetes and diabetes in a cost-effective way. The power of simulation tools has been recently recognized by the FDA (Food and Drug Administration) which accepted a simulator of Type 1 Diabetes (T1DM) [7, 8] as an alternative to the animal studies for the validation of control algorithms before their use in human clinical trials [9]. No such simulator of T2DM has existed heretofore.

SUMMARY OF THE INVENTION

An aspect of an embodiment of the present invention extends the simulation of T1DM to T2DM. It is important to emphasize that due to the profound physiological differences between T1DM and T2DM, the mathematical model and the simulated “subjects” with T2DM are very different from the model and simulated “population” of T1DM.

Realistic computer simulation can provide invaluable information about the safety and the limitations of various treatments of T2DM, can guide and focus the emphasis of clinical studies, and can rule-out ineffective treatment scenarios in a cost-effective manner prior to human use. While simulators of diabetes exist, most are based on general population models. As a result, their capabilities are generally limited to prediction of population averages that would be observed during clinical trials.

Therefore, for the purpose of personalized treatment development, a different type of computer simulator is needed—a system that is capable of simulating the glucose-insulin dynamics of a particular person. In other words, a simulator of T2DM should be equipped with a “cohort” of in silico “subjects” that spans sufficiently well the observed inter-person variability of key metabolic parameters in the general population of people with T2DM. Because large-scale simulations would account better for inter-subject variability than small-size animal trials and would allow for more extensive testing of the limits and robustness of various treatments, the following paradigm has emerged: (i) in silico modeling could produce credible pre-clinical results that could be substituted for certain animal trials, and (ii) in silico testing yields these results in a fraction of the time required for animal trials.

Following this paradigm, this invention provides a comprehensive simulation environment, which has the potential to accelerate studies on T2DM and prediabetes. Two exemplary principal components of the simulation environment are: (1) A mathematical model of the human metabolic system which has been derived from a unique data set, including both T2DM and prediabetic patients who underwent a triple tracer meal protocol, and (2) A population of virtual subjects including N=100 subjects with pre-diabetes and N=100 subjects with T2DM. As previously demonstrated by our simulator of Type 1 Diabetes, a comprehensive simulation environment has the potential for performing rapid and cost-effective in silica experiments. T2DM specific experiments on virtual subjects could test the efficacy of drugs and other treatments, e.g. exercise or diet, for improving prediabetes and T2DM control.

In accordance with a first aspect of the invention, 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)),
  • an electronic module that models meal glucose rate of appearance (Ra(t)),
  • an electronic module that models glucose utilization (U(t)),
  • an electronic module that models renal excretion of glucose (E(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 with values within a range of values derived from in vivo T2DM or prediabetic subjects; 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 said plurality of metabolic parameter values including said at least one varied metabolic parameter value into said glucose concentration and insulin concentration subsystems.

In accordance with a second aspect of the invention, a computer-executable program product embodied as computer executable code in a computer-readable storage medium is provided, wherein said computer-executable program product simulates a glucose-insulin metabolic system of a T2DM or prediabetic subject, said computer-executable code including subsystem code that models dynamic glucose concentration in a T2DM or prediabetic subject, including

• • an electronic code module that models endogenous glucose production (EGP(t)),
  • an electronic code module that models meal glucose rate of appearance (Ra(t)),
  • an electronic code module that models glucose utilization (U(t)),
  • an electronic code module that models renal excretion of glucose (E(t));

subsystem code that models dynamic insulin concentration in said T2DM or prediabetic subject, including

• • an electronic code 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 with values within a range, of values derived from in vivo T2DM or prediabetic subjects; and

computer-executable code 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 said plurality of metabolic parameter values including said at least one varied metabolic parameter value into said glucose concentration and insulin concentration subsystems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A, FIG. 1B, FIG. 1C, FIG. 1D, FIG. 1E and FIG. 1F are graphs illustrating plasma glucose and insulin concentrations, meal rate of appearance and endogenous glucose production, and glucose utilization and insulin secretion rate;

FIG. 2 illustrates unit process models for endogenous glucose production, glucose rate of appearance, glucose utilization, and insulin secretion in accordance with the present invention;

FIGS. 3A and 3B are graphs illustrating observed versus simulated range of glucose concentration in prediabetes and type 2 diabetes, in accordance with the present invention;

FIG. 4A and FIG. 4B illustrates glucose and insulin concentrations in untreated prediabetic subjects versus prediabetic subjects treated with drugs x and y; and

FIG. 5 is a schematic block diagram for a system or related method of an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Two key components of the simulator of the glucose-insulin metabolic system in prediabetes and T2DM in accordance with the present invention are:

(1) A physiological model of glucose-insulin metabolism in prediabetes and T2DM, and

(2) A population of virtual subjects with prediabetes (N-100) and T2DM (N-100).

Physiological Model of Glucose-Insulin Metabolism in Prediabetes and T2DM

Both model equations and the procedures which were used to identify model parameter distributions from prediabetes and T2DM meal data and to generate the virtual subject population are now described in accordance with an embodiment of the invention.

a. Model Equations

The model structure consists of a glucose subsystem and an insulin subsystem [7], each characterized by various unit processes, e.g. endogenous glucose production (EGP), meal glucose rate of appearance (Ra), glucose utilization (U), insulin secretion (S), and renal excretion (E).

Glucose Subsystem

The glucose subsystem model is defined by the following group of equations (1):

$\begin{array}{cc}\{\begin{array}{cc}\begin{array}{c}{\stackrel{.}{G}}_p\left(t\right)=\mathrm{EGP}\left(t\right)+\mathrm{Ra}\left(t\right)-U_{\mathrm{ii}}\left(t\right)-E\left(t\right)-k_1·\\ G_p\left(t\right)+k_2·G_{t}t\end{array}& G_p\left(0\right)=G_{\mathrm{pb}}\\ {\stackrel{.}{G}}_t\left(t\right)=-U_{\mathrm{id}}\left(t\right)+k_1·G_p\left(t\right)-k_2·G_t\left(t\right)& G_t\left(0\right)=G_{\mathrm{tb}}\\ G\left(t\right)=\frac{G_p}{V_g}& G\left(0\right)=G_b\end{array}& \left(1\right)\end{array}$

Where

G_p and G_t (mg/kg) are glucose masses in plasma and rapidly-equilibrating tissues, and in slowly-equilibrating tissues, respectively;G (mg/dl) is plasma glucose concentration;Suffix b denotes the basal state;EGP is endogenous glucose production (mg/kg/min);Ra is glucose rate of appearance in plasma (mg/kg/min);E is renal excretion (mg/kg/min);U_ii and U_id are insulin-independent and dependent glucose utilizations, respectively (mg/kg/min);V_G is the distribution volume of glucose (dl/kg); andk₁ and k₂ (min⁻¹) are rate parameters.

At the basal steady state, endogenous glucose production, EGP_b, equals glucose disappearance, i.e. the sum of glucose utilization and renal excretion (which is zero in normal subjects), U_b+E_b:

EGP_b=U_bE_b   (2)

Insulin Subsystem

The insulin subsystem model is defined by the following group of equations (3):

$\begin{array}{cc}\{\begin{array}{cc}{\stackrel{.}{I}}_l\left(t\right)=-\left(m_1+m_3\left(t\right)\right)·I_l\left(t\right)+m_2{I}_p\left(t\right)+S\left(t\right)& I_l\left(0\right)=I_{\mathrm{lb}}\\ {\stackrel{.}{I}}_p\left(t\right)=-\left(m_2+m_4\right)·I_p\left(t\right)+m_1·I_l\left(t\right)& I_p\left(0\right)=I_{\mathrm{pb}}\\ I\left(t\right)=\frac{I_p}{V_I}& I\left(0\right)=I_b\end{array}& \left(3\right)\end{array}$

where I_p and I_l (pmol/kg) are insulin masses in plasma and in the liver, respectively;I (pmol/l) is plasma insulin concentration;S is insulin secretion (pmol/kg/min);V_I is the distribution volume of insulin (1/kg); andm₁-m₄ (min⁻¹) are rate parameters.

Degradation, D, occurs both in the liver and peripherally. Peripheral degradation has been assumed to be linear (m₄). Hepatic extraction of insulin, HE, i.e. the insulin flux which leaves the liver irreversibly, divided by the total insulin flux leaving the liver, is assumed to be dependent from insulin secretion, S:

HE(t)=−m₅·S(t)+m₆ HE(0)=HE_b   (4)

thus one has:

$\begin{array}{cc}{m}_3\left(t\right)=\frac{HE\left(t\right)·m_1}{1-HE\left(t\right)}& \left(5\right)\end{array}$

At basal steady state one has:

$\begin{array}{cc}{m}_6=m_5·S_b+H{E}_b& \left(6\right)\\ m_3\left(0\right)=\frac{H{E}_b·m_1}{1-H{E}_b}& \left(7\right)\\ S_b=m_3\left(0\right)·I_{lb}+m_4·I_{pb}=D_b& \left(8\right)\end{array}$

Moreover, given that the liver is responsible for 60% of insulin clearance in the steady state, one has:

$\begin{array}{cc}{m}_2=\left(\frac{s_b}{I_{pb}}-\frac{m_4}{1-H{E}_b}\right)·\frac{1-H{E}_b}{H{E}_b};m_4=\frac{2}{5}·\frac{S_b}{I_{pb}}·\left(1-{\mathrm{HE}}_h\right)& \left(9\right)\end{array}$

with S_b and D_b basal secretion and degradation, respectively (HE_b was fixed to 0.6).

Endogenous Glucose Production

Endogenous glucose production is defined by the following equation (10):

EGP(t)=k_p1−k_p2·G_p(t)−k_p3·I_d(t)−k_p4·I_p0(t) EGP(0)=EGP_b   (10)

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

$\begin{array}{cc}\{\begin{array}{cc}{\stackrel{.}{I}}_1\left(t\right)=-k_i·\left[I_1\left(t\right)-I\left(t\right)\right]& I_1\left(0\right)=I_b\\ {\stackrel{.}{I}}_d\left(t\right)=-k_i·\left[I_d\left(t\right)-I_1\left(t\right)\right]& I_d\left(0\right)=I_b\end{array}& \left(11\right)\end{array}$

k_p1 (mg/kg/min) is the extrapolated EGP at zero glucose and insulin;k_p2 (min⁻¹) is liver glucose effectiveness;k_p3 (mg/kg/min per pmol/l) is a parameter governing amplitude of insulin action on the liver;k_p4 (mg/kg/min/(pmol/kg)) is a parameter governing amplitude of portal insulin action on the liver; andk_i (min⁻¹) is a rate parameter accounting for the delay between an insulin signal and insulin action.EGP is obviously constrained to be non-negative.At basal steady state one has:

k _p1=EGP_b+k_p2·G_pb+k_p3·I_b+k_p4·I_pob   (12)

Glucose Rate of Appearance

The glucose rate of appearance (Ra) is defined by the following group of equations (13):

$\begin{array}{cc}\{\begin{array}{cc}{Q}_{\mathrm{sto}}\left(t\right)=Q_{\mathrm{sto}1}\left(t\right)+Q_{\mathrm{sto}2}\left(t\right)& Q_{\mathrm{sto}}\left(0\right)=0\\ {\stackrel{.}{Q}}_{\mathrm{sto}1}\left(t\right)=-k_{\mathrm{gri}}·Q_{\mathrm{sto}1}\left(t\right)+D·\delta \left(t\right)& Q_{\mathrm{sto}1}\left(0\right)=0\\ {\stackrel{.}{Q}}_{\mathrm{sto}2}\left(t\right)=-k_{\mathrm{empt}}\left(Q_{\mathrm{sto}}\right)·Q_{\mathrm{sto}2}\left(t\right)+k_{\mathrm{gri}}·Q_{\mathrm{sto}1}\left(t\right)& Q_{\mathrm{sto}2}\left(0\right)=0\\ {\stackrel{.}{Q}}_{\mathrm{gut}}=-k_{\mathrm{abs}}·Q_{\mathrm{gut}}\left(t\right)+k_{\mathrm{empt}}\left(Q_{\mathrm{sto}}\right)·Q_{\mathrm{sto}2}\left(t\right)& Q_{\mathrm{gut}}\left(0\right)=0\\ \mathrm{Ra}\left(t\right)=\frac{f·k_{\mathrm{abs}}·Q_{\mathrm{gut}}\left(t\right)}{\mathrm{BW}}& \mathrm{Ra}\left(0\right)=0\end{array}& \left(13\right)\end{array}$

where Q_sto (mg) is the amount of glucose in the stomach (solid phase, Q_sto1, and liquid phase, Q_sto2);Q_gut (mg) is the glucose mass in the intestine;k_gri (min⁻¹) is the rate of grinding;k_empt(Q_sto) (min⁻¹) is a rate constant of gastric emptying which is a nonlinear function of Q_sto;k_abs (min⁻¹) is a rate constant of intestinal absorption;ƒ is a fraction of intestinal absorption which actually appears in the plasma;D (mg) is an amount of ingested glucose;BW (kg) is body weight; andRa (mg/kg/min) is the appearance rate of glucose in the plasma.

Glucose Utilization

Glucose utilization is made up of two components: insulin-independent utilization and insulin-dependent utilization. Insulin-independent utilization takes place in the first compartment, is constant and represents glucose uptake by the brain and erythrocytes (F_cns):

U _ii(t)=F_cns   (14)

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

$\begin{array}{cc}{U}_{\mathrm{id}}\left(t\right)=\frac{V\left(X\left(t\right)\right)·G_t\left(t\right)}{K_{m0}+G_t\left(t\right)}& \left(15\right)\end{array}$

where Vm(X(t)) is assumed to be linearly dependent from a remote insulin, X(t):

V _m(X(t))=V_m0+V_mx·X(t)   (16)

X (pmol/L) is insulin in the interstitial fluid described by:

{dot over (X)}(t)=−p_2U·X(t)+p_2U[I(t)−I_b]X(0)=0   (17)

where I is plasma insulin and p_2U (min⁻¹) is rate constant of insulin action on the peripheral glucose utilization.Total glucose utilization, U, is thus:

U(t)=U_ii(t)+U_id(t)   (18)

At basal steady state one has:

$\begin{array}{cc}{G}_{\mathrm{tb}}=\frac{F_{cns}-EG{P}_b+k_1·G_{pb}}{k_2}\mathrm{and}\text{:}& \left(19\right)\\ V_{m0}=\frac{\left(EG{P}_b-F_{cns}\right)·\left(K_{m0}+G_{\mathrm{tb}}\right)}{G_{tb}}& \left(20\right)\end{array}$

Insulin Secretion

Insulin secretion, S, is defined by the following equations (21)-(24):

$\begin{array}{ccc}S\left(t\right)=\gamma ·I_{\mathrm{po}}\left(t\right)& & \left(21\right)\\ {\stackrel{.}{I}}_{\mathrm{po}}\left(t\right)=-\gamma ·I_{\mathrm{po}}\left(t\right)+S_{\mathrm{po}}\left(t\right)I_{\mathrm{po}}\left(0\right)=I_{\mathrm{pob}}& & \left(22\right)\\ S_{\mathrm{po}}\left(t\right)=\{\begin{array}{cc}Y\left(t\right)+K·\stackrel{.}{G}\left(t\right)+S_b& \mathrm{for}\stackrel{.}{G}>0\\ Y\left(t\right)+S_b& \mathrm{for}\stackrel{.}{G}\le 0\end{array}\mathrm{and}& & \left(23\right)\\ \stackrel{.}{Y}\left(t\right)=\{\begin{array}{cc}-\alpha ·\left[Y\left(t\right)-\beta ·\left(G\left(t\right)-h\right)\right]& \mathrm{if}\beta ·\left(G\left(t\right)-h\right)\ge -S_b\\ -\alpha ·Y\left(t\right)-\alpha ·S_b& \mathrm{if}\beta ·\left(G\left(t\right)-h\right)<-S_b\end{array};& Y\left(0\right)=0& \left(24\right)\end{array}$

where γ (min⁻¹) is the transfer rate constant between the portal vein and the liver;K (pmol/kg per mg/dl) is the pancreatic responsivity to glucose rate of change;a (min⁻⁴) is the delay between the glucose signal and insulin secretion;β (pmol/kg/min per mg/dl) is the pancreatic responsivity to the glucose level; andh (mg/dl) is the threshold level of glucose above which the β-cells initiate to produce new insulin (h was set to the basal glucose concentration G_b to guarantee system steady state in basal condition).

Renal Glucose Excretion

Renal glucose excretion, E, is defined by the following equation (25):

$\begin{array}{cc}E\left(t\right)=\{\begin{array}{cc}{k}_{e1}·\left[G_p\left(t\right)-k_{e2}\right]& \mathrm{if}{G}_p\left(t\right)>k_{e2}\\ 0& \mathrm{if}{G}_p\left(t\right)\le k_{e2}\end{array}& \left(25\right)\end{array}$

where k_e1 (min⁻¹) is the glomerular filtration rate; andk_e2 (mg/kg) is the renal threshold of glucose.b. Parameter Identification

The data base used to identify the model consisted of 35 subjects with either IFG or IGT, or both (prediabetes), and 23 T2DM patients who underwent a triple tracer meal protocol, thus allowing us to obtain in a virtually model-independent fashion the time course of all of the relevant glucose and insulin fluxes during a meal [6, 11]. Subject characteristics are reported in Table 1. Average plasma glucose and insulin concentration, Ra, EGP, U and SR in prediabetes and T2DM are shown in FIG. 1 together with the profile obtained in a matched healthy population [6]. In FIG. 1, plasma glucose and insulin concentrations are shown in the upper panels, meal glucose rate of appearance and endogenous glucose production are shown in the middle panels, and glucose utilization and insulin secretion rate are shown in the lower panels, in prediabetes, T2DM, and matched healthy subjects respectively.

TABLE 1
Anthropometric Characteristics of Prediabetes and Type 2 Diabetes
	Prediabetes	Type 2 Diabetes
	Gender	(F/M)	19/16	11/12
	Age	(years)	53.4	56.1
	Weight	(kg)	91.6	91.0
	BMI	(kg/m2)	30.4	31.8
	LBM	(kg)	51.7	52.2
	%Fat	(%)	37.8	38.6
	Visceral Fat	(cm2)	159.5	160.1

The system model described in section (a) has been identified in each subject by using a subsystem decomposition and forcing function strategy, as shown in FIG. 2. In FIG. 2, a unit process model for endogenous glucose production is shown in the top left panel; unit process model for glucose rate of appearance is shown in the top right panel; unit process model for glucose utilization is shown in the bottom left panel; an unit process model for insulin secretion is shown in the bottom right panel.

Entering arrows represent forcing function variables, and outgoing arrows are model output. For example, to estimate glucose utilization parameters (equations 14-20), we use as known inputs endogenous glucose production, EGP, glucose rate of appearance, Ra, and insulin concentration, I, and as the model output glucose utilization, U, and plasma glucose concentration, G.

Model parameters were thus identified in each subject and log-transformed. The average parameter vector and the covariance matrix have been thus calculated for both prediabetic and T2DM populations. Assuming that the parameter vector is a log-normally distributed random vector, the average of log-transformed parameters and the covariance matrix univocally define the joint parameter distribution. In order to prove that the generated populations reflect the observed variability, the range of simulated plasma glucose concentrations in both populations (prediabetes and T2DM) is shown in FIG. 3, superimposed with the observed (measured) range of variability.

A potential application of the simulator is the in silico study of the effect of a drug on glucose metabolism. FIG. 4 shows plasma glucose and insulin concentrations in untreated prediabetics versus a profile obtained with the administration of a drug x, which increases insulin sensitivity, and a drug y, which enhance beta-cell responsivity to glucose.

Population of Virtual “Subjects” with Prediabetes (N=100) and T2DM (N=100)

As noted above, the key to successful simulation is the availability of a comprehensive population of simulated “subjects” that encompasses the distribution of key metabolic parameters observed in T2DM in vivo. From the joint parameter distributions described in the previous section we have generated N=200 virtual subjects: N=100 with prediabetes and N=100 with T2DM.

Each virtual subject is uniquely identified by a set of 26 parameters:

k_abs=rate constant of glucose absorption by the intestine

k_max=maximum rate constant of gastric emptying

k_min=minimum rate constant of gastric emptying

b=percentage of the dose for which k_empt decreases at (k_max−k_min)/2

c=percentage of the dose for which k_empt is back to (k_max−k_min)/2

k_i=rate parameter accounting for delay between insulin signal and insulin action on the liver

k_p2=liver glucose effectiveness

k_p3=parameter governing amplitude of insulin action on the liver

k_p4=parameter governing amplitude of portal insulin action on the liver

V_g=distribution volume of glucose

V_mx=parameter governing amplitude of insulin action on glucose utilization

k_m0=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

P_2U=rate parameter accounting for delay between insulin signal and insulin action on glucose utilization

V_i=distribution volume of insulin

K=beta-cell responsivity to glucose rate of change

β=beta-cell responsivity to glucose level

α=rate parameter accounting for delay between glucose signal and insulin secretion

m₁=rate parameter of insulin kinetics

m₅=coefficient linking insulin hepatic extraction to insulin secretion rate

G_b=basal plasma glucose concentration

EGP_b=basal endogenous glucose production

BW=body weight

I_b=basal plasma insulin concentration

SR_b=basal insulin secretion rate

Provided below in Tables 2 and 3, are sample lists of model parameters for 10 pre-diabetes virtual subjects and 10 T2DM virtual subjects.

TABLE 2
Model Parameters in 10 Prediabetic Subjects
parameter	unit	subj #1	subj #2	subj #3	subj #4	subj #5	subj #6	subj #7	subj #8	subj #9	subj #10
kabs	min{circumflex over ( )}-1	0.0735	0.1099	0.1661	0.0729	0.0793	0.0356	0.1177	0.0297	0.0765	0.1105
kmax	min{circumflex over ( )}-1	0.0251	0.0292	0.0338	0.019	0.0354	0.036	0.0279	0.0314	0.0416	0.0315
kmin	min{circumflex over ( )}-1	0.0125	0.009	0.0081	0.0145	0.0085	0.0129	0.0139	0.0112	0.0073	0.0106
b	dimensionless	0.8197	0.6448	0.578	0.5239	0.6299	0.5467	0.6803	0.5534	0.5219	0.7486
c	dimensionless	0.0871	0.0783	0.0752	0.0514	0.0711	0.0194	0.061	0.0419	0.1386	0.0601
ki	min{circumflex over ( )}-1	0.0149	0.0112	0.007	0.0081	0.0077	0.0144	0.0083	0.0091	0.01	0.0089
kp2	min{circumflex over ( )}-1	0.0023	0.0025	0.0032	0.0052	0.0107	0.0025	0.0048	0.0021	0.0024	0.0146
kp3	mg/kg/min/	0.0038	0.0035	0.0042	0.0201	0.0086	0.0124	0.0035	0.0103	0.0025	0.0041
	(pmol/1)
kp4	mg/kg/min/	0.0106	0.015	0.0298	0.0377	0.0218	0.0252	0.0144	0.0291	0.0119	0.0233
	(pmol/kg)
Vg	dl/kg	0.7703	1.5072	1.1806	1.35	1.2329	1.1499	1.4451	1.1974	1.417	1.151
Vmx	mg/kg/min	0.0762	0.0398	0.0219	0.1018	0.0224	0.1533	0.0103	0.0746	0.0111	0.0142
	per pmol/L
km0	mg/kg	278.3481	262.9952	232.3868	285.9935	265.9869	306.6452	186.6311	317.4536	183.8099	194.9743
K2	min{circumflex over ( )}-1	0.0987	0.0891	0.0641	0.1284	0.0512	0.0798	0.0506	0.0754	0.026	0.0396
K1	min{circumflex over ( )}-1	0.1313	0.0917	0.0907	0.1006	0.0798	0.1316	0.0814	0.1402	0.0689	0.0854
p2U	min{circumflex over ( )}-1	0.084	0.0579	0.041	0.1024	0.0311	0.0383	0.0366	0.0688	0.0691	0.0303
Vi	l/kg	0.0465	0.0456	0.0456	0.0499	0.0449	0.047	0.048	0.0468	0.0476	0.0468
K	pmol/kg/(mg/dl)	2.7727	1.579	2.5114	1.2439	3.9261	2.2416	3.3872	1.2828	3.4512	4.1856
β	pmol/kg/min/	0.2467	0.1839	0.1027	0.1169	0.0827	0.2034	0.1765	0.0905	0.148	0.1157
	(mg/dl)
α	min{circumflex over ( )}-1	0.0163	0.0179	0.105	0.0263	0.115	0.0154	0.0504	0.0362	0.0494	0.045
m1	min{circumflex over ( )}-1	0.0597	0.1086	0.1871	0.0805	0.2383	0.1657	0.1842	0.0845	0.2373	0.2718
m5	min*kg/pmol	0.0163	0.0114	0.0164	0.0237	0.0276	0.0173	0.0085	0.0285	0.0205	0.0166
Gb	mg/dl	92.0683	110.7642	99.985	98.4865	88.3801	103.674	90.955	96.9737	87.9565	98.3493
EGPb	mg/kg/min	1.1875	1.2351	1.3047	1.9595	1.1917	1.199	1.412	1.3213	1.1577	1.14
BW	kg	74.0362	77.0909	94.1644	76.6412	123.539	83.1689	84.8108	89.3894	106.9975	91.6455
lb	pmol/l	22.9192	39.453	34.552	21.7494	43.6105	19.9288	42.5819	27.45	50.242	38.5558
SRb	pmol/kg/min	1.3529	1.6791	1.4999	1.687	1.1615	1.4634	1.1764	1.4149	1.736	1.1567

TABLE 3
Model Parameters in 10 Type 2 Diabetic Subjects
parameter	unit	subj #1	subj #2	subj #3	subj #4	subj #5	subj #6	subj #7	subj #8	subj #9	subj #10
kabs	min{circumflex over ( )}-1	0.0514	0.0276	0.0643	0.0768	0.0276	0.0499	0.04	0.0753	0.0188	0.1003
kmax	min{circumflex over ( )}-1	0.0315	0.0594	0.0188	0.0266	0.0593	0.0522	0.0605	0.0149	0.059	0.0336
kmin	min{circumflex over ( )}-1	0.0066	0.0041	0.007	0.0142	0.0041	0.0119	0.0047	0.0042	0.0057	0.0135
b	dimensionless	0.8244	0.5521	0.8097	0.7339	0.5518	0.4931	0.6156	1.018	0.4749	0.7751
c	dimensionless	0.1662	0.0778	0.2434	0.0434	0.0778	0.0231	0.1176	0.6368	0.0409	0.0233
ki	min{circumflex over ( )}-1	0.0152	0.0059	0.016	0.0118	0.0059	0.0056	0.0072	0.0111	0.0059	0.003
kp2	min{circumflex over ( )}-1	0.0037	0.0007	0.0053	0.0023	0.0007	0.0008	0.0027	0.0033	0.0009	0.0006
kp3	mg/kg/min/	0.0055	0.0161	0.0052	0.0076	0.0162	0.003	0.0054	0.0023	0.0066	0.0064
	(pmol/l)
kp4	mg/kg/min/	0.0448	0.0925	0.0214	0.073	0.0925	0.0152	0.0656	0.0214	0.1546	0.1207
	(pmol/kg)
Vg	dl/kg	0.7907	0.8542	0.5878	1.7279	0.8542	1.4555	0.9845	0.6865	0.7526	2.4251
Vmx	mg/kg/min	0.0409	0.0363	0.0244	0.0591	0.0363	0.017	0.0689	0.0137	0.0274	0.054
	per pmol/L
km0	mg/kg	357.0175	719.292	372.048	432.325	719.293	158.8016	331.7172	271.744	598.0593	457.3901
K2	min{circumflex over ( )}-1	0.0272	0.0891	0.018	0.0504	0.0887	0.0476	0.0853	0.0335	0.0107	0.0376
K1	min{circumflex over ( )}-1	0.0814	0.0937	0.0906	0.0624	0.0933	0.0484	0.0758	0.071	0.0485	0.0194
p2U	min{circumflex over ( )}-1	0.0207	0.0853	0.0164	0.1419	0.0853	0.065	0.0131	0.0137	0.4861	0.0471
Vi	l/kg	0.04	0.0385	0.0404	0.0391	0.0385	0.0528	0.044	0.043	0.0401	0.0394
K	pmol/kg/(mg/dl)	1.5045	0.3015	1.5882	0.7135	0.3012	1.918	1.3548	2.2587	0.3735	1.4973
β	pmol/kg/min/	0.0711	0.0122	0.0373	0.071	0.0122	0.1722	0.1482	0.0798	0.0425	0.1191
	(mg/dl)
α	min{circumflex over ( )}-1	0.0127	0.0415	0.0392	0.0182	0.0415	0.0083	0.0082	0.0123	0.0087	0.0067
m1	min{circumflex over ( )}-1	0.3068	0.0957	0.1807	0.5012	0.0956	0.0391	0.3772	0.1075	0.1218	0.1871
m5	min*kg/pmol	0.0523	0.1129	0.0499	0.0439	0.1129	0.0169	0.0276	0.0354	0.0926	0.0237
Gb	mg/dl	141.7659	265.4568	179.435	127.8186	265.4568	64.313	126.1426	178.2554	178.9323	114.0941
EGPb	mg/kg/min	2.0356	2.5077	1.6258	2.1968	2.5077	0.8218	2.0573	1.3879	2.0212	2.0332
BW	kg	88.9266	106.4649	128.5865	75.4929	106.4648	81.3829	81.3337	129.8287	87.0632	59.7271
lb	pmol/l	27.6839	94.1748	78.8107	20.3449	94.1748	37.6358	32.3702	148.7886	29.1458	42.3448
SRb	pmol/kg/min	2.4889	7.2006	3.453	1.896	7.2004	1.659	3.3775	3.4148	3.1313	3.6781

Table 4 includes the mean, SD, and the range of all model parameters defining the span of the simulated populations:

TABLE 4
Mean, Standard Deviation and the Range of Prediabetic and Type 2
Diabetic Populations
		Prediabetics	Type 2 Diabetics
parameter	unit	Mean	SD	range	Mean	SD	range
kabs	min{circumflex over ( )}-1	0.0883	0.0491	0.0191 ÷ 0.3564	0.0809	0.0627	0.0154 ÷ 0.2927
kmax	min{circumflex over ( )}-1	0.0325	0.0088	0.0174 ÷ 0.0734	0.0435	0.0273	0.0101 ÷ 0.1853
kmin	min{circumflex over ( )}-1	0.0107	0.0035	0.0050 ÷ 0.0201	0.0077	0.0033	0.0021 ÷ 0.0190
b	dimensionless	0.7011	0.1598	0.3538 ÷ 1.2667	0.7578	0.1849	0.4589 ÷ 1.3582
c	dimensionless	0.0829	0.0457	0.0194 ÷ 0.2882	0.1288	0.1113	0.0215 ÷ 0.6368
ki	min{circumflex over ( )}-1	0.0104	0.0044	0.0024 ÷ 0.0242	0.0086	0.0035	0.0030 ÷ 0.0187
kp2	min{circumflex over ( )}-1	0.0047	0.0029	0.0009 ÷ 0.0215	0.0025	0.0022	0.0004 ÷ 0.0101
kp3	mg/kg/min/(pmol/1)	0.0078	0.0061	0.0016 ÷ 0.036	0.0062	0.0030	0.0020 ÷ 0.0162
kp4	mg/kg/min/(pmol/kg)	0.0337	0.0247	0.0072 ÷ 0.112	0.0553	0.0354	0.0081 ÷ 0.1760
Vg	dl/kg	1.3850	0.3900	0.7259 ÷ 2.6317	1.1908	0.5288	0.4002 ÷ 3.2804
Vmx	mg/kg/min per pmol/L	0.0465	0.0471	0.0044 ÷ 0.3949	0.0496	0.0558	0.0031 ÷ 0.4820
km0	mg/kg	220.46	42.94	143.22 ÷ 326.27	388.56	154.61	142.99 ÷ 925.26
K2	min{circumflex over ( )}-1	0.0825	0.0368	0.0240 ÷ 0.1982	0.1021	0.2655	0.0035 ÷ 1.4187
K1	min{circumflex over ( )}-1	0.0895	0.0192	0.0498 ÷ 0.1543	0.0570	0.0256	0.0164 ÷ 0.1214
p2U	min{circumflex over ( )}-1	0.0513	0.0176	0.0157 ÷ 0.1024	0.0543	0.0611	0.0032 ÷ 0.4861
Vi	l/kg	0.0486	0.0028	0.0417 ÷ 0.0562	0.0425	0.0041	0.0337 ÷ 0.0539
K	pmol/kg/(mg/dl)	2.6275	1.1470	0.7647 ÷ 6.6455	1.2288	0.4852	0.3011 ÷ 2.6059
β	pmol/kg/min/(mg/dl)	0.1246	0.0451	0.0610 ÷ 0.3056	0.0763	0.0468	0.0122 ÷ 0.2719
α	min{circumflex over ( )}-1	0.0535	0.0313	0.0151 ÷ 0.1425	0.0208	0.0123	0.0031 ÷ 0.0722
m1	min{circumflex over ( )}-1	0.1657	0.0708	0.0544 ÷ 0.4519	0.1885	0.1207	0.0391 ÷ 0.6578
m5	min*kg/pmol	0.0215	0.0096	0.0029 ÷ 0.0504	0.0446	0.0235	0.0074 ÷ 0.1129
Gb	mg/dl	95.60	9.31	78.49 ÷ 117.21	143.82	38.71	64.31 ÷ 265.46
EGPb	mg/kg/min	1.3350	0.2506	0.9019 ÷ 2.1415	1.8151	0.4213	0.8218 ÷ 3.2570
BW	kg	92.0528	20.3911	53.1937 ÷ 150.256	91.3930	21.2427	54.6762 ÷ 192.8718
lb	pmol/1	38.0648	20.4097	9.3908 ÷ 143.376	52.7363	33.7269	13.8310 ÷ 187.1251
SRb	pmol/kg/min	1.4479	0.4901	0.5841 ÷ 3.3658	3.2732	1.5889	1.0706 ÷ 7.5345

FIG. 5 is a functional block diagram for a computer system 500 for implementation of an exemplary embodiment or portion of an embodiment of the 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 50 as illustrated in FIG. 5. The computer system 500 may includes one or more processors, such as processor 504. The Processor 504 is connected to a communication infrastructure 506 (e.g., a communications bus, cross-over bar, or network). The computer system 500 may include a display interface 502 that forwards graphics, text, and/or other data from the communication infrastructure 506 (or from a frame buffer not shown) for display on the display unit 530. Display unit 530 may he digital and/or analog.

The computer system 500 may also include a main memory 508, preferably random access memory (RAM), and may also include a secondary memory 510. The secondary memory 510 may include, for example, a hard disk drive 512 and/or a removable storage drive 514, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. The removable storage drive 514 reads from and/or writes to a removable storage unit 518 in a well known manner. Removable storage unit 518, represents a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 514. As will be appreciated, the removable storage unit 518 includes a computer usable storage medium having stored therein computer software and/or data.

In alternative embodiments, secondary memory 510 may include other means for allowing computer programs or other instructions to be loaded into computer system 500. Such means may include, for example, a removable storage unit 522 and an interface 520. 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 522 and interfaces 520 which allow software and data to be transferred from the removable storage unit 522 to computer system 500.

The computer system 500 may also include a communications interface 524. Communications interface 124 allows software and data to be transferred between computer system 500 and external devices. Examples of communications interface 524 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 524 are in the form of signals 528 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 524. Signals 528 are provided to communications interface 524 via a communications path (i.e., channel) 526. Channel 526 (or any other communication means or channel disclosed herein) carries signals 528 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.

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 514, a hard disk installed in hard disk drive 512, and signals 528. These computer program products (“computer program medium” and “computer usable medium”) are means for providing software to computer system 500. 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.

Computer programs (also called computer control logic or computer program logic) are may be stored in main memory 508 and/or secondary memory 510. Computer programs may also be received via communications interface 524. Such computer programs, when executed, enable computer system 500 to perform the features of the present invention as discussed herein. In particular, the computer programs, when executed, enable processor 504 to perform the functions of the present invention. Accordingly, such computer programs represent controllers of computer system 500.

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 500 using removable storage drive 514, hard drive 512 or communications interface 524. The control logic (software or computer program logic), when executed by the processor 504, causes the processor 504 to perform the functions of the invention as described herein.

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).

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

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.

PUBLICATIONS

The following patents, applications and publications as listed below and throughout this document are hereby incorporated by reference in their entirety herein.

The devices, systems, compositions, computer program products, and methods of various embodiments of the invention disclosed herein may utilize aspects disclosed in the following references, applications, publications and patents and which are hereby incorporated by reference herein in their entirety:

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Title: System Coordinator and Modular Architecture for Open-Loop and Closed-Loop Control of Diabetes, Filed May 28, 2010.

Claims

1. A computer-implemented method for simulating a glucose-insulin metabolic system of a T2DM or prediabetic subject, comprising: modeling, by at least one processor dynamic glucose concentration in a T2DM or prediabetic subject, by modeling endogenous glucose production (EGP(t)) of a T2DM or prediabetic subject,modeling meal glucose rate of appearance (Ra(t)) of a T2DM or prediabetic subject,modeling glucose utilization (U(t)) of a T2DM or prediabetic subject, andmodeling renal excretion of glucose (E(t)) of a T2DM or prediabetic subject;modeling, by at least one processor, dynamic insulin concentration in said T2DM or prediabetic subject, by modeling insulin secretion (S(t)) of a T2DM or prediabetic subject;providing an electronic database containing a population of virtual T2DM or prediabetic subjects representative of the T2DM and prediabetic population, each virtual subject having a plurality of metabolic parameters with values encompassing a distribution of parameters observed in vivo across the population of T2DM or prediabetic subjects; andcalculating, by at least one processor, an effect of variation of at least one metabolic parameter value on the glucose-insulin metabolic system of a virtual T2DM or prediabetic subject by inputting said plurality of metabolic parameter values of said T2DM or prediabetic subject into said glucose concentration and insulin concentration subsystems and varying at least one of said metabolic parameter values to determine its effect on the glucose-insulin metabolic system of said T2DM or prediabetic subject.