U.S. patent application number 17/590659 was filed with the patent office on 2022-07-21 for system, method and computer simulation environment for in silico trials in pre-diabetes and type 2 diabetes.
The applicant listed for this patent is University of Virginia Patent Foundation. Invention is credited to Claudio Cobelli, Chiara Dalla Man, Boris P. Kovatchev.
Application Number | 20220230762 17/590659 |
Document ID | / |
Family ID | |
Filed Date | 2022-07-21 |
United States Patent
Application |
20220230762 |
Kind Code |
A1 |
Kovatchev; Boris P. ; et
al. |
July 21, 2022 |
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.
Inventors: |
Kovatchev; Boris P.;
(Charlottesville, VA) ; Cobelli; Claudio; (Padova,
IT) ; Dalla Man; Chiara; (Venezia, IT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
University of Virginia Patent Foundation |
Charlottesville |
VA |
US |
|
|
Appl. No.: |
17/590659 |
Filed: |
February 1, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13380839 |
Feb 10, 2012 |
11238990 |
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PCT/US10/40097 |
Jun 25, 2010 |
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17590659 |
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61220839 |
Jun 26, 2009 |
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International
Class: |
G16H 50/50 20060101
G16H050/50; G16H 50/70 20060101 G16H050/70; G06N 5/00 20060101
G06N005/00; G16H 10/60 20060101 G16H010/60; A61B 5/00 20060101
A61B005/00; G16H 50/20 20060101 G16H050/20 |
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, and modeling 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; and calculating, 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.
Description
BACKGROUND OF THE INVENTION
[0001] 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.
[0002] 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
[0003] 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.
[0004] 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.
[0005] 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.
[0006] 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.
[0007] 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 [0008] an
electronic module that models endogenous glucose production
(EGP(t)), [0009] an electronic module that models meal glucose rate
of appearance (Ra(t)), [0010] an electronic module that models
glucose utilization (U(t)), [0011] an electronic module that models
renal excretion of glucose (E(t));
[0012] a subsystem that models dynamic insulin concentration in
said T2DM or prediabetic subject, including [0013] an electronic
module that models insulin secretion (S(t));
[0014] 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
[0015] 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.
[0016] 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 [0017] an electronic code module that models endogenous
glucose production (EGP(t)), [0018] an electronic code module that
models meal glucose rate of appearance (Ra(t)), [0019] an
electronic code module that models glucose utilization (U(t)),
[0020] an electronic code module that models renal excretion of
glucose (E(t));
[0021] subsystem code that models dynamic insulin concentration in
said T2DM or prediabetic subject, including [0022] an electronic
code module that models insulin secretion (S(t));
[0023] 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
[0024] 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
[0025] 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;
[0026] 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;
[0027] 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;
[0028] FIG. 4A and FIG. 4B illustrates glucose and insulin
concentrations in untreated prediabetic subjects versus prediabetic
subjects treated with drugs x and y; and
[0029] 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
[0030] Two key components of the simulator of the glucose-insulin
metabolic system in prediabetes and T2DM in accordance with the
present invention are:
[0031] (1) A physiological model of glucose-insulin metabolism in
prediabetes and T2DM, and
[0032] (2) A population of virtual subjects with prediabetes
(N-100) and T2DM (N-100).
Physiological Model of Glucose-Insulin Metabolism in Prediabetes
and T2DM
[0033] 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.
[0034] a. Model Equations
[0035] 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
[0036] The glucose subsystem model is defined by the following
group of equations (1):
{ G . p .function. ( t ) = EGP .function. ( t ) + Ra .function. ( t
) - U ii .function. ( t ) - E .function. ( t ) - k 1 G p .function.
( t ) + k 2 G t .times. t G p .function. ( 0 ) = G pb G . t
.function. ( t ) = - U id .function. ( t ) + k 1 G p .function. ( t
) - k 2 G t .function. ( t ) G t .function. ( 0 ) = G tb G
.function. ( t ) = G p V g G .function. ( 0 ) = G b ( 1 )
##EQU00001##
Where
[0037] G.sub.p and G.sub.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.sub.ii and
U.sub.id are insulin-independent and dependent glucose
utilizations, respectively (mg/kg/min); V.sub.G is the distribution
volume of glucose (dl/kg); and k.sub.1 and k.sub.2 (min.sup.-1) are
rate parameters.
[0038] At the basal steady state, endogenous glucose production,
EGP.sub.b, equals glucose disappearance, i.e. the sum of glucose
utilization and renal excretion (which is zero in normal subjects),
U.sub.b+E.sub.b:
EGP.sub.b=U.sub.bE.sub.b (2)
Insulin Subsystem
[0039] The insulin subsystem model is defined by the following
group of equations (3):
{ I . l .function. ( t ) = - ( m 1 + m 3 .function. ( t ) ) I l
.function. ( t ) + m 2 .times. I p .function. ( t ) + S .function.
( t ) I l .function. ( 0 ) = I lb I . p .function. ( t ) = - ( m 2
+ m 4 ) I p .function. ( t ) + m 1 I l .function. ( t ) I p
.function. ( 0 ) = I pb I .function. ( t ) = I p V I I .function. (
0 ) = I b ( 3 ) ##EQU00002##
where I.sub.p and I.sub.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.sub.I is the
distribution volume of insulin (1/kg); and m.sub.1-m.sub.4
(min.sup.-1) are rate parameters.
[0040] Degradation, D, occurs both in the liver and peripherally.
Peripheral degradation has been assumed to be linear (m.sub.4).
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)=-m.sub.5S(t)+m.sub.6 HE(0)=HE.sub.b (4)
thus one has:
m 3 .function. ( t ) = H .times. E .function. ( t ) m 1 1 - H
.times. E .function. ( t ) ( 5 ) ##EQU00003##
At basal steady state one has:
m 6 = m 5 S b + H .times. E b ( 6 ) m 3 .function. ( 0 ) = H
.times. E b m 1 1 - H .times. E b ( 7 ) S b = m 3 .function. ( 0 )
I l .times. b + m 4 I p .times. b = D b ( 8 ) ##EQU00004##
Moreover, given that the liver is responsible for 60% of insulin
clearance in the steady state, one has:
m 2 = ( s b I p .times. b - m 4 1 - H .times. E b ) 1 - H .times. E
b H .times. E b ; m 4 = 2 5 S b I p .times. b ( 1 - HE h ) ( 9 )
##EQU00005##
with S.sub.b and D.sub.b basal secretion and degradation,
respectively (HE.sub.b was fixed to 0.6).
Endogenous Glucose Production
[0041] Endogenous glucose production is defined by the following
equation (10):
EGP(t)=k.sub.p1-k.sub.p2G.sub.p(t)-k.sub.p3I.sub.d(t)-k.sub.p4I.sub.p0(t-
) EGP(0)=EGP.sub.b (10)
where I.sub.po is the amount of insulin in the portal vein
(pmol/kg); I.sub.d (pmol/l) is a delayed insulin signal realized
with a chain of two compartments:
{ I . 1 .function. ( t ) = - k i [ I 1 .function. ( t ) - I
.function. ( t ) ] I 1 .function. ( 0 ) = I b I . d .function. ( t
) = - k i [ I d .function. ( t ) - I 1 .function. ( t ) ] I d
.function. ( 0 ) = I b ( 11 ) ##EQU00006##
k.sub.p1 (mg/kg/min) is the extrapolated EGP at zero glucose and
insulin; k.sub.p2 (min.sup.-1) is liver glucose effectiveness;
k.sub.p3 (mg/kg/min per pmol/l) is a parameter governing amplitude
of insulin action on the liver; k.sub.p4 (mg/kg/min/(pmol/kg)) is a
parameter governing amplitude of portal insulin action on the
liver; and k.sub.i (min.sup.-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.sub.p1=EGP.sub.b+k.sub.p2G.sub.pb+k.sub.p3I.sub.b+k.sub.p4I.sub.pob
(12)
Glucose Rate of Appearance
[0042] The glucose rate of appearance (Ra) is defined by the
following group of equations (13):
{ Q sto .function. ( t ) = Q sto .times. .times. 1 .function. ( t )
+ Q sto .times. .times. 2 .function. ( t ) Q sto .function. ( 0 ) =
0 Q . sto .times. .times. 1 .function. ( t ) = - k gri Q sto
.times. .times. 1 .function. ( t ) + D .delta. .function. ( t ) Q
sto .times. .times. 1 .function. ( 0 ) = 0 Q . sto .times. .times.
2 .function. ( t ) = - k empt .function. ( Q sto ) Q sto .times.
.times. 2 .function. ( t ) + k gri Q sto .times. .times. 1
.function. ( t ) Q sto .times. .times. 2 .function. ( 0 ) = 0 Q .
gut = - k abs Q gut .function. ( t ) + k empt .function. ( Q sto )
Q sto .times. .times. 2 .function. ( t ) Q gut .function. ( 0 ) = 0
Ra .function. ( t ) = f k abs Q gut .function. ( t ) BW Ra
.function. ( 0 ) = 0 ( 13 ) ##EQU00007##
where Q.sub.sto (mg) is the amount of glucose in the stomach (solid
phase, Q.sub.sto1, and liquid phase, Q.sub.sto2); Q.sub.gut (mg) is
the glucose mass in the intestine; k.sub.gri (min.sup.-1) is the
rate of grinding; k.sub.empt(Q.sub.sto) (min.sup.-1) is a rate
constant of gastric emptying which is a nonlinear function of
Q.sub.sto; k.sub.abs (min.sup.-1) is a rate constant of intestinal
absorption; f 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; and Ra (mg/kg/min) is the appearance rate of
glucose in the plasma.
Glucose Utilization
[0043] 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.sub.cns):
U.sub.ii(t)=F.sub.cns (14)
Insulin-dependent utilization takes place in the remote compartment
and depends nonlinearly (Michaelis Menten) from glucose in the
tissues:
U id .function. ( t ) = V .function. ( X .function. ( t ) ) G t
.function. ( t ) K m .times. .times. 0 + G t .function. ( t ) ( 15
) ##EQU00008##
where Vm(X(t)) is assumed to be linearly dependent from a remote
insulin, X(t):
V.sub.m(X(t))=V.sub.m0+V.sub.mxX(t) (16)
X (pmol/L) is insulin in the interstitial fluid described by:
{dot over (X)}(t)=-p.sub.2UX(t)+p.sub.2U[I(t)-I.sub.b]X(0)=0
(17)
where I is plasma insulin and p.sub.2U (min.sup.-1) is rate
constant of insulin action on the peripheral glucose utilization.
Total glucose utilization, U, is thus:
U(t)=U.sub.ii(t)+U.sub.id(t) (18)
At basal steady state one has:
G tb = F c .times. n .times. s - E .times. G .times. P b + k 1 G p
.times. b k 2 .times. .times. and .times. : ( 19 ) V m .times. 0 =
( E .times. G .times. P b - F c .times. n .times. s ) ( K m .times.
.times. 0 + G tb ) G t .times. b ( 20 ) ##EQU00009##
Insulin Secretion
[0044] Insulin secretion, S, is defined by the following equations
(21)-(24):
.times. S .function. ( t ) = .gamma. I po .function. ( t ) ( 21 )
.times. I . po .function. ( t ) = - .gamma. I po .function. ( t ) +
S po .function. ( t ) .times. .times. I po .function. ( 0 ) = I pob
( 22 ) .times. S po .function. ( t ) = { Y .function. ( t ) + K G .
.function. ( t ) + S b for .times. .times. G . > 0 Y .function.
( t ) + S b for .times. .times. G . .ltoreq. 0 .times. .times.
.times. and ( 23 ) Y . .function. ( t ) = { - .alpha. [ Y
.function. ( t ) - .beta. ( G .function. ( t ) - h ) ] if .times.
.times. .beta. ( G .function. ( t ) - h ) .gtoreq. - S b - .alpha.
Y .function. ( t ) - .alpha. S b if .times. .times. .beta. ( G
.function. ( t ) - h ) < - S b ; Y .function. ( 0 ) = 0 ( 24 )
##EQU00010##
where .gamma. (min.sup.-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.sup.-4)
is the delay between the glucose signal and insulin secretion;
.beta. (pmol/kg/min per mg/dl) is the pancreatic responsivity to
the glucose level; and h (mg/dl) is the threshold level of glucose
above which the .beta.-cells initiate to produce new insulin (h was
set to the basal glucose concentration G.sub.b to guarantee system
steady state in basal condition).
Renal Glucose Excretion
[0045] Renal glucose excretion, E, is defined by the following
equation (25):
E .function. ( t ) = { k e .times. .times. 1 [ G p .function. ( t )
- k e .times. .times. 2 ] if .times. .times. G p .function. ( t )
> k e .times. .times. 2 0 if .times. .times. G p .function. ( t
) .ltoreq. k e .times. .times. 2 ( 25 ) ##EQU00011##
where k.sub.e1 (min.sup.-1) is the glomerular filtration rate; and
k.sub.e2 (mg/kg) is the renal threshold of glucose. b. Parameter
Identification
[0046] 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-US-00001 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/m.sup.2) 30.4 31.8 LBM (kg) 51.7 52.2 %Fat (%) 37.8 38.6
Visceral Fat (cm.sup.2) 159.5 160.1
[0047] 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.
[0048] 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.
[0049] 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.
[0050] 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)
[0051] 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.
[0052] Each virtual subject is uniquely identified by a set of 26
parameters:
[0053] k.sub.abs=rate constant of glucose absorption by the
intestine
[0054] k.sub.max=maximum rate constant of gastric emptying
[0055] k.sub.min=minimum rate constant of gastric emptying
[0056] b=percentage of the dose for which k.sub.empt decreases at
(k.sub.max-k.sub.min)/2
[0057] c=percentage of the dose for which k.sub.empt is back to
(k.sub.max-k.sub.min)/2
[0058] k.sub.i=rate parameter accounting for delay between insulin
signal and insulin action on the liver
[0059] k.sub.p2=liver glucose effectiveness
[0060] k.sub.p3=parameter governing amplitude of insulin action on
the liver
[0061] k.sub.p4=parameter governing amplitude of portal insulin
action on the liver
[0062] V.sub.g=distribution volume of glucose
[0063] V.sub.mx=parameter governing amplitude of insulin action on
glucose utilization
[0064] k.sub.m0=parameter governing glucose control on glucose
utilization
[0065] K.sub.2=rate parameter accounting for glucose transit from
tissue to plasma
[0066] K.sub.1=rate parameter accounting for glucose transit from
plasma to tissue
[0067] P.sub.2U=rate parameter accounting for delay between insulin
signal and insulin action on glucose utilization
[0068] V.sub.i=distribution volume of insulin
[0069] K=beta-cell responsivity to glucose rate of change
[0070] .beta.=beta-cell responsivity to glucose level
[0071] .alpha.=rate parameter accounting for delay between glucose
signal and insulin secretion
[0072] m.sub.1=rate parameter of insulin kinetics
[0073] m.sub.5=coefficient linking insulin hepatic extraction to
insulin secretion rate
[0074] G.sub.b=basal plasma glucose concentration
[0075] EGP.sub.b=basal endogenous glucose production
[0076] BW=body weight
[0077] I.sub.b=basal plasma insulin concentration
[0078] SR.sub.b=basal insulin secretion rate
[0079] 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-US-00002 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 .beta.
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) .alpha. 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-US-00003 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
.beta. 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) .alpha. 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
[0080] Table 4 includes the mean, SD, and the range of all model
parameters defining the span of the simulated populations:
TABLE-US-00004 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 .beta. pmol/kg/min/(mg/dl) 0.1246 0.0451
0.0610 / 0.3056 0.0763 0.0468 0.0122 / 0.2719 .alpha.
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
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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).
[0089] In yet another embodiment, the invention is implemented
using a combination of both hardware and software.
[0090] 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
[0091] The following patents, applications and publications as
listed below and throughout this document are hereby incorporated
by reference in their entirety herein.
[0092] 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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