U.S. patent application number 12/060142 was filed with the patent office on 2009-03-12 for method and apparatus fo computer modeling diabetes.
This patent application is currently assigned to Entelos, Inc.. Invention is credited to Paul Brahznik, Kevin Hall, Dave Polidori, Scott Siler, Jeff Trimmer.
Application Number | 20090070088 12/060142 |
Document ID | / |
Family ID | 26717016 |
Filed Date | 2009-03-12 |
United States Patent
Application |
20090070088 |
Kind Code |
A1 |
Brahznik; Paul ; et
al. |
March 12, 2009 |
Method and Apparatus fo Computer Modeling Diabetes
Abstract
The present invention relates generally to a mathematical and
computer model of diabetes related disorders (e.g., human type 2
diabetes) within the framework of multiple macronutrient
metabolism. The model includes a representation of complex
physiological control mechanisms directing, for example, fat
metabolism, protein metabolism and/or carbohydrate metabolism. In
one embodiment, for example, the model can account for the
interconversion between macronutrients, as well as their digestion,
absorption, storage, mobilization, and adaptive utilization, as
well as the endocrine control of these processes. In this
embodiment, the model can simulate, for example, a heterogeneous
group of diabetes related disorders, from insulin resistant to
severe diabetic, and can predict the likely effects of therapeutic
interventions.
Inventors: |
Brahznik; Paul; (Blacksburg,
VA) ; Hall; Kevin; (Edmonton, CA) ; Polidori;
Dave; (Palo Alto, CA) ; Siler; Scott;
(Hayward, CA) ; Trimmer; Jeff; (Burlingame,
CA) |
Correspondence
Address: |
ENTELOS, INC.;c/o Law Offices of Karen E. Flick
P.O. Box 515
El Granada
CA
94018-0515
US
|
Assignee: |
Entelos, Inc.
|
Family ID: |
26717016 |
Appl. No.: |
12/060142 |
Filed: |
March 31, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10040373 |
Jan 9, 2002 |
7353152 |
|
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12060142 |
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60287702 |
May 2, 2001 |
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Current U.S.
Class: |
703/11 |
Current CPC
Class: |
G16H 50/50 20180101;
A61P 7/12 20180101; G16B 5/00 20190201 |
Class at
Publication: |
703/11 |
International
Class: |
G06G 7/60 20060101
G06G007/60 |
Claims
1-6. (canceled)
7. A computer model of a disease state of diabetes, comprising: a
computer-readable memory storing: instructions defining a set of
biological processes related to the disease state of diabetes, at
least two biological processes from the set of biological processes
being associated with a set of mathematical relationships related
to interactions among biological variables associated with the
biological processes, the instructions defining a simulation of
glucose metabolism in the context of multiple macronutrient
metabolism; and a processor coupled to the computer-readable
memory, the processor configured to execute the instructions.
8. The computer model of claim 7, wherein, upon execution of the
instruction, the processor is configured to produce a simulated
biological attribute for the disease state of diabetes, the
simulated biological attribute is substantially consistent with at
least one biological attribute associated with a reference pattern
of diabetes.
9. The computer model of claim 7, wherein the instructions further
define a set of defects associated with diabetes, the set of
defects including a first defect and a second defect, the first
defect is a modification of a first biological process from the set
of biological processes, the first biological process is related to
biological attributes of diabetes in a reference pattern of
diabetes, the second defect is a modification of the first
biological process or a second biological process from the set of
biological processes, the second biological process is related to
biological attributes of diabetes in the reference pattern of
diabetes.
10. A computer executable software code, comprising: code to define
a normal biological state through a set of biological processes,
each biological process from the set of biological processes having
its own associated parameter set, the set of biological processes
being related to glucose metabolism in the context of multiple
macronutrient metabolism; code to provide a plurality of predefined
defect indicators, each predefined defect indicator from the
plurality of predefined defect indicators being uniquely associated
with a defect from a plurality of defects associated with a disease
state of diabetes, each defect from the plurality of defects being
associated with at least one biological process from the set of
biological processes; and code to receive a user-specified
identification oaf first defect indicator from the plurality of
predefined defect indicators, a first defect from the plurality of
defects being associated with the first defect indicator, the
parameter set associated with each biological processes that is
associated with the first defect being changed based on the
user-specified identification.
11. The computer executable software code of claim 10, further
comprising: code to determine at least one simulated biological
attribute based on the modified biological process associated with
the first defect, the simulated biological attribute being
substantially consistent with at least one corresponding biological
attribute associated with diabetes in a reference pattern of
diabetes.
12. The computer executable software code of claim 10, further
comprising: code to receive a user-specified identification of a
second defect indicator from the plurality of predefined defect
indicators, a second defect from the plurality of defects being
associated with the second defect indicator, the parameter set
associated with each biological processes that is associated with
the second defect being changed based on the user-specified
identification.
13. The computer executable software code of claim 12, wherein: the
first defect has an associated severity based on the change to the
at least one associated parameter set; and the second defect has an
associated severity based on the change to the at least one
associated parameter set, the severity associated with the first
defect being different from the severity associated with the second
defect.
14. The computer executable software code of claim 12, wherein: the
first defect has an associated severity based on the change to the
at least one associated parameter set; and the second defect has an
associated severity based on the change to the at least one
associated parameter set, the severity associated with the first
defect being substantially similar to the severity associated with
the second defect.
15. The computer executable software code of claim 10, further
comprising: code to produce a simulated biological attribute based
on the parameter set associated with each biological processes that
is associated with the first defect, the simulated biological
attribute being substantially consistent with biological attributes
of a reference pattern of diabetes.
16. A computer executable software code, comprising: code to
provide a plurality of predefined defect indicators, each
predefined defect indicator from the plurality of predefined defect
indicators being uniquely associated with a defect from a plurality
of defects associated with a disease state, each defect from the
plurality of defects being associated with at least one biological
process from a set of biological processes, the set of biological
processes being related to glucose metabolism in the context of
multiple macronutrient metabolism; code to receive a user-specified
identification oaf first defect indicator from the plurality of
predefined defect indicators, a first defect from the plurality of
defects being associated with the first defect indicator, the first
defect being associated with at least one biological process and
its associated parameter set, the at least one parameter set
associated with the first defect being changed based on the
user-specified identification; and code to receive a user-specified
identification of a second defect indicator from the plurality of
predefined defect indicators, a second defect from the plurality of
defects being associated with the second defect indicator, the
second defect being associated with at least one biological process
and its associated parameter set, the at least one parameter set
associated with the second defect being changed based on the
user-specified identification.
17. The computer executable software code of claim 16, wherein: the
first defect having an associated severity based on the change to
the at least one associated parameter set, the second defect having
an associated severity based on the change to the at least one
associated parameter set, the severity associated with the first
defect being different from the severity associated with the second
defect.
18. The computer executable software code of claim 16, further
comprising: code to define a normal biological state through the
set of biological processes, each biological process from the set
of biological processes being associated with its own parameter
set.
19. The computer executable software code of claim 16, wherein the
plurality of defects are associated with type 2 diabetes.
20-28. (canceled)
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] The present invention is related to and claims priority to
U.S. Provisional Patent Application Ser. No. 60/287,702, entitled
"Method and Apparatus for Computer Modeling Type 2 Diabetes," which
is incorporated herein by reference.
COPYRIGHT NOTICE
[0002] A portion of the disclosure of the patent document contains
material that is subject to copyright protection. The copyright
owner has no objection to the facsimile reproduction by anyone of
the patent document of the patent disclosure, as it appears in the
Patent and Trademark Office patent file or records, but otherwise
reserves all copyright rights whatsoever.
BACKGROUND OF THE INVENTION
[0003] The present invention relates generally to a computer model
of diabetes. More specifically, the present invention relates to a
computer model of diabetes (e.g., human type 2 diabetes) within the
framework of multiple macronutrient metabolism.
[0004] The process of extracting energy from the environment and
using it to maintain life is called metabolism. Every cell in the
human body requires a constant supply of energy in order to avoid
the decay to thermodynamic equilibrium (i.e. death). The required
energy comes from the ingestion of food and the carefully
controlled oxidation of
SUMMARY OF THE INVENTION
[0005] The present invention relates generally to a mathematical
and computer model of diabetes related disorders (e.g., human type
2 diabetes) within the framework of multiple macronutrient
metabolism. The model includes a representation of complex
physiological control mechanisms related to, for example, fat
metabolism, protein metabolism and/or carbohydrate metabolism. In
one embodiment, for example, the model can account for the
interconversion between macronutrients, as well as their digestion,
absorption, storage, mobilization, and adaptive utilization, as
well as the endocrine control of these processes. In this
embodiment, the model can simulate, for example, a heterogeneous
group of diabetes related disorders, from insulin resistant to
severe diabetic, and can predict the likely effects of therapeutic
interventions. In another embodiment, the model includes modeling
of fat and/or protein metabolism without explicitly modeling
carbohydrate metabolism.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] FIG. 1 illustrates an example of an Effect Diagram, which
shows the dynamic relationships that exist among these elements of
the physiologic system.
[0007] FIG. 2 illustrates an enlargement of the upper left portion
of the Effect Diagram shown in FIG. 1.
[0008] FIG. 3 illustrates an example of a Summary Diagram from the
Effect Diagram of FIG. 1.
[0009] FIG. 4 illustrates an example of a module diagram for one of
the anatomical elements shown in the Summary Diagram of FIG. 3.
[0010] FIG. 5 illustrates an example of a browser screen that
lists, by biological areas, lesions (or defects) for type 2
diabetes that can be modeled.
[0011] FIG. 6 illustrates an example of a user-interface screen for
the parameter set of a type 2 diabetes lesion.
[0012] FIG. 7 illustrates a graph comparing the model results
against measured data for an oral glucose tolerance test.
[0013] FIGS. 8A-H graphically illustrate an example of the model
results for a 24-hour simulation of an obese diabetic patient
eating 3 typical meals.
[0014] FIG. 9 illustrates a graph showing an example of the model
results for an oral glucose tolerance test.
[0015] FIG. 10 shows a system block diagram of a computer system
within which the methods described above can operate via software
code, according to an embodiment of the present invention.
[0016] FIG. 11 shows an example of the module diagram for the
glucose uptake functions of the muscle, according to an embodiment
of the present invention.
[0017] FIG. 12 shows a graph of the function f(i) (representing the
effect of insulin on GLUT4 membrane content) versus the
interstitial insulin concentration, i.
DETAILED DESCRIPTION
Overview
[0018] Embodiments of the present invention relate to a computer
model of diabetes (e.g., human type 2 diabetes) within the
framework of multiple macronutrient metabolism. The computer model
of diabetes-related disorders includes modeling the metabolism of
fat and/or protein metabolism in addition to, or in place of,
carbohydrate metabolism. Furthermore, the present invention relates
to a computer model of diabetes-related disorders that includes
modeling fat and/or protein metabolism without explicitly modeling
carbohydrate metabolism.
[0019] In one embodiment, the computer executable software code
numerically solves the mathematical equations of the model under
various simulated experimental conditions. Furthermore, the
computer executable software code can facilitate visualization and
manipulation of the model equations and their associated parameters
to simulate different patients subject to a variety of stimuli.
See, e.g., U.S. Pat. No. 6,078,739, entitled "Managing objects and
parameter values associated with the objects within a simulation
model," the disclosure of which is incorporated herein by
reference. Thus, the computer model can be used to rapidly test
hypotheses and investigate potential drug targets or therapeutic
strategies.
Mathematical Model
[0020] The mathematical model of the computer-executable software
code represents the dynamic biological processes controlling
multiple macronutrient metabolism. The form of the mathematical
equations employed may include, for example partial differential
equations, stochastic differential equations, differential
algebraic equations, difference equations, cellular automata,
coupled maps, equations of networks of Boolean or fuzzy logical
networks, etc. In one embodiment, the form of the mathematical
equations used in the model are ordinary differential
equations:
dx/dt=f(x,p,t),
where x is an N dimensional vector whose elements represent the
biological variables of the system (for example plasma glucose,
insulin, free fatty acids, etc.), t is time, dx/dt is the rate of
change of x, p is an M dimensional set of system parameters (for
example basal muscle glucose uptake rate, level of physical
activity, nutrient composition of diet, etc.), and f is a function
that represents the complex interactions among biological
variables.
[0021] The term "multiple macronutrient metabolism" refers to the
biological processes related to the metabolism of at least one of
the macronutrients, i.e., carbohydrates, fats, and/or proteins. In
particular, in the present invention, this term could refer to
processes related to metabolism of at least two of the
macronutrients, i.e. carbohydrates and fats, or carbohydrates and
proteins, or fats and proteins. In one embodiment, the diabetes
model only includes the biological processes related to fat
metabolism. In another embodiment, the diabetes model only includes
the biological processes related to protein metabolism.
[0022] The term "biological variables" refers to the extra-cellular
and/or intra-cellular constituents that make up a biological
process. For example, the biological variables can include
metabolites, DNA, RNA, proteins, enzymes, hormones, cells, organs,
tissues, portions of cells, tissues, or organs, subcellular
organelles, chemically reactive molecules like H.sup.+,
superoxides, ATP, citric acid, protein albumin, as well as
combinations or aggregate representations of these types of
biological variables.
[0023] The term "biological process" is defined herein to mean an
interaction or series of interactions between biological variables.
Thus, the above function f mathematically represents the biological
processes in the model. Biological processes can include, for
example, digestion, absorption, storage, and oxidation of
carbohydrate, fat, and protein, as well as the endocrine control of
these processes. Each biological variable of the biological process
can be influenced, for example, by at least one other biological
variable in the biological process by some biological mechanism,
which need not be specified or even understood.
[0024] The term "biological state" is used herein to mean the
result of the occurrence of a series of biological processes. As
the biological processes change relative to each other, the
biological state also undergoes changes. One measurement of a
biological state, is the level of activity of biologic variables,
parameters, and/or processes at a specified time and under
specified experimental or environmental conditions.
[0025] In one embodiment the biological state can be mathematically
defined by the values of x and p at a given time. Once a biological
state of the model is mathematically specified, numerical
integration of the above equation using a computer determines, for
example, the time evolution of the biological variables x(t) and
hence the evolution of the biological state over time.
[0026] The term "simulation" is used herein to mean the numerical
or analytical integration of a mathematical model. For example,
simulation can mean the numerical integration of the mathematical
model of the biological state defined by the above equation, i.e.
dx/dt=f(x, p, t).
[0027] A biological state can include, for example, the state of an
individual cell, an organ, a tissue, and/or a multi-cellular
organism. A biological state can also include the state of a
nutrient or hormone concentration in the plasma, interstitial
fluid, intracellular fluid, and/or cerebrospinal fluid; e.g. the
states of hypoglycemia orhypoinsulinemia are low blood sugar or low
blood insulin. These conditions can be imposed experimentally, or
may be conditions present in a patient type. For example, a
biological state of a neuron can include the state in which the
neuron is at rest, the state in which the neuron is firing an
action potential, and the state in which the neuron is releasing
neurotransmitter. In another example, the biological states of the
collection of plasma nutrients can include the state in which the
person awakens from an overnight fast, the state just after a meal,
and the state between meals.
[0028] The term "biological attribute" is used herein to mean
clinical signs and diagnostic criteria associated with a disease
state. The biological attributes of a disease state can be
quantified with measurements of biological variables, parameters,
and/or processes. For example, for the disease state of diabetes,
the biological attributes can include fasting plasma glucose,
casual plasma glucose, or oral glucose tolerance test (OGTT)
value.
[0029] The term "disease state" is used herein to mean a biological
state where one or more biological processes are related to the
cause or the clinical signs of the disease. A disease state can be,
for example, of a diseased cell, a diseased organ, a diseased
tissue, and/or a diseased multi-cellular organism. Such diseases
can include, for example, diabetes, asthma, obesity, and rheumatoid
arthritis. A diseased multi-cellular organism can be, for example,
an individual human patient, a specific group of human patients, or
the general human population as a whole. A diseased state could
also include, for example, a diseased protein (such as a defective
glucose transporter) or a diseased process, such as defects in
clearance, degradation or synthesis or a system constituent, which
may occur in several different organs.
[0030] The term "reference pattern of the disease state" is used
herein to mean a set of biological attributes that are measured in
a diseased biological system under specified experimental
conditions. For example, the measurements may be performed on blood
samples at some specified time following a particular glucose or
insulin stimulus. Alternatively, measurements may be performed on
biopsy samples, or cell cultures derived from a diseased human or
animal. Examples of diseased biological systems include cellular or
animal models of diabetes, including a human diabetic patient.
[0031] The computer model of diabetes includes the biological
processes related to multiple macronutrient metabolism. In one
embodiment, the model includes the processes related to the
metabolism of all three macronutrients, i.e., carbohydrates, fats,
and proteins. In another embodiment, the model includes the
processes related to fat metabolism. In yet another embodiment, the
model includes the processes related to protein metabolism. In
other embodiments of the invention, the model includes processes
related to the metabolism of two macronutrients, i.e.,
carbohydrates and fats, carbohydrates and proteins, or fats and
proteins. These different embodiments enable a researcher to
understand the pathophysiology of diabetes in the presence of one,
two, or all three macronutrients.
[0032] To represent metabolism of macronutrients, the biological
processes can include the processes of digestion and absorption of
carbohydrates, fat, and/or proteins. In addition, the appropriate
hormonal responses to carbohydrates, fat, and/or proteins can be
included.
[0033] To represent carbohydrate metabolism, the model can include,
for example, muscle glucose uptake regulation; muscle glycogen
regulation; lactate metabolism; hepatic carbohydrate regulation
including gluconeogenesis (i.e. creation of glucose 6-phosphate)
from lactate, glycerol, and amino acids, glycogenolysis and
glycogen synthesis, and glucose uptake and output; brain glucose
uptake and utilization; adipose tissue glucose uptake for
triglyceride esterification (i.e. fat storage); carbohydrate
oxidation in tissues other than the brain and skeletal muscle; and
renal glucose excretion.
[0034] To represent fat metabolism, the model can include, for
example, the regulation of adipose tissue uptake of free fatty
acids (FFA) from circulating FFA and lipoproteins (chylomicra and
VLDL (very low density lipoprotein)); the regulation of adipose
tissue lipolysis (i.e. the release of FFA and glycerol from fat
cells); regulation of adipose tissue triglyceride esterification;
hepatic lipoprotein regulation; and muscle FFA uptake and
utilization.
[0035] To represent amino acid metabolism, the model can include,
for example, the regulation of skeletal muscle protein turnover in
response to activity, exercise, fat mass, dietary composition, and
insulin; production of amino acids from carbohydrate in the muscle;
hepatic gluconeogenesis from amino acid substrate; and oxidation of
amino acids in muscle and other tissues (primarily the liver).
Computer System
[0036] FIG. 10 shows a system block diagram of a computer system
within which the methods described above can operate via software
code, according to an embodiment of the present invention. The
computer system 100 includes a processor 102, a main memory 103 and
a static memory 104, which are coupled by bus 106. The computer
system 100 can further include a video display unit 108 (e.g., a
liquid crystal display (LCD) or cathode ray tube (CRT)) on which a
user interface can be displayed. The computer system 100 can also
include an alpha-numeric input device 110 (e.g., a keyboard), a
cursor control device 112 (e.g., a mouse), a disk drive unit 114, a
signal generation device 116 (e.g., a speaker) and a network
interface device medium 115. The disk drive unit 114 includes a
computer-readable medium 115 on which software 120 can be stored.
The software can also reside, completely or partially, within the
main memory 103 and/or within the processor 102. The software 120
can also be transmitted or received vai the network interface
device 118.
[0037] The term "computer-readable medium" is used herein to
include any medium which is capable of storing or encoding a
sequence of instructions for performing the methods described
herein and can include, but not limited to, optical and/or magnetic
storage devices and/or disks, and carrier wave signals.
Computer Model
[0038] Suitably, a computer model can be used to implement at least
some embodiments of the present invention. The computer model can
be used for a variety of purposes. For example, the computer model
can enable a researcher to: (1) simulate the dynamics of the
biological state associated with type 2 diabetes, (2) visualize key
metabolic pathways and the feedback within and between these
pathways, (3) gain a better understanding of the metabolism and
physiology of type 2 diabetes, (4) explore and test hypotheses
about type 2 diabetes and normal metabolisms, (5) identify and
prioritize potential therapeutic targets, (6) identify patient
types and their responses to various interventions, (7) identify
surrogate markers of disease progression, and (8) organize
knowledge and data that relate to type 2 diabetes.
[0039] In addition to simulation capabilities, the computer model
can include a built-in database of references to the scientific
literature on which the model is based. Users can augment this
database with additional references or other commentary and can
link the information to the relevant disease component. The
computer model can be a multi-user system in which the information
can be shared throughput an organization. Thus, the computer model
can be a specialized knowledge management system focused on
diabetes.
Effect Diagram and Summary Diagram
[0040] In one embodiment, the computer model contains software code
allowing visual representation of the mathematical model equations
as well as the interrelationships between the biological variables,
parameters, and processes. This visual representation can be
referred to as an "Effect Diagram", illustrated in FIG. 1. The
Effect Diagram comprises multiple modules or functional areas that,
when grouped together, represent the large complex physiology
model. These modules represent and encode sets of ordinary
differential equations for numerical integration, as discussed more
fully below in the section entitled "Mathematical Equations Encoded
in the Effect Diagram."
[0041] The Effect Diagram depicted in FIG. 1 includes a Summary
Diagram in the upper left corner 1. FIG. 2 is an enlargement of the
upper left portion of the Effect Diagram showing that the Summary
Diagram can provide navigational links to modules of the model. The
navigational tools can relate to a functional view or the
anatomical view since the Effect Diagram can include the modules
for the various anatomical elements of the human physiologic
system, and a given function may involve multiple anatomical
structures. From the Summary Diagram, a user can select any of
these related user-interface screens by selecting such a screen
from the Summary Diagram (e.g., by clicking a hyperlink to a
related user-interface screen).
[0042] FIG. 3 illustrates an example of a Summary Diagram from the
Effect Diagram of FIG. 1. As shown in FIG. 3, the Summary Diagram
can provide an overview of the contents of the Effect Diagram and
can contain nodes that link to modules in the Effect Diagram. These
modules can be based on, for example, the anatomical elements of
the human physiology such as stomach and intestines, portal vein,
liver, pancreas, etc. (as shown in the Anatomical View of the
Summary Diagram).
[0043] FIG. 4 illustrates an example of a module diagram for one of
the anatomical elements shown in the Summary Diagram of FIG. 3.
More specifically, FIG. 4 illustrates a module diagram for the
carbohydrate storage and oxidation functions of the muscle. Both
the biological relationships as well as the mathematical equations
are represented through the use of diagrammatic symbols. Through
the use of these symbols, the complex and dynamic mathematical
relationships for the various elements of the physiologic system
are represented in a user-friendly manner.
[0044] Pages A-1 through A-39 of Appendix A lists additional
examples of user-interface screens for other modules for anatomical
elements and physiologic functions shown in the Summary Diagram.
For purposes of clarity, pages A-40A through A-78B of Appendix A
lists enlarged versions of pages A-1 through A-39, respectively;
each user-interface screen from pages A-1 through A-39 are
vertically divided over two enlarged pages (e.g., page A-1 is
enlarged over two pages A-40A and A-40B).
Mathematical Equations Encoded in the Effect Diagram
[0045] As mentioned above, the Effect Diagram is a visual
representation of the model equations. This section describes how
the diagram encodes a set of ordinary differential equations. Note
that although the discussion below regarding state and function
nodes refers to biological variables for consistency, the
discussion also relates to variables of any appropriate type and
need not be limited to just biological variables.
State and Function Nodes
[0046] State and function nodes display the names of the biological
variables they represent and their location in the model. Their
arrows and modifiers indicate their relation to other nodes within
the model. State and function nodes also contain the parameters and
equations that are used to compute the values or their biological
variables in simulated experiments. In one embodiment of the
computer model, the state and function nodes are generated
according to the method described in U.S. Pat. No. 6,051,029 and
co-pending application Ser. No. 09/588,855, both of which are
entitled "Method of generating a display for a dynamic simulation
model utilizing node and link representations," and both of which
are incorporated herein by reference. Further examples of state and
function nodes are further discussed below.
##STR00001##
[0047] State nodes, the single-border ovals in the Effect Diagram,
represent biological variables in the system the values of which
are determined by the cumulative effects of its inputs over
time.
[0048] State node values are defined by differential equations. The
predefined parameters for a state node include its initial value
(S.sub.o) and its status. State nodes that have a half-life have
the additional parameter of a half-life (h) and are labeled with a
half-life symbol.
##STR00002##
[0049] Function nodes, the double-border ovals in the Effect
Diagram, represent biological variables in the system the values of
which, at any point in time, are determined by inputs at that same
point in time.
[0050] Function nodes are defined by algebraic functions of their
inputs. The predefined parameters for a function node include its
initial value (F.sub.o) and its status.
[0051] Setting the status of a node effects how the value of the
node is determined. The status of a state or function node can
be:
[0052] Computed--the value is calculated as a result of its
inputs
[0053] Specified-Locked--the value is held constant over time
[0054] Specified Data--the value varies with time according to
predefined data points.
[0055] State and function nodes can appear more than once in the
Effect Diagram as alias nodes. Alias nodes are indicated by one or
more dots, as in the state node illustration above. All nodes are
also defined by their position, with respect to arrows and other
nodes, as being either source nodes (S) or target nodes (T). Source
nodes are located at the tails of arrows, and target nodes are
located at the heads of arrows. Nodes can be active or inactive.
Active nodes are white. Inactive nodes match the background color
of the Effect Diagram.
State Node Equations
[0056] The computational status of a state node can be Computed,
Specified-Locked, or Specified Data.
State Node Computed ##EQU00001## S t = { sum of arrowterms when h =
0 ln 1 2 h S ( t ) + sum of arrowterms when h > 0
##EQU00001.2##
[0057] Where S is the node value, t is time, S(t) is the node value
at time, t, and h is the half-life. The three dots at the end of
the equation indicate there are additional terms in the equation
resulting from any effect arrows leading into it and by any
conversion arrows that lead out of it. If h is equal to 0, then the
half-life calculation is not performed and dS/dt is determined
solely by the arrows attached to the node.
State Node Specified--Locked S(t)=S.sub.0 for all t
[0058] State Node Specified Data S(t) is defined by specified data
entered for the state node.
[0059] State node values can be limited to a minimum value of zero
and a maximum value of one. If limited at zero, S can never be less
than zero and the value for S is reset to zero if it goes negative.
If limited at one, S cannot be greater than one and is reset to one
if it exceeds one.
Function Node Equations
[0060] Function node equations are computed by evaluating the
specified function of the values of the nodes with arrows pointing
into the function node (arguments), plus any object and Effect
Diagram parameters used in the function expression. To view the
specified function, click the Evaluation tab in the function node
Object window.
The Effect Diaram--Arrows
[0061] Arrows link source nodes to target nodes and represent the
mathematical relationship between the nodes. Arrows can be labeled
with circles that indicate the activity of the arrow. A key to the
annotations in the circles is located in the upper left corner of
each module in the Effect Diagram. If an arrowhead is solid, the
effect is positive. If the arrowhead is hollow, the effect is
negative.
[0062] Arrow Types
##STR00003##
[0063] Effect arrows, the thin arrows on the Effect Diagram, link
source state or function nodes to target state nodes. Effect arrows
cause changes to target nodes but have no effect on source nodes.
They are labeled with circles that indicate the activity of the
arrow.
##STR00004##
[0064] Conversion arrows, the thick arrows on the Effect Diagram,
represent the way the contents of state nodes are converted into
the contents of the attached state nodes. They are labeled with
circles that indicate the activity of the arrow. The activity may
effect the source node or the target node or both nodes. The
conversion can go either way.
[0065] Argument arrows specify which nodes are input arguments for
function nodes. They do not contain parameters or equations and are
not labeled with activity circles.
Arrow Characteristics
[0066] Effect or conversion arrows can be constant, proportional,
or interactive.
##STR00005##
[0067] Arrows that are constant have a break in the arrow shaft.
They are used when the rate of change of the target is independent
of the values of the source and target nodes.
##STR00006##
[0068] Arrows that are proportional have solid, unbroken shafts and
are used when the rate of change is dependent on, or is a function
of, the values of the source node.
##STR00007##
[0069] Arrows that are interactive have a loop from the activity
circle to the target node. They indicate that the rate of change of
the target is dependent on, or a function of, the value of both the
source node and the target node.
[0070] Arrow Properties can be displayed in an Object window (not
shown). The window may also include tabs for displaying Notes and
Arguments associated with the arrow. If Notes are available in the
Object window, the arrow is labeled with a red dot (.andgate.).
Arrow Equations Effect Arrows
[0071] Proportional Effect Arrow: The rate of change of target
tracks source node value.
T t = C S ( t ) a + ##EQU00002## [0072] Where T is the target node,
C is a coefficient, S is the source node, and a is an exponent.
[0073] Constant Effect Arrow: The rate of change of the target is
constant.
T t = K + ##EQU00003##
[0074] Where T is the target node and K is a constant.
[0075] Interaction Effect Arrow: The rate of change of the target
depends on both the source node and target node values.
T t = C ( S ( t ) .alpha. - T ( t ) .delta. ) + ##EQU00004## [0076]
Where T is the target node, S is the source node, and a and b are
exponents. This equation can vary depending on the operation
selected in the Object window. The operations available are S+T,
S-T, SET, T/S, and S/T.
Arrow Equations Conversion Arrows
[0077] Proportional Conversion Arrow: The rate of change of the
target tracks the value of source node.
T t = C R S ( t ) a + ##EQU00005## S t = - C S ( t ) a +
##EQU00005.2## [0078] Where T is the target node, S is the source
node, C is a coefficient, R is a conversion ratio, and a is an
exponent.
[0079] Constant Conversion Arrow: The rates of change of target and
source are constant such that an increase in target corresponds to
a decrease in source.
T t = K R + ##EQU00006## S t = - K + ##EQU00006.2## [0080] Where T
is the target node, S is the source node, K is a constant, and R is
a conversion ratio.
[0081] Interaction Conversion Arrow: The rates of change of the
target and source depend on both source and target node values such
that an increase in target corresponds to a decrease in source.
T t = R C ( S ( t ) .alpha. - T ( t ) .delta. ) + ##EQU00007## S t
= - C ( S ( t ) .alpha. - T ( t ) .delta. ) + ##EQU00007.2## [0082]
Where T is the target node, S is the source node, a and b are
exponents, and R is a conversion ratio. This equation can vary
depending on the operation selected in the Object window. The
operations available are S+T, S-T, S*T, T/S, and S/T.
Modifiers
[0083] Modifiers indicate the effects nodes have on the arrows to
which they are connected. The type of modification is qualitatively
indicated by a symbol in the box. For example, a node can allow ,
block regulate inhibit or stimulate an arrow rate.
[0084] A key to the modifier annotations is located in the upper
left corner of each module.
[0085] Modifier Properties can be displayed in the Object Window.
The window may also include tabs for displaying the notes,
arguments, and specified data associated with the modifier. If
notes are available in the Object window, the modifier is labeled
with a red dot (.andgate.).
[0086] Effect Arrow, Modifier Equation:
T t = M f ( u N ) arrowterm + ##EQU00008## [0087] Where T is the
target node, M is a multiplier constant, N is a normalization
constant, f( ) is a function (either linear or specified by a
transform curve), and arrowterm is an equation fragment from the
attached arrow.
Modifier Effect
[0088] By default, conversion arrow modifiers affect both the
source and target arrow terms. However, in some cases, a
unilateral, modifier is used. Such modifier will affect either a
source arrow term or on target arrow term; it does not affect both
arrow terms.
[0089] Conversion arrow, Source Only Modifier Equation:
S t = M f ( u N ) arrowterm + other attached arrowterms
##EQU00009##
[0090] Conversion arrow, Target Only Modifier Equation:
T t = M f ( u N ) arrowterm + other attached arrowterms
##EQU00010##
[0091] The equation for a source and target modifier uses both the
Source Only equation and the Target Only equation.
[0092] When multiplicative and additive modifers are combined,
effect is given precedence. For example, if the following modifiers
are on an arrow, [0093] a1,a2: Additive, Source and Target [0094]
m1,m2: Multiplicative, Source and Target [0095] A1,A2: Additive,
Target Only [0096] M1,M2: Multiplicative, Target Only then the
rates are modified by [0097] Target node:
(a1+a2+A1+A2)*(m1*m2)*(M1*M2) [0098] Source node:
(a1+a2)*(m1*m2)
EXAMPLE OF A MODEL COMPONENT
Skeletal Muscle Glucose Uptake
[0099] The following discussion provides an example of a process by
which the modules of the above-described computer model can be
developed. As discussed above, the various elements of the
physiologic system are represented by the components shown in the
Effect Diagram. These components are denoted by state and function
nodes, which represent mathematical relationships that define the
elements of the physiologic system. In general, these mathematical
relationships are developed with the aid of appropriate publicly
available information on the relevant physiological components. The
development of the mathematical relationships underlying the module
diagram for glucose uptake functions of the muscle will be
discussed here as an example.
[0100] FIG. 11 shows an example of a module diagram for the glucose
uptake functions of the muscle. Note that for illustration
purposes, this module diagram is a rearranged version of the module
diagram depicted on pages A9 and A48 in Appendix A. FIG. 11
illustrates the primary factors involved in the muscle glucose
uptake, whereas the module depicted on pages A9 and A48 in Appendix
A also includes the secondary effects of free fatty acids, activity
and exercise.
[0101] As FIG. 11 illustrates, the relevant physiological
components for the glucose uptake functions of the muscle include:
node 200, muscle glucose uptake rate (MGU); node 210, GLUT1
kinetics; node 220, GLUT4 kinetics; node 230, Vmax for GLUT1; node
240, Vmax for GLUT4; and node 250, insulin effect on GLUT4 Vmax.
The following discussion relates to deriving the underlying
mathematical relationships for these physiological components based
on the appropriate publicly available information. Although not
discussed herein, the remaining physiological components for the
glucose uptake functions can be similarly derived from publicly
available information.
[0102] Skeletal muscle glucose uptake is a facilitated diffusion
process mediated primarily by transmembrane GLUT1 and GLUT4
proteins. Both GLUT1 and GLUT4 obey Michaelis Menten kinetics and
the rate of glucose uptake is distributed through GLUT1 and GLUT4
according to their relative membrane content and their kinetic
parameters. Following meals, glucose levels in the circulation rise
causing increased pancreatic insulin secretion and concomitant
elevations in muscle interstitial insulin. Increased insulin leads
to a complex signaling cascade finally causing an increased number
of transmembrane GLUT4 thereby increasing glucose uptake. These
biological processes are well known and are reviewed in (P R
Shepherd et al. New Eng. J. Med. 341:248-57, 1999).
[0103] Since GLUT1 and GLUT4 obey Michaelis Menton kinetics, the
equation for muscle glucose uptake (MGU) has two terms:
bi-directional glucose mediated flux by GLUT1 and bi-directional
glucose meditated flux by GLUT4:
MGU = V max 1 K m 1 ( g e - g i ) ( K m 1 + g e ) ( K m 1 + g i ) +
V max 4 ( i ) K m 4 ( g e - g i ) ( K m 4 + g e ) ( K m 4 + g i )
##EQU00011##
where, g.sub.e is extracellular glucose concentration; g.sub.i is
intracellular glucose concentration; i is interstitial insulin
concentration; K.sub.m1 and K.sub.m4 are the Michaelis Menten
constants for GLUT1 and GLUT4, respectively; V.sub.max1 is the
maximal unidirectional flux for GLUT1 mediated transportation;
V.sub.max4(i) is the maximal unidirectional flux for GLUT4 mediated
transportation as a function of insulin.
[0104] Insulin's action on MGU is via an increase in effective
GLUT4 number. Consequently, interstitial insulin concentration only
enters the computation for MGU through V.sub.max4. Under basal
concentrations of glucose and insulin (.about.g.sub.e,
.about.g.sub.i,), the basal MGU, denoted by B, and the ratio of the
membrane GLUT4 and the GLUT1 denoted by r; the values for
V.sub.max1 and V.sub.max4 can be obtained from the following
equations
V max 1 = B g ~ e - g ~ i [ K m 1 ( K m 1 + g ~ e ) ( K m 1 + g ~ i
) + rK m 4 ( K m 4 + g ~ e ) ( K m 4 + g ~ i ) ] ##EQU00012## V max
4 ( i ) = rV max 1 f ( i ) ##EQU00012.2##
[0105] The function, f(i), represents the effect of insulin on
GLUT4 membrane content. The function f(i) is a sigmoidal function
having a value under basal concentrations of f( ) equal to 1. The
function f(i) is selected to match steady state MGU during
hyperinsulinemic clamps. Some studies, for example, use leg A-V
balance technique to measure leg glucose uptake. See, e.g., Dela,
F. et al., Am. J. Physiol. 263:E1134-43 (1992). Thus, for each
steady state, the MGU can be computed as the LGU divided by the leg
fraction of body muscle, f. The leg fraction of body muscle, f, is
for example, about 1/4 for normal people.
[0106] The values for the parameters within equations for
V.sub.max1 and V.sub.max4 can be obtained, for example, from
publicly available information. For example, the normal basal MGU,
B, can be assigned a value of 30 mg/min and the normal basal
extracellular concentration, .about.g.sub.e, can be assigned a
value of 90 mg/dl; see, e.g., Dela, F., et al., Am. J. Physiol.
263:E1134-43 (1992). The normal basal intracellular concentration,
.about.g.sub.i, can be assigned a value of 2 mg/dl; see, e.g.,
Cline, G. W., et al., NEJM 341:240-6 (1999). The normal basal
interstitial insulin concentration, can be assigned a value of 5
iU/ml; see, e.g., Sjostrand, M., et al., Am. J. Physiol. 276:E151-4
(1999). The normal basal ratio of membrane GLUT4 and GLUT 1, r, can
be assigned a value 4; see, e.g., Marette, A., et al., Am. J.
Physiol. 263:C443-52 (1992). The normal Michaelis constant for
GLUT1, K.sub.m1, can be assigned a value of 2 mM or 36 mg/dl; see,
e.g., Shepherd, P. R., et al., NEJM 341:248-57 (1999). The normal
Michaelis constant for GLUT4, K.sub.m4, can be assigned a value of
16 mM or 290 mg/dl; see, e.g., Ploug, T., et al., Am. J. Physiol.,
264:E270-8 (1993).
[0107] Returning to FIG. 11, the above-described equations can be
related to nodes 200 through 250 of FIG. 11. More specifically, the
mathematical relationships associated with node 200 corresponds to
the equation for MGU above, where nodes 210 and 220 correspond to
each of the respective GLUT1 and GLUT4 transport terms in the MGU
equation. The above-derived equations for V.sub.max1 and
V.sub.max4(i) are defined in nodes 230 and 240 respectively.
Similarly, the mathematical relationship associated with node 250
(for the insulin effect on GLUT4.sub.Vmax) corresponds to the
above-derived function f(i).
[0108] As this example of glucose uptake model component generally
illustrates, the components of the Effects Diagram, denoted by
state and function nodes, represent mathematical relationships that
define the elements of the physiologic system. These mathematical
relationships can be developed with the aid of appropriate publicly
available information on the relevant physiological components. In
other words, the Effect Diagrams indicate that type of mathematical
relationships that are modeled within a given model component. The
publicly available information can then put into a form that
matches the structure of the Effect Diagram. In this way, the
structure of the model and can be developed.
Simulation of Biological Attributes of Diabetes
[0109] Once a normal physiology has been defined, a user can then
select specific defects in the normal physiology by which the
physiology for diabetes (e.g., type 2 diabetes) can be modeled and
simulated. The term "defect" as used herein means an imperfection,
failure, or absence of a biological variable or a biological
process associated with a disease state. Diabetes, including type 2
diabetes, is a disease resulting from a heterogenous combination of
defects. The computer model can be designed so that a user can
simulate defects of varying severity, in isolation or combination,
in order to create various diabetic and prediabetic patient types.
The model thus can provide several simulated patient types of
varying degrees of diabetes.
[0110] For example, it is known that skeletal muscle glucose uptake
is defective in patients with type 2 diabetes. In spite of having
abnormally high basal glucose and insulin levels, people with type
2 diabetes generally have basal rates of MGU comparable to that of
normal people without type 2 diabetes. Consequently, type 2
diabetic skeletal muscle is likely insulin resistant. Such a defect
can be introduced within the computer model by altering the shape
of the function f(i) (representing the effect of insulin on GLUT4
membrane content), as shown in FIG. 12.
[0111] FIG. 12 shows a graph of the function f(i) (representing the
effect of insulin on GLUT4 membrane content) versus the
interstitial insulin concentration, i. FIG. 12 shows curve 300 for
a normal person and curve 310 for a person with type 2 diabetes.
The curves differ in that insulin has less effect in the case of
curve 310 compared to curve 300 thereby representing insulin
resistance known to occur in the type 2 diabetic skeletal muscle.
Mathematically, the curves 300 and 310 differ by parameter values
that define the shape of the curve.
[0112] In one embodiment, a user can select the specific defects
(relevant for diabetes) from a browser screen. FIG. 5 illustrates
an example of a browser screen that lists, by biological areas,
defect indicators associated with defects for diabetes that can be
modeled. The term "defect indicators" relates to the display, for
example, via the browser screen of defects relevant for diabetes.
The user can select a particular defect indicator, for example, by
a mouse click or keyboard selection.
[0113] For example, FIG. 5 illustrates various biologic areas such
as adipose issue and lipid metabolism, other tissues, pancreas,
muscle and liver. For each of the biologic areas, the browser
illustrated in FIG. 5 lists various defect indicators associated
with defects that can be specified for that biologic area. To
define a specific diabetes physiology, a user can select specific
defect indicators to indicate defects for modeling and then can
customize the parameters for that defect.
[0114] For each selected defect, the user can then specify the
values for parameters associated with physiology of the various
elements of the physiology system. FIG. 6 illustrates an example of
a user-interface screen for the parameter set of a type 2 diabetes
defect. More specifically, FIG. 6 illustrates the user-interface
screen for the parameter set to modify the physiology of muscle
glucose uptake and phosphorylation. In one embodiment of the
computer model, a parameter set is based on the method described in
U.S. Pat. No. 6,069,629, entitled "Method of providing access to
object parameters within a simulation model," the disclosure of
which is incorporated herein by reference.
[0115] As FIG. 6 illustrates, the user-interface screen allows a
user to specify alternative value sets to the baseline value sets
associated with a normal physiology. The baseline value sets and
the alternative value sets associated with the various type 2
diabetes defects can be based on, for example, real physiological
values relied upon from the related literature. In one embodiment
of the computer model, the user can specify alternative value sets
according to the method described in U.S. Pat. No. 6,078,739,
entitled "Managing objects and parameter values associated with the
objects within a simulation model," the disclosure of which is
incorporated herein by reference. Although FIG. 6 only shows a
single example of a user-interface screen for a parameter set of a
type 2 diabetes defect, many other parameters sets are possible
relating to other various physiological elements.
[0116] Thus, a user can select the defect relating to insulin
resistance of the type 2 diabetic skeletal muscle through a browser
screen described above in reference to FIG. 5. In other words, the
browser screen that lists defects for diabetes can include an entry
for insulin resistance of the type 2 diabetic skeletal muscle. When
a user selects such an entry, curve 300 (for a normal person
without type 2 diabetes) is substituted within the computer model
with curve 310 (for a person with type 2 diabetes). Of course, when
a user deselects such an entry curve 310 is substituted with curve
300.
[0117] In addition to the defects listed above, parameter sets and
value sets can be created for processes not listed above. Many
systems not involved in creating the pathophysiology of diabetes
are nevertheless affected by those changes (e.g. gastric emptying).
Some of these systems can use alternate parameterization to that
representing a normal individual.
[0118] As described above, simulation of the biological attributes
of diabetes is done in a cross-sectional manner, where defects are
introduced statically via parameter changes. Alternatively, the
computer model can represent the progression of diabetes. For
example, one means of including diabetes progression in the
computer model can involve replacing defect parameters, formerly
fixed at a particular value, with biological variables (defect
variables) that evolve over time. The time-evolution of the new
defect variables can be specified either as a direct function of
time, an algebraic function of other biological or defect
variables, or via a dynamical systems equation such as an ordinary
differential equation. As the defect variables change over time,
the progression of the disease can be modeled. For example, the
parameters that specify the insulin sensitivity of skeletal muscle
GLUT4 translocation to can be made to decrease over time. The
depiction of progression of diabetes in the computer model can be
used to study, for example, the progress of a normal human to an
obese patient to an obese-insulin-resistant patient to ultimately a
diabetic patient. Also, pharmaceutical treatments can be explored
to prevent or reverse the progression of diabetes.
Numerical solution of the Mathematical Equations and Outputs of the
Computer Model
[0119] Since the Effect Diagram defines a set of ordinary
differential equations as described above, once the initial values
of the biological variables are specified, along with the values
for the model parameters, the equations can be solved numerically
by a computer using standard algorithms. See, for example, William
H. Press et al. Numerical Recipes in C: The Art of Scientific
Computing, 2nd edition (January 1993) Cambridge Univ. Press. As
illustrated above in the muscle glucose uptake example, one can
derive equations, obtain initial conditions, and estimate parameter
values from the public literature. Likewise, other initial
conditions and parameter values can be estimated under different
conditions and can be used to simulate the time evolution of the
biological state.
[0120] Note that parameters can also be used to specify stimuli and
environmental factors as well as intrinsic biological properties.
For example, model parameters can be chosen to simulate in vivo
experimental protocols including: pancreatic clamps; infusions of
glucose, insulin, glucagon, somatostatin, and FFA; intravenous
glucose tolerance test (IVGTT); oral glucose tolerance test (OGTT);
and insulin secretion experiments demonstrating acute and steady
state insulin response to plasma glucose steps. Furthermore, model
parameters can be chosen to represent various environmental changes
such as diets with different nutrient compositions, as well as
various levels of physical activity and exercise.
[0121] The time evolution of all biological variables in the model
can be obtained, for example, as a result of the numerical
simulation. Thus, the computer model can provide, for example,
outputs including any biological variable or function of one or
more biological variables. The outputs are useful for interpreting
the results of simulations performed using the computer model.
Since the computer model can be used to simulate various
experimental tests (e.g. glucose-insulin clamps, glucose tolerance
tests, etc.), and clinical measurements (e.g. % HbA1c,
fructosamine), the model outputs can be compared directly with the
results of such experimental and clinical tests.
[0122] The model can be configured so as to compute many outputs
including: biological variables like plasma glucose, insulin,
C-peptide, FFA, triglycerides, lactate, glycerol, amino acids,
glucagon, epinephrine, muscle glycogen, liver glycogen; body weight
and body mass index; respiratory quotient and other measures of
substrate utilization; clinical indices of long-term hyperglycemia
including glycosylated hemoglobin (% HbA1c) and fructosamine;
substrate and energy balances; as well as metabolic fluxes
including muscle glucose uptake, hepatic glucose output, glucose
disposal rate, lipolysis rate, glycogen synthesis, and
glycogenolysis rates. The outputs can also be presented in several
commonly used units.
[0123] FIGS. 7 through 9 provide examples of outputs of the
computer model under various conditions. FIG. 7 illustrates a graph
comparing the model results against measured data for an oral
glucose tolerance test. An oral glucose tolerance test was
simulated based on the metabolic characteristics of a simulated
lean control, simulated lean type 2 diabetic and a simulated obese
type 2 diabetic. The simulation time for the patients considered
was two years. The measurements were made at a time that
corresponds to an overnight-fasted individual shortly after waking.
The model results were compared to measured data from Group et al.,
J. Clin. Endocrin. Metab., 72:96-107 (1991). The results shown in
FIG. 7 demonstrate the ability of the model to simulate accurately
oral glucose tolerance tests in lean and obese type 2 diabetic
patients as well as controls.
[0124] FIGS. 8A-H illustrate an example of model outputs for a
24-hour simulation of an obese diabetic patient consuming three
meals (55% carbohydrates, 30% fat, 15% protein). While all model
biological variables are simulated, the results are shown for
circulating levels of glucose (FIG. 8A), insulin (FIG. 8B), free
fatty acids (FFA) (FIG. 8G), gluconeogenic precursors (FIG. 8E):
lactate, amino acids, and glycerol, as well as the dynamics of
processes like hepatic glucose output (FIG. 8C), muscle glucose
uptake (FIG. 8D), relative contributions of whole-body
carbohydrate, fat and amino acid oxidation (FIG. 8H). The expansion
and depletion of the muscle and liver glycogen storage pools are
also shown (FIG. 8F). The simulated responses of these and other
biological variables are in agreement with data measured in obese
type 2 diabetic patients. For example, the glucose and insulin
results can be compared with data presented in Palonsky et al., N.
Engl. J. Med., 318(19): 1231-1239 (1988).
[0125] Note that the computer model can simulate therapeutic
treatments. For example, a therapy can be modeled in a static
manner by modifying the parameter set of the appropriate tissue(s)
to represent the affect of the treatment on that tissue(s).
Alternatively, therapeutic treatments can be modeled in a dynamic
manner by allowing the user to specify the delivery of a
treatment(s), for example, in a time-varying (and/or periodic)
manner. To do this, the computer model includes pharmacokinetic
representations of various therapeutic classes (e.g., injectable
insulins, insulin secretion enhancers, and/or insulin sensitizers)
and how these therapeutic treatments can interact with the various
tissues in a dynamic manner.
[0126] FIG. 9 illustrates a graph showing an example of the model
results for an oral glucose tolerance test. The graph shown in FIG.
9 is based on a simulated obese type 2 diabetic patient following
treatment with muscle insulin sensitizer or pancreatic
glucose-induced insulin secretion enhancer. An oral glucose
tolerance test was simulated in obese diabetic patients with or
without two theoretical interventions. One simulated patient
received a muscle insulin sensitizer, while the other received a
pancreatic glucose-induced insulin secretion enhancer. Note that
the simulated post-prandial glucose excursions were considerably
lower in treated patients as compare to simulated diabetic
controls, indicating the potential effectiveness of these
theoretical agents.
[0127] The computer model allows a user to simulate a variety of
diabetic and pre-diabetic patients by combining defects in various
combinations where those defects have various degrees of severity.
This can allow a more effective modeling of the type 2 diabetes
population, which is heterogeneous. In other words, diabetes can
have a wide range of impairment, some of which can be distinguished
clinically. Furthermore, clinically similar diabetics can have
differences in their physiology that can be modeled by using
different defect combinations. Consequently, the computer model can
be used to better understand and classify the real patient
population for type 2 diabetes and to anticipate what drug target
may work best on certain classes of patients, thereby improving the
design of clinical trials and target prioritization.
[0128] In sum, the computer model can enable a researcher, for
example, to: (1) simulate the dynamics of hyperglycemia in type 2
diabetes, (2) visualize key metabolic pathways and the feedback
within and between these pathways, (3) gain a better understanding
of the metabolism and physiology of type 2 diabetes, (4) explore
and test hypotheses about type 2 diabetes and normal metabolisms,
(5) identify and prioritize potential therapeutic targets, (6)
identify patient types and their responses to various
interventions, and (7) organize knowledge and data that relate to
type 2 diabetes.
Validation of the Computer Model
[0129] Typically, the computer model should behave similar to the
biological state they represent as closely as possible. Thus, the
responses of the computer model can be validated against biological
responses. The computer model can be validated, for example, with
in vitro and in vivo data obtained using reference patterns of the
biological state being modeled. Methods for validation of computer
models are described in co-pending application entitled
"Developing, analyzing and validating a computer-based model,"
filed on May 17, 2001, Application No. 60/292,175.
[0130] The diabetic patients produced with the diabetes computer
model can be validated by running the following tests on the
computer model: overnight-fasted concentrations of glucose,
post-prandial concentrations of glucose, metabolic response to 24
hour fast, oral glucose tolerance test (OGTT), intravenous glucose
tolerance test (IVGTT), euglycemic-hyperinsulinemic clamp,
hyperglycemic clamp, normal everyday behaviour. The computer model
of diabetes can be considered a valid model if the simulated
biological attribute obtained is substantially consistent with a
corresponding biological attribute obtained from a cellular or
whole animal model of diabetes or human diabetic patient. The term
"substantially consistent" as used herein does not mean that the
biological attributes have to be identical. The term "substantially
consistent" can be, for example, relative changes that are similar
but with different absolute values. FIG. 7 shows examples of model
simulation results that are "substantially consistent" with the
corresponding biological attributes obtained from glucose following
a glucose tolerance test. Table 1 lists the values for the
responses that can be evaluated in a non-diabetic and diabetic
following over night fasting. One means of validation of a diabetes
computer model would be to verify that the model produces results
substantially consistent with those present in Table 1 for a
non-diabetic and a diabetic. As the understanding of diabetes
evolves in the art, the responses against which the computer model
is validated can be modified.
TABLE-US-00001 TABLE 1 Response Value for [Overnight fasted]
Non-diabetic Value for Diabetic Plasma glucose 90 mg/dl 126-300
mg/dll Plasma insulin 10 .mu.U/ml 5-30 .mu.U/ml Plasma FFA 500
.mu.M 500-900 .mu.M Plasma lactate 8 mg/dl 8-10 mg/dl Plasma
glycerol 0.5 mg/dl 0.65 mg/dll Plasma amino acids 32 mg/dl 32 mg/dl
Plasma triglycerides 100 mg/dl 150-1000 mg/dl Plasma glucagon 75
mg/dl 80 mg/dl Muscle glycogen 400 g 200 g Liver glycogen 72 g 40 g
Muscle glucose uptake rate 28 mg/min 28-35 mg/min Hepatic glucose
output 140 mg/min 155-275 mg/min
[0131] Table 2 lists the values for post-prandial responses that
can be evaluated in a non-diabetic and a diabetic. Another means of
validation of a diabetes computer model would be to verify that the
model produces results substantially consistent with those present
in Table 2 for a non-diabetic and a diabetic. As the understanding
of diabetes evolves in the art, the responses against which the
computer model is validated can be modified.
TABLE-US-00002 TABLE 2 Response Value for [Post-prandial]
Non-diabetic Value for Diabetic Plasma glucose Increase 40%
Increase 50% Plasma insulin Increase 490% Increase 240% Plasma FFA
Decrease 38% Decrease 50% Plasma lactate Increase 10% Increase
20%
[0132] Table 3 lists other tests that can be used to obtain
responses in a non-diabetic and a diabetic. Yet another means of
validation of a diabetes computer model would be to verify that the
model produces results substantially consistent with those present
in Table 3 for a non-diabetic and a diabetic. As the understanding
of diabetes evolves in the art, the responses against which the
computer model is validated can be modified.
TABLE-US-00003 TABLE 3 Response Value for [Other tests]
Non-diabetic Value for Diabetic 2 hr OGTT glucose value 98-120
mg/dl 230-350 mg/dl Euglycemic, 7.2 mg/kg LBM/min 3.42 mg/kg
LBM/min hyperinsulinemic clamp glucose disposal rate Hyperglycemic
clamp 1.sup.st phase, 2.sup.nd phase only insulin response 2.sup.nd
phase
[0133] While various embodiments of the invention have been
described above, it should be understood that they have been
presented by way of example only, and not limitation. Thus, the
breadth and scope of the present invention should not be limited by
any of the above-described embodiments, but should be defined only
in accordance with the following claims and their equivalents.
[0134] The previous description of the embodiments is provided to
enable any person skilled in the art to make or use the invention.
While the invention has been particularly shown and described with
reference to embodiments thereof, it will be understood by those
skilled in the art that various changes in form and details may be
made therein without departing from the spirit and scope of the
invention.
[0135] For example, although a certain embodiment of a computer
system is described above, other embodiments are possible. Such
computer system embodiments can be, for example, a networked or
distributed computer system.
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