WO2008154515A1 - Procédé et appareil servant à modéliser une polyarthrite rhumatoïde - Google Patents

Procédé et appareil servant à modéliser une polyarthrite rhumatoïde Download PDF

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WO2008154515A1
WO2008154515A1 PCT/US2008/066364 US2008066364W WO2008154515A1 WO 2008154515 A1 WO2008154515 A1 WO 2008154515A1 US 2008066364 W US2008066364 W US 2008066364W WO 2008154515 A1 WO2008154515 A1 WO 2008154515A1
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biological
computer
cells
cell
mathematical
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PCT/US2008/066364
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English (en)
Inventor
Saroja Ramanujan
Daneil L. Young
Jason Chan
Nadine Defanoux
Todd Dubnikoff
Sirid Kellermann
Kosmas Kretsos
Rukmini Kumar
Peter C. Maisonpierre
Mike Mckay
Lisl K.M Shoda
Cynthia Stokes
Herbert K. Struemper
Chan Whiting
Yanan Zheng
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Entelos, Inc.
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Publication of WO2008154515A1 publication Critical patent/WO2008154515A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention relates generally to the field of simulating rheumatoid arthritis.
  • RA rheumatoid arthritis
  • One aspect of the invention provides computer models of rheumatoid arthritis comprising: a) a serum compartment comprising a mathematical representation of one or more mediators; b) a synovial compartment comprising i) a mathematical representation of one or more mediators; and ii) a mathematical representation of synovial tissue.
  • the computer model of the invention can further comprise a cartilage compartment comprising a mathematical representation of cartilage metabolism.
  • the computer model can further comprise a mathematical representation of bone erosion.
  • the mathematical representation of bone erosion comprises a representation of osteoclast life cycle and/or osteoblast life cycle.
  • Another aspect of the invention provides computer models of rheumatoid arthritis comprising a) one or more mathematical representation of a biological process associated with FLS; b) one or more mathematical representation of a biological process associated with T cells; c) one or more mathematical representation of a biological process associated with B cells; d) one or more mathematical representation of a biological process associated with endothelial cells; e) one or more mathematical representation of a biological process associated macrophages; and f) a set of mathematical relationships between the representations of biological processes to form the model.
  • Yet another aspect of the invention provides computer-readable media having computer-readable instructions stored thereon that, upon execution by a processor, cause the processor to simulate rheumatoid arthritis, wherein the instructions comprise: a) mathematically representing one or more biological processes associated with one or more mediator in serum; b) mathematically representing one or more biological processes associated with synovial tissue; c) defining a set of mathematical relationships between the representations of biological processes to form a model of rheumatoid arthritis.
  • Yet another aspect of the invention provides computer-readable media having computer-readable instructions stored thereon that, upon execution by a processor, cause the processor to simulate rheumatoid arthritis, wherein the instructions comprise: a) mathematically representing one or more biological process associated with FLS; b) mathematically representing one or more biological process associated with T cells; c) mathematically representing one or more biological process associated with B cells; d) mathematically representing one or more biological process associated with endothelial cells; e) mathematically representing one or more biological process associated macrophages; and f) defining a set of mathematical relationships between the representations of biological processes to form a model of rheumatoid arthritis.
  • the invention also provides methods of simulating rheumatoid arthritis, said method comprising executing a computer model of the invention.
  • the method further comprises applying a virtual protocol to the computer model to generate set of outputs representing a phenotype of the biological system.
  • the virtual protocol comprises a therapeutic regimen, a diagnostic procedure, passage of time, exposure to environmental toxins, or physical exercise.
  • the phenotype can represent a diseased state.
  • method can also further comprise accepting user input specifying one or more parameters or variable associated with one or more mathematical representations prior to executing the computer model.
  • the user input comprises a definition of a virtual patient.
  • Yet another aspect of the invention provides systems comprising: a) a computer- executable data editor, capable of accepting biological data relating to a subject; b) a computer executable integrator, capable of executing a computer model of rheumatoid arthritis with the biological data to generate at least one simulated biological attribute, wherein the computer model comprises: i) a serum compartment comprising a mathematical representation of at least one mediator in serum; ii) a synovial compartment comprising A) a mathematical representation of one or more mediators; and B) a mathematical representation of synovial tissue; c) a first user terminal, the first user terminal operable to receive a user input specifying one or more parameters associated with one or more mathematical representations defined by the computer readable instructions; and d) a second user terminal, the second user terminal operable to provide the simulated biological attribute to a second user.
  • a computer- executable data editor capable of accepting biological data relating to a subject
  • a computer executable integrator capable of executing a computer model
  • Another aspect of the invention provides system comprising: a) a computer- executable data editor, capable of accepting biological data relating to a subject; b) a computer executable integrator, capable of executing a computer model of rheumatoid arthritis with the biological data to generate at least one simulated biological attribute, wherein the computer model comprises: i) one or more mathematical representation of a biological process associated with FLS; ii) one or more mathematical representation of a biological process associated with T cells; iii) one or more mathematical representation of a biological process associated with B cells; iv) one or more mathematical representation of a biological process associated with endothelial cells; v) one or more mathematical representation of a biological process associated with macrophages; and vi) a set of mathematical relationships between the representations of biological processes to form the model; c) a first user terminal, the first user terminal operable to receive a user input specifying one or more parameters associated with one or more mathematical representations defined by the computer readable instructions; and d) a
  • Figure 3 illustrates an exemplary Summary Diagram that links modules for synovial tissue, cartilage metabolism and other related biological processes.
  • Figures 4-10 provide Effect Diagrams describing various biological processes related to synovial tissue composition.
  • Figures 11-14 illustrate various biological processes related to cell adhesion in synovial tissue.
  • Figure 15 provides an Effect Diagram illustrating leukocyte recruitment to synovial tissue.
  • Figures 16-19 describe various biological processes relating to synovial macrophages.
  • Figures 20-22 describe various biological processes relating to synovial fibroblasts and fibroblast-like synovial (FLS) cells.
  • Figures 23-28 describe various biological processes relating to synovial T cells.
  • Figures 29-33 describe various biological processes relating to synovial B cells and plasma cells.
  • Figures 34-38 describe various biological processes relating to synovial endothelium.
  • Figures 39 and 40 provide Effect Diagrams illustrating biological processes in the serum compartment.
  • Figures 41-47 describe various biological processes relating to cartilage metabolism.
  • Figures 48-52 describe various biological processes relating to osteoblasts, osteoclasts and bone erosion in rheumatoid arthritis.
  • Figures 53-57 illustrate various biological processes relating to therapies for rheumatoid arthritis.
  • Figures 58 and 59 illustrate certain calibration tools that may be used in conjunction with the computer models of the invention.
  • Figures 6OA and 6OB illustrate certain virtual patients that can be used in conjunction with the models of the invention to simulate development or progression of rheumatoid arthritis.
  • Figures 61A-61T describes a high-level sensitivity analysis tool that can be used in conjunction with the computer models of the invention to study progress, development and potential treatments for rheumatoid arthritis.
  • the invention encompasses novel methods for developing a computer model of rheumatoid arthritis.
  • the models include representations of biological processes associated with a serum compartment and a synovial compartment.
  • the model also may include representations of synovial cells, endothelial cells, T cells and B cells in a joint.
  • the invention also encompasses computer models of rheumatoid arthritis, methods of simulating rheumatoid arthritis and computer systems for simulating rheumatoid arthritis.
  • a “biological system” can include, for example, an individual cell, a collection of cells such as a cell culture, an organ, a tissue, a multi-cellular organism such as an individual human patient, a subset of cells of a multi-cellular organism, or a population of multi-cellular organisms such as a group of human patients or the general human population as a whole.
  • a biological system can also include, for example, a multi-tissue system such as the nervous system, immune system, or cardio-vascular system.
  • the term "biological component” refers to a portion of a biological system.
  • a biological component that is part of a biological system can include, for example, an extracellular constituent, a cellular constituent, an intra-cellular constituent, or a combination of them.
  • suitable biological components include, but are not limited to, metabolites, DNA, RNA, proteins, surface and intracellular receptors, enzymes, lipid molecules (Ae., free cholesterol, cholesterol ester, triglycerides, and phospholipid), hormones, cells, organs, tissues, portions of cells, tissues, or organs, subcellular organelles, chemically reactive molecules like H + , superoxides, ATP, as well as, combinations or aggregate representations of these types of biological variables.
  • biological components can include therapeutic agents such as non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, methotrexate, and the like.
  • NSAIDs non-steroidal anti-inflammatory drugs
  • glucocorticoids glucocorticoids
  • methotrexate and the like.
  • biological process is used herein to mean an interaction or series of interactions between biological components.
  • suitable biological processes include, but are not limited to, activation, apoptosis or recruitment of certain cells, such as macrophages, bone erosion, vascular permeability, mediator production, and the like.
  • biological process can also include a process comprising one or more therapeutic agents, for example the process of binding a therapeutic agent to a cellular mediator.
  • 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.
  • parameter is used herein to mean a value that characterizes the interaction between two or more biological components.
  • parameters include affinity constants, K m , K 0 , /c cat , half life, or net flux of cells, such macrophages, into particular tissues.
  • variable refers to a value that characterizes a biological component. Examples of variables include the total number of T cells, the number of active or inactive macrophages, and the concentration of a mediator, such as TGF- ⁇ , IL-6, or TNF- ⁇ .
  • phenotype 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 phenotype also undergoes changes.
  • One measurement of a phenotype is the level of activity of variables, parameters, and/or biological processes at a specified time and under specified experimental or environmental conditions.
  • a phenotype can include, for example, the state of an individual cell, an organ, a tissue, and/or a multi-cellular organism. Organisms useful in the methods and models disclosed herein include animals.
  • the term "animal” as used herein includes mammals, such as humans.
  • a phenotype can also include, but is not limited to, behavior of the system as a whole, as measured by bone erosion, cartilage effects, or ACR-N.
  • the conditions defined by a phenotype can be imposed experimentally, or can be conditions present in a patient type.
  • a phenotype of a non-rheumatic joint can include normal amount of cartilage and regulators of inflammation for a healthy subject.
  • the phenotype of rheumatoid arthritis can include increased amounts of inflammatory mediators and decreased cartilage for a rheumatic patient.
  • the phenotype can include the amounts of inflammatory mediators for a patient being treated with one or more of the therapeutic agents.
  • disease state is used herein to mean a phenotype where one or more biological processes are related to the cause or the clinical signs of the disease.
  • a disease state can be the state of a diseased cell, a diseased organ, a diseased tissue, or a diseased multi-cellular organism. Examples of diseases that can be modeled include a rheumatic joint.
  • a diseased multi-cellular organism can be, for example, an individual human patient, a group of human patients, or the human population as a whole.
  • a diseased state can also include, for example, a defective enzyme or the overproduction of an inflammatory mediator.
  • biological characteristic is used herein to refer to a trait, quality, or property of a particular phenotype of a biological system.
  • biological characteristics of a particular disease state include clinical signs and diagnostic criteria associated with the disease.
  • the biological characteristics of a biological system can be measurements of biological variables, parameters, and/or processes. Suitable examples of biological characteristics associated with a disease state of the rheumatoid arthritis include, but are not limited to, measurements of concentration of various mediators, bone erosion scores and cartilage effects.
  • 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.
  • a computer model can be designed to model one or more biological processes or functions.
  • the computer model can be built using a "top-down" approach that begins by defining a general set of behaviors indicative of a biological condition, e.g. a disease.
  • the behaviors are then used as constraints on the system and a set of nested subsystems are developed to define the next level of underlying detail. For example, given a behavior such as cartilage degradation in rheumatoid arthritis, the specific mechanisms inducing the behavior are each modeled in turn, yielding a set of subsystems, which can themselves be deconstructed and modeled in detail.
  • the control and context of these subsystems is, therefore, already defined by the behaviors that characterize the dynamics of the system as a whole.
  • the deconstruction process continues modeling more and more biology, from the top down, until there is enough detail to replicate a given biological behavior.
  • the model is capable of modeling biological processes that can be manipulated by a drug or other therapeutic agent.
  • FIGs. 1 and 2. An overview of the methods used to develop computer models of rheumatoid arthritis is illustrated in FIGs. 1 and 2.
  • the methods typically begin by identifying one or more biological processes associated with synovial tissue, one or more biological processes associated with endothelial cells, one or more biological processes associated with T cells and one or more biological processes associated with B cells.
  • the identification of biological process associated with synovial tissue, endothelial cells, T cells or B cells can be informed by data relating to the immune system, musculoskeletal system, circulatory system or any portion thereof.
  • the method can also comprise the step of identifying one or biological processes associated with chondrocytes, osteoblasts and/or osteoclasts.
  • the method next comprises the step of mathematically representing each identified biological process.
  • the representations of biological processes associated with synovial tissue, endothelial cells, T cells and B cells are combined, thus forming predictive models of rheumatoid arthritis
  • the methods may further include the steps of identifying and mathematically representing one or more biological processes associated with chondrocytes, osteoblasts and/or osteoclasts.
  • the methods can begin by identifying one or more biological processes associated with a serum compartment and one or more biological processes associated with a synovial compartment.
  • identifying a biological process associated with the synovial compartment comprises identifying a biological process related to macrophage, T cell or B cell recruitment and/or to fibroblast-like synovial (FLS) cell life cycle.
  • FLS fibroblast-like synovial
  • each biological process is mathematically represented.
  • the computer model can represent a first biological process using a first mathematical relation and a second biological process using a second mathematical relation.
  • a mathematical relation typically includes one or more variables, the behavior (e.g., time evolution) of which can be simulated by the computer model.
  • mathematical relations of the computer model can define interactions among variables describing levels or activities of various biological components of the biological system as well as levels or activities of combinations or aggregate representations of the various biological components.
  • variables can represent various stimuli that can be applied to the physiological system.
  • the mathematical model(s) of the computer-executable software code represents the dynamic biological processes related to joint inflammation or degradation.
  • 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.
  • the mathematical equations used in the model are ordinary differential equations of the form: where x is an N dimensional vector whose elements represent the biological variables of the system, t is time, dx/dt is the rate of change of x, p is an M dimensional set of system parameters, and f is a function that represents the complex interactions among biological variables.
  • the parameters are used to represent intrinsic characteristics (e.g., genetic factors) as well as external characteristics (e.g., environmental factors) for a biological system.
  • the phenotype can be mathematically defined by the values of x and p at a given time.
  • the methods can further comprise methods for validating the computer models described herein.
  • the methods can include generating a simulated biological characteristic associated with a joint of an animal, and comparing the simulated biological characteristic with a corresponding reference biological characteristic measured in a normal or diseased animal. The result of this comparison in combination with known dynamic constraints may confirm some part of the model, or may point the user to a change of a mathematical relationship within the model, which improves the overall fidelity of the model.
  • Methods for validating the various models described herein are taught in U.S. Patent Publication 2002-0193979, entitled “Apparatus And Method For Validating A Computer Model", and in U.S. Patent No. 6,862,561 , entitled “Method and Apparatus for Computer Modeling a Joint", the disclosures of which are incorporated herein by reference.
  • the invention provides methods of predicting a clinical outcome for a patient suffering or prone to rheumatoid arthritis comprising executing a computer model of rheumatoid arthritis, which model comprises a) a serum compartment comprising a mathematical representation of one or more mediators; b) a synovial compartment comprising i) a mathematical representation of one or more mediators; and ii) a mathematical representation of synovial tissue.
  • the computer model of the invention can further comprise a cartilage compartment comprising a mathematical representation of cartilage metabolism.
  • the computer model can further comprise a mathematical representation of bone erosion.
  • the mathematical representation of bone erosion comprises a representation of osteoclast life cycle and/or osteoblast life cycle.
  • the invention provides methods of predicting a clinical outcome for a patient comprising executing a computer model of rheumatoid arthritis, which model comprises a) one or more mathematical representation of a biological process associated with FLS; b) one or more mathematical representation of a biological process associated with T cells; c) one or more mathematical representation of a biological process associated with B cells; d) one or more mathematical representation of a biological process associated with endothelial cells; e) one or more mathematical representation of a biological process associated macrophages; and f) a set of mathematical relationships between the representations of biological processes to form the model.
  • a computer model of rheumatoid arthritis which model comprises a) one or more mathematical representation of a biological process associated with FLS; b) one or more mathematical representation of a biological process associated with T cells; c) one or more mathematical representation of a biological process associated with B cells; d) one or more mathematical representation of a biological process associated with endothelial cells; e) one or
  • the computer model of the present invention can be used to identify novel therapeutic targets, to prioritize existing therapeutic targets and to validate targets prior to investment or large amounts of capital and time in developing a therapy for rheumatoid arthritis. Methods of identifying therapeutic targets are described in detail in United States Patent Application Publication No. 2005-0130192, incorporated herein by reference.
  • a therapeutic target for use in treating rheumatoid arthritis can be identified by (a) executing a computer model of rheumatoid arthritis according to the present invention, (b) receiving user identification of a set of functions of a biological constituent of rheumatoid arthritis; (c) for each function of the set of functions: (i) receiving user input defining a modification of the function, the modification of the function corresponding to one of an inhibition of the function and a stimulation of the function, (ii) running the computer model based on the modification of the function to produce a comparison output associated with the function, and (iii) comparing the comparison output with the baseline output; and (d) identifying at least one function of the set of functions as a therapeutic target when a difference in its associated comparison output exists with respect to the baseline output.
  • Therapeutic targets for the treatment of rheumatoid arthritis can also be identified utilizing a product, stored on a computer-readable medium, comprising instructions operable to cause a programmable processor to: (a) execute a computer model of rheumatoid arthritis according to the present invention, (b) define a first virtual stimulus, the first virtual stimulus representing a modification of a first function of a biological constituent represented in the computer model of rheumatoid arthritis, (c) run the computer model based on the first virtual stimulus to produce a comparison output associated with the first function, and (d) identify the first function as a therapeutic target if a different exists in its associated comparison output with respect to the baseline output.
  • the product can further comprise instructions operable to cause a programmable processor to define a second virtual stimulus, the second virtual stimulus representing a modification of a second function of the biological constituent and run the computer model based on the second virtual stimulus to produce a comparison output associated with the second function.
  • the computer models of the present invention can also be used to identify biomarkers of therapies or of disease conditions or of patient sub-populations. Methods for identifying biomarkers utilizing computer models are described in detail in United States Patent Application Publication No. 2004-0115647, incorporated herein by reference.
  • methods of identifying a biomarker of a therapy for rheumatoid arthritis would comprise executing a computer model according to the present invention absent a virtual therapy to produce a result of a first virtual measurement for each configuration of a plurality of configurations associated with the computer model, the first virtual measurement being associated with a first measurement relevant to rheumatoid arthritis, each configuration of the plurality of configurations being associated with a different representation of the biological system; executing the computer model based on a virtual therapy to produce a result of a second virtual measurement for each configuration of the plurality of configurations, the virtual therapy being associated with the therapy, the second virtual measurement being associated with a second measurement relevant to rheumatoid arthritis, the second measurement being configured to evaluate an effect of the therapy; and comparing the results of the first virtual measurement for the plurality of configurations with the results of the second virtual measurement for the plurality of configurations to identify a biomarker of the therapy.
  • the first and second measurements correspond to a clinical outcome of rheumatoid arthritis.
  • one or more biological attributes will be identified as biomarker(s) of therapy based on a correlation coefficient associated with the results of the first and second virtual measurements.
  • Biomarkers can also be identified graphically by (a)executing a computer model of rheumatoid arthritis in the absence of a virtual stimulus to produce a first set of results; (b) applying a the virtual stimulus while executing the computer model of rheumatoid arthritis to produce a second set of results; (c) displaying one or both of the first and second sets of results; and (d) analyzing one or both of the first and second sets of results to identify one or more biomarkers.
  • the invention provides computer models of rheumatoid arthritis comprising one or more mathematical representations of a biological process associated with a serum compartment; one or more mathematical representations of a biological process associated with a synovial compartment; and a set of mathematical relationships between the representations of biological processes to form the model.
  • the computer model can also comprise one or mathematical representations of a biological process associated with cartilage metabolism and/or bone erosion.
  • the invention also provides computer models of rheumatoid arthritis comprising one or more mathematical representation of a biological process associated with FLS, one or more mathematical representation of a biological process associated with T cells, one or more mathematical representation of a biological process associated with B cells, one or more mathematical representation of a biological process associated with endothelial cells; one or more mathematical representation of a biological process associated with macrophages; and a set of mathematical relationships between the representations of biological processes to form the model.
  • the methods of developing models of biological systems described above may be used to generate a model for rheumatoid arthritis.
  • the simulation model may include hundreds or even thousands of objects, each of which may include a number of parameters.
  • it is useful to access and observe the input values of certain key parameters prior to performance of a simulation operation, and also possibly to observe output values for these key parameters at the conclusion of such an operation.
  • a modeler may also need to examine certain parameters at either end of such a relationship.
  • a modeler may wish to examine parameters that specify the effects a specific object has on a number of other objects, and also parameters that specify the effects of these other objects upon the specific object.
  • Complex models are also often broken down into a system of submodels, either using software features or merely by the modeler's convention. It is accordingly often useful for the modeler simultaneously to view selected parameters contained within a specific sub-model. The satisfaction of this need is complicated by the fact that the boundaries of a sub-model may not be mutually exclusive with respect to parameters, i.e., a single parameter may appear in many sub-models. Further, the boundaries of sub-models often change as the model evolves.
  • the created computer model represents biological processes at multiple levels and then evaluates the effect of the biological processes on biological processes across all levels.
  • the created computer model provides a multi-variable view of a biological system.
  • the created computer model also provides cross-disciplinary observations through synthesis of information from two or more disciplines into a single computer model or through linking two computer models that represent different disciplines.
  • An exemplary, computer model reflects a particular biological system, e.g., a joint or the immune or circulatory system, and anatomical factors relevant to issues to be explored by the computer model.
  • the level of detail incorporated into the model is often dictated by a particular intended use of the computer model.
  • biological components being evaluated often operate at a subcellular level; therefore, the subcellular level can occupy the lowest level of detail represented in the model.
  • the subcellular level includes, for example, biological components such as DNA, mRNA, proteins, chemically reactive molecules, and subcellular organelles.
  • the model can be evaluated at the multicellular level or even at the level of a whole organism. Because an individual biological system, i.e.
  • the individual biological system e.g., represented in the form of clinical outcomes
  • the individual biological system is the highest level represented in the system.
  • Disease processes and therapeutic interventions are introduced into the model through changes in parameters at lower levels, with clinical outcomes being changed as a result of those lower level changes, as opposed to representing disease effects by directly changing the clinical outcome variables.
  • the level of detail reported to a user can vary depending on the level of sophistication of the target user.
  • This higher level of abstraction can show, for example, major physiological subsystems and their interconnections, but need not report certain detailed elements of the computer model - at least not without the user explicitly deciding to view the detailed elements.
  • This higher level of abstraction can provide a description of the virtual patient's phenotype and underlying physiological characteristics, but need not include certain parametric settings used to create that virtual patient in the computer model.
  • this higher level of abstraction can describe what the therapy does but need not include certain parametric settings used to simulate that therapy in the computer model.
  • a subset of outputs of the computer model that is particularly relevant for subjects and doctors can be made readily accessible.
  • the computer model is configured to allow visual representation of mathematical relations as well as interrelationships between variables, parameters, and biological processes.
  • This visual representation includes multiple modules or functional areas that, when grouped together, represent a large complex model of a biological system.
  • simulation modeling software is used to provide a computer model, e.g., as described in U.S. Pat. No. 5,657,255, issued Aug. 12, 1997, titled “Hierarchical Biological Modeling System and Method”; U.S. Pat. No. 5,808,918, issued Sep. 15, 1998, titled “Hierarchical Biological Modeling System and Method”; U.S. Pat. No. 6,051 ,029, issued Apr. 18, 2000, titled “Method of Generating a Display for a Dynamic Simulation Model Utilizing Node and Link Representations”; U.S. Pat. No. 6,539,347, issued Mar.
  • a Summary Diagram can provide an overview of the various pathways modeled in the methods and models described herein.
  • the Summary Diagram illustrated in Figure 3, provides an overview of pathways that can affect the development or progression of arthritis, as measured by a bone erosion score or ACR-N.
  • the Summary Diagram can also provide links to individual modules of the model.
  • the modules model the relevant components of the phenotype through the use of "state” and "function” nodes whose relations are defined through the use of diagrammatic arrow symbols.
  • the complex and dynamic mathematical relationships for the various elements of the phenotype are easily represented in a user-friendly manner. In this manner, a normal phenotype can be represented.
  • An Effect Diagram can be a visual representation of the model equations and illustrate the dynamic relationships among the elements of the model.
  • Figure 4 illustrates an example of an Effect Diagram, in which synovial tissue volume and synovial tissue composition are described.
  • the Effect Diagram is organized into modules, or functional areas, which when grouped together represent the large complex physiology of the phenotype being modeled.
  • State and function nodes show the names of the variables they represent and their location in the model.
  • the arrows and modifiers show the relationship of the state and function nodes to other nodes within the model.
  • State and function nodes also contain the parameters and equations that are used to compute the values of the variables represented in simulated experiments.
  • the state and function nodes are represented according to the method described in U.S. Patent Number 6,051 ,029, entitled "Method of Generating a Display for a Dynamic Simulation Model Utilizing Node and Link Representations," incorporated herein by reference. Further examples of state and function nodes are further discussed below.
  • State nodes are represented by single-border ovals and represent variables in the system, the values of which are determined by the cumulative effects of inputs over time (see, e.g., Figure 10A).
  • "Input" refers to any parameter that can affect the variable being modeled by the state node.
  • input for a state node representing synovial IL-1 can be macrophage IL-1 production rate or B cell IL-1 production rate.
  • State node values are defined by differential equations.
  • the predefined parameters for a state node include its initial value (S 0 ) and its status.
  • state nodes can have a half-life. In these embodiments, a circle containing an "H" is attached to the node that has a half-life.
  • Function nodes are represented by double-border ovals and represent variables in the system, the values of which, at any point in time, are determined by inputs at the same point in time. Function nodes are defined by algebraic functions of their inputs.
  • the predefined parameters for a function node include its initial value (F 0 ) and its status. 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: 1 ) Computed, i.e., the value is calculated as a result of its inputs; 2) Specified-Locked, i.e., the value is held constant over time; or 3) Specified Data, i.e., the value varies with time according to predefined data points.
  • State and function nodes can appear more than once in the module diagram as alias nodes. Alias nodes are indicated by one or more dots (see, e.g., state node "synovial IL-1 " in Figure 10A). State and Function 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.
  • 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 Diagrams. If an arrowhead is solid, the effect is positive. If the arrowhead is hollow, the effect is negative.
  • arrow types, arrow characteristics, and arrow equations see, e.g., U.S. Patent No. 6,051 ,029, U.S. Patent No. 6,069,629, U.S. Patent No,. 6,078,739, and U.S. Patent No. 6,539,347.
  • Figures 4-10 provide Effect Diagrams describing various biological processes related to synovial tissue composition.
  • Figure 4 provides an Effect Diagram of the volume and composition of synovial tissue.
  • the number of synovial tissue (ST) cells is affected by the amount of macrophages, fibroblast-like synovial (FLS) cells, T cells, B cells, plasma cells, endothelial cells (EC), osteoclasts (OC) and other cells in a reference volume of synovium.
  • the composition of the synovial tissue is dictated by the relative amounts of these different cell types.
  • Figures 5A and 5B illustrate the calculation of cell densities and fractions of a variety of cell types in synovial tissue.
  • the computer models of the invention provide for explicit calculation of synovial cell density and fraction for macrophages, fibroblast-like synovial cells, osteoclasts, T cells, B cells and/or plasma cells.
  • Figure 6 provides an Effect Diagram illustrating endothelial cell and vasculature calculations as well as vascular supply to the synovial compartment. Vascular supply in the synovial compartment affects vascular sufficiency and synovial hypoxia.
  • Figure 7 provides an Effect Diagram illustrating calculations relating to soluble adhesion molecules in the synovial compartment.
  • the computer models of the invention can assess the effects of E-selectin, P-selectin, ICAM-1 and VCAM-1 , generated by endothelial cells and/or fibroblast- like synovial cells.
  • Figures 8A and 8B illustrate mediator binding and natural inhibitor interactions within the synovial compartment.
  • the computer models of the invention can account for the interactions of IL-1 with its receptor IL-1 Ra, TNF- ⁇ with its receptors TNF-RI and THF-RII, MMP-1 with TIMP-1 , MMP-2 with TIMP-1 , MMP-3 with TIMP-1 , MMP-9 with TIMP-1 and other MMPs with TIMP-1 , OPG with soluble RANKL, and/or IL-6 with its receptor IL-6-R and the resulting bipartite complex with soluble gp130 to form tripartite IL-6/IL6R/gp130 complexes.
  • Figure 9 illustrates the calculation of various clinical outputs relating to the synovial tissue compartment.
  • the computer model of the invention can calculate the American College of Rheumatology (ACR) clinical classification, joint space narrowing (JSN), bone erosion scores, central cartilage thickness, change in bone volume, bone loss rates and central cartilage degradation rates.
  • ACR American College of Rheumatology
  • JSN joint space narrowing
  • bone erosion scores
  • the computer model of the invention can also account for various cytokines and soluble factors, as illustrated in Figures 10A-10C.
  • the computer model can perform various calculations relating to IL-1 , IL-2, IL-4, IL-6, IL-8, IL-10, IL-17, IL-18, IL-23, IL-6 receptor, soluble gp130, TNF- ⁇ , TNF- ⁇ receptors (TNF-RI and TNF-RII), IL-1 receptor, interferon ⁇ (IFN-beta), prostaglandin E2 (PGE2), soluble RANKL, osteoprotegerin (OPG), FGF-2, GM-CSF, M-CSF, PDGF, IGF-1 , TGF- ⁇ , VEGF, angiopoietin-1 (Ang-1 ), angiopoietin-2 (Ang-2), endostatin, angiostatin, MCP-1 , MIP- 1 ⁇ , RANTES, IP-10
  • Figures 11-14 illustrate various biological processes related to cell adhesion in synovial tissue.
  • Figures 11A-1 1 C provide effect diagrams relating to a variety of cell-cell interactions, include macrophage-FLS, activated macrophage-FLS, T cell-macrophage, T cell-FLS, activated macrophage-macrophage, activated endothelial cell-T-cell, activated endothelial cell-macrophage, T cell-T cell, T cell-B cell, B cell-FLS, T cell-monocyte, T cell- plasma cell, FLS-plasma cell, activated endothelial cell-B cell, osteoblast-monocyte and osteoblast-osteoclast contact.
  • Figure 12 illustrates various calculations relating to adhesion and surface molecules, including T cell adhesion molecules (such as FAS, RANKL, and CD40L), macrophage adhesion molecules (such as FAS and CD40), FLS adhesion molecules.
  • Figure 12 also illustrates T cell surface stimulation by a variety of cytokines including IL-15, IL-2, PGE2, TGF- ⁇ and IL-10.
  • Figure 13 provides an Effect Diagram illustrating biological processes relating to cell co-localization.
  • Figure 14 illustrates biological processes relating to endothelial adhesion molecules, including regulation of production, normalized expression endothelial density and total endothelial expression of TGF- ⁇ , TNF- ⁇ , IL-1 , FGF-2, IFN- ⁇ and IL-4/13.
  • Figure 15 provides an Effect Diagram illustrating leukocyte recruitment to synovial tissue.
  • Figures 16-19 describe various biological processes relating to synovial macrophages.
  • Figure 16 provides an Effect Diagram illustrating the life cycle and phagocytic activity of synovial macrophages. Macrophage and monocyte activation is affected by TGF- ⁇ , IL-10, IL4/13, T cell ligation, IL-18, IL-17, IL-1 , TNF- ⁇ , GM-CSF, M-CSF, receptor-bound IL-6, IFN- ⁇ , collagen Il fragments and synovial immune complexes. Activation of these cells is also responsive to monocyte recruitment, monocyte influx rates, and monocyte and macrophage proliferation.
  • computer models according to the invention also account for monocyte and macrophage apoptosis, which can be responsive (positively or negatively) to IL-10, FAS, TNF- ⁇ , the fraction of highly activated macrophages, TGF- ⁇ , IL-1 , IL-17 and PGE2 synthesis by macrophages.
  • Figure 17 provides an Effect Diagram illustrating calculations relating to protein synthesis of a variety of cytokines and mediators utilized in the computer models according to the present invention.
  • Figures 18A- 18E illustrate the regulation of synthesis of these cytokines, proteases and mediators.
  • TNF- ⁇ synthesis by macrophages is responsive to IFN- ⁇ , PGE2, steroids, Cyclosporin A, TGF- ⁇ , IL-4/13, IL-10, IFN- ⁇ , synovial hypoxia, synovial immune complexes, RANTES, T cell ligation, IL-18, IL-17, TNF- ⁇ , receptor-bound IL-6, IL-2, and GM-CSF.
  • Macrophage IL-1 synthesis is responsive to IFN- ⁇ , steroids, Cyclosporin A, TGF- ⁇ , IL-4/13, IL-10, IFN- ⁇ , synovial hypoxia, MIP-1 ⁇ , T cell ligation, IL-17, IL-2, and TNF- ⁇ .
  • IL-6 synthesis by Macrophages is responsive to methotrexate, IL-1 , steroids, Cyclosporin, IL-4/13, IL-10, T cell ligation, PGE2, synovial immune complexes, MIP-1 ⁇ , IL-18 and IL-17.
  • Macrophage IL- 10 synthesis is responsive to steroids, TGF- ⁇ , IL-4.13, IFN- ⁇ , IFN- ⁇ , T cell ligation, synovial immune complexes, IL-17, TNF- ⁇ and PGE2.
  • Synthesis of IL-12 in macrophages is responsive to steroids, IL-10, TGF- ⁇ , MIP-1 ⁇ , MCP-1 , PGE2, T cell ligation, IL-17, TNF- ⁇ and IFN- ⁇ .
  • Synthesis of IL-15 is responsive to IFN- ⁇ and GM-CSF.
  • IL-18 synthesis is responsive to steroids, IL-1 and TNF- ⁇ .
  • Synthesis of IL-23 by macrophages is responsive to T cell ligation, TGF- ⁇ , steroids, IL-10, IL-17, GM-CSF, PGE2, IFN- ⁇ and IL-1.
  • Macrophage synthesis of soluble TNF-R1 is responsive to receptor-bound IL-6, steroids, and IFN- ⁇
  • macrophage synthesis of soluble TNF-RII is responsive to steroids, IL-4/13, IFN- ⁇ , IL-10 and receptor-bound IL-6.
  • Synthesis of IL-1 receptor (IL-I Ra) is responsive to synovial hypoxia, steroids, IFN- ⁇ , T cell ligation, IL-17, IL-10, receptor-bound IL-6 and IL-4/13.
  • GM- CSF production by macrophages is responsive to steroids, synovial IL-4/13, IL-10, TGF- ⁇ , IL-1 and TNF- ⁇ .
  • Macrophage synthesis of GM-CSF is responsive to steroids, IL-4/13, IL-10 TGF- ⁇ , IL1 and TNF- ⁇ .
  • Synthesis of M-CSF by macrophages is responsive to steroids, PGE2, IL-10, receptor-bound IL-6, IL-4/13, IL-1 , GM-CSF, and TNF- ⁇ .
  • Interferon beta synthesis is responsive to IL-10, IL-4/13, MMP-9, RANKL and IFN- ⁇ .
  • Macrophage synthesis of prostaglandin E2 is responsive to TNF- ⁇ , NSAIDs, steroids, IL-4/13, IL-10, phagocytosis of apoptotic cells, TGF- ⁇ , IL-17 and IL-1.
  • PDGF synthesis is responsive to steroids, IFN- ⁇ , synovial hypoxia, TGF- ⁇ and IL-2.
  • Macrophage synthesis of TGF- ⁇ is responsive to IFN- ⁇ , steroids, PDGF, MIP-1 ⁇ , phagocytosis of apoptotic cells, PGE2 and TGF- ⁇ .
  • Synthesis of VEGF by macrophages is responsive to synovial hypoxia, steroids, IL-10, IL-4/13, FLS ligation, T cell ligation, TGF- ⁇ and TNF- ⁇ .
  • MCP-1 synthesis is responsive to steroids, IL- 4/13, PGE2, immune complexes, TFN- ⁇ , IL-2/15, IL-10 and IL-1.
  • CXCL13 synthesis is responsive to IFN- ⁇ , IL-4 and IL-1.
  • Synthesis of MIP-1 ⁇ by macrophages is responsive to steroids, IFN- ⁇ , PGE2, IL-4/13, IL-10, TGF- ⁇ , TNF- ⁇ , IL-1 and immune complexes.
  • IL-8 synthesis is responsive to steroids, Cyclosporin A, IL-10, IL-4/13, TGF- ⁇ , IFN- ⁇ , T cell ligation, IL-1 , and IL-2/15.
  • RANTES synthesis by macrophages is responsive to steroids, TNF- ⁇ and synovial immune complexes.
  • IP-10 Interferon-inducible protein 10
  • GM-CSF GM-CSF
  • IL-4 IL-10
  • IL-2 IFN- ⁇
  • FLS ligation FLS ligation
  • endothelial cell ligation MMP-1 synthesis by macrophages is responsive to steroids, TGF- ⁇ , synovial immune complexes, and T cell ligation.
  • MMP-2 synthesis is responsive to IFN- ⁇ , steroids, endostatin, IFN- ⁇ and TGF- ⁇ .
  • Macrophage synthesis of TIMP- 1 is responsive to IL-4/13, PGE2, steroids, IL-8, methotrexate, IL-2/15, IL-17 and IL-10.
  • FIG. 19 provides an Effect Diagram illustrating biological processes relating to monocyte and macrophage recruitment.
  • the monocyte recruitment rate is related to monocyte tethering, monocyte-endothelial cell adhesion and monocyte extravasation.
  • Monocyte tethering is responsive to endothelial P-selectin and E-selectin density.
  • Monocyte-endothelial adhesion responds to adhesion molecule expression, VCAM-1 density, ICAM-1 density, MCP-1 , IL-8 and PGE2. Extravasation is responsive to MCP-1 , VEGF, MIP-1 ⁇ , MIP-3 ⁇ , fractalkine, IL-8, CXCL12, RANTES, TGF- ⁇ , soluble E-selectin, collagen Il fragments, and PGE2.
  • Figures 20-22 describe various biological processes relating to synovial fibroblasts and fibroblast-like synovial (FLS) cells. Synovial fibroblast life cycle is illustrated in the Effect Diagram of Figure 20.
  • FLS proliferation is responsive to OPG acting on RANKL, FGF-2, PDGF, methotrexate, PGE2, IL-13, IL-4, IFN- ⁇ , IFN- ⁇ , TNF- ⁇ , IL-1 and IL-15.
  • the transition of viable FLS cells to apoptotic FLS cells is affected by TNF- ⁇ , IL-1 , IL-4/13, IL-5 and Cyclosporin A.
  • Figure 21 illustrates FLS synthesis of a variety of proteins, including TNF- ⁇ , IL-1 , IL-6, IL-10, IL-15, IL-23, soluble TNF receptors, IL-1 receptor, CXCL12, fractalkine, RANTES, IP-10, MCP-1 , MIP-1 ⁇ , MIP-3 ⁇ , IL-8, TIMP-1 , MMP-3, MMP-1 , MMP-2, MMP-9, BAFF, COMP, VEGF, FGF-2, TGF- ⁇ , Ang-1 , OPG, IFN- ⁇ , M-CSF, GM-CSF, and PGE2.
  • Figures 22A-22E illustrate regulation of protein synthesis by synovial fibroblast and fibroblast-like synovial cells.
  • TNF- ⁇ is responsive to IL-10, TNF- ⁇ , cyclosporine, IFN- ⁇ and IL-1.
  • IL-1 synthesis by fibroblasts is responsive to T cell ligation and steroids.
  • IL-6 synthesis is responsive to steroids, methotrexate, IL-10, T cell ligation, CXCL12, MCP-1 , RANTERS, macrophage ligation to FLS, IL-4/13, PGE2, IL-17, TNF- ⁇ and IL-1.
  • IL-10 synthesis is responsive to PGE2.
  • IL-15 synthesis is responsive to IL-10, PGE2, IL-1 and IFN- ⁇ .
  • IL-23 synthesis is responsive to IL-17.
  • TNF receptor, sTNF-RI synthesis is responsive to PDGF, IL-1 IL-4/13, and TGF- ⁇ .
  • TNF receptor, sTNF-RII synthesis is responsive to TGF- ⁇ , TNF- ⁇ , IL-1 and IL-4/13.
  • IL-1 receptor synthesis is responsive to steroids, TGF- ⁇ , methotrexate, IL- 4/13, IFN- ⁇ , PDGF, TNF- ⁇ and IL-1.
  • M-CSF synthesis is responsive to PGE2, PDGF, TNF- ⁇ , IL-4, and IL-1.
  • GM-CSF synthesis by fibroblasts is responsive to IL-4/13, PGE2, IFN- ⁇ , IL- 17, FGF-2, IL-1 , TNF- ⁇ and steroids.
  • IFN- ⁇ synthesis is responsive to FGF-2, MMP-9, IFN- ⁇ , TNF- ⁇ , IL-1 and steroids.
  • PGE2 synthesis is responsive to IFN- ⁇ , IL-4/13, NSAIDs, methotrexate, IL-10, steroids, Cyclosporin A, FGF-2, TNF- ⁇ , IL-1 , PDGF, IL-17 and T cell ligation.
  • TGF- ⁇ synthesis is responsive to IFN- ⁇ and TGF- ⁇ .
  • VEGF synthesis is responsive to IFN- ⁇ , steroids, Cyclosporin A, receptor-bound IL-6, T cell ligation, macrophage ligation, PDGF, FGF-2, TGF- ⁇ , synovial hypoxia, PGE2, IL-17, IL-1 and TNF- ⁇ .
  • Angiopoietin-1 synthesis is regulated by PDGF, TGF- ⁇ and TNF- ⁇ .
  • IL-8 synthesis is responsive to TNF- ⁇ , steroids, IFN- ⁇ , IL-10, IL-4/13, T cell ligation, RANTES, MCP-1 , IL-17, PGE2, IL-18, CXCL12, and IL-1.
  • MCP-1 synthesis is responsive to IL-1 , steroids, cyclosporin A, receptor- bound IL-6, IL-2, IL-4, TNF- ⁇ and TFN- ⁇ .
  • CXCL12 synthesis by FLS cells is responsive to synovial hypoxia, T cell ligation and IL-17.
  • MIP-1 ⁇ synthesis is responsive to TNF- ⁇ , IL-1 , T cell ligation, and macrophage ligation.
  • MIP-3 ⁇ synthesis is responsive to IL-4/13, IL-1 , IL- 18, TNF- ⁇ and IL-17. Fractalkine synthesis is regulated by IL-1 and TNF- ⁇ .
  • FLS synthesis of RANTES is responsive to IL-17, steroids, IL-4, IFN- ⁇ , TNF- ⁇ , and IL-1.
  • IP-10 synthesis is responsive to TNF- ⁇ , IFN- ⁇ and IL-1.
  • MMP-1 synthesis is responsive to IFN- ⁇ , IL-4/13, PGE2, steroids, TNF- ⁇ , IL-1 , IL-17, and T cell ligation.
  • MMP-3 synthesis is responsive to PGE2, IL-4/13, TNF- ⁇ and IL-1.
  • TIMP- 1 synthesis is responsive to steroids, PGE2, IL-10, IL- 17, IL-4/13, FGF-2, IL-1 , TGF- ⁇ and receptor-bound IL-6.
  • MMP-2 synthesis is responsive to steroids, endostatin, fractalkine, and TGF- ⁇ .
  • MMP-9 synthesis is responsive to steroids, endostatin, IL-1 , and TNF- ⁇ .
  • COMP production is responsive to IL-1 , TNF- ⁇ and TGF- ⁇ .
  • Figures 23-28 describe various biological processes relating to synovial T cells.
  • Figure 23 provides an Effect Diagram of biological processes relating to the life cycle of synovial T cells, including T cell recruitment, proliferation and apoptosis.
  • Figure 24 illustrates protein synthesis by synovial T cells. The synthesis of specific proteins is responsive to the density of active Th17-like, Th1 -like, Th2-like and Treg-like cells as wells as the density of apoptotic T cells.
  • Figure 25 illustrates the regulation of protein synthesis by synovial T cells.
  • IL-2 synthesis is auto-regulated by IL-2 as well as by IL-10.
  • IL-4 synthesis responds to steroids.
  • IL-6 synthesis is affected by steroids.
  • IL-10 synthesis is responsive to steroids and I FN- ⁇ .
  • IL-13 production is affected by steroids and Cyclosporin A.
  • Synthesis of IL-17 A and F responds to IL-23.
  • IFN- ⁇ production is affected by steroids and Cyclosporin A.
  • T cell synthesis of TNF- ⁇ is affected by steroids and IL-15.
  • sTNF-RI synthesis is responsive to TNF- ⁇ .
  • sTNF-RII synthesis is responsive to IL-4 and TNF- ⁇ .
  • GM-CSF production is responsive to steroids.
  • RANKL synthesis by T cells is responsive to steroids, TGF- ⁇ , IL-17 and receptor-bound IL-6.
  • TGF- ⁇ synthesis is responsive to TGF- ⁇ , I FN- ⁇ and Cyclosporin A.
  • RANTES synthesis is inhibited by steroids.
  • TIMP-1 synthesis is responsive to steroids and TNF- ⁇ .
  • MMP-9 synthesis is responsive to IFN- ⁇ .
  • Figure 26 provides an Effect Diagram illustrating biological processes relating to T cell receptor stimulation, including antigen presentation and an effective antigen pool.
  • Figure 27A illustrates biological processes relating to the regulation of T cell activation by cytokines, such as TNF- ⁇ , IL-6, IL-18, IL-23, IL-2/15, PGE2, TGF- ⁇ and IL-10, as well as T cell receptor stimulation and effector costimulation.
  • Figure 27B illustrates regulated proliferation and apoptosis of synovial T cells.
  • Figure 28 provides an Effect Diagram illustrating the biological processes relating to T cell recruitment to the synovial compartment. T cell recruitment is related to T cell tethering, T cell-endothelial adhesion and T cell extravasation.
  • T cell extravasation is responsive to RANTES, expression of T cell adhesion molecules, MCP-1 , MIP-3 ⁇ , MIP-1 ⁇ , IP-10, fractalkine, IL-18, CXCL12, IL-4/13, IL- 10 and PGE2.
  • Figures 29-33 describe various biological processes relating to synovial B cells and plasma cells.
  • Figure 29 illustrates biological processes relating to B cell and plasma cell life cycle in the synovial compartment. The amount of viable synovial B cells or plasma cells is affected by recruitment of B cells and plasma cells as well as B cell proliferation. Both B cells and plasma cells become apoptotic at a designated rate.
  • Figure 30 illustrates protein synthesis by B cells and plasma cells. B cells can synthesize IL1 , IL-6, IL-10, MIP-1 ⁇ , TGF- ⁇ , and TNF- ⁇ . Production of auto-antibodies by plasma cells can be altered by methotrexate or Cyclosporin A. B cell synthesis of IL-6 is regulated by IL-10.
  • MIP-1 ⁇ production is responsive to T cell ligation and IL-4/13.
  • Figure 31 provides an Effect Diagram illustrating the regulation of the life cycle of B cells and plasma cells in the synovial compartment.
  • B cell proliferation is responsive to B cell activation, T cell ligation, IL-1 , IL-2, IL-4/13, receptor- bound IL-6, IL-10, GM-CSF, IFN- ⁇ , TNF- ⁇ , IFN- ⁇ , PGE2, TGF- ⁇ , methotrexate, steroids and Cyclosporin A.
  • B cell differentiation is responsive to IL-10, receptor-bound IL-6, IL-2, B cell activation and T cell ligation.
  • Apoptosis of plasma cells is responsive to FLS ligation, T cell ligation, IL-17 and BAFF.
  • B cell apoptosis is responsive to rituxan, IL-10, TGF- ⁇ , the fraction of the synovial compartment that are B cells, I L-2, T cell ligation, BAFF, I L-4/13 and B cell activation.
  • Figure 32 illustrates biological processes relating to bone marrow production of B cells.
  • Figure 33 illustrates recruitment of B cells to the synovial compartment. B cell recruitment is affected by B cell tethering, B cell-endothelial adhesion, and B cell extravasation.
  • B cell extravasation is responsive to B cell adhesion molecule expression, CXCL12, CXCL13, MIP-1 ⁇ , MCP-1 and MIP-3 ⁇ .
  • B cell-endothelial adhesion is affected by B cell adhesion molecule expression, VCAM-1 density, ICAM-1 density and CXCL13.
  • B cell tethering is responsive to endothelial VCAM-1 , P-selectin and E-selectin density as well as B cell adhesion molecule expression.
  • Figures 34-37 describe various biological processes relating to synovial endothelium.
  • Figure 34 provides an Effect Diagram illustrating the life cycle of synovial endothelium.
  • the figure illustrates the transition of endothelial cells between a quiescent, active organized, active disorganize, and apoptotic state.
  • Figure 35 illustrates a variety of proteins that can be synthesized by active endothelial cells, including IL-1 , IL-6, IL-15, IL-12, TNF- ⁇ , M-CSF, GM-CSF, PDGF, FGF-2, VEGF, TGF- ⁇ , MMP-9, MMP-2, angiopoietin-2, OPG, IFN- ⁇ , PGE2, MCP-1 , fractalkine, RANTES, IP-10 and IL-8.
  • Figure 36 illustrates the regulation of the life cycle of endothelial cells, including activation, proliferation and migration of endothelial cells in the synovium. More particularly, IL-1 synthesis is responsive to T cell ligation, IL-6 synthesis is responsive to steroids, Cyclosporin A, TGF- ⁇ , T cell ligation, IL-4, IL-13, TNF- ⁇ , IFN- ⁇ , IL-1 and IL-17. IL-12 synthesis is responsive to steroids, IL-10, IFN- ⁇ and T cell ligation. TNF- ⁇ synthesis by endothelial cells is responsive to steroids and IL-1. PGE2 synthesis is responsive to steroids, NSAIDs, IL-1 , TGF- ⁇ , TNF- ⁇ and VEGF.
  • IFN- ⁇ synthesis is responsive to MMP-9 and TNF- ⁇ .
  • Osteoprotegerin (OPG) synthesis is responsive to TGF- ⁇ , TNF- ⁇ and IL-1.
  • FGF-2 synthesis by endothelial cells is responsive to IFN- ⁇ , VEGF, IL-2, TNF- ⁇ and IL-1.
  • PDGF synthesis is responsive to FGF-2, IFN- ⁇ , TNF- ⁇ , T cell ligation, synovial hypoxia, TGF- ⁇ , VEGF, and IL-1.
  • TGF- ⁇ synthesis is responsive to VEGF and FGF-2.
  • VEGF synthesis is regulated by CXCL12, T cell ligation, TGF- ⁇ and synovial hypoxia.
  • M-CSF synthesis is responsive to TNF- ⁇ , IL-1 and TGF- ⁇ .
  • GM-CSF synthesis is responsive to steroids, Cyclosporin A, IFN- ⁇ , TNF- ⁇ and IL-1.
  • angiopoietin-2 (Ang-2) synthesis is responsive to TGF- ⁇ , Ang-1 , Ang-2, FGF-2, VEGF, and synovial hypoxia.
  • MCP-1 synthesis is responsive to IL-2, IFN- ⁇ , VEGF, T cell ligation, TNF- ⁇ , receptor-bound IL-6, IL-4 and IL-1.
  • Fractalkine synthesis is responsive to IL-4/13, IFN- ⁇ , TNF- ⁇ and IL-1.
  • RANTES production by endothelial cells is responsive to IL-4/13, IFN- ⁇ , TNF- ⁇ and IL-1.
  • IL-8 synthesis is responsive to steroids, IL-10, TGF- ⁇ , B cell ligation, synovial hypoxia, T cell ligation, VEGF, TNF- ⁇ , IL-1 , receptor-bound IL-6, and Cyclosporin A.
  • IP-10 synthesis is responsive to IL-10, macrophage ligation, T cell ligation, TNF- ⁇ , IL-1 and IFN- ⁇ .
  • MMP-2 synthesis is responsive to steroids, endostatin, FGF-2, VEGF, and IL-8.
  • MMP-9 synthesis is responsive to steroids, endostatin, T cell ligation, FGF-2 and VEGF.
  • Figure 38 provides and Effect Diagram illustrating biological processes relating to the regulation and stabilization of the synovial endothelium.
  • Figures 39 and 40 illustrate biological processes relating the serum compartment.
  • Figure 39 provides an Effect Diagram illustrating biological processes in the serum compartment.
  • a joint involvement factor describes the transfer of various factors, such as TNF- ⁇ , IL-1 , IL-6, IL-I Ra, COMP, VEGF and NTX, from the synovial compartment to a serum compartment.
  • the computer models of the invention preferably, can also account for transfer of MMP-3 from cartilage to the serum.
  • the computer model can represent the transfer of synovial MMP-3 and MMP-3 in the synovial lining to a serum compartment.
  • the computer model can also provide a representation of the transfer of synovial IL-6 receptor and gp130 to the serum, the amount of serum soluble IL-6 receptor or gp130 can be augmented by IL-6 receptor or gp130 produced in the serum.
  • Figure 40 illustrates mediator binding and natural inhibitor interactions in the serum compartment.
  • Figures 41-47 describe various biological processes relating to cartilage metabolism.
  • Figure 41 provides an Effect Diagram of biological processes relating to the life cycle of chondrocytes.
  • Figure 42 illustrates the variety of proteins that can be synthesized by chondrocytes. These proteins include IL-1 , IL-6, GM-CSF, M-CSF, IL- 1 receptor, PGE2, IL- 8, MCP-1 , RANTES, COMP, TIMP-1 , MMP-13, MMP-3, MMP-1 , VEGF, FGF-2, IGF-1 , TGF- ⁇ and OPG.
  • Figures 43A and 43B illustrate the regulation of protein synthesis by chondrocytes. IL-1 production by chondrocytes is regulated by TNF- ⁇ , IFN- ⁇ and IL-17.
  • IL-6 synthesis is responsive to steroids, IL-1 , TNF- ⁇ , IFN- ⁇ , TGF- ⁇ , and IL-17.
  • IL-1 receptor, IL- 1 Ra, synthesis is responsive to receptor-bound IL-6, IL-1 , TNF- ⁇ and IFN- ⁇ .
  • GM-CSF synthesis is responsive to steroids, TGF- ⁇ , FGF-2, TNF- ⁇ and IL-1.
  • M-CSF synthesis is responsive to steroids, receptor-bound IL-6, FGF-2, TNF- ⁇ and IL-1.
  • PGE2 synthesis is responsive to steroids, NSAIDs, IL-17, IFN- ⁇ , TNF- ⁇ , and IL-1.
  • OPG and FGF-2 synthesis is regulated by IL-1.
  • VEGF synthesis is responsive to steroids, IL-17, TNF- ⁇ , IGF-1 , TGF- ⁇ , FGF-2, and IL-1.
  • TGF- ⁇ synthesis is responsive to receptor-bound IL-6 and TGF- ⁇ .
  • IGF-1 synthesis is regulated by TGF- ⁇ and FGF-2.
  • RANTES synthesis is responsive to TNF- ⁇ and IL-1.
  • IL-8 synthesis is responsive to IFN- ⁇ , TNF- ⁇ and IL-1.
  • MCP-1 synthesis is responsive to steroids, IL-1 and TNF- ⁇ .
  • MMP-3 synthesis is responsive to steroids, IL-4/13, IFN- ⁇ , VEGF, IL-17, TNF- ⁇ , CXCL12, receptor-bound IL-6, IL-1 and CXCL13.
  • TIMP-1 synthesis is responsive to steroids, TNF- ⁇ , IL-1 , receptor-bound IL-6, and TGF- ⁇ .
  • MMP-13 synthesis is responsive to steroids, IL-10, IL-4/13, TGF- ⁇ , IL-17, TNF- ⁇ , FGF-2, PDGF, CXCL12, CXCL13, receptor-bound IL-6 and IL-1.
  • MMP-1 synthesis is responsive to steroids, methotrexate, IL-10, TGF- ⁇ , PDGF, receptor-bound IL-6, FGF-2, VEGF, TNF- ⁇ , and IL-1.
  • Collagen Il synthesis is responsive to IL-1 , TNF- ⁇ , IFN- ⁇ , TGF- ⁇ and IGF-1.
  • Aggrecan synthesis is responsive to TNF- ⁇ , IL-1 , IL-17, receptor-bound IL-6, TGF- ⁇ and IGF-1.
  • COMP synthesis is responsive to TNF- ⁇ , IL-1 and TGF- ⁇ .
  • Figures 44A and 44B illustrate mediator binding and natural inhibitor interactions in the cartilage compartment
  • Figure 45 provides an Effect Diagram of the biological processes relating to matrix metabolism in cartilage, including collagen production, incorporation and degradation.
  • the computer model of rheumatoid arthritis can also account for aggrecan production and incorporation into cartilage, as wells as the effect of aggrecan depletion in lysis of collagen Il fibrils and the resulting cartilage degradation.
  • Figure 46 illustrates various cytokines and soluble factors that can be found in the cartilage compartment, including IL-1 , IL-2, IL-15, IL-4, IL-13, IL-6, IL-10, IL-6 receptor, soluble gp130, IL-17, IL-18, IL-23, TNF- ⁇ , IFN- ⁇ , GM-CSF, IFN- ⁇ , TGF- ⁇ , FGF-2, PDGF, PGE2, IGF-1 , MCP-1 , MIP-1 ⁇ , IL-8, RANTES, IP-10, VEGF, endostatin, angiostatin, MMP-1 , MMP-3, MMP-13, TIMP-1 , TNF-RI, TNF-RII, IL-I Ra, M-CSF, osteoprotegerin (OPG), soluble RANKL, CXCL13 and CXCL12.
  • Figures 47A and 47B illustrate infiltration factors from synovial tissue into cartilage.
  • Figures 48-52 describe various biological processes relating to osteoblasts, osteoclasts and bone erosion in rheumatoid arthritis.
  • Figure 48 provides Effect Diagrams illustrating osteoblasts life cycle and osteoclasts life cycle.
  • Figure 49 illustrates biological processes relating to protein synthesis by osteoblasts (OB) and osteoclasts (OC).
  • OB osteoblasts
  • OC osteoclasts
  • osteoblasts produce OPG responsive, in part, to TNF- ⁇ .
  • Osteoblasts are also capable of producing sRANKL and osteocalcin.
  • Osteoclasts produce TRAP 5b, responsive, in part, to contact with bone surfaces.
  • Figure 50 illustrates Osteoclast differentiation, osteoclast apoptosis and osteoclast activation.
  • Figure 51 illustrates biological processes relating to bone matrix metabolism.
  • Figure 52 illustrates various bone biochemical markers. In particular, the figure illustrates biological processes relating to bone synthesis and bone resorption.
  • Figures 53-57 illustrate various biological processes relating to therapies for rheumatoid arthritis.
  • Figure 53 illustrates biological processes relating to methotrexate and NSAID therapies.
  • Figures 54A and 54B illustrate biological processes relating to therapy with corticosteroids.
  • Figure 55 illustrates biological processes relating to cyclosporine A therapy.
  • Figures 55 and 56 illustrate certain calibration tools that may be used in conjunction with the computer models of the invention.
  • Figures 56A and 56B illustrate biological processes relating to therapy with soluble TNF-RII (Etanercept), anti-TNF- ⁇ antibody (infliximab). IL-1 receptor (anakinra), and rituximab.
  • Figure 57 illustrates biological processes relating to therapy with tocilizumab.
  • Figures 58-60 illustrate certain virtual patients that can be used in conjunction with the models of the invention to simulate development or progression of rheumatoid arthritis.
  • Figure 61 describes a high-level sensitivity analysis tool that can be used in conjunction with the computer models of the invention to study progress, development and potential treatments for rheumatoid arthritis.
  • the computer model dynamically integrates the contributions of immune cells (T cells, B cells, and macrophages), resident cells (fibroblast-like synoviocytes and chondrocytes) and mediators to the joint inflammation and structural damage observed in rheumatoid arthritis (RA).
  • T cells immune cells
  • B cells fibroblast-like synoviocytes and chondrocytes
  • mediators to the joint inflammation and structural damage observed in rheumatoid arthritis (RA).
  • RA rheumatoid arthritis
  • the B cell lifecycle is represented in the platform, as well as effector functions such as antigen presentation, mediator and autoantibody production, and immune complex formation
  • B cell proinflammatory cytokine production contributes minimally to synovial hyperplasia, but plays a role in the progression of structural damage.
  • Immune complexes result in monocyte activation, increased macrophage mediator production, and increased antigen presentation by macrophages and dendritic cells. Biosimulation research in the computer model advances our understanding of the mechanisms underlying effective B cell-targeting RA therapies, and may guide the development of improved second-generation therapeutic approaches.
  • This invention can include a single computer model that serves a number of purposes.
  • this layer can include a set of large-scale computer models covering a broad range of physiological systems.
  • the system can include complementary computer models, such as, for example, epidemiological computer models and pathogen computer models.
  • computer models can be designed to analyze a large number of subjects and therapies.
  • the computer models can be used to create a large number of validated virtual patients and to simulate their responses to a large number of therapies.
  • the invention and all of the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structural means disclosed in this specification and structural equivalents thereof, or in combinations of them.
  • the invention can be implemented as one or more computer program products, i.e., one or more computer programs tangibly embodied in an information carrier, e.g., in a machine readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers.
  • a computer program also known as a program, software, software application, or code
  • a computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program does not necessarily correspond to a file.
  • a program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).
  • a computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
  • the processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions of the invention by operating on input data and generating output.
  • the processes and logic flows can also be performed by, and apparatus of the invention can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
  • processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.
  • a processor will receive instructions and data from a read only memory or a random access memory or both.
  • the essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data.
  • a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.
  • Information carriers suitable for embodying computer program instructions and data include all forms of non volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks.
  • the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
  • the invention can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer.
  • a display device e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor
  • a keyboard and a pointing device e.g., a mouse or a trackball
  • Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.
  • the invention can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of such back end, middleware, or front end components.
  • the components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network ("LAN”) and a wide area network (“WAN”), e.g., the Internet.
  • LAN local area network
  • WAN wide area network
  • the computing system can include clients and servers.
  • a client and server are generally remote from each other and typically interact through a communication network.
  • the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
  • the invention also provides methods of simulating rheumatoid arthritis said method comprises executing a computer model of rheumatoid arthritis as described above.
  • Methods of simulating rheumatoid arthritis can further comprise applying a virtual protocol to the computer model to generate set of outputs represent a phenotype of the biological system.
  • the phenotype can represent a normal state or a diseased state.
  • the methods can further include accepting user input specifying one or more parameters or variables associated with one or more mathematical representations prior to executing the computer model.
  • the user input comprises a definition of a virtual patient or a definition of the virtual protocol.
  • Running the computer model produces a set of outputs for a biological system represented by the computer model.
  • the set of outputs represent one or more phenotypes of the biological system, i.e., the simulated subject, and includes values or other indicia associated with variables and parameters at a particular time and for a particular execution scenario.
  • a phenotype is represented by values at a particular time.
  • the behavior of the variables is simulated by, for example, numerical or analytical integration of one or more mathematical relations to produce values for the variables at various times and hence the evolution of the phenotype over time.
  • the computer executable software code numerically solves the mathematical equations of the model(s) under various simulated experimental conditions.
  • 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. Patent Number 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.
  • the computer model(s) can be used to rapidly test hypotheses and investigate potential drug targets or therapeutic strategies.
  • the computer model can represent a normal state as well as an abnormal (e.g., a diseased or toxic) state of a biological system.
  • the computer model includes parameters that are altered to simulate an abnormal state or a progression towards the abnormal state.
  • the parameter changes to represent a disease state are typically modifications of the underlying biological processes involved in a disease state, for example, to represent the genetic or environmental effects of the disease on the underlying physiology.
  • various mathematical relations of the computer model, along with a modification defined by the virtual stimulus can be solved numerically by a computer using standard algorithms to produce values of variables at one or more times based on the modification.
  • values of the variables can, in turn, be used to produce the first set of results of the first set of virtual measurements.
  • One or more virtual patients in conjunction with the computer model can be created based on an initial virtual patient that is associated with initial parameter values.
  • a different virtual patient can be created based on the initial virtual patient by introducing a modification to the initial virtual patient.
  • modification can include, for example, a parametric change (e.g., altering or specifying one or more initial parameter values), altering or specifying behavior of one or more variables, altering or specifying one or more functions representing interactions among variables, or a combination thereof.
  • a parametric change e.g., altering or specifying one or more initial parameter values
  • altering or specifying behavior of one or more variables altering or specifying one or more functions representing interactions among variables, or a combination thereof.
  • Alternative parameter values can be defined as, for example, disclosed in U.S. Pat. No. 6,078,739.
  • parameter values can be grouped into different sets of parameter values that can be used to define different virtual patients of the computer model.
  • the initial virtual patient itself can be created based on another virtual patient (e.g., a different initial virtual patient) in a manner as discussed above.
  • one or more virtual patients in the computer model can be created based on an initial virtual patient using linked simulation operations as, for example, disclosed in the following publication: "Method and Apparatus for Conducting Linked Simulation Operations Utilizing A Computer-Based System Model", (U.S. Application Publication No. 20010032068, published on October 18, 2001 ).
  • This publication discloses a method for performing additional simulation operations based on an initial simulation operation where, for example, a modification to the initial simulation operation at one or more times is introduced.
  • additional simulation operations can be used to create additional virtual patients in the computer model based on an initial virtual patient that is created using the initial simulation operation.
  • a virtual patient can be customized to represent a particular subject.
  • one or more simulation operations may be performed for a time sufficient to create one or more "stable" virtual patient of the computer model.
  • a "stable" virtual patient is characterized by one or more variables under or substantially approaching equilibrium or steady-state condition.
  • Various virtual patients of the computer model can represent variations of the biological system that are sufficiently different to evaluate the effect of such variations on how the biological system responds to a given therapy.
  • one or more biological processes represented by the computer model can be identified as playing a role in modulating biological response to the therapy, and various virtual patients can be defined to represent different modifications of the one or more biological processes.
  • the identification of the one or more biological processes can be based on, for example, experimental or clinical data, scientific literature, results of a computer model, or a combination of them.
  • various virtual patients can be created by defining different modifications to one or more mathematical relations included in the computer model, which one or more mathematical relations represent the one or more biological processes.
  • a modification to a mathematical relation can include, for example, a parametric change (e.g., altering or specifying one or more parameter values associated with the mathematical relation), altering or specifying behavior of one or more variables associated with the mathematical relation, altering or specifying one or more functions associated with the mathematical relation, or a combination of them.
  • the computer model may be run based on a particular modification for a time sufficient to create a "stable" configuration of the computer model. Simulations utilizing the computer model of the invention can lead to new insights into the development and progression of rheumatoid arthritis.
  • the simulations of the invention have determined that synovial immune complexes drive several processes associated with B cell/plasma cell-mediated RA pathology, including B cell activation, monocyte/ macrophage activation, and macrophage mediator synthesis. Targeting these specific processes may lead to the development of novel RA therapies that are superior to those currently available. Further, in silico analysis can prioritize these processes based on their individual impact on clinical outcome. Finally, in silico analyses in the RA PhysioLab platform can be used to better understand critical pathophysiological mechanisms for targeted drug development.
  • the model of rheumatoid arthritis is executed while applying a virtual stimulus or protocol representing, e.g., administration of a drug.
  • a virtual stimulus can be associated with a stimulus or perturbation that can be applied to a biological system.
  • Different virtual stimuli can be associated with stimuli that differ in some manner from one another.
  • Stimuli that can be applied to a biological system can include, for example, existing or hypothesized therapeutic agents, treatment regimens, and medical tests. Additional examples of stimuli include exposure to existing or hypothesized disease precursors. Further examples of stimuli include environmental changes such as those relating to changes in level of exposure to an environmental agent (e.g., an antigen), and changes in level of physical activity or exercise.
  • an environmental agent e.g., an antigen
  • a virtual protocol e.g., a virtual therapy, representing an actual therapy can be applied to a virtual patient in an attempt to predict how a real-world equivalent of the virtual patient would respond to the therapy.
  • Virtual protocols that can be applied to a biological system can include, for example, existing or hypothesized therapeutic agents and treatment regimens, mere passage of time, exposure to environmental toxins, increased exercise and the like.
  • a virtual protocol can be created, for example, by defining a modification to one or more mathematical relations included in a model, which one or more mathematical relations can represent one or more biological processes affected by a condition or effect associated with the virtual protocol.
  • a virtual protocol can define a modification that is to be introduced statically, dynamically, or a combination thereof, depending on the particular conditions and/or effects associated with the virtual protocol.
  • the computer model is capable of simulating a therapy or action of a therapeutic agent selected from the group consisting of methotrexate, NSAIDs, corticosteroids, cyclosporine, TNF- ⁇ neutralization, IL-1 receptor antagonism, B cell antagonism (e.g. rituximab), and IL-6 antagonism (e.g., tocilizumab).
  • anti-rheumatic therapy is simulated as described in any of
  • the computer models of the invention can be used to identify one or more biomarkers.
  • a biomarker can refer to a biological characteristic that can be evaluated to infer or predict a particular result. For instance, biomarkers can be predictive of effectiveness, biological activity, safety, or side effects of a therapy. Biomarkers can be identified to select or create tests that can be used to differentiate subjects. Biomarkers that differentiate responders versus non-responders may be sufficient if the specific goal is to identify a recommended therapy for a subject. Similarly, biomarkers can be identified to diagnose or categorize subjects. For example, utilizing the computer model of the invention, the relative contribution of B cell recruitment to a subject's symptoms can be determined based on B cell protein synthesis. Identification of the relative contributions of B cell recruitment can guide appropriate therapy for the subject. Further, biomarkers can be identified to monitor the actual response of a subject to a therapy.
  • One aspect of the invention comprises identifying one or more biomarkers by executing a computer model of the invention absent a virtual protocol to produce a first set of results; executing the computer model based on the virtual protocol to produce a second set of results; comparing the first set of results with the second set of results; and identifying a correlation between one or more variables or parameters and a virtual measurement indicative of a pre-selected biological characteristic. Preferable the correlated variable(s) and/or parameter(s) is present in only one of the first or second set of results.
  • Results of two or more virtual measurements can be determined to be substantially correlated based on one or more standard statistical tests.
  • Statistical tests that can be used to identify correlation can include, for example, linear regression analysis, nonlinear regression analysis, and rank correlation test.
  • a correlation coefficient can be determined, and correlation can be identified based on determining that the correlation coefficient falls within a particular range. Examples of correlation coefficients include goodness of fit statistical quantity, r 2 , associated with linear regression analysis and Spearman Rank Correlation coefficient, rs, associated with rank correlation test.
  • a virtual patient in the computer model can be associated with a particular set of values for the parameters of the computer model
  • virtual patient A may include a first set of parameter values
  • virtual patient B may include a second set of parameter values that differs in some fashion from the first set of parameter values.
  • the second set of parameter values may include at least one parameter value differing from a corresponding parameter value included in the first set of parameter values.
  • virtual patient C may be associated with a third set of parameter values that differs in some fashion from the first and second set of parameter values.
  • a biological process that modulates biological response to the therapy can be associated with a knowledge gap or uncertainty, and various virtual patients of the computer model can be defined to represent different plausible hypotheses or resolutions of the knowledge gap.
  • biological processes associated with cartilage metabolism can be identified as playing a role in modulating biological response to a therapy for rheumatoid arthritis. While it may be understood that inflammatory mediators have an effect on arthritis, the relative effects of the different types of mediators on bone erosion as well as baseline concentrations of the different types of mediators may not be well understood.
  • various virtual patients can be defined to represent human subjects having different baseline concentrations of inflammatory mediators. Knowledge gaps can be identified and explored as described in Friedrich and Paterson, "In Silico Predictions of Target Clinical Efficacy," Drug Discovery Today: Targets 3(5):216-222 (2004).
  • Figure 29 shows an example of an effect diagram for the B cell and plasma cell life cycle in the synovial tissue.
  • the physiological components modeled for the life cycle of the B cells and plasma cells include: viable synovial B cells; viable synovial plasma cells; B cell recruitment rate; plasmas cell recruitment rate; B cell proliferation rate constant; B cell differentiation rate constant; B cell apoptosis rate constant; plasma cell apoptosis rate constant; apoptotic B cells; and apoptotic plasma cells.
  • B cells and plasma cells accumulate in the inflamed synovium of RA patients and may drive disease activity through antigen presentation, autoantibody production, and possibly, mediator secretion and complement fixation. The importance of B cells is also supported by their role in RA-related pathological processes such as T cell and macrophage activation.
  • clinical trials with anti-CD20 monoclonal antibody include: viable synovial B cells; viable synovial plasma cells; B cell recruitment rate; plasmas cell recruitment rate; B cell proliferation rate constant; B cell differentiation rate constant; B cell
  • B cells and plasma cells In the synovial tissue of patients with rheumatoid arthritis, B cells and plasma cells accumulate where they interact with other cell types via soluble mediators and direct cell-cell contact. These interactions are shaped by the number and activation state of the B cells and plasma cells.
  • Figure 29 and the following description address only the calculation of the number of B cells and plasma cells in the synovial tissue reference volume.
  • the main processes of B cell and plasma cell turnover modeled in the synovial tissue are cell recruitment, proliferation, differentiation, and apoptosis. In the model, the numerical balance of these processes determines the number of viable B cells and plasma cells, which modulate their net cellular activities in other parts of the model.
  • the number of apoptotic B cells and plasma cells reflect the number of viable B cells and plasma cells, their apoptosis rate, and clearance of apoptotic cells by phagocytosis or other means (as modeled by an apoptotic cell half-life).
  • Figure 29 provides the graphical representation for the differential equations used to track the population of viable synovial B cells and plasma cells, and apoptotic B cells and plasma cells. As these differential equations depend on calculations of the recruitment, proliferation, differentiation, and apoptosis rates, the latter are described first, followed by the description of the differential equations governing the population dynamics.
  • the B cell recruitment rate (r b ; 1e6 cells/hr) is calculated elsewhere in the model and is dependent on chemoattraction of B cells, upregulation of endothelial adhesion molecules by cytokine activation, and systemic preactivation of B cells.
  • B-cell trafficking is affected by the probabilities of three sequential processes: B cell tethering, endothelial adhesion, and extravasation through the vasculature.
  • each of these processes is determined in the model by dynamic expression level of surface molecules on endothelial cells (such as VCAM-1 and ICAM-1 ) and chemokine levels (such as CXCL12 and Ml P-1 alpha) in the synovial tissue as well as activation status of B cells in the peripheral blood.
  • the B cell recruitment rate is modulated by the density of B cells in the peripheral blood and the vascular surface area in the synovial tissue reference volume. The overall B cell recruitment rate has been adjusted to achieve the reported B cell density in the synovial tissue while maintaining a balance between the population's dependence of cell recruitment vs. cell proliferation.
  • the plasma cell recruitment rate (r p ; 1e6 cells/hr) is calculated elsewhere in the model and is determined by the density of plasma cells in the peripheral blood and the vascular surface area in the synovial tissue reference volume.
  • the overall plasma cell recruitment rate has been adjusted to achieve the reported plasma cell density in the synovial tissue while maintaining a balance between the population's dependence of cell recruitment vs. differentiation.
  • the proliferation (or clonal expansion) of B cells in the synovial tissue is determined in part from the fraction of cells entering mitosis at a specific moment (f p ), as determined elsewhere in the model.
  • the proliferative fraction is driven by activation of B cells (via direct encounter with antigen, encounter with un-complexed auto-antibodies, or by immune complexes which may or may not be loaded with antigen) and further regulated by both soluble mediators (such as IL-6 and I L- 10 ) and cell-cell contact (such as with T cells).
  • the differentiation of B cells into plasma cells in the synovial tissue is determined in part by the fraction of cells differentiating at a specific moment (f d ), as determined elsewhere in the model.
  • the differentiating fraction is driven by activation of B cells (via direct encounter with antigen, encounter with un-complexed auto-antibodies, or by immune complexes which may or may not be loaded with antigen) and further regulated by both soluble mediators (such as IL-2 and IL-10) and cell-cell contact (such as with T cells).
  • the apoptosis of B and plasma cells is determined from the fraction of cells entering the apoptotic cascade at a given time (f ab ) and (f ap ), respectively, as determined elsewhere in the model.
  • the apoptotic fraction for these cells is regulated independently by multiple soluble mediators and cell-cell contact effects.
  • half-lives for viable B cells t vb V_- and plasma cells (t vp 1 /4) are specified to account for B cell and plasma cell efflux from the synovial tissue into the synovial fluid, through vascular vessels and lymphatic ducts, or into any other compartment. These efflux rates are distinct from loss of B cells and plasma cells due to apoptosis.
  • the populations of viable B cells (B v ) and apoptotic B cells (B a ) are determined using the values obtained from the evaluation of B cell recruitment rate (r b ), B cell proliferation rate constant (p), B cells proliferative fraction (f p ), B cell differentiation rate constant (d), B cells differentiation fraction (f d ), half-lives for viable B cells (W/ ⁇ -), B cell apoptosis rate constant (a), and B cell apoptotic fraction (f ab ).
  • the viable B cells proliferate at a rate proportional to the population of viable cells (B v ), the fraction of B cells proliferating (f p ), and the proliferation rate constant (p).
  • the dynamics of the viable B cell population are represented by the following equation:
  • the viable B cells enter apoptosis at a rate proportional to the population of viable B cells (B v ), the B cell apoptotic fraction (f ab ) and the apoptosis rate constant (a), and exit from the synovial tissue via phagocytosis/degradation characterized by the half-life (t a 1 /_-).
  • B v population of viable B cells
  • f ab B cell apoptotic fraction
  • the populations of viable plasma cells (P v ) and apoptotic plasma cells (P a ) are determined using the values obtained from the evaluation of plasma cell recruitment rate (r p ), B cell differentiation rate constant (d), B cells differentiation fraction (f d ), half-lives for viable plasma cells (t vp V_-), plasma cell apoptosis rate constant (a), and plasma cell apoptotic fraction (f ap ).
  • the viable plasma cells enter apoptosis at a rate proportional to the population of viable plasma cells (P v ), the plasma cell apoptotic fraction (f ap ) and the apoptosis rate constant (a), and exit from the synovial tissue via phagocytosis/degradation characterized by the half-life (t a 1 /_-).
  • P v population of viable plasma cells
  • f ap the plasma cell apoptotic fraction
  • apoptosis rate constant characterized by the half-life
  • these equations specify the population dynamics of viable and apoptotic plasma cells in the synovial tissue.
  • the values of the parameters used in the various functions within this module were determined so as to match experimental data and the guidelines described below.
  • these guidelines are manifested as the following constraints: (1 ) populations of viable and apoptotic B cells and plasma cells (B v , B a , P v , P a ) in the untreated virtual patient are consistent with ranges reported in clinical data, namely populations of B cells and plasma cells comprise -2% and -6%, respectively, of the total synovial cell density based on RA biopsy studies (Jimenez-Boj, J Immunol.
  • B cells in the RA joint are predominantly either memory B cells or plasma cells (Reparon-Schuijt, Arthritis and Rheumatism 44:2029-2037 (2001 )); (3) B cell activation in the synovial tissue (-50%) is consistent with ranges in the reported measurements in peripheral blood of RA patients (De Miguel, Clinical and Experimental Rheumatology ' 21 :726-732 (2003)); (4) regulation of B cell and plasma cell lifecycle by mediators reproduce in vitro data, e.g., data from Tangye, J Immunol.
  • proliferative rate constant (p) 0.029 (1/hours)
  • apoptosis rate constant (a) 0.029 (1/hours)
  • differentiation rate constant (d) 0.029 (1/hours)
  • p proliferative rate constant
  • a 0.029 (1/hours
  • d differentiation rate constant
  • the components of the Effect Diagram represent mathematical relationships that define the elements of the biological state being modeled. These mathematical relationships can be developed with the aid of appropriate information on the relevant biological variables and biological processes.
  • the Effect Diagram indicates the type of mathematical relationships that are modeled within a given model component. The information can then be put into a form that matches the structure of the Effect Diagram. In this way, the structure of the model was developed and other components of this model were developed in a similar fashion.

Abstract

L'invention comprend de nouveaux procédés servant à développer un modèle informatique de polyarthrite rhumatoïde. En particulier, les modèles comprennent des représentations des processus biologiques associés à un compartiment de sérum et un compartiment synovial. Le modèle peut également comprendre des représentations de cellules synoviales, de cellules endothéliales, de cellules T et de cellules B dans une jointure. L'invention comprend également des modèles informatiques de polyarthrite rhumatoïde, des procédés de simulation de la polyarthrite rhumatoïde et des systèmes informatiques pour simuler la polyarthrite rhumatoïde.
PCT/US2008/066364 2007-06-08 2008-06-09 Procédé et appareil servant à modéliser une polyarthrite rhumatoïde WO2008154515A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030014232A1 (en) * 2001-05-22 2003-01-16 Paterson Thomas S. Methods for predicting biological activities of cellular constituents
US6862561B2 (en) * 2001-05-29 2005-03-01 Entelos, Inc. Method and apparatus for computer modeling a joint

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030014232A1 (en) * 2001-05-22 2003-01-16 Paterson Thomas S. Methods for predicting biological activities of cellular constituents
US6862561B2 (en) * 2001-05-29 2005-03-01 Entelos, Inc. Method and apparatus for computer modeling a joint

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