WO2014089478A1

WO2014089478A1 - Methods of numerical analysis for platelet disorders and computer-readable media and systems for performing the same

Info

Publication number: WO2014089478A1
Application number: PCT/US2013/073656
Authority: WO
Inventors: Ted S. STROM
Original assignee: University Of Tennessee Research Foundation
Priority date: 2012-12-07
Filing date: 2013-12-06
Publication date: 2014-06-12

Abstract

The present invention provides a method, system and a computer-readable medium for determining the kinetic basis for a platelet disorder such as thrombocytopenia in an individual patient. The computer-implemented method may include the steps of receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters relating to platelet consumption rate and platelet lifespan, and diagnosing or determining the characteristics of a platelet disorder based upon the numerical models. The numerical analysis model described herein allows for the determination of random and lifespan-dependent platelet consumption rates from in vivo platelet consumption data, and the determination of a cause of a platelet disorder based upon the numerical models.

Description

METHODS OF NUMERICAL ANALYSIS FOR PLATELET DISORDERS AND COMPUTER-READABLE MEDIA AND SYSTEMS FOR PERFORMING THE SAME

RELATED APPLICATIONS:

[0001] The presently disclosed application claims priority from U.S. Provisional Application

Serial No. 61/734,555, filed December 7, 2012, the entire disclosure of which is incorporated herein by reference.

GOVERNMENT INTEREST

[0002] The presently disclosed application was developed under a contract from

R01AI071163 (David J. Rawlings, Principal Investigator), by AID 1R21AI079757-01A1 (TS), and by the Department of Veterans Affairs the United States Government may retain some rights in this invention.

TECHNICAL FIELD

[0003] The presently disclosed application relates in general to a numerical analysis method for platelet disorders and computer-readable media and systems for performing the same. More particularly, the presently disclosed application relates to a numerical analysis method for determining the causes of platelet disorders in a subject, such as impaired production, accelerated consumption, or a combination of impaired production and accelerated consumption.

INTRODUCTION

[0004] Platelet disorders such as thrombocytopenias occur in a number of clinical contexts, including bone marrow failure (which can in turn be caused by hematologic malignancies, myelodysplasias, a number of viral syndromes, and a number of therapeutic agents), a number of inherited conditions, in association with several well characterized autoimmune conditions, and in a both alcoholic and non-alcoholic liver disease such as that induced by Hepatitis C Virus (HCV). The latter two categories affect tens of thousands of individuals including numerous veterans. Specifically, one study used diagnostic codes to estimate the incidence of cirrhosis among veterans at approximately 2.3% of inpatients (Persson EC, et al. (2012) British journal of cancer). Another study, however, found those codes to underestimate the incidence of acute hepatic decompensation among veterans by over 50% (Lo Re V, et al. (2011) Pharmacoepidemiology and drug safety).

[0005] Significant thrombocytopenia (platelet count below 100 x 109/L) is found in approximately 13% of patients with chronic liver disease (Afdhal, (2008) Journal of hepatology), with the subcategory of HCV patients showing a similar or higher incidence (16%; Louie et al., (2011) Journal of viral hepatitis). Although incidences as high as 45% have been reported in HCV patients with cirrhosis, the correlation between disease severity and thrombocytopenia is in fact weak or nonexistent in that context (Louie et al., (2011) Journal of viral hepatitis). Thrombocytopenia in HCV patients can also be induced, or exacerbated, by antiviral therapies. This both obscures estimates of the true incidence of HCV-associated thrombocytopenia and increases its clinical impact, since thrombocytopenia often limits the use of antiviral agents.

[0006] Thrombocytopenia can place patients with liver disease at risk of severe hemorrhage when even minor invasive procedures (such as paracentesis or liver biopsy) are clinically indicated. Such procedures may be delayed by the need for platelet transfusion, and the latter intervention carries its own risks (including volume overload and often overlooked risk of hemolysis induced by the incompatible plasma in which the platelets may be resuspended). A better understanding of the pathophysiologic basis for this condition could therefore be of enormous clinical utility.

[0007] It is not clear what causes thrombocytopenia in patients with liver failure, and it is not clear how best to treat it. The most commonly cited mechanisms are splenic clearance (sometimes inaccurately referred to as "sequestration"), which is thought to increase in association with cirrhosis- induced splenomegaly, and reduced platelet production due to impaired hepatic synthesis of thrombopoietin (TPO) (Afdhal, (2008) Journal of hepatology; Witters P, et al. (2008) Alimentary pharmacology & therapeutics). But efforts to reduce portal hypertension, or to reduce splenic platelet clearance via splenectomy or splenic embolization, have produced inconsistent results in liver disease patients (Afdhal, (2008) Journal of hepatology ; Akahoshi T, et al. (2012) Journal of gastroenterology and hepatology). And TPO levels have not been shown to correlate consistently with platelet count in these patients (discussed in more detail below), raising questions about whether treatment with TPO receptor agonists in fact target the fundamental problem in these patients or not. Lack of understanding of the pathophysiology of thrombocytopenia, therefore, parallels the lack of reliable clinical options for treating it.

[0008] In many cases, thrombocytopenias occur when none of the clinical conditions occur, such as bone marrow failure, a number of inherited conditions, in association with several well characterized autoimmune conditions, and in a both alcoholic and non-alcoholic liver disease such as that induced by Hepatitis C Virus (HCV), and are diagnosed (by exclusion) as immune thrombocytopenic purpura (ITP). The latter term is technically a misnomer, since a diagnosis of exclusion is by definition idiopathic, but it is used because about 2/3 of ITP patients demonstrate serum anti-platelet antibodies capable of inducing rapid clearance of platelets from the circulation[7]. About 2/3 of them also show serum antibodies capable of inhibiting ex vivo platelet production by cultured megakaryocytes [8]. It is not know which of the two is of greater clinical significance, or whether ITP patients can be categorized as those for whom impaired platelet production vs. rapid platelet consumption constitutes the fundamental cause of the condition.

[0009] ITP occurs in adults in the general population at an annual incidence of 5.8-6.6 per

100,000 population (Gernsheimer T. (2008) European journal of haematology Supplementum) . Patients over the age of 60 have the highest incidence, making it a particular concern for the aging Veteran population. Although older references and text books often cite a significantly increased incidence in women, more recent studies have found an overall female to male ratio of only 1.2 (Gernsheimer T. (2008) European journal of haematology Supplementum). Veterans are at greater risk than non- Veterans, with an annual incidence in HCV(-) Veterans of 18.5 per 100,000. The risk is even higher in HCV(+) Veterans (30.2 per 100,000) (Chiao EY, et al. (2009) Archives of internal medicine), a rate which takes on greater significance in light of the increased prevalence of HCV infection in Veterans (estimated at between 5.4% and 7.3%, in comparison to the 1.6% prevalence in the general population) (Chak E, et al., (2011) official journal of the International Association for the Study of the Liver). This increased incidence of thrombocytopenia in HCV+ patients raises the question of whether antibody- induced platelet clearance causes thrombocytopenia in those HCV+ patients who develop severe liver disease. We will address that question in this proposal.

[00010] Although most ITP patients respond to treatment with corticosteroids, for those who do not the outcomes obtained with "second line" therapies, such as splenectomy or the use of more potent immunosuppressives, are highly variable (Neunert C, et al. (201 1) Blood). While TPO-receptor agonists (TPO-RAs) have been more effective in this context, the expense (currently approximately $60,000 per year) and partially-explored long term side effects of this life-long treatment option (one study found an 88% incidence of reticulin fibrosis in bone marrows of treated patients (Ghanima W, et al. (2011) British journal of haematology)) raise serious questions about over-reliance on it. Here again, understanding the pathophysiologic basis of the thrombocytopenia in individual patients should provide better guidance for therapy than empirical findings have so far.

[00011] Pathophysiology of thrombocytopenia: By analogy to anemias, there are only three ways for thrombocytopenias to occur: impaired platelet production, rapid platelet consumption, or a combination of the two. But in contrast to anemias, for which in most cases these kinetic bases are routinely identified, most thrombocytopenias cannot be described in these terms. This enormous blind spot in our understanding of these conditions has to do primarily with the complex kinetics of platelet consumption, which differs from red cell consumption in one key aspect. While red cell consumption is normally lifespan dependent, normal platelet consumption necessarily, and functionally, includes a large component of random (or exponential) consumption. This makes it hard to interpret the available laboratory measures of platelet turnover.

[00012] While rapid clearance of labeled platelets from the bloodstream can be followed in thrombocytopenic individuals, no model exists for quantitatively inferring from autologous or allogeneic platelet consumption data what changes in random consumption, lifespan dependent consumption, and platelet production rate may have caused the thrombocytopenia. Accordingly, there is a need for new technologies that will allow distinctions to be made between platelet thrombocytopenias due primarily to impaired platelet production and those due to acceleration of random or lifespan- dependent platelet consumption.

SUMMARY OF THE INVENTION

[00013] This Summary describes several embodiments of the presently-disclosed subject matter, and, in many cases, lists variations and permutations of these embodiments. However, this Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently-disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.

[00014] Further provided, in some embodiments of the presently-disclosed subject matter, is a computer-implemented method for determining one or more characteristics of a platelet disorder in a subject. More specifically, the method may include the following steps: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant exponential random platelet consumption rate, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model.

[00015] Yet in some further embodiments of the present invention, a computer-implemented method for diagnosing a platelet disorder in a subject is disclosed. This computer-implemented method may contain the following steps: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models generated to provide a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for corresponding parameters relating to platelet consumption; analyzing the platelet consumption data in view of the plurality of numerical models to identify a numerical model that provides a best fit approximation of a platelet population consumption curve for the observed platelet consumption data; and outputting an indication of a type of platelet disorder for the subject based on the best fit of a platelet population consumption curve for the observed platelet consumption data.

[00016] In some embodiments, the presently-disclosed subject matter further provides a computer-implemented method for determining the cause of a platelet disorder in a subject. More particularly, this method may include the steps of: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant random platelet consumption, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model.

[00017] Still further provided, in some embodiments of the present invention, is a method for differentiating between different platelet disorders in a subject. This method includes the steps of: obtaining in vivo platelet consumption data from a subject, wherein said data is obtained with at least one marker; comparing said platelet consumption data with platelet consumption curves generated from a numerical analysis model to determine the kinetic basis for the platelet disorder in the subject; and differentiating the type of platelet disorder in the subject based on said curves. More particularly, the presently-disclosed invention provides that the numerical analysis model can be achieved by receiving user input from an input device configured for receiving in vivo platelet consumption data; computing with a computational engine, said engine configured to implement a numerical analysis model to the determine the kinetic basis for the platelet disorder; and creating curves that correlate the kinetic basis with the type of the platelet disorder.

[00018] The presently-disclosed invention further provides a method of determining the cause of platelet disorder in a subject. The method includes: obtaining in vivo platelet consumption data from the subject, wherein said data are obtained with at least one marker; constructing platelet consumption curves from a numerical analysis model to determine the kinetic basis of the platelet disorder; comparing said platelet consumption data with said curves to determine the kinetic basis of the platelet disorder in the subject; and determining the cause of said platelet disorder in the subject based on the comparison of said curves.

[00019] Yet in further embodiments, the present invention provides a method of predicting the response to therapy of a subject with platelet disorders. More specifically, the method includes: obtaining in vivo platelet consumption data from the subject; wherein said data is obtained by administering a donor's platelets to the subject, wherein the donor's platelets are labeled with at least one marker; constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan; comparing said platelet consumption data with said curves to determine the kinetic basis for platelet disorder; determining the causes of said platelet disorder based on said curves; and predicting the response to therapy of said patient based on said determination.

[00020] Still further provided, in some embodiments of the present invention, is a method of treating a subject having a platelet disorder. The method may include the steps of: obtaining in vivo platelet consumption data from the subject based on data obtained from donor's platelets administered to the subject, wherein the donor's platelets are labeled with at least one marker; constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan; comparing said platelet consumption data with said curves to determine the kinetic basis for platelet disorder; determining the causes of said platelet disorder based on said curves; and treating the patient in accordance with the determined cause of said platelet disorders.

[00021] Exemplary embodiments of the present invention that are related to computer program products and data processing systems corresponding to each of the above-summarized computer- implemented methods are also described and claimed herein.

[00022] These and other objects of the present invention are obtained through the compositions and methods as set forth in the detailed description of the invention provided hereinbelow.

BRIEF DESCRIPTION OF THE DRAWING FIGURES:

[00023] For a fuller understanding of the nature and desired objects of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawing figures wherein:

[00024] FIG. 1 is a block diagram illustrating a platelet disorder data analysis tool in accordance with an exemplary embodiment of the present invention [00025] FIG. 2 shows the design of the platelet population matrix.

[00026] FIG. 3 includes graphs showing model cohort and population platelet kinetics in normal (WT) and thrombocytopenic (WASP(-) mouse models. A) The two model platelet cohorts shown were generated at 20.6 K/ul/hr (WT) and 24.3 K/ul/hr (WASP(-)), and consumed hourly at the following parameter values: WT, RD=1.16%/hr, SD = 1.20, LS = 105hr; WASP(-),RD=4.21 %/hr, SD=0.18, LS = 150 hr. B) 240 sequential cohorts were generated hourly and consumed as in (A). Their summed hourly cohort values are shown. Note that production ceases after 240 hr. C) The platelet age distribution is taken from the last row (time) of the equilibration phase (in this case, row i=240).

[00027] FIG. 4 includes graphs showing effect of parameter value changes on the consumption curve. A 500 hour equilibration phase was used. A) Consumption at LS =80, SD = 0.44. B) Consumption at RD= 1.0, SD = 0.44. C) Consumption at RD = 0.1, LS = 50. Equilibration metric values (e) are greater than 0.95 for all of the parameter sets shown (data not shown).

[00028] FIG. 5 shows an example of the least squares-based optimal parameter search method.

A) Shown are SS values for a 20 x 20 array of SD and LS parameter values at an RD value of 0.7%. The values shown are the squared residual values for a total of 120 WT platelet consumption measurements (30 WT recipients, four time points per recipient). The platelet consumption data used to generate the SS values is shown in FIG. 5B) Shown are SS minima for a series of RD planes. The white diamond indicates the global minimum identified in this set of analyses.

[00029] FIG. 6 is a block diagram illustrating an exemplary computer system that can be used for implementing exemplary embodiments of the present invention.

[00030] FIG. 7 shows optimal consumption curves. Black diamonds are data obtained with fluorescently labeled platelets for the "donor to recipient" data sets shown. Consumption curves are shown for the optimal parameter values shown in Table 1. For allogeneic platelet. Consumption curves, the age distribution histograms shown in FIG. 3 were used to determine the optimal parameter values (see text). Each fluorescently labeled platelet data set includes 14"n" data points at each of the time points shown, with the exception of the WASP(-) to WASP(-) data set at 5 hours (n=5) and at 89 hours (n=13). Error bars are standard deviations.

[00031] FIG. 8 includes examples of optimal modeled consumption curves. The data points shown for each patient were used to infer optimal parameter values (RD, LS, SD, and LL; table 2) predictive of the consumption curves shown.

[00032] FIG. 9 shows optimal parameter value searches and lifespan distributions. Top: The examples shown yielded optima in three different SD-defined parameter spaces. The low points on the SS value curves define the optimal parameter value sets. Resolution is 0.25%/hr (patients 35, 2), 1%/hr (patient 27). Bottom: the fractional lifespan dependent consumption rate distributions for these optima are shown. For example, the optimal parameter values for patient 35 demonstrate the distribution of lifespan-dependent consumption rates per cohort (peaking at 1%/hr) shown at left. Resolution is 0.5 hr.

[00033] FIG. 10 shows modeling the effect of reduced Production rate on RD and Platelet count. Schematic of the interpolation process described in the text.

[00034] FIG. 11 shows platelet production and consumption observed and predicted parameter values. Optimal RD and PR values from table 1 for patients responding or not responding to subsequent splenectomies are plotted for each of the patients in the study. Projected values for thrombocytopenias due to reduced production, increased consumption with no homeostatic increase in production, and increased consumption with a compensatory increase in production rate, were interpolated as described in the text. A) Population turnover rate, which at equilibrium equals platelet production rate, vs. platelet count. B) Random destruction rate (RD) vs. platelet count. C) RD and PR values from A and B.

[00035] FIG. 12 shows age distribution histograms and IPF modeling. A) Histograms were generated from the optimal kinetic parameter values shown in Table 2, using bin sizes of 10 hours. B) IPF and absolute IPF (alPF) values were generated from the modeled parameter values shown in FIG. 11 as described in the text.

[00036] FIG. 13 shows Projected interpretive guide to absolute IPF values. Predicted IPF and platelet values for different thrombocytopenia mechanisms (see FIG. 12). Data points falling in the boxes shown would be inferred to result from the first, second, or third mechanism shown.

[00037] FIG. 14 is a graph showing values of clinical platelet counts and RD plotted against the RD values expected for the HRD/RD ratios shown.

[00038] FIG. 15 is a diagram showing the sketch of proposed studies for evaluating cis

(platelet intrinsic) effects of murine thrombocytopenia on platelet consumption.

[00039] FIG. 16 is a diagram showing the schematic of follow up experiment for evaluating cis (platelet intrinsic) effects of murine thrombocytopenia on platelet consumption.

[00040] FIG. 17 is a graph showing the predicted result that RD1/RD2 (hvMPL to hvMPL /

WT to hvMPL) is anticipated to decline in proportion to the fraction (F) of the donor platelet age distribution (AD) that is younger than some threshold age (T).

DESCRIPTION OF EXEMPLARY EMBODIMENTS

[00041] The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.

[00042] While the terms used herein are believed to be well-understood by one of ordinary skill in the art, definitions are set forth to facilitate explanation of the presently-disclosed subject matter.

[00043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the presently-disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are now described.

[00044] Following long-standing patent law convention, the terms "a," "an," and "the" refer to

"one or more" when used in this application, including the claims. Thus, for example, reference to "a cell" includes a plurality of such cells, and so forth.

[00045] Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about." Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.

[00046] As used herein, the term "about," when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.

[00047] Accelerated platelet consumption rates are thought to underlie multiple types of platelet disorders such as thrombocytopenia, including immune thrombocytopenic purpura (ITP), thrombotic thrombocytopenic purpura (TTP), heparin induced thrombocytopenia (HIT), and disseminated intravascular coagulation (DIC). No current diagnostic test, however, is capable of directly distinguishing platelet disorders such as thrombocytopenias due to rapid platelet clearance from those due to impaired platelet production. A major stumbling block in this area is the lack of a mathematical model capable of simultaneously quantifying the effect of changes in random platelet consumption processes, and lifespan-dependent platelet consumption, from net in vivo platelet consumption data. Such a model would also allow inference of the platelet population turnover rate (i.e. the platelet production rate), and would allow the interpretation of data obtained with allogeneic as well as autologous platelets. [00048] Random platelet consumption occurs in association with hemostasis, but can also occur due to uptake by splenic macrophages, hepatic macrophages (Kupffer cells), or hepatocytes. Lifespan dependent platelet consumption is mediated by a platelet intrinsic process that terminates in apoptosis. Efforts to quantify the sum of these two types of process typically involve ex vivo labeling of platelets with a radioisotope (such as ¹¹ 'indium) or a fluorescent marker (such as CMFDA), injecting them into a recipient, and following the rate at which they are cleared from the circulation.

[00049] In accordance with the present invention, a computer-implemented method for determining one or more characteristics of a platelet disorder in a subject is provided. More specifically, the method may include the steps of receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant exponential random platelet consumption rate, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model. The method may be carried out utilizing the computer-related apparatus as set forth below.

[00050] While some exemplary embodiments of the present invention will be described below primarily in a general context of an application program that runs on an operating system in conjunction with a personal computer such as a desktop or portable computer, those skilled in the art will recognize that they also may be implemented in conjunction with other program modules such as, for example, platform software modules, user-written software modules (such as spreadsheet templates, word processor macros, graphics scripts, etc.), routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that exemplary embodiments of the present invention may be practiced with other computer system configurations, including hand-held devices, mobile communication devices, tablets, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like, as well as in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. As used herein, however, the terms "data processing system," "computer," and the like are intended to mean essentially any type of computing device or machine that is capable of receiving, storing, and running a software product, including such devices as communication devices (for example, pagers, telephones, electronic books, electronic magazines and newspapers, etc.) and personal and home consumer devices (for example, handheld computers, Web-enabled televisions, home automation systems, multimedia viewing systems, gaming consoles, etc.).

[00051] Referring now to Figure 1, a block diagram illustrating a platelet disorder data analysis tool 100 in accordance with an exemplary embodiment of the present invention is provided. Platelet disorder data analysis tool 100 provides a computational engine for determining one or more characteristics of a platelet disorder in a subject and can be implemented using one or more program modules and data storage units. As used herein, the term "modules", "program modules", "components", "systems", "tools", "utilities", and the like include routines, programs, objects, components, data structures, and instructions, or instructions sets, and so forth that perform particular tasks or implement particular abstract data types. As would be appreciated by one of ordinary skill in the art, the modules refer to computer-related entities that can be implemented as software, hardware, firmware and/or other suitable components that provide the described functionality, and which may be loaded into memory of a machine embodying exemplary embodiments of the present invention. Aspects of the modules may be written in a variety of programming languages, such as C, C++, Java, etc. As used herein, the terms "repository", "data storage unit," "data store", "storage unit", and the like can refer to any suitable memory device that may be used for storing data, including manual files, machine readable files, and databases.

[00052] The functionality provided by exemplary embodiments can be combined and/or further partitioned. The modules and/or storage units can all be implemented and run on the same computing system (for example, the exemplary computer system illustrated in Figures 1 and 6, and as described below) or they can be implemented and run on different computing systems. For example, one or more modules can be implemented on a personal computer operated by a user while other modules can be implemented on a remote server and accessed via a network. In exemplary embodiments, platelet disorder data analysis tool 100 can be configured for incorporation within any medical analysis or diagnostic software application or computing environment as a plug-in, add-on, or extension supported by the local computing system operated by the user or the server system. As used herein, the term "plug-in" can refer to a software application or module program, or one or more computer instructions, which may or may not be in communication with other software applications or modules, that interacts with a host application to provide specified functionality, and which may include any file, image, graphic, icon, audio, video, or any other attachment. In other exemplary embodiments, platelet disorder data analysis tool 100 can be implemented using a standalone program that is run as a separate computer process, a portable application, a native component of a medical analysis tool or application, a part of a software bundle, or any other suitable implementation. [00053] As illustrated in Figure 1, exemplary data analysis tool 100 generally includes a user interface 110, a numerical model acquirer 120, an analyzer 130, and a numerical model data store 140. User interface 1 10 is to configured to implement a set of user interface controls for receiving observed platelet consumption data for a subject from a user, as well as to provide a set of options permitting the user to configure particular settings for data analysis tool 100 to use in performing analysis of the observed platelet consumption data. For this purpose, user interface 110 can be implemented as a graphical user interface (GUI) to allow a user to interact with the functionality provided by data analysis tool 100 on a computer terminal through graphical icons and visual indicators or graphical elements called "widgets," along with text, labels, or text navigation to represent the information and actions available to the user. These actions may be performed by the user through direct manipulation of the graphical elements in response to control sequences such as, for example, keystrokes with the computer keyboard and movements of the computer mouse the user performs to use data analysis tool 100. In these embodiments, the functionality provided by user interface 1 10 can be implemented through any suitable user interface controls (for example, by way of menu selection, point-and-click, dialog box, or keyboard command).

[00054] As an alternative receiving the observed platelet consumption data for the subject via user input, the observed platelet consumption data can be stored in a data store provided within the computing system on which data analysis tool 100 is implemented or another system that is connected to the computing system on which data analysis tool 100 is implemented, and user interface 110 (or another component of data analysis tool 100) can be configured to access the data store to obtain the observed platelet consumption data or otherwise receive the observed platelet consumption data as input from the system that includes the data store in which the observed platelet consumption data is stored.

[00055] Numerical model acquirer 120 is configured to obtain a plurality of numerical models to use in perform the analysis of the observed platelet consumption data and to store the obtained numerical models in numerical model data store 140. Each obtained numerical model provides a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters representing random and lifespan platelet consumption. For instance, as explained in greater detail with reference to the examples provided below, the parameters can include a constant random platelet consumption rate, which can be used to model random platelet consumption, and two parameters that can be used to model which can be used to model a lifespan dependent platelet consumption: a lognormal distribution of platelet lifespan and a standard deviation of the lognormal distribution of platelet lifespan.

[00056] In the examples described below, each numerical model comprises a set of data values arranged in a data structure that is used to model a respective platelet population as a series of platelet cohorts that are respectively produced at a constant production rate at a plurality of sequential time points separated by a constant time interval and each discretely consumed at each of the plurality of sequential time points occurring after the time point at which the platelet cohort is produced according to the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for the numerical model.

[00057] More specifically, as depicted in Figure 2 and further detailed with reference to the examples described below, for each numerical model in these examples, the set of data values are arranged in the data structure to represent elements in a two-dimensional matrix having m rows and n columns (with m and n being integers, where m > n > 1). The columns of the matrix respectively correspond to the platelet cohorts in sequence, and the rows of the matrix respectively correspond to the time points in sequence. Each element Py of the matrix includes, as the data value for the element, a value indicating platelets remaining in the platelet cohort corresponding to the column for the element at the time point corresponding to the row for the element or a null value if the time point to which the element corresponds is earlier than the time point at which the platelet cohort to which the element corresponds was produced. For each column of the numerical model, the value indicating platelets remaining in the platelet cohort corresponding to the column in the element for each row corresponding to a time point subsequent to the time point at which the platelet cohort was produced is determined by subtracting a first amount representing random platelet consumption and a second amount representing lifespan dependent platelet consumption from the value indicating platelets remaining in the platelet cohort in the element of the row corresponding to the immediately preceding time point.

[00058] The first amount is determined according to the value for the constant random platelet consumption rate of the numerical model, and the second amount is calculated using a probability density function based on the values for the lognormal distribution of platelet lifespan and the standard deviation of the lognormal distribution of platelet lifespan for the numerical model in view of a quantity of time points from the time point at which the platelet cohort was produced to the time point to which the row corresponds. More particularly, for each platelet cohort of each numerical model, for each time point subsequent to the time point at which the platelet cohort was produced, a cumulative sum of the first amounts determined for the rows corresponding to time points prior to and including the time point is calculated, the cumulative sum is subtracted from the value indicating platelets remaining in the platelet cohort at the time point at which the platelet cohort was produced to determine a value indicating platelets in the platelet cohort not consumed by random platelet consumption at the time point, and the value indicating platelets in the platelet cohort not consumed by random platelet consumption at the time point is used when applying the probability density function to calculate the second amount. [00059] As a result of the above operations being performed when the matrix for each numerical model is generated, for each element of the matrix for the numerical model, the value indicating platelets remaining in the platelet cohort corresponding to the column for the element at the time point corresponding to the row for the element represents a concentration of platelets remaining in the platelet cohort.

[00060] In exemplary embodiments, user interface 110 can be implemented to provide options to the user for setting the range and the resolution for each of one or more of the parameters to be used for obtaining the numerical models. User interface 1 10 can also be implemented to provide an option to the user for setting the value for the constant production rate based on which the numerical models are obtained. Upon receiving these setting values from the user, user interface 110 can input the setting values to model acquirer 120, which can then obtain a corresponding numerical model for each set of values falling within the input range(s) based on the input resolution(s) and the value for the constant production rate. Alternatively, or in conjunction therewith, model acquirer 120 can be configured to obtain the numerical models in accordance with predetermined settings for one or more of the parameters that are used to obtain the numerical models, as well as a predetermined value for the constant production rate.

[00061] In exemplary embodiments, model acquirer 120 can be implemented to perform a discretization process to generate the plurality of numerical models based on the settings for the constant production rate and the set of parameters. Upon generating the numerical models in this manner, model acquirer 120 can access numerical model data store 140 to store the generated numerical models therein. Alternatively, as illustrated in Figure 1, model acquirer 120 can be configured to access a numerical model repository 142 can also be loaded with previously prepared numerical models that were generated in accordance with a plurality of different settings for the constant production rate and the set of parameters and to direct storage of copies of the numerical models from the numerical model repository 142 that were generated corresponding to the particular settings constant production rate and the set of parameters to be used for the performing the current analysis in data store 140.

[00062] Analyzer 130 is configured to, upon model acquirer 120 obtaining the numerical models to use in performing the analysis of the observed platelet consumption data and storing the obtained numerical models in numerical model data store 140 as described above, access the plurality of numerical models stored in data store 140, map the respective platelet population consumption curve for each of the numerical models in accordance with the set of data values are arranged in the numerical model, and perform an analysis of the observed platelet consumption data with respect to the respective platelet population consumption curve provided by each of the numerical model to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data. In the present exemplary embodiment, user interface 110 is configured to input the received platelet consumption data to analyzer 130 to use in performing the analysis. As discussed above, in alternative exemplary embodiments, analyzer 130 may configured to access the data store to obtain the observed platelet consumption data or otherwise receive the observed platelet consumption data as input from the system that includes the data store in which the observed platelet consumption data is stored.

[00063] As noted above, for each element of the matrix for the numerical model, the value indicating platelets remaining in the platelet cohort corresponding to the column for the element at the time point corresponding to the row for the element represents a concentration of platelets remaining in the platelet cohort. To map the respective platelet population consumption curve for each numerical model, analyzer 130 is configured to calculate a sum of the data values for the elements in each row of the matrix. For each row, this calculated value represents a total concentration of the respective platelet population for the numerical model at the time point corresponding to the row. In each valid numerical model, the total concentration of the respective platelet population will increase at each sequential time point from a first time point to a subsequent time point of the plurality of sequential time points, remain at an equilibrium concentration level at each sequential time point from the subsequent time point to an n^th time point of the plurality of sequential time points, and decrease at each sequential time point from the n"¹ time point to the m"¹ time point of the plurality of sequential time points. In view thereof, analyzer 130 is configured to provide the respective platelet population consumption curve that is mapped by each numerical model based on the total concentrations of the respective platelet population from the n"¹ time point to the m"¹ time point of the plurality of sequential time points for the numerical model.

[00064] In performing the analysis of the observed platelet consumption data with respect to the respective platelet population consumption curve for each numerical model, analyzer 130 is configured to upon mapping the respective platelet population consumption curve provided by the numerical model, utilize a data fitting procedure to identify the optimal numerical model for the observed platelet consumption data for which the respective platelet population consumption curve provides the best fit approximation of the observed platelet consumption data. To identify this optimal numerical model for the observed platelet consumption data, analyzer 130 can be implemented to utilize any suitable data fitting procedure. For example, analyzer 130 can be configured to perform a least squares analysis to calculate a sum of squared residuals between the platelet consumption data and the respective platelet population consumption curve provided by each numerical model and, based on the results of least square analysis for each platelet population consumption curve, identify the optimal numerical model for the observed platelet consumption data as the numerical model for which the sum of squared residuals is minimized. [00065] In the present exemplary embodiment, analyzer 130 is configured to, upon identifying the optimal numerical model for the observed platelet consumption data as described above, perform an analysis the identified optimal numerical model to make a determination of various platelet characteristics for the subject and utilize a set of user interface controls implemented by user interface 110 to present a formatted representation or indication of each of the various platelet characteristics for the subject to the user. For example, user interface 1 10 can be configured to output an indication of the platelet characteristics for the subject by generating a corresponding display on a display unit connected to the computing system operated by the user. In exemplary embodiments, data analysis tool 100 can be implemented to provide for the various platelet characteristics that are determined for the subject and presented to the user via user interface 110 to be selected by the user via user interface controls implemented by the user interface for receiving this selection from the user.

[00066] As described in greater details in the examples discussed below, the various platelet characteristics determined by analyzer 130 can include a platelet production rate for the subject and/or other platelet characteristics for the subject that are determined by the analyzer based on a determination of the platelet production rate. To determine the platelet production rate for the subject based on the identified numerical model, analyzer 130 can be implemented to calculate a net platelet consumption rate based on the total concentrations of the respective platelet population for the optimal numerical model at the n^th time point and the (n+l)^th time point, as this net platelet consumption rate will correspond a platelet population turnover rate, calculate the platelet production rate for the subject by multiplying the net platelet consumption rate by a platelet count for the subject.

[00067] In exemplary embodiments of the above method for in which the platelet disorder is thrombocytopenia, the platelet characteristics for the subject can include a type of thrombocytopenia indicated for the subject based on the observed platelet consumption data, which analyzer 130 can be configured to determine based on the platelet production rate for the subject in conjunction with characteristics of the identified optimal numerical model. For example, analyzer 130 can be configured to select an indicated type of thrombocytopenia for the subject from increased platelet consumption and decreased platelet production based on the platelet production rate for the subject and the value for the constant exponential random platelet consumption rate in the optimal numerical model. In another example, analyzer 130 can be configured to calculate an absolute random platelet destruction rate for the subject by multiplying the value for the constant random platelet consumption rate in the optimal numerical model by a platelet count for the subject, and then determine the type of thrombocytopenia in the subject by selecting one of increased platelet consumption with a corresponding increase in platelet production, increased platelet consumption without a corresponding increase in platelet production, and decreased platelet production based on the platelet production rate and the absolute random platelet destruction rate. [00068] In further exemplary embodiments in which the platelet disorder is thrombocytopenia and the platelet characteristics for the subject includes the type of thrombocytopenia indicated for the subject, analyzer 130 can be configured to determine the type of thrombocytopenia in further view of additional relevant data pertaining to the subject. In this regard, user interface 110 can be further configured to implement user interface controls for receiving this additional data, which may include, for example, a particular condition of the subject contributing to the thrombocytopenia (for instance, liver disease), a serum plasma thrombopoietin level of the subject, and/or an immature platelet fraction for the patient, and to input the received additional data to analyzer 130. In some such embodiments, analyzer 130 can be configured to select an indicated type of thrombocytopenia for the subject from a group of more specific medical conditions such as, for instance, a group consisting of immune thrombocytopenic purpura (ITP), thrombotic thrombocytopenic purpura, heparin induced thrombocytopenia, and disseminated intravascular coagulation.

[00069] In exemplary embodiments in which user interface 1 10 is configured to output an indication of the platelet characteristics for the subject by generating a corresponding display on a display unit connected to the computing system operated by the user, the display that is generated can include a graphical representation of the respective platelet population consumption curve provided by the optimal numerical model. This display may further include indications of the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for the optimal numerical model, which may be provided, for example, within a window element that includes the display of the graphical representation of the respective platelet population consumption curve. In additional examples, the display generated by user interface 110 can include indications of any number of various platelet characteristics for the subject that can be determined by analyzer 130 based on the identified optimal numerical model such as, for instance, a platelet population turnover rate, an absolute random platelet destruction rate, a hemostatic random destruction (HRD) rate, a non-hemostatic random destruction ( HRD) rate, and consumption rate characteristics of young platelets in view of characteristics of platelets showing a normal age distribution.

[00070] Accordingly, exemplary embodiments of the present invention can be implemented to provide a computer-implemented platelet disorder data analysis tool configured to perform a method for determining one or more characteristics of a platelet disorder in a subject, the method include the steps: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters which may include (1) a constant exponential random platelet consumption rate, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model. In this method, the observed platelet consumption data for a subject may be obtained by in vivo observations of platelet consumption in the subject by measuring clearance of labeled platelets that are autologous to the subject from a bloodstream of the subject over a time period, or may be obtained by in vivo observations of platelet consumption in the subject by measuring clearance of labeled platelets that are allogeneic to the subject from a bloodstream of the subject over a time period.

[00071] As used herein, the term "characteristics" refers to any characteristic of a platelet disorder such as type of disorder, or any characteristic that is symptomatic or caused by the disorder such as consumption rate, platelet turnover, production rate, etc. For example, the characteristic of the platelet disorder may be increased platelet consumption, decreased platelet production, or a combination of the two.

[00072] In some embodiments, the present invention provides a computer-implemented method for diagnosing a platelet disorder in a subject. The method includes the steps of: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models generated to provide a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for corresponding parameters relating to platelet consumption; analyzing the platelet consumption data in view of the plurality of numerical models to identify a numerical model that provides a best fit approximation of a platelet population consumption curve for the observed platelet consumption data; and outputting an indication of a type of platelet disorder for the subject based on the best fit of a platelet population consumption curve for the observed platelet consumption data. In some embodiments, the method further includes the step of determining the platelet production rate in the subject based on the identified numerical model; and outputting an indication of a type of platelet disorder for the subject based on the platelet production rate.

[00073] The terms "diagnosing" and "diagnosis" as used herein refer to methods by which the skilled artisan can estimate and even determine whether or not a subject is suffering from a given disease or condition. The skilled artisan often makes a diagnosis on the basis of one or more diagnostic indicators, such as for example a biomarker, the amount (including presence or absence) of which is indicative of the presence, severity, or absence of the condition.

[00074] In some embodiments, the present invention provides a computer-implemented method for determining the cause of a platelet disorder in a subject. The method includes: receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant random platelet consumption, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model. In some embodiments, the cause is selected from the group consisting of impaired platelet production, increased platelet consumption, and a combination of the two.

[00075] As indicated above, the presently disclosed invention provides a computer- implemented method for determining one or more characteristics of a platelet disorder in a subject. By platelet disorder is meant any disorder, disease or condition affecting platelets as would be readily understood by one of ordinary skill in the art. Such platelet disorders may include thrombocytopenias, disorders of platelet adhesion, disorders of aggregation, disorders of secretion, disorders of thromboxane synthesis, acquired disorders of platelet function, uremia, paraproteins, fibrin degradation products, myelodysplasia, myeloproliferative syndrome, von Willebrand disease, Bernard-Soulier syndrome, and Glanzmann thrombasthenia.

[00076] In another aspect of the present invention, a computer-implemented method for determining one or more characteristics of a platelet disorder in a subject is provide which may comprise the steps of receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant exponential random platelet consumption rate, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan; analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model. In another aspect of this method, the cause may be impaired platelet production, increased platelet consumption, or a combination of the two.

[00077] In another aspect of the present invention, a computer-implemented method for diagnosing a platelet disorder in a subject is provided which may comprise the steps of receiving observed platelet consumption data for a subject; obtaining a plurality of numerical models generated to provide a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for corresponding parameters relating to platelet consumption; analyzing the platelet consumption data in view of the plurality of numerical models to identify a numerical model that provides a best fit approximation of a platelet population consumption curve for the observed platelet consumption data; and outputting an indication of a type of platelet disorder for the subject based on the best fit of a platelet population consumption curve for the observed platelet consumption data. This method may further comprise determining the platelet production rate in the subject based on the identified numerical model; and outputting an indication of a type of platelet disorder for the subject based on the platelet production rate.

[00078] Further provided, in some embodiments of the presently-disclosed subject matter, is a method for differentiating between different platelet disorders in a subject. The method may include the steps of: obtaining in vivo platelet consumption data from a subject, wherein said data is obtained with at least one marker; comparing said platelet consumption data with platelet consumption curves generated from a numerical analysis model to determine the kinetic basis for the platelet disorder in the subject; and differentiating the type of platelet disorder in the subject based on said curves. In some embodiments, the numerical analysis model is obtained by the steps of: receiving user input from an input device configured for receiving in vivo platelet consumption data; computing with a computational engine, said engine configured to implement a numerical analysis model to the determine the kinetic basis for the platelet disorder; and creating curves that correlate the kinetic basis with the type of the platelet disorder.

[00079] Yet in some further embodiments, the present invention provides a method of determining the cause of platelet disorder in a subject. The method includes the steps of: obtaining in vivo platelet consumption data from the subject, wherein said data are obtained with at least one marker; constructing platelet consumption curves from a numerical analysis model to determine the kinetic basis of the platelet disorder; comparing said platelet consumption data with said curves to determine the kinetic basis of the platelet disorder in the subject; and determining the cause of said platelet disorder in the subject based on the comparison of said curves. In some embodiments, the numerical analysis model is based on random destruction (RD), platelet lifespan (PL) and a standard deviation of the platelet lifespan. In some embodiments, the method further includes the step of labeling platelets of a donor with at least one marker, and administrating the labeled platelets to a recipient. Further, non- limiting examples of markers includes a fluorescent marker, CMFDA, BMQC, a pH-sensitive marker, PhRodo, a radioactive marker, 11 lln-oxine, and an enzymatic marker.

[00080] Further, in some embodiments, the random destruction (RD) is broken down into hemostatic random destruction (HRD), and non-hemostatic random destruction (NHRD). Further still, the numerical analysis model is used to determine whether platelets from a subject with increased non- hemostatic random destruction (NHRD) are more susceptible to ex vivo phagocytosis or enhanced platelet phagocytosis.

[00081] In some embodiments, the numerical analysis model is based on a random consumption rate, a platelet lifespan and a standard deviation of the platelet lifespan. In some further embodiments, the numerical analysis model is further based on a platelet production rate.

[00082] In some embodiments, the numerical analysis model is used to determine if the consumption rate parameter values of young platelets are identical to those of platelets showing a normal age distribution. Yet in some embodiments, the numerical analysis model is used to determine if consumption rates are intrinsically different for young versus old platelets.

[00083] As indicated above, in some embodiments, an example of a platelet disorder is thrombocytopenia. Non-limiting examples of thrombocytopenia include immune thrombocytopenic purpura (ITP), thrombotic thrombocytopenic purpura, heparin induced thrombocytopenia, and disseminated intravascular coagulation. In some embodiments, the thrombocytopenia is associated with liver disease. In some further embodiments, the thrombocytopenia is caused by accelerated consumption or impaired production, or a combination of the two.

[00084] The presently-disclosed subject matter further provides a method of predicting the response to therapy of a subject with platelet disorders. The method includes: obtaining in vivo platelet consumption data from the subject; wherein said data is obtained with administering a donor's platelets to the subject, wherein the donor's platelets are labeled with at least one marker; constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan; comparing said platelet consumption data with said curves to determine the kinetic basis for platelet disorder; determining the causes of said platelet disorder based on said curves; and predicting the response to therapy of said patient based on said determination.

[00085] The phrase "predicting the response" as used herein refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term does not refer to the ability to predict the course or outcome of a condition with 100% accuracy, or even that a given course or outcome is predictably more or less likely to occur based on the presence, absence or levels of a therapy. Instead, the skilled artisan will understand that the term refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject exhibiting a given condition, when compared to those individuals not exhibiting the condition. For example, in individuals does not receiving a therapy, the chance of a given outcome (e.g., increase platelets number in a subject suffering thrombocytopenia) may be very low (e.g., <1%), or even absent. In contrast, in individuals receives the therapy, the chance of a given outcome (e.g., increase platelets number in a subject suffering thrombocytopenia) may be high. In certain embodiments, a predicted response is about a 5% chance of a given expected outcome, about a 7% chance, about a 10% chance, about a 12% chance, about a 15% chance, about a 20% chance, about a 25% chance, about a 30% chance, about a 40% chance, about a 50% chance, about a 60% chance, about a 75% chance, about a 90% chance, or about a 95% chance.

[00086] In another embodiment, the present invention provides a method of treating a subject with platelet disorders. The method includes the steps of: obtaining in vivo platelet consumption data from the subject; wherein said data is obtained with administering a donor's platelets to the subject, wherein the donor's platelets are labeled with at least one marker; constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan; comparing said platelet consumption data with said curves to determine the kinetic basis for platelet disorder; determining the causes of said platelet disorder based on said curves; and treating the patient in accordance with the determined cause of said platelet disorders. In some embodiments, the method further contains the step of administering to said subject an effective amount of at least one pharmaceutical composition specific to the cause of the platelet disorder. In some embodiments, the method further includes a splenectomy of the subject. Yet in some embodiments, non-limiting examples of the pharmaceutical composition include thrombopoietin receptor agonists and corticosteroids.

[00087] The term "treatment" relates to medical management of a subject with the intent to substantially cure, ameliorate, stabilize, or substantially prevent a condition of interest (e.g., disease, pathological condition, or disorder), including but not limited to prophylactic treatment to preclude, avert, obviate, forestall, stop, or hinder something from happening, or reduce the severity of something happening, especially by advance action.

[00088] As such, the terms "treatment" or treating include, but are not limited to: inhibiting the progression of a condition of interest; arresting or preventing the development of a condition of interest; reducing the severity of a condition of interest; ameliorating or relieving symptoms associated with a condition of interest; causing a regression of the condition of interest or one or more of the symptoms associated with the condition of interest; and preventing a condition of interest or the development of a condition of interest.

[00089] As used herein, the term "effective amount" refers to an amount that is sufficient to achieve the desired result or to have an effect on an undesired condition. For example, a "therapeutically effective amount" refers to an amount that is sufficient to achieve the desired therapeutic result or to have an effect on undesired symptoms, but is generally insufficient to cause adverse side effects. The specific therapeutically effective dose level for any particular patient will depend upon a variety of factors including the disorder being treated and the severity of the disorder; the specific composition employed; the age, body weight, general health, sex and diet of the patient; the time of administration; the route of administration; the rate of excretion of the specific compound employed; the duration of the treatment; drugs used in combination or coincidental with the specific compound employed and like factors well known in the medical arts. For example, it is well within the skill of the art to start doses of a compound at levels lower than those required to achieve the desired therapeutic effect and to gradually increase the dosage until the desired effect is achieved. If desired, the effective daily dose can be divided into multiple doses for purposes of administration. Consequently, single dose compositions can contain such amounts or submultiples thereof to make up the daily dose. The dosage can be adjusted by the individual physician in the event of any contraindications. Dosage can vary, and can be administered in one or more dose administrations daily, for one or several days. Guidance can be found in the literature for appropriate dosages for given classes of pharmaceutical products. In further various aspects, a preparation can be administered in a "prophylactically effective amount"; that is, an amount effective for prevention of a disease or condition.

[00090] Some portions of the exemplary embodiments described above are presented in terms of algorithms and symbolic representations of operations on data bits within a processor-based system. The operations are those requiring physical manipulations of physical quantities. These quantities may take the form of electrical, magnetic, optical, or other physical signals capable of being stored, transferred, combined, compared, and otherwise manipulated, and are referred to, principally for reasons of common usage, as bits, values, elements, symbols, characters, terms, numbers, or the like. Nevertheless, it should be noted that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the description, terms such as "executing" or "processing" or "computing" or "calculating" or "determining" or the like, may refer to the action and processes of a processor-based system, or similar electronic computing device, that manipulates and transforms data represented as physical quantities within the processor-based system's storage into other data similarly represented or other such information storage, transmission or display devices.

[00091] Exemplary embodiments of the present invention can be realized in hardware, software, or a combination of hardware and software. Exemplary embodiments can be realized in a centralized fashion in one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system - or other apparatus adapted for carrying out the methods described herein - is suited. A typical combination of hardware and software could be a general-purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein. [00092] Exemplary embodiments of the present invention can also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which - when loaded in a computer system - is able to carry out these methods. Computer program means or computer program as used in the present invention indicates any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code or, notation; and (b) reproduction in a different material form.

[00093] A computer system in which exemplary embodiments can be implemented may include, inter alia, one or more computers and at least a computer program product on a computer readable medium, allowing a computer system, to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium may include non-volatile memory, such as ROM, Flash memory, disk drive memory, CD- ROM, and other permanent storage. Additionally, a computer readable medium may include, for example, volatile storage such as RAM, buffers, cache memory, and network circuits. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network, that allow a computer system to read such computer readable information.

[00094] Figure 6 is a block diagram of an exemplary computer system 600 that can be used for implementing exemplary embodiments of the present invention. Computer system 600 includes one or more processors, such as processor 604. Processor 604 is connected to a communication infrastructure 602 (for example, a communications bus, cross-over bar, or network). Various software embodiments are described in terms of this exemplary computer system. After reading this description, it will become apparent to a person of ordinary skill in the relevant art(s) how to implement the invention using other computer systems and/or computer architectures.

[00095] Exemplary computer system 600 can include a display interface 608 that forwards graphics, text, and other data from the communication infrastructure 602 (or from a frame buffer not shown) for display on a display unit 610. Computer system 600 also includes a main memory 606, which can be random access memory (RAM), and may also include a secondary memory 612. Secondary memory 612 may include, for example, a hard disk drive 614 and/or a removable storage drive 616, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. Removable storage drive 616 reads from and/or writes to a removable storage unit 618 in a manner well known to those having ordinary skill in the art. Removable storage unit 618, represents, for example, a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 616. As will be appreciated, removable storage unit 618 includes a computer usable storage medium having stored therein computer software and/or data.

[00096] In exemplary embodiments, secondary memory 612 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such means may include, for example, a removable storage unit 622 and an interface 620. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 622 and interfaces 620 which allow software and data to be transferred from the removable storage unit 622 to computer system 600.

[00097] Computer system 600 may also include a communications interface 624.

Communications interface 624 allows software and data to be transferred between the computer system and external devices. Examples of communications interface 624 may include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, etc. Software and data transferred via communications interface 624 are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface 624. These signals are provided to communications interface 624 via a communications path (that is, channel) 626. Channel 626 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels.

[00098] In this document, the terms "computer program medium," "computer usable medium," and "computer readable medium" are used to generally refer to media such as main memory 606 and secondary memory 612, removable storage drive 616, a hard disk installed in hard disk drive 614, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as Floppy, ROM, Flash memory, Disk drive memory, CD-ROM, and other permanent storage. It can be used, for example, to transport information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface including a wired network or a wireless network that allow a computer to read such computer readable information.

[00099] Computer programs (also called computer control logic) are stored in main memory

606 and/or secondary memory 612. Computer programs may also be received via communications interface 624. Such computer programs, when executed, can enable the computer system to perform the features of exemplary embodiments of the present invention as discussed herein. In particular, the computer programs, when executed, enable processor 604 to perform the features of computer system 600. Accordingly, such computer programs represent controllers of the computer system.

[000100] The presently-disclosed subject matter is further illustrated by the following specific but non-limiting examples. Some of the following examples are prophetic, notwithstanding the numerical values, results and/or data referred to and contained in the examples.

EXAMPLES

Example 1. A Numerical Analysis Model for the Interpretation of In Vivo Platelet Consumption Data

[000101] Materials and Methods.

[000102] Ethics statement: All animal studies were approved by the institutional animal care and use committee of the Memphis VA Medical Center (protocol #281).

Reagents: CMFDA ("celltracker green") and BMQC ("celltracker violet") were obtained from

Invitrogen. PE-anti -mouse CD41 was obtained from BD Biosciences. PGE-1 was obtained from Sigma. Mouse strains: WASP(-) mice originally derived by Snapper et al. [Snapper SB, Rosen FS, Mizoguchi E, Cohen P, Khan W, et al. (1998) Wiskott-Aldrich syndrome protein-deficient mice reveal a role for WASP in T but not B cell activation. Immunity 9: 81-91.] were crossed onto the C57B1/6J background for at least 8 generations. All donors and recipients in this study were males. Platelet and reticulated platelet counts were routinely performed on WASP(-) mice prior to their use as platelet donors or recipients. All mice were bred and maintained in specific pathogen free environments at the Memphis VA Medical Center. In vivo platelet consumption assays: Platelet preparation, labeling of platelets with CMFDA, injection of labeled platelets, and quantification of the fraction of peripheral blood platelets labeled was performed as previously described [Marathe BM, Prislovsky A, Astrakhan A, Rawlings DJ, Wan JY, et al. (2009) Antiplatelet antibodies in WASP(-) mice correlate with evidence of increased in vivo platelet consumption. Exp Hematol 37: 1353-1363.]. In some cases, platelets labeled with BMQC CELLTRACKER VIOLET ( Invitrogen) were co-injected with CMFDA-labeled platelets. One skilled in the art can select other labeling reagents, from the CELLTRACKER( Invitrogen) product list as a wide variety of colors and functionalities provide efficient and sensitive methods for identifying and following specific cells within a population. In particular it should be noted that here are potential advantages to using two labeling reagents at once in the same patient in some circumstances (one labeling allogeneic platelets and the other one labeling autologous platelets).

[000103] Briefly, platelets were prepared (from blood obtained via tail clipping) using ficoll step gradients. Platelets at 2.9 x 105 per ul were fluorescently labeled in modified tyrode's buffer (20 mM Hepes, 137 mM NaCl, 13.8 mM NaHC03, 2.5 mM KCl, 0.36 mM NaH2P04-H20, 5.5 mM glucose, 0.25% bovine serum albumin, 1 mM MgC12) supplemented with 1 ug/ml PGE-1, and 1.25 uM

CMFDA or 1.25 uM BMQC at 37 degrees C for 20 minutes. After addition of five volumes modified tyrode's buffer + 1 ug/ml PGE-1, platelets were centrifuged at 2000 RCF for 10 minutes at room temperature, resuspended in modified tyrode's buffer, and counted using a Beckman-Coulter (Fullerton, CA, USA) Model Z2 particle count and size analyzer. Labeled platelets or mixtures of labeled platelets were brought to 2.5 x 105 per ul and injected via tail vein into the recipients described in the text. Typical injection volumes of 400 to 600 ul yielded platelet dosages of 1 to 1.5 x 108 platelets, resulting in most cases in labeling of 2% to 4% of circulating platelets at TO (5 minutes). Recipients were bled retro-orbitally at TO and at the intervals shown in FIG. 7. In some cases the fraction of platelets labeled was determined after gating platelets via forward vs. side scatter. In others, gating also employed the addition of a PE-CD41 marker. All flow cytometric data was analyzed using Flowjo software (Tree Star Inc., Ashland, OR).

[000104] Consumption of Indium-Ill -labeled platelets: Platelets prepared as described above were resuspended at approximately 3 x 105 per ul and incubated with 200 uCi 1 1 lln-oxyquinoline (GE Healthcare) per 1 x 109 platelets at 370 for 30 minutes, centrifuged at 6,000g for 5 minutes at room temperature, and resuspended in modified tyrode's buffer. A mock-labeling reaction was used to estimate platelet recovery from this step via Coulter counting. Specific activities of the labeled platelets ranged from 3 x 107 to 4 x 107 cpm per platelet. Approximately 1.2 xl08 platelets in a net volume of 500 ul were injected per recipient. Total cpm per ul of whole blood was measured with a gamma counter at 5 minutes post injection. CPM were measured again, and corrected for decay, at the times shown in FIG. 7.

[000105] Computational methods: All calculations were performed using Microsoft Excel on standard iMAC desktop computers. Recurrent calculations were written into Excel "macros" using visual basic programming language. Three dimensional plots of LML values were performed with Qtiplot (QtiPlot - Data Analysis and Scientific Visualization Craiova, Romania).

[000106] Results: Model: The numerical analysis model is constructed on the assumption that two kinetically distinct processes result in platelet consumption: a random destruction process (RD),designed to model the sum of hemostatic as well as other random platelet consumption processes, and applicable to all circulating platelets; and a platelet lifespan (LS), at the end of which platelets begin to apoptose. We assume that lifespan-dependent platelet consumption is determined by a series of internal pro-apoptotic processes as well as (random) recognition of apoptotic platelets by receptors on phagocytes. For this reason, LS is assumed to be lognormally distributed, an assumption supported by empirical cell fate observations in similar systems [Duffy KR, Wellard CJ, Markham JF, Zhou JH, Holmberg R, et al. (2012) Activation-induced B cell fates are selected by intracellular stochastic competition. Science 335: 338-341. Hawkins ED, Markham JF, McGuinness LP, Hodgkin PD (2009) A single-cell pedigree analysis of alternative stochastic lymphocyte fates. Proceedings of the National Academy of Sciences of the United States of America 106: 13457-13462]. It is then necessary to introduce a third parameter (SD, the standard deviation of ln(LS)) to model such a distribution. It is shown (below) (1) that the rate of platelet population consumption can be modeled numerically for any set of values of RD (%/hr), LS (hr), and SD; (2) that the difference between such a model curve and observed consumption data can be quantified as the sum of squared residuals (SS); and (3) that by generating SS values for a large range of possible combinations of RD, LS, and SD values (in a hypothetical volume termed "parameter space", or PS), and identifying a minimum value in the resultant array of SS values, we can identify the parameter values which optimally describe in vivo platelet consumption data. To perform that modeling, we constructed (in a spreadsheet) a dynamic population of platelet cohorts (produced hourly at a user defined production rate PR), each of which is consumed (hourly) by the aforementioned processes. This is shown schematically (FIG. 2) as a two dimensional matrix of dimensions n and m, in which the columns (1 to n) represent the concentrations (K/ul) of platelets in cohorts which are produced at time (i=j), and consumed at hourly intervals (j=i+l , j=i+2, . . .j=m) by two types of process (random and lifespan-dependent). The entry Pi,j denotes the concentration of platelets in cohort (j) at time (i). Random consumption at the end of a given interval (RD(i)) is equal to RD (%/hr)xP(i). The cumulative amount of platelets in a cohort consumed by random consumption processes is tracked in a separate column as cumulative random destruction (CRD(i)). Lifespan dependent consumption at the end of each interval is calculated using the excel probability density function lognorm.dist, applied to those platelets in the cohort not consumed by random processes (=PR-CRDi). The variables used by lognorm.dist are LS, SD, and the age of the cohort (i-j). The resultant value is the lifespan dependent consumption amount (LSDC(i)). Thus at interval (i+1), P = P(i) - RD(i+l) - LSDC(i+l). This value is calculated for each cell in the matrix. Range limitations are placed on the calculations to prevent the generation of negative P values. The consumption process for individual cohorts can therefore vary from predominantly linear to exponential, as shown for two examples in FIG. 3A. (The empirical choice of parameter values for the figure is described below). Values for sequentially produced cohorts sum to the net platelet count at time i. Over time, the net platelet count in this model increases to an equilibrium value as shown in FIG. 3B. This value is associated with a defined platelet age distribution, as shown in FIG. 3C. Platelet production rate (PR, K/ul/hr) can be manually adjusted to generate a net platelet count consistent with observed mean values such as those we have reported for WT and WASP(-) mice [A, Marathe B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906.e915.] To model in vivo platelet consumption, the matrix is extended vertically so as to contain more rows than columns, i.e. m = n + c, where a value of c=125 hr represents a useful value in modeling murine platelet consumption. Thus at row (time) values (i>n), platelet production ceases and cohort platelet consumption continues as determined by the parameter values and the age of each cohort (j-i). Platelet population consumption from this time point continues as the sequential sums of the cohort values, generating the consumption curves seen in FIG. 3B at the end of the equilibration phase. The validity of the model-generated consumption curves is dependent, for any set of parameter values, on the achievement of equilibrium at time j=m. We evaluate this for different sets of parameter values via an equilibration metric (e), which we define as the platelet count a time (i=j/2) divided by the platelet count at time (i=j). It is assumed that equilibration is functionally adequate when (e) exceeds 0.95 (i.e. the model has achieved at least 95% of its final platelet count over the first half of its equilibration phase).

[000107] The effect of varying the three consumption parameter values on the shape of the consumption curve is shown in FIG. 4. The mid-range of these curves was chosen to roughly approximate the behavior of WT platelets in WT recipients (see below). As expected, a relative increase in RD yields a curve that begins to resemble exponential decay, while a relative decrease in LS yields more linear behavior. High SD values also cause the curve to resemble exponential decay. These results indicate that the model can emulate the not-quite-linear, not quite-exponential platelet consumption processes seen in most published studies [Hanson SR, Slichter SJ (1985) Platelet kinetics in patients with bone marrow hypoplasia: evidence for a fixed platelet requirement. Blood 66: 1105-1 109. Tomer

A, Hanson SR, Harker LA (1991) Autologous platelet kinetics in patients with severe thrombocytopenia: discrimination between disorders of production and destruction. J Lab Clin Med 118: 546-554. Dowling MR, Josefsson EC, Henley KJ, Hodgkin PD, Kile BT (2010) Platelet senescence is regulated by an internal timer, not damage inflicted by hits. Blood 116: 1776-1778. Hill- Zobel RL, McCandless B, Kang SA, Chikkappa G, Tsan MF (1986) Organ distribution and fate of human platelets: studies of asplenic and splenomegalic patients. American journal of hematology 23: 231-238. Wandall HH, Hoffmeister KM, Sorensen AL, Rumjantseva V, Clausen H, et al. (2008) Galactosylation does not prevent the rapid clearance of long-term, 4 degrees C-stored platelets. Blood 111 : 3249-3256.] The model also allows direct inference of the platelet population turnover rate (hence, platelet production rate) as the rate observed in the first interval after platelet production ceases. Optimal parameter search method: In vivo platelet consumption data was obtained by labeling platelet preparations with a fluorescent marker (either CMFDA or BMQC); injecting them into recipient tail veins; quantifying their initial concentration (% of total platelets) in peripheral blood at 5 minutes after injection; and quantifying their concentration again at four subsequent intervals. [Prislovsky A, Marathe

B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906. e915.] The resultant data comprises a time zero (TO) measurement and four subsequent measurements of the fraction of the TO value remaining. This data has been obtained from prior studies, for a total of 30 WT recipients of WT platelets (WT to WT), and 18 WASP(-) recipients of WASP(-) platelets (WASP(-) to WASP(-)). These were usually obtained as control data in the course of studying other consumption rates [Prislovsky A, Marathe B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906.e915. Marathe BM, Prislovsky A, Astrakhan A, Rawlings DJ, Wan JY, et al. (2009) Antiplatelet antibodies in WASP(-) mice correlate with evidence of increased in vivo platelet consumption. Exp Hematol 37: 1353-1363.]

[000108] A single combination of the three parameter values (RD, LS, and SD) can generate, via the model, platelet consumption curves which optimally match those observed experimentally. To determine whether the model can do this, we used a least squared residuals procedure to search for parameter values which best fit the data. We evaluated 8000 points in a parameter space (PS) defined by 20 equally spaced values along three orthogonal axes. This entails generating a matrix (FIG. 2) for each point in PS, i.e. each possible combination of parameter values. The range evaluated for each parameter is user defined. In all cases, model equilibration within the PS volume searched is confirmed, (Squared residual values are calculated for each data point (for WT to WT studies, 30 x 4 = 120 data points), allowing calculation of a sum of squared residuals value (SS) for all data points at each point in PS.

[000109] To visualize the distribution of SS values in parameter space, we evaluated all points in an RD-defined plane. The resultant SS values can be displayed as a surface in a volume defined by the axes LS, SD, and SS, as shown in FIG. 5A. We term a minimum SS value (and its associated parameter values) on such a surface a "local minimum" (LM). The L can in turn be followed across sequential planes to define a "local minimum line" (LML) in PS. Evaluation of SS for all values on the LML is then used to identify a "global minimum" (GM) (FIG. 5B), which is subsequently resolved more precisely with a higher resolution search Optimal parameters for autologous platelet consumption: Optimal parameter values (at the identified global minima) for WT to WT and WASP(-) to WASP(-) platelet consumption data are shown in Table 1. The associated consumption curves (and the data to which they were fit) are shown in FIG. 7.

[000110] FIG. 7 also shows that similar consumption data was obtained with Indium-I l l labeled platelets. This suggests that an artifactual effect of fluorescent labeling does not contribute significantly to our findings. Note that the high rate of random consumption of WASP(-) platelets in WASP(-) mice precludes estimation of LS from this data, as the fraction of platelets consumed by lifespan-dependent processes is in this case less than 1% as sown in Table 1. Standard deviation estimates for the parameter values were calculated using a "jackknife" resampling method [Shao J, Wu CFJ (1989) A general theory for jackknife variance estimation. The Annals of Statistics 17: 1 176-1197], described in more detail in supporting information.

[000111] Table 1. Optimal platelet consumption parameters and associated values.

Donor WT WT WASP(-) WASP(-)

Recipient WT WASP(-) WASP(-) WT

n 30 13 18 25

RD, %/hr 1.16 (0.09) 1.55 (0.03) 4.20 (0.07) 2.28 (0.04) LS, hr 105 (2.3) 105* n/a 106 (1.3)

0.180

SD (of In LS) (0.057) 0.275 (0.033) n/a 0.231 (0.043)

Platelet

population

turnover rate,

%/hr 1.66 2.03 4.22 2.35

Random

destruction, %

of turnover

rate 69.9 76.5 99.5 97.2

Platelet

turnover rate,

K/ul/hr 20.5 n/a 24.4 n/a

[000112] Table 1: Optimal platelet consumption parameters and associated values. Values in parentheses are standard errors determined by "jackknife" resampling as described in the text. The differences between the four RD values are all significant (two sample t-test, p < 0.05). The differences between columns for the SD and LS values are not significant. Population turnover rates for the allogeneic platelet consumption studies refer to the donor populations. For WT to- WASP(-) data, LS is not greater than 105 hr.

[000113] Optimal parameters for allogeneic platelet consumption: As shown in FIG. 2C, the model generates both an equilibrated population of platelet cohorts and a consumption curve. The latter' s shape is determined by the number or platelets (P(i)) remaining in each cohort when platelet production ceases; the associated CRD values; platelet production rate (PR, used to calculate LSDC(i)); and the parameter values applied during the consumption phase. To find optimal parameter values describing the consumption of allogeneic platelets, we "graft" the P and CRD values for each donor strain cohort (in the spreadsheet, the last row in the equilibration phase) into an identical matrix. Using the same procedures described above, and applying the PR value associated with the donor platelets, we then search for the optimal parameter values which generate a consumption curve which optimally fits the observed data. Results for consumption of WT platelets in WASP(-) recipients, and the converse type of study, are shown in Table 1 and FIG. 7.

[000114] A study concluded that a lifespan-dependent platelet consumption model provided a better "fit" to in vivo murine platelet consumption data than did a model based on a hypothetical "multiple hit" mechanism [Dowling MR, Josefsson EC, Henley KJ, Hodgkin PD, Kile BT (2010) Platelet senescence is regulated by an internal timer, not damage inflicted by hits. Blood 116: 1776- 1778.]. Here we extend that observation by demonstrating that the combined effects of lifespan- dependent and random platelet consumption processes provide a still better fit to such normal platelet consumption data. Because it generates an age distribution for platelets studied in autologous transfusion studies, the model can also be used to interpret their consumption rate in allogeneic recipients. The importance of taking the donor platelet age distribution into account is evident when the consumption of WT platelets in WASP(-) recipients (FIG. 7C) is compared to that of WASP(-) platelets in WT recipients (FIG. 7D). Although the raw data is quite similar, the optimal parameter values are markedly different (Table 1). This is due to the strikingly different age distributions of WT and WASP(-) platelets (FIG. 3), inferred from their markedly different autologous consumption rates (FIGS. 7A and 7B).

[000115] Applied to a large body of in vivo platelet consumption data from 30 WT mice, the model finds that 71% of WT platelets are consumed by random processes (Table 1). Because this differs radically from a previous estimate of 17% in humans [Hanson SR, Slichter SJ (1985) Platelet kinetics in patients with bone marrow hypoplasia: evidence for a fixed platelet requirement. Blood 66: 1105-1109.], we obtained consumption data using an alternative platelet labeling method (Indium-111). This data does not differ substantially from that obtained with fluorescently labeled platelets (FIG. 7). Resampling statistics (Table 1) generate a 95% confidence interval for the random consumption rate of 0.97 to 1.35 %/hr. These findings suggest that the rapid random platelet consumption we see is not artifactual However, if most of this consumption resulted from a baseline requirement for normal hemostasis, we would expect that a reduction in platelet count by roughly 30% or more would result in spontaneous hemorrhages. This is not the case. Mpl(-/-) strains show platelet count reductions of over 80% [Levin J, Cocault L, Demerens C, Challier C, Pauchard M, et al. (2001) Thrombocytopenic cmpl(- /-) mice can produce a normal level of platelets after administration of 5-fluorouracil: the effect of age on the response. Blood 98: 1019-1027. Alexander WS, Roberts AW, Nicola NA, Li R, Metcalf D (1996) Deficiencies in progenitor cells of multiple hematopoietic lineages and defective megakaryocytopoiesis in mice lacking the thrombopoietic receptor c-Mpl. Blood 87: 2162-2170. Kauppi M, Murphy JM, de Graaf CA, Hyland CD, Greig KT, et al. (2008) Point mutation in the gene encoding p300 suppresses thrombocytopenia in Mpl-/- mice. Blood 112: 3148-3153], and demonstrate no abnormal bleeding. This suggests that in mice, most random platelet consumption is not directly involved in hemostasis. It should be noted that a similar issue arises when human platelet consumption is considered. If 17% of human platelet turnover is required for normal hemostasis, a platelet count below that fraction of the normal human mean of approximately 290 x 109/L (i.e. below approximately 49 x 109/L) would be expected to result in spontaneous hemorrhage. The latteris in fact rare above a platelet count of 10 x 109/L [Slichter SJ (2004) Relationship between platelet count and bleeding risk in thrombocytopenic patients. Transfusion medicine reviews 18: 153-167]. These considerations suggest that most random platelet consumption could predominantly serve non-hemostatic biological purposes such as immune function [Semple JW, Italiano JE, Jr., Freedman J (2011) Platelets and the immune continuum. Nature reviews Immunology 11 : 264-274]. Present results suggest that rapid random platelet consumption is the largest contributing factor to the thrombocytopenia of murine WAS, since platelet production is in fact increased (Table 1) but is insufficient to correct their approximately 53% reduction in platelet count [Prislovsky A, Marathe B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906. e915.]). However, WASP(-) mice also show a nearly twofold increase in bone marrow megakaryocytes [Prislovsky A, Marathe B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906.e915.] as well as a two-fold increase in spleen size that is predominantly due to extramedullary hematopoiesis [Andreansky S, Liu CH, Turner SJ, McCullers J, Lang R, et al. (2005) WASP- mice Exhibit Defective Immune Responses to Influenza A Virus, Streptococcus pneumoniae, and Mycobacterium bovis BCG. Experimental Hematology 33: 443-451]. This suggests that platelet production per megakaryocyte must be significantly reduced by WASP deficiency, as has been reported in ex vivo thrombopoiesis studies [Sabri S, Foudi A, Boukour S, Franc B, Charrier S, et al. (2006) Deficiency in the Wiskott-Aldrich protein induces premature proplatelet formation and platelet production in the bone marrow compartment. Blood 108: 134-140. Kajiwara M, Nonoyama S, Eguchi M, Morio T, Imai K, et al. (1999) WASP is involved in proliferation and differentiation of human haemopoietic progenitors in vitro. Br J Haematol 107: 254-262]. The numerical analysis model also allows us to ask whether the rapid random clearance of WASP(-) platelets in WASP(-) recipients is due primarily to cz^'s-acting (platelet intrinsic) or trans acting factors. The findings in Table 1 allow the following conclusions. (1) A platelet intrinsic (cis) defect contributes significantly to the rapid clearance of WASP(-) platelets (compare the RD of WASP(-) to WT platelets in WT recipients). (2) Platelet lifespan is not affected by WASP deficiency (compare the LS of WT vs. WASP(-) platelets in WT recipients). (3) An additional platelet extrinsic (trans) effect of recipient WASP deficiency results in a still more rapid random consumption rate for WASP(-) donor platelets (RD is increased by 84% for WASP(-) platelets in WASP(-) vs WT recipients). (4) The trans appears to largely require platelet WASP deficiency as well (i.e. it is a cis/trans effect), since it is much weaker when WT platelets are infused (RD is increased by only 34% for WT platelets in WASP(-) vs WT recipients). [Dowling MR, Josefsson EC, Henley KJ, Hodgkin PD, Kile BT (2010) Platelet senescence is regulated by an internal timer, not damage inflicted by hits. Blood 116: 1776-1778.] No positive trans effect of the WASP(-) environment on the LS of WT platelets is evident. The current model does not allow assessment of a negative trans effect on LS. For WASP(-) platelets, we cannot assess either type of effect because random consumption in WASP(-) recipients is too rapid to allow quantification of LS. An increased susceptibility of WASP(-) platelets to phagocytosis by splenic macrophages is the most likely mechanism for the cis effect. This is supported by the consistent positive impact of splenectomy on the thrombocytopenia of both clinical [Mullen C, Anderson, KD, and Blaese, RM (1993) Splenectomy and/or bone marrow transplantation in the management of the wiskott-aldrich syndrome: long-term follow-up of 52 cases. Blood 82: 2961-2966.] and murine Prislovsky A, Marathe B, Hosni A, Bolen AL, Nimmerjahn F, et al. (2008) Rapid platelet turnover in WASP(-) mice correlates with increased ex vivo phagocytosis of opsonized WASP(-) platelets. Exp Hematol 36: 906-906.e915] WAS (in contrast to the variable efficacy of splenectomy in treating ITP), and by the increased amount of detectable platelet antigens seen in splenic macrophages in WAS patients [Shcherbina A, Rosen FS, Remold-O'Donnell E (1999) Pathological events in platelets of Wiskott-Aldrich syndrome patients. Br J Haematol 106: 875-883]. Our observations of increased susceptibility of both murine and human WASP(-) platelets to ex vivo phagocytosis also support this mechanism [Strom TS, Anur P, Prislovsky A (201 1) A numerical analysis model for interpretation of flow cytometric studies of ex vivo phagocytosis. PLoS ONE 6: e26657. Prislovsky A, Zeng X, Sokolic RA, Garabedian EN, Anur P, et al. (2012 [Epub ahead of print]) Platelets from WAS patients show an increased susceptibility to ex vivo phagocytosis. Platelets.]. We do not know the molecular mechanism for either the cis or the cis/trans effect. It should be noted that Falet et al. [Falet H, Marchetti MP, Hoffmeister KM, Massaad MJ, Geha RS, et al. (2009) Platelet associated] reported more rapid consumption of ex vivo labeled murine WASP(-) platelets in WT recipients (in terms of the fraction cleared at two hours post injection) than we report here (n=4, vs n=25 in our studies). Possible contributors to these different findings include the use of different platelet preparation and labeling methods, and the different genetic background (129SvEv) on which the experiments were performed. Also, their evaluation of an earlier (2 hr) time point than we did could bring the poorly understood phenomenon of reversible splenic platelet sequestration into their study.

[000116] The utility of the numerical analysis model for interpretation of murine platelet consumption data suggests that it could be applied to other types of thrombocytopenia, particularly in cases where the relative contributions of impaired platelet production and accelerated platelet consumption are not known. This is the current status of most cases of immune thrombocytopenic purpura ITP, as the antiplatelet antibodies known to be present in many such cases can have either effect. The model allows autologous or allogeneic platelet consumption data from such subject patients to be interpreted as either reflecting only an increased hemostatic burden (in the case of impaired platelet production), or that and a further increase in platelet consumption due to, for example, antibody- dependent clearance. Applied to data from individuals with bone marrow failure, it should also allow reassessment of the fraction of human platelet consumption which occurs due to hemostasis.

Example 2. Numerical Analysis Of Platelet Consumption Data Demonstrates Reduced Or Baseline Platelet Production In 90% Of ITP Patients Unresponsive To Corticosteroids

[000117] Immune thrombocytopenic purpura (ITP) is a heterogeneous diagnosis of exclusion

(Rodeghiero F., et al., 2009, Blood) for which approximately 2/3 of patients demonstrate platelet- clearance-inducing antibodies, and a similar fraction demonstrate antibodies which inhibit ex vivo thrombopoiesis. Diagnostic methods capable of distinguishing the effects of impaired platelet production and rapid platelet consumption would enable classification of ITP patients on the basis of these basic pathophysiologic mechanisms. Data from in vivo platelet consumption studies may allow such distinctions to be made, but it has been difficult to interpret such studies in terms of the concurrent and interactive effects of random (hemostatic and non-hemostatic) consumption, lifespan-dependent platelet consumption, and altered platelet production rates. A numerical analysis model in Example 1 describes which, applied to murine platelet consumption data, allowed quantification of each of those processes. Here a modified form of the model in Example 1 is used to interpret platelet consumption data obtained in ITP patients following infusion of autologous ^{n i}In-labeled platelets.

[000118] Methods

[000119] Patients: ¹¹ 'indium-labeled autologous platelet consumption data from 41 consecutive adult patients with prednisone non-responsive primary ITP was reviewed. The data is obtained either (A) at the time of diagnosis, or (B) after failure to sustain a platelet count response to prednisone treatment. Diagnostic criteria for all patients includes exclusion of other malignant, metabolic, or pharmacologic causes, as well as causes of "secondary" ITP such as HCV infection. For those in group (A), rapid consumption of autologous ¹¹ 'indium-labeled platelets (interpreted via the multiple hit model(7)) is an additional diagnostic criterion. For those in group (B), demonstration of antiplatelet antibodies on the platelet surface via indirect immunofluorescence is used for this purpose.

[000120] All of the patients in this study underwent splenectomy after failing to respond adequately to prednisone treatment. Patients were deemed to have had a complete response to splenectomy if their platelet count persistently exceeded 100 x 109/L thereafter, with no significant bleeding episodes. They were considered "non-responders" if their subsequent platelet count did not exceed either 30 x 109/L, or twice their baseline count, or if they had persistent significant bleeding episodes. Two previously reported patients were excluded: one because the initial time point was taken six hours after injection, and a second due to an initial rate of consumption that was 4 SD greater than the overall mean (see Results).

[000121] Platelet kinetics studies: Briefly, platelet rich plasma was prepared by differential centrifugation, and platelets prepared by a subsequent high speed centrifugation were labeled with ^{i n}Indium oxine by standard methods. Peripheral blood specimens obtained at 30 minutes after injection were considered "baseline" measurements (for patient 30, a 1.5 hour time point was used), and all subsequent measurements were normalized to these for each patient. All post-injection specimens for each patient were evaluated in a gamma counter at the same time to eliminate decay-related effects on recovery.

[000122] Numerical analysis: The numerical model posits that an in vivo platelet population can be visualized in a spreadsheet as a series of small platelet cohorts produced at a constant rate (PR, K/ul/hr) in short sequential time periods and individually consumed, by both random and lifespan- dependent processes, at the end of each such time period. Consumption rate of individual cohorts is determined by a random destruction rate constant (RD, %/hr), by the lognormally distributed cohort lifespan (LS, hr), and by the standard deviation of the latter (SD). By quantitatively comparing clinical platelet consumption data to a large range of possible values of the parameters RD, LS, and SD, the model finds those parameter value sets which optimally describe the data. For the current study, the model was modified to evaluate a fourth parameter, the fraction of long lived ¹¹ 'in-labeled species (LL), and the number of evaluated SD values was reduced.

[000123] Results

[000124] Model and modifications: Platelet consumption was modeled as the summed effects of two concurrent processes: random (exponential) consumption, and lifespan dependent consumption. The first process can be quantified via an exponential "random destruction" rate constant (RD, %/hr), while the second is quantified as a lognormally distributed lifespan defined by the parameters LS (hr) and the standard deviation of In LS (SD). A least squared residuals method was previously used to find values of these three parameters which optimally fit observed murine fluorescently-labeled platelet consumption data in WT and (thrombocytopenic) WASP(-) mice (Strom TS. 2013, PLoS ONE). An optimal value of the net platelet turnover rate (which, at equilibrium, equals the platelet production rate) can be inferred at the same time. The same method is applied to ¹¹ 'in-labeled platelet consumption data obtained in a series of ITP patients.

[000125] To do so, the data for each patient is first normalized to the first measurement, taken

0.5 hr after intravenous administration of the ^{i n}In-labeled platelets. Visual inspection of the data (FIG. 8) strongly suggests that many of the labeled platelet preparations contained long lived species. This is evident in the plateau phase seen at late times in the consumption data (patients 35, 27). For this reason, instead of searching for only three optimal parameter values (RD, LS, and SD of In LS) to describe the consumption curve associated with each data set, a fourth parameter is added: the fraction of the labeled cells/platelets consisting of long lived (LL) species. This parameter simply "shrinks" the scope of the analysis to the consumption of platelets from 100% of the time zero value to an optimal minimum percentage (LL). Lifespans of these long lived species were assumed to be infinite.

[000126] In some cases, such as patient 35 (FIG. 8), some of the data obtained at early (< 3.5 hr) time points fell above any trend lines evident from the later data points. While the source of this variation is unclear, splenic sequestration could be responsible. One data set is rejected (patient 16, not shown) because the initial rate of platelet clearance (46% over 1.5 hr; the fastest other clearance rate in the study is 21% in the same time period) was four SD faster than the mean of all the other cases, a value which suggests platelet activation during labeling (occasional similar results have been seen in murine platelet clearance studies - TS, unpublished). [000127] Optimal parameter value searches: These are performed in the context of only three

SD values, as this reduced computation time while generating a plausible range of distribution widths for the resultant lifespan-dependent consumption rates (FIG. 9). Optimal parameter value sets were identified by visualizing the minimum SS values within a series of RD-defined planes in each of the three SD-defined parameter spaces. Examples are shown in FIG. 9.

[000128] The results of these searches, and the parameter value ranges in order to identify them, are shown in Table 2. For each data set we first searched a "baseline" range of RD, LS, and LL values is searched first (those shown for patient 4. In some cases, such as patient 8, the range of LL values searched was empirically shifted). In cases where the resultant optimal RD value was less than 5.0%, and for patient 15, then the narrower range of RD values shown is searched. Single "global" SS minima were identified in most cases, but for some of the patients two minima (associated with two sets of parameter values) were evident in parameter space. The minimum with the higher SS value (i.e. poorer fit) is termed a "local minimum." This was seen exclusively in cases where the global minimum showed a high RD value (4.5 %/hr or greater). This means that at high consumption rates it becomes more difficult to distinguish rapid, predominantly random consumption from rapid, predominantly lifespan-dependent consumption with these data sets.

[000129] Modeling of the effects of reduced platelet production and increased random destruction: As a guide to interpreting the parameter values in Table 2, the model is used to predict how a normal platelet population's parameter values might shift in response to A) impaired production, B) increased consumption, or C) increased consumption in association with a homeostatic increase in platelet production. The assumptions were:

[000130] i) The optimal parameter values (RD₀, LS₀, and SD₀) and the associated platelet production rate (PRo) obtained for the three patients in the study whose platelet counts transiently normalized in response to prednisone (patients 3, 33, and 38, Table 2) are representative of normal. (Most clinical laboratories use a normal platelet count range of 150 to 400 K/ul).

[000131] ii) A hemostatic minimum absolute random destruction rate (aRDo) is normally maintained, as platelet count diminishes, via an increase in RD.

[000132] iii) LS is not affected by reduced platelet production.

[000133] To generate predicted parameter values for the effect of reducing platelet production, the modeling process shown in FIG.9 is used. The process begins (step A) with the optimal (baseline) parameter values for pooled data from the three patients who transiently normalized their platelet counts (Table 2). From this set, a "target" reduced platelet production rate (PRi) is generated, corresponding to 90% of PRo. Using that value, the model generates the expected (reduced) aRD value. Then (step B) incrementally increase RD until the model generated value of aRD (aRD;) = aRDo. The associated RD and platelet count values are those predicted to occur at PRi. Finally (step C), repetition of steps A and B with a series of reduced platelet production rates (PRi). This generates predicted RD, platelet count, and aRD values for each PR; value. For the baseline parameter values, aRDo (RD x platelet count) is equal to 45% of PRo. No quantitative predictions is made for the effect of reducing PR below aRD₀.

[000134] To model the effect of increasing platelet consumption, a series of incrementally reduced target platelet counts (Pt)(range: 90% to 10% of baseline) is generated, and for each we incrementally increase RD until the model generated value of P (Pm) is equal to Pt. To model the concurrent effects of increasing platelet consumption and homeostatically increasing platelet production, the same series of target platelet counts (Pt) is used, and for each we increase PR in a manner proportional to the reduction in platelet count (to a maximum of twice the baseline PR value, a conservative theoretical starting point). For each such combination of Pt and PRt, then RD is incrementally increased until Pm = Pt.

[000135] The results of these three modeling approaches are plotted with the values obtained for the patients in FIG. 11.

[000136] Optimal patient parameter values in comparison to modeled values: there are only four patients in the study showing a platelet production rate that is even modestly increased (>50%) in comparison to the presumed normals (FIG. 11 A). The latter patients showed a mean platelet production rate (2.12 K/ul/hr) comparable to the 1.7 K/ul/hr rate estimated for normals in a previous study(l l). The finding of predominantly low production rates in this study is corroborated by the distribution of random consumption rates (FIG. 11B), where rates consistent with no increase in platelet production are seen for, again, all but a handful of the patients. A surprisingly large number of cases (in FIG. 11B and IOC, approximately 14 of 39) fall near the RD rates predicted to result solely from impaired platelet production.

[000137] Modeling of immature platelet fraction values: An ability to take up fluorescent marker dyes such as thiazole orange (a marker of "reticulated platelets", RP) or the proprietary dyes used in Sysmex hematology analyzers (marking the "immature platelet fraction", IPF) is thought to be characteristic of those platelets which have recently been released into the bloodstream. The age threshold (T) at which "young' platelets stop taking up these marker dyes is not known. Because the numerical analysis model generates a platelet age distribution for any given set of parameter values, it can be used both to estimate T and to predict the effect of altered production and consumption rates on the fraction of platelets of age less than T (i.e. the IPF).

[000138] Specifically, the normal range for the IPF is approximately 4.5% (each clinical laboratory typically establishes its own range; this is the value in use at the Memphis VA Medical Center). Per the age distribution predicted by the model for our normalized controls (FIG. 12A), the youngest 4.5% of platelets corresponds to those aged less than 4 hr (i.e. T=4 hr). Application of that cutoff to the age distributions generated during modeling of the effects of altered production and/or consumption (FIG. 11), generates the predicted IPF and absolute IPF (alPF) for thrombocytopenias induced by those mechanisms, as shown in FIG. 12B

[000139] Several of the patients in this study had transiently normalized their platelet count.

One such patient was used as a reference point from which to infer the absolute normal random destruction rate (aRDo). (The aRD by this estimate accounts for approximately 30% of platelet turnover). From there the aRD assumption was used to predict the effects on RD of reduced PR, increased non-hemostatic RD, and of an increased RD with a compensatory increase in PR. Those predictions, and the inferred PR and RD values for each of the patients in the study, are shown in FIG. 11.

[000140] These findings comprise proof of principle that thrombocytopenic patients can be separated into those primarily due to (A) impaired production (those close to the dashed line), (B) accelerated random destruction at baseline PR (those close to the solid line), and (C) those for which augmented production cannot compensate for rapid destruction (those close to the gray line).

[000141] This analysis suggests that impaired platelet production, or a failure to increase it in the face of rapid platelet clearance, is a more common feature of ITP than is increased RD (since only a handful of the patients in the study showed a significant increase in PR). In fact approximately one third of the patients in the study showed RD values consistent with the effects of impaired PR alone, although we cannot rule out the possibility that some of these involved both reduced PR and increased RD. More importantly, this study demonstrates (A) the feasibility of applying the numerical analysis model to clinical data, (B) the need to use a fluorescent platelet marker rather than an isotopic one (since following platelet consumption by flow cytometry would eliminate the need to estimate the fraction of long-lived labeled species), and (C) the need to obtain data from a normal control group.

[000142] Predicting the effects of changes in PR and RD on the IPF and absolute IPF. As noted above, the numerical model generates a platelet age distribution for each input, or optimal, set of parameter values (RD, LS, SD). Examples of the resultant age distribution histograms for two of the patients in the above study are shown in FIG. 12A.

[000143] Using a midpoint of the normal range for the immature platelet fraction (IPF) of 4.5%

(empirically determined at the Memphis VA Medical Center), it is estimated from normalized control patient's histogram that the immature platelets comprise those with an age of 5.0 hr or less. That cutoff was applied to the age distributions generated during the modeling of the effects of altered production and/or consumption. This approach generated the IPF and absolute IPF (alPF) values predicted to occur when platelet count is reduced due to impaired production, increased consumption, or a combination of the two, as shown in FIG. 12B.

TABLE 2 Patient characteristics Search parameter ranges Optimal parameter values splenectomy Platelets (x n (data D RD SD (of In

Patient response 10e9/L) points) (%/hr) LS (hr) LL (%) (%/hr) LS (hr) LS) LL (%) PR (k/ul/hr)

4 n 24 9 0-19 20-267 0-15.2 7 215 0.2 5.6 1.68

17 n 8 9 0-4.75 20-267 0-15.2 1.75 267 0.1 8.8 0.14

30 n 13 6 0-4.75 20-267 0-15.2 1.75 137 0.2 6.4 0.26

31 n 37 7 0-4.75 20-267 0-15.2 2.25 254 0.2 15.2 0.84

1 y 30 6 0-4.75 20-267 0-15.2 2.5 189 0.2 6.4 0.77

2 y 119 9 0-4.75 20-267 0-15.2 0.75 267 0.2 7.2 1.06

3 y 165 9 0-4.75 20-267 0-15.2 2 215 0.3 6.4 3.45

5 y 80 9 0-19 20-267 0-15.2 6 189 0.2 5.6 4.80

6 y 21 9 0-4.75 20-267 0-15.2 0 33 0.3 4 0.80

7 y 41 9 0-4.75 20-267 0-15.2 3.5 176 0.1 6.4 1.44

8 y 58 8 0-4.75 20-267 10.4-25.6 2 202 0.1 23.2 1.19

9 y 19 9 0-4.75 20-267 0-15.2 0.75 59 0.3 9.6 0.48

10 y 24 7 3.0-7.75 20-267 0-15.2 4.5 33 0.2 8 1.50

11 y 10 5 0-19 20-267 0-15.2 12 267 0.2 6.4 1.20

12 y 21 8 3.0-7.75 20-267 0-15.2 5.75 33 0.1 9.6 1.45

13 y 44 6 0-4.75 20-267 0-15.2 1.5 189 0.1 9.6 0.71

14 y 17 5 0-19 20-267 0-15.2 12.03 72 0.3 0 2.05

15 y 39 6 3.0-7.75 20-267 0-15.2 4.5 137 0.1 8.8 1.76

18 y 88 7 0-4.75 20-267 0-15.2 2 150 0.2 8 1.91

19 y 13 9 0-19 20-267 0-15.2 6 33 0.3 3.2 0.99

20 y 45 9 0-19 20-267 0-15.2 5 254 0.2 5.6 2.25

21 y 143 9 0-4.75 20-267 10.4-25.6 0 124 0.2 11.2 1.34

22 y 85 8 0-4.75 20-267 0-15.2 1.25 111 0.3 5.6 1.61

23 y 119 9 0-4.75 20-267 10.4-25.6 0 124 0.3 22.4 1.20

24 y 20 6 0-4.75 20-267 0-15.2 2.5 33 0.3 1.6 1.04

25 y 39 9 0-4.75 20-267 0-15.2 4.5 228 0.2 6.4 1.76

26 y 39 9 0-19 20-267 0-15.2 5 267 0.2 2.4 1.95

27 y 2 9 0-19 20-267 0-15.2 8 163 0.1 11.2 0.16

28 y 22 9 0-4.75 20-267 0-15.2 2.25 254 0.1 13.6 0.50

29 y 34 9 0-4.75 20-267 0-15.2 4.5 189 0.1 6.4 1.53

32 y 36 5 3.0-7.75 20-267 0-15.2 7.25 72 0.1 2.4 2.63

33 y 238 7 0-4.75 20-267 10.4-25.6 0.25 163 0.2 20.8 2.02

34 y 77 6 0-4.75 20-267 10.4-25.6 4.5 124 0.1 24 3.49

35 y 37 9 0-4.75 20-267 0-15.2 3 137 0.3 5.6 1.17

36 y 32 6 0-19 20-267 0-15.2 6 111 0.2 7.2 1.93

37 y 102 6 0-4.75 20-267 10.4-25.6 3 228 0.1 17.6 3.07

38 y 170 7 0-4.75 20-267 10.4-25.6 0.25 150 0.1 24.8 1.45

39 y 21 6 3.0-7.75 20-267 0-15.2 5.25 111 0.2 12.8 1.11

40 y 43 6 0-19 20-267 0-15.2 5 124 0.2 14.4 2.16 normali

zed na 191 (mean) 23 0.05-1.0 124-162 na 0.5 140 0.2 na 2.12 Example 3. Use Of CMFDA-Labeled Platelets To Quantify Platelet Consumption And Production

Parameters In Thrombocytopenic Patients

[000144] The product to be used in these studies is termed CMFDA-labeled platelets. The product consists of human platelets which have been (A) prepared for transfusion by standard FDA- approved procedures, and (B) covalently modified via thio-ester linkage of the fluorescent marker CMFDA (5-chloromethylfluorescein diacetate; Invitrogen) to intracellular gluthathione. CMFDA is a cell permeable dye which is transformed by intracellular esterases into a fluorescent marker.

[000145] CMFDA labeled platelets is used to study in vivo platelet consumption in individuals with thrombocytopenia due to liver failure, and in patients who carry the diagnosis of exclusion called Immune thrombocytopenic purpura (ITP). To perform essential model validation steps, these studies is also performed in a series of healthy control patients, and in patients whose thrombocytopenia is due to bone marrow failure.

[000146] Dosage form, route of administration, and dosing regimen: Platelets for labeling is obtained from FDA-approved suppliers of platelets for transfusion. Platelets is labeled according to the CMFDA manufacturer's recommended methods. Specifically, CMFDA (resuspended in DMSO) is mixed with normal donor platelets (the only study limitation on donors will be ABO compatibility of the platelets with recipient sera) for 30 minutes. After addition of 1 volume of Intersol (Fenwal), an FDA-approved serum-free platelet additive, the platelets undergo a high speed spin and be resuspended in Intersol. (This step eliminates the risks associated with infusion of donor serum, which include hemolysis and allergic reactions).

[000147] The study's objective is to transfuse a sufficient quantity of CMFDA+ platelets such that they will comprise approximately 4% of total circulating platelets immediately after transfusion. This fraction has been sufficient for murine studies, and is necessary in order to allow adequate confidence intervals for the fraction of labeled platelets after up to 95% of the CMFDA+ platelets have been consumed. Specifically, at that point (a hypothetical true value of 0.2 % CMFDA+ platelets), flow cytometric analyses of 50,000 platelets per specimen (larger specimens present technical difficulties) would be projected to have a coefficient of variation of 0.1 (i.e. the 95% confidence interval for the values obtained at this point in consumption would be +/- 20%). Example 1 and 2 indicate that 90% of thrombocytopenic ITP patients demonstrate a random platelet consumption rate of under 6%/hr. Based on that value, the 4% initial CMFDA+ target allows to obtain five interpretable data points from the vast majority of thrombocytopenic patients.

[000148] This in turn will require transfusion in the range of 45 billion platelets (for normal controls) or 14 billion platelets (for patients with a platelet count of 75 K/ul). There are a minimum of 55 billion platelets in a single "random donor" platelet unit (most clinical transfusions involve the use of five or more such units, or one unit prepared via apheresis). Thus the total transfused platelets will be in the range of 20% of the number typically used for transfusion.

[000149] The normal platelet count range in most hospitals is quite wide (ranging typically from

150 K/ul to 400 K/ul). Thus the normal range varies by roughly 45% from the mean of 275 K/ul. On that background, it is expected that a study-induced platelet count increase of 4% will be clinically insignificant.

[000150] Transfusion of 45 billion CMFDA+ platelets requires labeling of approximately twice that number (to allow for possible losses in processing). That in turn requires approximately 40 ug of CMFDA at the start of labeling. It is not known what fraction of the input CMFDA results in the final glutathione-linked product, but it can be said A) that the vast majority of unreacted CMFDA will be removed via the subsequent centrifugation step, and B) that thrombocytopenic patients will be exposed to still less CMFDA and CMFDA+ platelets. This process contrasts markedly with the 500 mg intravenous quantities of the parent compound (fluorescein) that have been routinely used, for over 50 years, for retinal angiography 1. Adverse reactions, summarized in a recent retrospective review of over 11,000 such studies2, are rare (affecting ~ 1% of patients). No drug-related fatalities were found in that study, although uriticaria was seen in 0.2% of cases.

[000151] Scientific Rationale: A basic understanding of any thrombocytopenia would necessarily involve knowing whether it is due primarily to impaired production, rapid consumption, or some combination of the two. For most thrombocytopenias, that understanding is lack. In vivo platelet consumption studies, in which the rate of disappearance of labeled platelets is quantified, might be expected to solve this problem, but the complex kinetics of platelet consumption has until recently made interpretation of such studies difficult. Specifically, platelets are consumed by both random (hemostatic or immunologic) and lifespan-dependent processes, and it has been difficult to infer from in vivo consumption data which process might be affected in thrombocytopenic patients, and by how much. However, a numerical model for interpreting such data as shown in Example 1, has made it possible to use in vivo platelet consumption data to quantify the contributions of either or both processes to rapid platelet consumption in thrombocytopenic mice, and to concurrently quantify any change in platelet production rate. This development means that in vivo platelet consumption studies, using CMFDA-labeled platelets, can now be used to reach key pathophysiologic conclusions about the causes of clinical thrombocytopenias.

[000152] Previous clinical studies of platelet consumption have primarily used ^mIn-labeled autologous platelets, and problematic mathematical models, to evaluate thrombocytopenias. The numerical model by re-interpreting the data from one such study of 39 ITP patients is validated (Example 2). But there are several problems associated with the use of ¹¹ 'in for these studies. The isotope is a high energy gamma emitter, resulting in problematic levels of radiation exposure for both patients and staff. Also, the data acquisition method (net radioactivity per unit blood volume) does not allow precise enumeration of the platelets themselves (the isotope is found, for unclear reasons, in other cell types as well, and at levels that are difficult to approximate). CMFDA-labeled platelets, which we propose to use here, resolve both problems.

[000153] The objective is primarily to investigate the pathophysiologic basis of the thrombocytopenias in liver failure and ITP patients, while using data from bone marrow failure patients, and from healthy controls, to validate our approach. However, the clinical implications of our findings could be immediately evident because current therapeutic choices in the first two groups are multiple, have been arrived at largely by empirical means, and produce highly variable clinical outcomes. Some interventions are aimed primarily at reducing platelet consumption (splenectomy, corticosteroids, other immunosuppressants) while others are aimed primarily at augmenting platelet production (thrombopoietin receptor agonists (TPO-RA's) such as eltrombopag and romiplostim). The proposed studies are expected to determine, for many of the patients, whether their thrombocytopenia is primarily due to one or the other of these two basic mechanisms (rapid consumption vs. impaired production). That in turn is expected to be useful in determining which patients best respond to which existing therapeutic options. Simply put, understanding what you are treating should help you decide how to treat it.

[000154] CMFDA chemistry and utility: The fluorescent ligand proposed to use, CMFDA, is a modified form of one of the first fluorescent dyes ever used in biology and medicine, fluorescein. Derivatives of fluorescein include the isothiocyanate form (FITC), which is widely used to fluorescently tag antibodies, and the tetrabromo form (Eosin-Y), which is used routinely in histology and clinical hematology laboratories. An unmodified form, sodium fluorescein, has been administered intravenously to study ophthalmologic disorders for over 50 years.

[000155] CMFDA is fluorescein with two chemical modifications. A chloromethyl group has been added, which allows the molecule to form the desired thioester linkage with glutathione, and two acetate moieties have been added, enhancing its uptake by intact cells. Purity of the product is routinely assessed by the manufacturer (Invitrogen) at over 97% by HPLC (see attached certificates of analysis). The reagent is provided in solid form, and is resuspended in DMSO for use in labeling platelets. The final concentration of DMSO in the labeling reaction is less than 1% (v/v), prior to the rinsing step and resuspension of the labeled platelets in Intersol.

[000156] CMFDA-labeled platelets have been used to study in vivo platelet kinetics in animal models for over 16 years. For the proposed studies, platelets will be labeled via a simple and brief (30 minute) mixing process that exposes the platelets only to CMFDA, in the same solution (plasma) in which they are prepared, and to one other compound, the solvent used to resuspend CMFDA (DMSO, at a final concentration of under 1% (v/v)). After labeling the platelets will be rinsed and resuspended in Intersol (Fenwal).

[000157] Potential toxicities: Because the parent compound has been safely used at over 10,000 fold higher dosages for over 50 years (as discussed above), toxic effects are not anticipated. Theoretically, they could occur via the following mechanisms.

[000158] i) Potential toxicity of CMFDA-S-glutathione: Glutathione's intracellular biological role involves the reduction of oxidized proteins. Thus high levels of CMFDA-S-glutathione could increase the amount of such proteins in the transfused platelets. Reduced glutathione levels have been well characterized in the clinical condition G6PD deficiency, where their only known clinical consequence involves red cells (hemolysis). However, reduced glutathione levels also correlate with an increase in red cell and platelet-derived microparticles lO. If labeling with CMFDA were to have this effect on platelets, it would be expected to also change their FSC vs SSC characteristics, and their clearance rate. No such effects have been reported in studies making such comparisons3,4.

[000159] ii) Potential toxic effects on cells taking up CMFDA-labeled platelets: In those thrombocytopenias induced by impaired platelet production, platelets are thought to be predominantly cleared via their hemostatic function (they aggregate at sites of vascular damage). The extent to which they are cleared directly by other processes in the patients we will study is not known. At normal platelet counts, lifespan-dependent platelet consumption predominates, a process thought to be mediated predominantly by hepatic and splenic macrophages as well as hepatocytes. Potential toxic effects of CMFDA-labeled platelets on these cell types will be evaluated in our general toxicity studies in experimental animals.

[000160] iii) Potential side effects of platelet transfusions: In general, risks of platelet transfusions include A) allergic reactions to the associated plasma component (which can range in severity from mild urticarial reactions to anaphylaxis); and B) development of an immune response to the platelets themselves, which can in turn reduce the utility of future transfusions. In the proposed project, C) the risk of thrombosis due to increased platelet count, must also be considered. Risk (A) will be reduced in this study, relative to the most common current platelet transfusion methods, by replacement (after CMFDA labeling) of most of the original plasma with Intersol (as described in section 4, above). Risk (B) will be reduced by leukoreduction, either via the "acrodose" system or apheresis-based preparation. Risk (C) cannot be completely eliminated for the normal recipients in the study, but must be viewed in the context of the normal variability of platelet count. In most hospitals, normal clinical platelet count ranges, routinely defined as those which encompass 95% of normal platelet counts, lie between 150 K/ul and 400 K/ul. Thus from the midpoint of the normal range, platelet counts normally vary by as much as 45%. The number of platelets we plan to infuse will increase the platelet count by 4% (at the midpoint of the normal range, this amounts to 1 1 K/ul), or less than one tenth of the normal clinical variability. This could nonetheless increase the platelet count to a level outside the normal range in a small subset of patients. To eliminate that risk, we propose to not enroll any normal potential participants whose pre-infusion platelet counts are within 4% of the upper limit of normal (i.e. above 384 K/ul).

[000161] Proposed clinical studies: the objective will be to enroll approximately 24 thrombocytopenic patients per year for studies of their platelet turnover rate. A cohort of 6-10 healthy controls will also be enrolled. The data obtained from these controls will be required for interpretation of the data obtained from the thrombocytopenic patients (specifically, the age distribution of normal platelets will be needed). The number of participants in subsequent years will depend on the results obtained in the first year.

[000162] A detailed description of entry and exclusion criteria for this portion of the study follows:

[000163] Clinical entry requirements for all study participants will include submission of a current routine blood specimen to the Blood Bank for a "tyP^{e an}d screen." The participant's ABO type will be used to select ABO-compatible platelets for CMFDA-labeling and infusion. This will be necessary in part because infusion of platelets that are incompatible with recipient plasma is known to result in more rapid platelet clearance. Also required at entry will be serum TPO levels (to be performed at Quest Diagnostics); a CBC with IPF value (Memphis VAMC clinical lab); and peripheral blood smear review by the PI (whose clinical duties routinely involve this function).

[000164] Clinical entry requirements for healthy controls will include no history of liver disease, alcohol abuse, or documented thrombocytopenia, and no recent use of any antiplatelet agents such as aspirin or clopidogrel.

[000165] Clinical entry requirements for liver disease patients will include a platelet count of <

100,000 on at least two occasions; known HCV status; and liver disease by the following criteria: Abnormal liver function tests (ALT and/or AST) on at least two occasions, and supporting imaging studies to include evaluation of spleen dimensions.

[000166] It is planned to accrue 20 such patients for each full year of the study.

[000167] Clinical entry requirements for ITP patients will include exclusion of other causes of thrombocytopenia, and a platelet count of < 100,000 on at least two occasions; known HCV status; and normal liver function tests (ALT and/or AST) performed at diagnosis. We plan to accrue at least 4 such patients for each full year of the study.

[000168] Exclusion criteria for all potential participants will include the use of aspirin, clopidogrel, or other anti-platelet agents within 10 days of entry into the study; a history of fluorescein angiography (which would in theory enhance the risk of an allergic reaction to CMFDA-labeled platelets); current treatment with TPO receptor agonists (eltrombopag or romiplostim); and current corticosteroid treatment. Patients with platelet counts below 15K/ul at the time of the study will be excluded due to their risk of hemorrhage in association with any invasive procedures.

[000169] Additionally, it is anticipated enrolling a cohort of 6-10 bone marrow failure patients, in order to quantify platelet consumption in thrombocytopenias due entirely to impaired platelet production.

[000170] Details of the proposed turnover studies follow:

[000171] Preparation of CMFDA-labeled platelets: it is anticipated to use "ABO compatible" platelets in all cases. That means that their ABO phenotype of the infused platelets will be compatible with the study participants' plasma. Incompatibility in this regard can lead to more rapid platelet consumption. Because commercially available platelet preparations are resuspended in donor plasma, routine platelet transfusions with non-ABO-identical platelets are associated with a poorly characterized risk of immune-mediated post transfusion hemolysis. That risk will be eliminated by resuspending labeled platelets in an FDA-approved plasma substitute (Intersol, also called PAS-3) which has been shown to be clinically safe and effective.

[000172] ABO-forward-compatible "random donor" platelet preparations (prepared via the acrodose pre -pooling and leukoreduction method) will be obtained from one of the Memphis VAMC's current suppliers (American Red Cross, or Lifeblood Midsouth regional blood center). If necessary, a (larger) apheresis unit will be obtained. An aliquot of the preparation will be removed and used to determine the unit's platelet concentration via flow cytometry, using a quantitative internal control.

[000173] Based on the patient's most recent platelet count, and weight, an estimate of the patient's blood volume and absolute platelet number will be made. The study will target preparation of enough CMFDA-labeled platelets to yield an initial result of 4% CMFDA+ circulating platelets. This value has given adequate platelet consumption data in animal studies.

[000174] The amount infused will therefore differ for healthy controls vs. thrombocytopenic patients. Projected examples are shown in Table 3.

[000175] Table 3 is projected number of platelets needed per participant. The number of platelets shown in a random donor unit is the minimum required to meet the standards of the American Association of Blood Banks (AABB). Actual fractions of an RD unit needed for labeling are therefore expected to be lower than those shown. Due to possible losses during labeling, we will start the labeling process with twice the number needed for each transfusion.

TABLE 3

Healthy Thrombocytopenic

control patient

Platelet count (x 10E9/L) 250 75 blood volume (L) 4.5 4.5

total platelets (x 10E9) 1125 338

CMFDA+ platelets 45 13.5

needed (x 10E9)

Platelets in "random 55 55

donor" unit (x10E9)

fraction of unit needed to 82 25

transfuse

Fraction of RD unit 164 49

needed to label

[000176] All procedures is anticipated to perform under the same aseptic conditions used in the blood bank to perform other manipulations such as pooling of random donor platelet units. CMFDA will be purchased from Invitrogen. For labeling, the required number of platelets (in their original plasma) will be mixed with CMFDA for 30 minutes at room temperature, in the dark, with gentle agitation. After addition of one volume of Intersol, platelets will undergo a "high speed spin", as is performed during routine platelet preparation, and will be resuspended for infusion in one volume of Intersol. Typical quantities of CMFDA required per labeling will range (for the examples shown in table 1) from 4 to 40 ug per patient. This contrasts markedly with the 500 mg quantities of the parent compound (fluorescein) that are routinely injected directly into patients during angiography studies.

[000177] In vivo platelet turnover studies: Routine EDTA-anticoagulated (4.5 ml) blood specimens will be obtained from healthy controls and thrombocytopenic patients per the optimal sampling schedule shown in Table 4. Table 4 indicates optimal sampling schedules. Unites are hours after the initial transfusion of CMFDA+ platelets. These time points were chosen to best assess the type of consumption rates seen in similar clinical circumstances for ITP patients (see Example 2) while minimizing the effects of transient splenic sequestration. Precise adherence to this schedule is not needed as long as the time at which each specimen is obtained is recorded accurately.

TABLE 4

[000178] Specimens will be analyzed by flow cytometry as we have in animal studies. Whole blood specimens will be diluted 1 : 100 in PBS, and platelets will be marked with PE-anti-CD61. Flow cytometric analysis will gate platelets both on log FSC vs log SSC and on PE fluorescence. The fraction of CMFDA-positive platelets will be determined for that gate. It is anticipated being able to count a minimum of 1000 CMFDA+ platelets for each specimen, although we may find fewer positives at late time points.

[000179] Data analysis: Each measurement of the fraction of CMFDA+ platelets will be normalized to the baseline (3 hour) measurement. Data for each patient will be used to determine the optimum consumption parameter values.

[000180] Expected outcomes: For healthy controls, it is expected to be able to establish a set of mean parameter values (RD, LS, SD). From these and the mean platelet count of this group, we will infer the normal platelet production rate (PR) as well as the normal age distribution (AD). It is anticipated that the AD to be similar to that inferred from the ¹¹ 'in-labeled platelet consumption data (described in Example 2) for an ITP patient who had a transiently normalized platelet count (FIG. 12, top left). The AD will be required for interpretation of data from the thrombocytopenic patients.

[000181] Also for healthy controls, it is expected to establish the normal absolute random destruction rate (aRD₀). Previous clinical estimates, using a different model, placed this value at -17% of total turnover, while the studies yielded an estimate, for a transiently normalized ITP patient, of approximately 29% of total turnover. This value will in turn allow us to generate predicted effects of reduced PR, increased RD at baseline PR, and increased RD with an inadequate homeostatic increase in PR (FIG. 11).

[000182] For both liver disease and ITP patients, it is expected to be able to plot platelet count,

RD, and PR values for each patient exactly as Example 2 (FIG. 11). Note that this refined version of FIG. 1 1 cold be refined further if the experiments proposed in the Examples below lead to a reassessment of one of the underpinnings of the prediction method (the aRD hypothesis). The anticipated outcomes of those studies could be:

[000183] Clustering of patient data on the left side of the a refined graph comparable to that shown in FIG. 11C, implying that most of them are thrombocytopenic due solely to reduced platelet consumption;

[000184] Clustering of that data in the top center portion of that graph, implying that most of them are thrombocytopenic due to increased platelet consumption in the absence of a homeostatic increase in platelet production; [000185] Clustering on the right side of the graph, implying that most of them are mounting an inadequate homeostatic platelet production increase in the face of rapid platelet consumption.

[000186] Separation of liver disease and/or ITP patients into subsets clustering in any of the above three areas.

[000187] For any of these possible outcomes, it can also be asked whether TPO levels correlate with PR. If they do not, that would imply that some other regulatory mechanism is acting in some or all of the patients in the study, and open up new research avenues aimed at identifying it. If they do, the value of TPO levels will be established as a marker of platelet production rates in these clinical contexts.

[000188] Concerning IPF values: Based on the mean platelet age distribution of the normal control patients, more reliable predictions of the effects of changes in PR and RD on the IPF shown in FIG. 12 will be regenerated (bottom). It is anticipated that the actual alPF values, and platelet counts, for the patients in this study against the predicted values will be plotted. Based on where the actual values fall on the plot (in FIG. 13, whether they fall for a given platelet count range in the areas marked A, B, or C for the relevant platelet count ranges), IPF-based inferences will be made regarding which of the three main pathophysiologic categories these thrombocytopenias fall into.

[000189] The next step will be to ask whether these IPF-based inferences about platelet production and consumption rates are correct. That can be done by comparing them to the same type of inferences, for the same patients, that we will make based on where their platelet counts and measured PR and RD values fall in the (refined) version of FIG. 11. This comparison will determine whether the IPF is in fact a valid predictor of the rates of platelet production and consumption in thrombocytopenic patients.

[000190] The ultimate outcome of all of these studies will be an ability to ask whether the mechanism responsible for these thrombocytopenias predicts their response to therapy. The findings obtained here will allow retrospective assessment of whether therapies aimed at increasing platelet production (such as the use of TPO-RA's) are more effective in patients whose PR is low than in those for whom it is not. Similarly, they will allow assessment of whether therapies aimed at reducing platelet consumption (including splenectomy and splenic embolization) are more effective in patients for whom NHRD is clearly elevated. It is anticipated that these findings will provide a basis for the design and implementation of prospective randomized trials of therapies chosen on the basis of pathophysiology instead of platelet count alone.

Example 4. Evaluate The Contributions Of Hemostatic Random Platelet Destruction (HRD) And Non- Hemostatic Random Destruction CNHRD) To Net Random Platelet Destruction (RD) In Normal Controls And In Thrombocytopenias Caused By Impaired Platelet Production. [000191] The aRD assumption: random human platelet destruction was estimated to comprise approximately 17% of overall normal platelet turnover. It is assumed that this was due in normal and thrombocytopenic patients to normal hemostatic consumption (referred to here as the aRD assumption), and in support of that assumption they noted that RD increased in thrombocytopenic patients. It follows from this formulation, however, that a platelet count reduced to less than 17% of normal should begin to impair normal hemostasis. That is not the case, as patients do not experience spontaneous hemorrhage until their platelet counts drop below approximately 10 K/ul (approximately 4% of the midpoint of the normal range). This discrepancy calls the aRD assumption into question. Specifically, it suggests that aRD consists of two components: hemostatic RD (HRD), and non-hemostatic RD ( HRD). The latter might be expected to be significant if the known immunologic functions of platelets play a larger normal biological role than is currently appreciated.

[000192] In the studies shown in FIG. 3, the patients who transiently normalized their platelet counts demonstrated random platelet consumption comprising approximately 30% of total platelet turnover. This value is higher than expected, and suggests that not all normal random platelet destruction is hemostatic.

[000193] Studies in WT mice amplify this concern, as the normal RD in that system comprised an even higher fraction of platelet turnover (approximately 70%). Yet thrombocytopenias down to below -20% of normal (in Mpl-/- mice, in which platelet production is impaired due to absence of the receptor for TPO) result in no spontaneous hemorrhages, and recent estimates place the hemorrhagic threshold for murine thrombocytopenias at below 2.5% of normal.

[000194] To study how a significant NHRD component of normal human random platelet consumption would alter the predicted effect of reduced PR shown in FIG. 3, it is posited the following:

[000195] IF A defined fraction of RD were due to NHRD; and, the affinity of platelets for hemostatic targets were markedly higher than their affinity for non-hemostatic targets (a reasonable assumption given their vital hemostatic function); then, sequential reductions in platelet count should result in a shift of platelet consumption from NHRD to HRD, with little or no change in RD, up to the point where absolute HRD made up all of absolute RD; and, below that threshold platelet count, RD would have to increase to maintain baseline aHRD.

[000196] Based on the above reasoning, and using the same interpolation process shown in

FIG. 3B the predicted concurrent effects of reduced PR on human platelet count (FIG. 13, top) and RD (FIG. 13, middle) are generated for conditions in which HRD makes up 100%, 50%, and 25% of normal RD. The predicted RD values for the resultant platelet count values "fall out" of these calculations (FIG. 13, bottom). While the random clearance of platelets from the circulation has long been thought to be associated with the primary biological role of platelets (hemostasis), our preliminary studies, and other published reports, suggest a random clearance rate that is higher than expected. Specifically, if the observed absolute normal platelet clearance rates were indeed required for normal hemostasis, then even moderate thrombocytopenias should impair that function (i.e. should result in hemorrhage). This calls into question the aRD assumption: that normal aRD (aRDO) is maintained, via increased RD, in thrombocytopenic individuals.

[000197] It is proposed to test the aRD assumption against an alternative hypothesis: A) that two random processes result in platelet clearance: Hemostatic random destruction (HRD) and non- hemostatic (NHRD), and B) that the affinity of platelets for their non-hemostatic targets is significantly lower than it is for their hemostatic targets. It follows from this scheme that a reduced platelet count (if due to impaired platelet production, PR) would result in a baseline absolute random destruction rate (aRD) maintained by an increased hemostatic rate (HRD) and a reduced non-hemostatic rate (NHRD). As platelet count declines, this would result in only a small increase in RD - until aRD approaches aHRD. At that point, RD would begin to increase more rapidly, an effect that would be magnified as aRD approaches PR.

[000198] The numerical analysis model is used to project these effects (FIG. 14) for models in which HRD comprises all, 50%, or 25% of RD. To test the hypothesis, RD (and aRD) in circumstances in which PR is reduced will be measured (in both animal models and clinical conditions). The extent to which RD increases with decreasing platelet count will allow us to estimate normal HRD as a fraction of RD.

[000199] Experimental design: The hypothesis is anticipated to be tested in animal models via syngeneic CMFDA-labeled platelet consumption studies in Mpl(-/-) mice. Mpl(-/-) mice lack the thrombopoietin receptor. As a consequence their platelet production rate is low, and their platelet counts are reduced by over 80%. No platelet function defect is expected in these mice, and none has been described. While these mice can be used to evaluate the effects of reduced PR, these mice can also be used to show a more severe thrombocytopenia due to a more severe reduction in PR.

[000200] To achieve that, a series of sub-lethal irradiation studies on Mpl(-/-) mice will be performed in order identify radiation dosages at which their platelet count is transiently reduced to two additional sub-lethal levels. The recent findings of Morowski et al., defining with some precision the platelet count reduction that results in spontaneous hemorrhage at less than 2.5% of normal (Morowski M, et al., 2013, Blood), strongly suggest that levels between that and the approximately 20%-of-normal platelet count seen in Mpl(-/-) mice will be achievable. The process will simply entail irradiation in the irradiator at dosages between 500 and 1000 Gray, followed by evaluation of platelet counts at 1, 3, 5, and 7 days . The rate of their recovery from irradiation will be measured to evaluate the extent to which their platelet populations are age-equilibrated. Since it is expected fairly rapid platelet turnover in this circumstance (exponential consumption at half lives of less than one day), this should pose no theoretical problems. These mice will be termed hvMPLa and hvMPLb mice. [000201] Preparation of CMFDA-labeled platelets: Platelets will be prepared from blood obtained via tail clipping, and ficoll step gradients, as described in our previous studies (Prislovsky A., et al., 2008, Exp Hematol). They will be quantified using a Beckman Coulter model Z2 particle analyzer, and labeled with CMFDA as described in previous studies. Adequate labeling will be verified by flow cytometry. In our experience, labeling at too high a mean fluorescence intensity level can result in artifactually rapid platelet consumption, and any study in which that is detected will be aborted.

[000202] Because these mice will be very thrombocytopenic, A) a smaller number of labeled platelets will need to be injected in order to generate the baseline fraction of labeled platelets (~4%) that we have used for previous studies, but B) the yield of platelets per donor will also be lower. On this basis it is anticipated needing approximately the same number of donors per recipient (two) that we have used previously.

[000203] In vivo platelet turnover studies: labeled platelets will be injected into 6-12 week old

C57B1/6J recipients via tail vein, and the fraction of circulating CMFDA+ platelets will be determined exactly 5 minutes later. Recipients will then undergo small volume (50 ul) retro-orbital bleeds at appropriate intervals chosen based on the anticipated clearance rates of the infused platelets. Based on the parameter value standard deviations we have seen with similar studies, we anticipate a need for 6-10 allogeneic in vivo cmfda-labeled platelet consumption studies for each of the three types of recipients (Mpl, hvMPLa, and hvMPLb). Platelet counts will be obtained for each recipient 24 hours before each study.

[000204] Accrual of patients: Bone marrow failure patients will be recruited from Memphis

VAMC Hematology/Oncology clinic. It is anticipated a need to perform 6-10 such studies, which could take 1-2 years of recruitment efforts. Platelet preparation and labeling with CMFDA will be performed as described in previous examples, with optimization of the number of platelets labeled and infused so as to target an initial 4% fraction of CMFDA+ platelets.

[000205] Data analysis: Data from both the murine and clinical studies will be analyzed with the numerical analysis model as described in preliminary studies. Standard deviations of the parameter values will be determined as Example 1. P-values of any differences seen will be calculated from the mean and standard deviation values by standard methods.

[000206] To identify the optimal fraction of RD that defines HRD in this study, a least squares curve fitting approach will be used. Specifically, for hypothetical data such as that shown in FIG. 14, the sum of squared residual values (SS) will be determined for a series of possible values for (HRD/RD). The PI has experience designing similar procedures (Strom TS, A numerical analysis model for the interpretation of in vivo platelet consumption data, 2013, PLoS One). [000207] Clinical entry requirements: Patients will include those showing therapy-related thrombocytopenia as well as those showing chronic bone marrow failure due to myelofibrosis in the context of myelodysplasia. Patients must demonstrate a consistently low platelet count (<100 K/ul on at least two consecutive occasions), and (if post chemotherapy) must be free of the treated malignancy the standard clinical and laboratory criteria used to make that evaluation for their condition. In addition to these requirements, all of the entry requirements for the patients in previous examples will apply.

[000208] It is anticipated obtaining RD and platelet count data for bone marrow failure patients that could be distributed as shown in FIG. 14. In this hypothetical case, the results are consistent with HRD constituting approximately 50% of RD at baseline. Similar predicted curves and data distributions for our murine studies will be evaluable by the same method.

[000209] These findings will in turn allow us to produce more accurate predictions concerning how reduced platelet production affects platelet count and RD.

Example 5. Evaluation of Cis (Platelet Intrinsic) Effects Of Murine Thrombocytopenia On Platelet Consumption.

[000210] Rationale and Hypothesis: The interpretation of allogeneic platelet consumption data is difficult because consumption rate in this circumstance is affected both by trans acting (recipient) factors (such as the rate of hemostatic consumption required by the host, which is itself a function of the host platelet count) and cis acting (platelet intrinsic) factors. The most obvious of the latter factors, when donor and host demonstrate different platelet counts, is the age distribution of the donor platelets. The numerical analysis model takes that variable into account. But other platelet intrinsic factors could also contribute to the consumption rate. There is evidence, for example, that younger platelets are more hemostatically active than older ones. If that is true, their rate of random consumption should be increased relative to WT platelets. It is assumed that the consumption rate parameter values of young platelets are identical to those of platelets showing a normal age distribution (the equal hemostatic activity assumption, Hypothesis 1). Here that assumption is tested against the Hypothesis 2: that consumption rates are intrinsically different for young versus old platelets.

[000211] As sketched in FIG. 15, these hypotheses can be distinguished by evaluating the consumption of allogeneic platelets from Mpl(-/-) mice in WT recipients. Suppose that platelet consumption studies of Mpl(-/-) platelets in Mpl(-/-) mice are performed. From those the age distribution for these platelets will be obtained. Now suppose that their consumption in WT recipients is evaluated, taking their left shifted age distribution into account as we did (in the preliminary studies section) for WASP(-) platelets. This would allow us to obtain (A) their consumption rate parameter values in that context. (This analysis needs to be refined to take NHRD into account, depending on the results obtained in Example 4). [000212] Now suppose that it is obtained in parallel (B) consumption rate parameter values for

WT platelets in WT recipients. Hypothesis 1 predicts that (A) and (B) will be identical. Hypothesis 2 predicts that they will differ.

[000213] Experimental design: Design of the allogeneic platelet consumption studies will be as described in Example 4. To reduce inter-experimental error, each study will include all three of the layouts shown in figure 14 (Mpl -to Mpl, Mpl to WT, and WT to WT). Based on the standard deviations of the parameter values we previously calculated for WT-to-WT platelets, it is anticipated that studies of 8 recipients in each of the three groups shown in the figure will allow to detect an approximately 15% difference (for WT-to-WT vs Mpl -to-WT) in any of the parameter values we will estimate (RD, LS, SD).

[000214] Preparation of CMFDA labeled platelets, performance of the in vivo platelet consumption studies, and data analysis will be as described in previous examples.

[000215] Expected outcomes: Either confirm hypothesis 1 or hypothesis 2 is anticipated to be confirmed.

[000216] If hypothesis 1 is confirmed, this will support the use of the model in similar situations. For example, for any thrombocytopenias associated with both impaired platelet production and platelet function defects, this finding would suggest that studies of both syngeneic and allogeneic platelet turnover will reliably serve to quantify the two contributors to the thrombocytopenia.

[000217] If hypothesis 2 is confirmed, interpretation will depend on which parameter values differ between Mpl(-/-) and WT platelet consumption, and in what way they differ. The most likely outcome here would be an increased RD for Mpl(-/-) platelets. This result would have several significant implications. First, it would support previous studies that suggested that nascent platelets are more hemostatically active. Second, it would necessitate a re-evaluation of our previous interpretation of allogeneic WASP(-) platelet consumption. Depending on the magnitude of the effect seen with Mpl(-/-) platelets, the findings might suggest that only some, or all, of the increased RD seen with WASP(-) platelets is due solely to their left shifted age distribution. By allowing a preliminary estimate of the magnitude of the effect, this result would allow more accurate interpretation of the allogenic (normal - to - thrombocytopenia) platelet consumption studies proposed in Example 4. (A means for obtaining a more precise estimate of its magnitude is described below).

[000218] Less likely but possible outcomes would include a reduced RD, which would be completely at odds with previous studies; an altered LS, which would call into question our understanding of the intrinsic nature of platelet lifespan; and a reduced SD, which might be expected based on the partial synchronization of Mpl(-/-) platelet lifespans, and would have little impact on other studies. [000219] The finding of an effect of the donor platelet age distribution on RD in WT recipients, if we detect it, would imply that the RD inferred from the consumption of normal platelets in thrombocytopenic recipients would underestimate the hemostatic RD of thrombocytopenic platelets in those recipients. IF this finding is obtained, it would mandate an effort to quantify this effect as a function of the donor platelet age distribution. Specifically, the hypothesis that RD increases in proportion to the fraction of the donor platelet age distribution (AD) that is younger than some threshold age (T) is anticipated to be tested. That fraction can be termed F(T, AD).

[000220] To do that, the effects of different degrees of donor thrombocytopenia (and associated changes in platelet age distribution) on consumption rates are assessed. This could be done with the use of the Mpl(-/-) and hvMPL mice described in Example 4.

[000221] Although it would be optimal to test the hypothesis via in vivo consumption studies in the same format shown in FIG. 15, this would be impractical due to the large number of thrombocytopenic donors required for the hvMPL-to-WT portion of that experiment. To overcome that problem, the WT recipients could be replaced with hvMPL recipients, as sketched in FIG. 16. It is anticipated to perform three sets of such studies, as sketched in the FIG. 16: one using non-irradiated Mpl donors and recipients (termed here hvMPL(O)), and two using the more thrombocytopenic irradiated mice (hvMPLa, hvMPLb). As the platelet counts of the Mpl donors decline, the ratio of RD2 to RDl would be expected to decrease.

[000222] For each such value of RD2/RD1, candidate values can be quantified for F(T, AD), the fraction of platelets under a candidate threshold age T for each of the three (known) hvMPL platelet age distributions. The threshold platelet age (T) that defines the hyper-reactive group could be obtained by plotting RD1/RD2 against the candidate F(T, AD) values, such as the three sketched in FIG. 17. The hypothesis predicts that RD2/RD1 will decline in proportion to F(T, AD). On that basis, the candidate T value could be inferred by which results in a linear decline in RD2/RD1 is the threshold value defining the population of younger, more hemostatically active platelets.

[000223] In the hypothetical case shown, the data would identify an optimal T value of 10 hours. This type of result would constitute a clear and quantified observation of the effect we will seek to identify (or rule out) under this Specific Aim.

[000224] Experimental testing of a similar effect of platelet age on measurable RD in human platelets would be difficult because it would entail infusion of CMFDA-labeled platelets from thrombocytopenic individuals into healthy volunteers. Such studies would pose risks to both the donors (a high volume bleed would be needed) and the recipients (since the donors in most cases carry diagnoses associated with disease transmission risks) that would be hard to justify. However, should the above studies demonstrate that such an effect is significant in mice, studies in humans could be designed to test it. Specifically, those studies would be laid out exactly as shown in FIG. 16 (minus the large brackets), substituting "bone marrow failure" (BMF) for MPL or hvMPL. Two thirds of that experiment will have already been done: assessment of normal-to-normal platelet consumption, and assessment of normal-to-BMF. By pursuing a small number of studies of autologous CMFDA-labeled platelet consumption in bone marrow failure patients, we would be able to use the type of interpretive process shown in FIG. 16 to confirm an effect of platelet age on hemostatic activity in humans. (As this would be a highly contingent study, the details are omitted here.) The equal hemostatic activity assumption would be confirmed at an RD2/RD1 ratio of 1.0; the alternative hypothesis would be confirmed if the two RD values differ significantly. These findings would either validate the interpretive methods used in previous examples or allow a more accurate refinement of those methods (i.e. correction for unequal hemostatic activity) to be formulated.

Example 6. Test whether platelets from patient showing increase non-hemostatic random destruction ( HRD) are more susceptible than controls to ex vivo phagocytosis (enhanced platelet phagocytosis, or EPP).

[000225] Rationale and Hypothesis: Approximately 2/3 of ITP patients demonstrate clearance- inducing antibodies. It is unknown whether patients with liver disease are in general predisposed to developing similar antibodies, but the increased incidence of ITP (sometimes termed "secondary ITP") in HCV seropositive patients without severe liver disease raises the question of whether such antibodies could contribute to the thrombocytopenia of HCV+ patients with severe liver disease. The possible role of increased phosphatidyl serine exposure in any patients showing increased NHRD has also not been explored.

[000226] Experimental Design:

[000227] Specimens: Citrate anticoagulated blood (5 cc) will be obtained from patients whose testing demonstrates increased NHRD. A healthy control specimen will be obtained on the same day.

[000228] Platelet preparations: Specimens will be layered over a ficoll cushion (Fico/lyte) and centrifuged at 350g for 15 minutes at room temperature. The plasma layer will be stored at -80 in aliquots. The platelet layer will be diluted in modified tyrode's buffer (20 mM Hepes, 137 mM NaCl, 13.8 mM NaHC03, 2.5 mM KC1, 0.36 mM NaH2P04-H20, 5.5 mM glucose, 0.25% bovine serum albumin, 1 mM MgC12) supplemented with 1 ug/ml of PGE1, and centrifuged at 6000g for 5 minutes. Pellets will be resuspended in modified tyrode's buffer supplemented with PGE1, and counted with a Beckman Coulter model Z2 particle analyzer. An aliquot of the platelet preparation will be exposed to FITC-labeled lactadherin to assess phosphatidyl serine exposure.

[000229] DIP labeling of platelets: Platelet preparations will be labeled by mixing them 1 : 1 with a solution of pre-warmed DIO (1.0 uM), Tween 20 (0.7 mM) and DMSO (92%). Final platelet concentrations in the reaction will range from 9 x 104 to 8 x 105 per ul . The reaction will be allowed to proceed for 30 minutes at 37 degrees in darkness. After addition of 5 volumes of modified tyrode's buffer supplemented with PGE-1, platelets will be left on a rotating platform for 10 minutes at room temperature in darkness (to facilitate removal of DMSO), centrifuged at 6000g for 5 minutes at room temperature, and resuspended in modified tyrode's buffer supplemented with PGE-1.

[000230] Antibody binding: DIO-labeled control and patient platelets will be exposed to either an opsonizing antibody (mouse anti-human CD61, clone VP-PL2) or an isotype control antibody as follows. Antibodies will be bound for 2 hour at room temperature on a rotating platform protected from light. Five volumes of modified tyrode's buffer supplemented with PGE1 will be added, platelets will be centrifuged at 6000g for 5 minutes at room temperature, and platelets will be resuspended in 10% IFBS RPMI media. This method has yielded enhanced phagocytosis for opsonized control specimens consistently in our hands[54].

[000231] THP-1 cells: THP-1 cells will be grown in 10% IFBS RPMI supplemented with

Penicillin/streptomycin, L-glutamine, 5 um beta mercaptoethanol, and 10% inactivated fetal bovine serum (IFBS). Cells will be activated for 3 hours 37C with 50 ng/ml of PMA at a concentration of 1 x 106 cells/ml. After activation, cells will be centrifuged at 450g for 10 minutes at room temperature, then resuspended in the above medium at 3 x 105 cells/ml, and distributed in 48 well dishes.

[000232] Phagocytosis: Platelets will be added to wells containing activated THP-1 cells at 10 platelets/cell, using duplicate or triplicate wells. Cells and platelets will be centrifuged for 1 minute at 200g, at room temperature, then placed in a 37 degree, 5% C02 incubator for 1 hour. The majority of the cells will be non-adherent at this point. They will be collected by centrifugation of the supernatant at 400g for 10 minutes at 40C. Cells remaining on the dish will be rinsed 3 times with HBSS , treated with trypsin for 15min 37C, centrifuged at 400g for 10 minutes at 40C , resuspended in RPMI/10% IFBS, and pooled with the supernatant cells. The cells will be centrifuged again as above, and resuspended in PBS containing PE labeled mouse anti-human CD-61. Cells will be Incubated for 30min at 40C in the dark, then analyzed with a Becton Dickinson LSRII flow cytometer. Quantification of the mean adsorption(+)(Q2+3) and adsorption(-), phagocytosis(+/-)(Ql) populations will be performed using Flowjo software (Treestar, Inc).

[000233] Computation: The numerical analysis model is performed in a Microsoft Excel workbook. For comparison to predicted histograms, flow cytometry data will be exported from flowjo as scale values. "Negative" fluorescence values, a consequence of automated baseline correction by the flow cytometer, will be corrected by addition of a constant equal to the lowest value in an individual file. Calculations will be performed on standard iMac desktop computers.

[000234] Previously published results show that ex vivo phagocytosis is increased in Wiskott-

Aldrich syndrome (WAS) patient. The thrombocytopenia of WAS is thought to be due to both reduced platelet production and accelerated platelet consumption. Studies demonstrated that platelets from WASP-deficient mice are consumed more rapidly in vivo than are WT platelets, and that opsonization accelerates their uptake by bone marrow- derived macrophages more than it does that of WT platelets. Platelets from WAS patients show similar features. Ex vivo phagocytosis by activated THP-1 cells of DIO-labeled platelets from a series of WAS or XLT patients is increased in comparison to that of normal control platelets. Using a numerical analysis method, this effect is distinguished from a concurrent effect on the amount of detectable fluorescent signal transferred to the macrophage per phagocytosed platelet. It is shown that the latter quantity is reduced by platelet WASP deficiency, as might be expected if the fluorescence transferred from these smaller platelets is more rapidly quenched. The study was not able to detect a differential effect of opsonization with anti-CD61 antibody on the uptake of WASP(-) vs. WT platelets. However, the high probability of phagocytosis per adsorbed WASP(-) platelet could limit the sensitivity of the assay in this case. Also, no effect of sera from WAS patients on the uptake of normal control platelets is seen, suggesting that in vivo opsonization is not the cause of increased uptake of WASP(-) platelets. Finally, little, if any, increase in the reticulated platelet fraction in WAS patients is seen, suggesting that impaired production of reticulated platelets contributes to the thrombocytopenia. These findings suggest that rapid in vivo platelet consumption contributes significantly to the thrombocytopenia of WAS. They also demonstrate the feasibility of routinely performing functional assays of phagocytosis of small numbers of platelets obtained at remote locations, a method which should be applicable to the study of other types of thrombocytopenia such as ITP. (Prislovsky A. et al., Platelets from WAS patients show an increased susceptibility to ex vivo phagocytosis. (2012) Platelets)

[000235] Expected outcomes: it is anticipated that in the range of 2/3 of initially diagnosed ITP patients will show positive findings.

[000236] INCORPORATION BY REFERENCE

[000237] All patents, published patent applications, and other references disclosed herein are hereby expressly incorporated by reference in their entireties by reference.

[000238] EQUIVALENTS

[000239] The functions of several elements may, in alternative embodiments, be carried out by fewer elements, or a single element. Similarly, in some embodiments, any functional element may perform fewer, or different, operations than those described with respect to the illustrated embodiment. Also, functional elements (e.g., modules, databases, computers, clients, servers and the like) shown as distinct for purposes of illustration may be incorporated within other functional elements, separated in different hardware or distributed in a particular implementation.

[000240] While certain embodiments according to the invention have been described, the invention is not limited to just the described embodiments. Various changes and/or modifications can be made to any of the described embodiments without departing from the spirit or scope of the invention. Also, various combinations of elements, steps, features, and/or aspects of the described embodiments are possible and contemplated even if such combinations are not expressly identified herein.

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Claims

CLAIMS What is claimed is:

1. A computer-implemented method for determining one or more characteristics of a platelet disorder in a subject, the method comprising:

receiving observed platelet consumption data for a subject;

obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters representing random and lifespan platelet consumption;

analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models so as to identify an optimal numerical model for which the respective platelet population consumption curve provides a best fit approximation of the observed platelet consumption data; and

outputting an indication of one or more platelet characteristics for the subject determined based on the identified optimal numerical model.

2. The method of claim 1 wherein the parameters include a constant random platelet consumption, a lognormal distribution of platelet lifespan, and a standard deviation of the lognormal distribution of platelet lifespan.

3. The method of claim 1 or claim 2, further comprising a step of determining a platelet production rate for the subject based on the identified numerical model, and outputting an indication of one or more platelet characteristics for the subject based on the platelet production rate.

4. The method of claim 2, wherein each numerical model comprises a set of data values arranged in a data structure that is used to model a respective platelet population as a series of platelet cohorts that are respectively produced at a constant production rate at a plurality of sequential time points separated by a constant time interval and each discretely consumed at each of the plurality of sequential time points occurring after the time point at which the platelet cohort is produced according to the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for the numerical model.

5. The method of claim 4, wherein, for each numerical model: the set of data values represent elements in a two-dimensional matrix having m rows and n columns, where m and n are integers and m > n > 1 , in which the columns of the matrix respectively correspond to the platelet cohorts in sequence and the rows of the matrix respectively correspond to the time points in sequence such that each element of the matrix includes, as the data value for the element, a value indicating platelets remaining in the platelet cohort corresponding to the column for the element at the time point corresponding to the row for the element or a null value if the time point to which the element corresponds is earlier than the time point at which the platelet cohort to which the element corresponds was produced,

for each column of the numerical model, the value indicating platelets remaining in the platelet cohort corresponding to the column in the element for each row corresponding to a time point subsequent to the time point at which the platelet cohort was produced is determined by subtracting a first amount representing random platelet consumption and a second amount representing lifespan dependent platelet consumption from the value indicating platelets remaining in the platelet cohort in the element of the row corresponding to the immediately preceding time point, and

wherein the first amount is determined according to the value for the constant random platelet consumption rate of the numerical model and the second amount is calculated based on the values for the lognormal distribution of platelet lifespan and the standard deviation of the lognormal distribution of platelet lifespan for the numerical model in view of a quantity of time points from the time point at which the platelet cohort was produced to the time point to which the row corresponds.

6. The method of claim 5, wherein, for each numerical model, a probability density function based on the values for the lognormal distribution of platelet lifespan and the standard deviation of the lognormal distribution of platelet lifespan for the numerical model is applied to calculate the second amount used to determine the value indicating platelets remaining in the platelet cohort corresponding to each column in the element for each row corresponding to a time point subsequent to the time point at which the platelet cohort was produced.

7 The method of claim 6, wherein, for each platelet cohort of each numerical model, for each time point subsequent to the time point at which the platelet cohort was produced, a cumulative sum of the first amounts determined for the rows corresponding to time points prior to and including the time point is calculated, the cumulative sum is subtracted from the value indicating platelets remaining in the platelet cohort at the time point at which the platelet cohort was produced to determine a value indicating platelets in the platelet cohort not consumed by random platelet consumption at the time point, and the value indicating platelets in the platelet cohort not consumed by random platelet consumption at the time point is used when applying the probability density function to calculate the second amount.

8. The method of claim 5, wherein, for each element of the matrix for each numerical model, the value indicating platelets remaining in the platelet cohort corresponding to the column for the element at the time point corresponding to the row for the element is a concentration of platelets remaining in the platelet cohort, and wherein, for each numerical model a sum of the data values for the elements in each row of the matrix represents a total concentration of the respective platelet population for the numerical model at the time point corresponding to the row.

9. The method of claim 8, wherein, for each numerical model:

the total concentration of the respective platelet population for the numerical model increases at each sequential time point from a first time point to a subsequent time point of the plurality of sequential time points, remains at an equilibrium concentration level at each sequential time point from the subsequent time point to an nth time point of the plurality of sequential time points, and decreases at each sequential time point from the n"¹ time point to the m"¹ time point of the plurality of sequential time points, and

the respective platelet population consumption curve that is mapped by the numerical model is provided based on the total concentrations of the respective platelet population from the n^th time point to the m^th time point of the plurality of sequential time points for the numerical model.

10. The method of claim 4, further comprising receiving a range and a resolution for each of the parameters of the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan, receiving a value for the constant production rate to use for generating the numerical models, and obtaining the plurality of numerical models according to the value for the constant production rate and the range and the resolution for each of the parameters of the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan.

11. The method of claim 10, wherein the value for the constant production rate is received as user input via a user interface component, and wherein the range and the resolution for one or more of the parameters of the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan is received as user input via the user interface component.

12. The method of claim 10, wherein obtaining the plurality of numerical models comprises generating the plurality of numerical models using discretization based on the constant production rate and the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for each numerical model.

13. The method of claim 10, wherein obtaining the plurality of numerical models comprises accessing a data store of previously generated numerical models to obtain the numerical models corresponding to the constant production rate and the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for each numerical model.

14. The method of claim 5, wherein n is at least 240, m is at least 365, and the constant time interval is one hour.

15. The method of claim 1, wherein analyzing the platelet consumption data with respect to the respective platelet population consumption curve provided by each of the plurality of numerical models comprises using a data fitting procedure to identify the optimal numerical model for the observed platelet consumption data.

16. The method of claim 15, wherein the data fitting procedure comprises performing a least squares analysis to calculate a sum of squared residuals between the platelet consumption data and the respective platelet population consumption curve provided by each numerical model and identifying the optimal numerical model for the observed platelet consumption data as the numerical model for which the sum of squared residuals is minimized.

17. The method of claim 9, further comprising determining a platelet population turnover rate as a net platelet consumption rate calculated based on the total concentrations of the respective platelet population for the optimal numerical model at the n^th time point and the (n+l)"¹ time point.

18. The method of claim 17, further comprising determining a platelet production rate for the subject by multiplying the platelet population turnover rate by a platelet count for the subject, and wherein the one or more platelet characteristics for the subject include the platelet production rate.

19. The method of claim 1, wherein the platelet disorder is thrombocytopenia, and the characteristic comprises the type of thrombocytopenia.

20. The method of claim 19, further comprising determining a platelet production rate for the subject based on the identified optimal numerical model and determining the type of thrombocytopenia in the subject based on the platelet production rate and the identified optimal numerical model.

21. The method of claim 20, wherein the type of thrombocytopenia for the subject is selected from increased platelet consumption and decreased platelet production based on the platelet production rate.

22. The method of claim 19, further comprising determining an absolute random platelet destruction rate for the subject by multiplying the value for the constant random platelet consumption rate in the optimal numerical model by a platelet count for the subject.

23. The method of claim 22, further comprising determining the type of thrombocytopenia in the subject by selecting one of increased platelet consumption with a corresponding increase in platelet production, increased platelet consumption without a corresponding increase in platelet production, and decreased platelet production based on the platelet production rate and the absolute random platelet destruction rate.

24. The method of claim 23, wherein the type of thrombocytopenia in the subject is further determined in view of a particular condition of the subject contributing to the thrombocytopenia.

25. The method of claim 23, wherein the type of thrombocytopenia for the subject is further determined in view of at least one of a serum plasma thrombopoietin level of the subject and an immature platelet fraction for the patient.

26. The method of claim 2, wherein outputting the indication of the one or more platelet characteristics for the subject comprises generating a display of the indication on a display unit via a user interface component.

27. The method of claim 26, further comprising generating a graphical representation of the respective platelet population consumption curve provided by the optimal numerical model on the display unit via the user interface component.

28. The method of claim 26, further comprising displaying indications of the respective set of values for the constant random platelet consumption rate, the lognormal distribution of platelet lifespan, and the standard deviation of the lognormal distribution of platelet lifespan for the optimal numerical model on the display unit via the user interface component.

29. The method of claim 26, further comprising determining a platelet population turnover rate and an absolute random platelet destruction rate for the subject based on the optimal numerical model and a platelet count for the subject, and displaying indications of the platelet population turnover rate and the absolute random platelet destruction rate on the display unit via the user interface component.

30. The method of claim 1, wherein the observed platelet consumption data for a subject is obtained by in vivo observations of platelet consumption in the subject by measuring clearance of labeled platelets that are autologous to the subject from a bloodstream of the subject over a time period.

31. The method of claim 1, wherein the observed platelet consumption data for a subject is obtained by in vivo observations of platelet consumption in the subject by measuring clearance of labeled platelets that are allogeneic to the subject from a bloodstream of the subject over a time period.

32. The method of claim 1 wherein one of the parameters is constant exponential random platelet consumption rate.

33. A computer-implemented method for determining one or more characteristics of a platelet disorder in a subject, the method comprising:

receiving observed platelet consumption data for a subject;

obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant exponential random platelet consumption rate, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan;

34. A computer-implemented method for diagnosing a platelet disorder in a subject comprising: receiving observed platelet consumption data for a subject;

obtaining a plurality of numerical models generated to provide a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for

corresponding parameters relating to platelet consumption ;

analyzing the platelet consumption data in view of the plurality of numerical models to identify a numerical model that provides a best fit approximation of a platelet population consumption curve for the observed platelet consumption data; and

outputting an indication of a type of platelet disorder for the subject based on the best fit of a platelet population consumption curve for the observed platelet consumption data.

35. The method of claim 34 further comprising determining the platelet production rate in the subject based on the identified numerical model; and outputting an indication of a type of platelet disorder for the subject based on the platelet production rate.

36. A computer-implemented method for determining the cause of a platelet disorder in a subject, the method comprising:

receiving observed platelet consumption data for a subject;

obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters including (1) a constant random platelet consumption, (2) a lognormal distribution of platelet lifespan, and (3) a standard deviation of the lognormal distribution of platelet lifespan;

37. The method of claim 36 wherein the cause is selected from the group consisting of impaired platelet production, increased platelet consumption, and a combination of the two.

38. A method for differentiating between different platelet disorders in a subject comprising the steps of: a. obtaining in vivo platelet consumption data from a subject, wherein said data is obtained with at least one marker;

b. comparing said platelet consumption data with platelet consumption curves generated from a numerical analysis model to determine the kinetic basis for the platelet disorder in the subject; and

c. differentiating the type of platelet disorder in the subject based on said curves.

39. The method of claim 38, wherein the numerical analysis model is obtained by:

a. receiving user input from an input device configured for receiving in vivo platelet consumption data;

b. computing with a computational engine, said engine configured to implement a numerical analysis model to the determine the kinetic basis for the platelet disorder; and

c. creating curves that correlate the kinetic basis with the type of the platelet disorder.

40. A method of determining the cause of platelet disorder in a subject, comprising:

obtaining in vivo platelet consumption data from the subject, wherein said data are obtained with at least one marker;

constructing platelet consumption curves from a numerical analysis model to determine the kinetic basis of the platelet disorder;

comparing said platelet consumption data with said curves to determine the kinetic basis of the platelet disorder in the subject; and

determining the cause of said platelet disorder in the subject based on the comparison of said curves.

41. The method of claim 40 wherein the numerical analysis model is based on random destruction (RD), platelet lifespan (PL) and a standard deviation of the platelet lifespan.

42. The method of claim 41 wherein the random destruction (RD) is broken down into hemostatic random destruction (HRD), and non-hemostatic random destruction (NHRD).

43. The method of claim 42 wherein the numerical analysis model is used to determine whether platelets from a subject with increased non-hemostatic random destruction (NHRD) are more susceptible to ex vivo phagocytosis or enhanced platelet phagocytosis.

44. The method of claim 40 wherein the numerical analysis model is based on a random consumption rate, a platelet lifespan and a standard deviation of the platelet lifespan.

45. The method of claim 44, wherein the numerical analysis model is further based on a platelet production rate.

46. The method of claim 40, further comprising labeling platelets of a donor with at least one marker, and administrating the labeled platelets to a recipient.

47. The method of claim 40, wherein the marker is selected from the group consisting of a fluorescent marker, CMFDA, BMQC, a pH-sensitive marker, PhRodo, a radioactive marker, 111 In- oxine, and an enzymatic marker.

48. The method of claim 40 wherein the numerical analysis model is used to determine if the consumption rate parameter values of young platelets are identical to those of platelets showing a normal age distribution.

49. The method of claim 40 wherein the numerical analysis model is used to determine if consumption rates are intrinsically different for young versus old platelets.

50. The method of claim 40 wherein the platelet disorder is selected from the group consisting of disorders of platelet adhesion, disorders of aggregation, disorders of secretion, disorders of thromboxane synthesis, acquired disorders of platelet function, uremia, paraproteins, fibrin degradation products, myelodysplasia, myeloproliferative syndrome, von Willebrand disease, Bernard-Soulier syndrome, and Glanzmann thrombasthenia.

51 The method of claim 40 wherein the platelet disorder is thrombocytopenia.

52. The method of claim 50, wherein the thrombocytopenia is selected from the group consisting of immune thrombocytopenic purpura (ITP), thrombotic thrombocytopenic purpura, heparin induced thrombocytopenia, and disseminated intravascular coagulation.

53. The method of claim 51 , wherein the thrombocytopenia is associated with liver disease.

54. The method of claim 51, wherein the thrombocytopenia is caused by accelerated consumption or impaired production, or a combination of the two.

55. A method of predicting the response to therapy of a patient with a platelet disorder, comprising obtaining in vivo platelet consumption data from the patient; wherein said data is obtained by administering a donor's platelets to the patient, wherein the donor's platelets are labeled with at least one marker;

constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan in said patient;

comparing said platelet consumption data with said curves to determine the kinetic basis for the platelet disorder;

determining the causes of said platelet disorder based on said curves; and

predicting the response to therapy of said patient based on said determination.

56. A method of treating a patient with a platelet disorder, comprising:

obtaining in vivo platelet consumption data from the subject; wherein said data is obtained by administering a donor's platelets to the subject, wherein the donor's platelets are labeled with at least one marker;

constructing platelet consumption curves from a numerical analysis model based on parameters including a constant platelet consumption rate, platelet lifespan, and a standard deviation of platelet lifespan;

comparing said platelet consumption data with said curves to determine the kinetic basis for platelet disorder;

determining the causes of said platelet disorder based on said curves; and

treating the patient in accordance with the determined cause of said platelet disorder.

57. The method of claim 56, further comprising administering to said patient an effective amount of at least one pharmaceutical composition specific to the cause of the platelet disorder.

58. The method of claim 57 wherein the pharmaceutical composition is selected from the group consisting of thrombopoietin receptor agonists and corticosteroids.

59. The method of claim 55, further comprising a splenectomy of the subject.

60. A method of determining the cause of thrombocytopenia in a subject, comprising: obtaining in vivo platelet consumption data from the subject; wherein said data is obtained by administering a donor's platelets to the subject, wherein the donor's platelets are labeled with at least one marker;

determining the cause of said thrombocytopenia in the subject based on the comparison of said curves.

61. A computer program product, comprising a non-transitory computer readable storage medium having a computer executable program instructions stored thereon, wherein the computer executable program instructions, when executed by a computer processor, direct the computer processor to perform a method of determining one or more characteristics of a platelet disorder in a subject, the method comprising:

receiving observed platelet consumption data for a subject;

obtaining a plurality of numerical models each providing a discrete mapping of a respective platelet population consumption curve in terms of a respective set of values for parameters representing random consumption data and platelet lifespan;

62. The computer program product of claim 61 wherein the parameters include a constant random platelet consumption, a lognormal distribution of platelet lifespan, and a standard deviation of the lognormal distribution of platelet lifespan.

63. A data processing system, comprising:

at least one processor;

a random access memory for storing data and programs for execution by the at least one processor; and computer readable instructions stored in the random access memory for execution by the at least one processor to perform a method of determining one or more characteristics of a platelet disorder in a subject, the method comprising:

receiving observed platelet consumption data for a subject;

64. The data processing system of claim 63, wherein the parameters include a constant random platelet consumption, a lognormal distribution of platelet lifespan, and a standard deviation of the lognormal distribution of platelet lifespan.