TW202230389A - Methods and apparatuses for modeling adamts13 and von willebrand factor interactions - Google Patents

Abstract

Aspects of the present application provide for methods and apparatuses for simulating interactions between von Willebrand factors and ADAMTS13 in endogenous or recombinant form. Some aspects provide for a computer-implemented method for modeling ADAMTS13 and VWF interactions, comprising obtaining a quantitative systems pharmacology (QSP) model representing ADAMTS13 and VWF interactions including a mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and inhibition thereof by extracellular hemoglobin; determining disease predictive descriptors, assigning the disease predictive descriptors to a virtual patient population, and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises a concentration of at least one biomarker. The biomarker may include ULVWF multimers, cleaved VWF fragments, lactate dehydrogenase and/or platelet cells. The QSP model may represent ADAMTS13 interactions with stretched and globular VWF multimers. The QSP model may simulate a conversion of stretched VWF multimers to globular VWF multimers.

Description

Method and apparatus for modeling the interaction of ADAMTS13 with the Win-Weber factor

The present application relates to methods and apparatus for modeling the interaction of ADAMTS13 with the Wein-Weber factor.

This application claims, under 35 U.S.C. § 119(e), filed on October 9, 2020 in Attorney Docket No. D0617.70137US00, entitled "METHODS AND APPARATUS FOR MODELING THE INTERACTION OF ADAMTS13 AND WINNER'S FACTOR (METHODS AND APPARATUSES FOR MODELING ADAMTS 13 AND VON WILLEBRAND FACTOR INTERACTIONS", US Provisional Application No. 63/089,935, which is incorporated herein by reference in its entirety.

Sickle cell disease (SCD) is an inherited blood disorder characterized by the formation of sickle-shaped hemoglobin, which results in rigid and deformed sickle-shaped red blood cells. These sickle cells are unable to traverse the microcirculation, creating blockages that result in tissue hypoxia and severe pain during vaso-occlusive crises (VOC) events.

Von Willebrand factor (VWF) is an adhesive and polysaccharide protein that plays an important role in maintaining hemostatic balance. VWF promotes platelet aggregation and clot formation at sites of endothelial injury. VOC events in SCD patients are triggered by the accumulation of high concentrations of uncleaved ultra-large von Willebrand factor (ULVWF) multimers in the patient's plasma to which extracellular hemoglobin (Hb) binds body, causing excessive coagulation.

To prevent unnecessary coagulation, VWF is negatively regulated by the catalytic enzyme "A Disintegrin and Mettaloproteinase with Thrombospondin Type 1 Motifs 13; ADAMTS13". Typically, ADAMTS13 is used to prevent extracellular Hb binding to ULVWF by cleaving ULVWF to form smaller VWF fragments. However, in patients with ADAMTS13 deficiency or acquired ADAMTS13 autoinhibition, this function does not perform well, resulting in high concentrations of uncleaved ULVWF in plasma.

Thrombotic thrombocytopenic purpura (TTP) is a condition that causes blood clots (thrombosis) to form in small blood vessels throughout the body. Serious medical problems can result if these clots block blood vessels and restrict blood flow to organs such as the brain, kidneys, and heart. In patients with TTP, thrombosis can also be caused by high concentrations of uncleaved VWF multimers in plasma. Thrombotic thrombocytopenic purpura includes immune-mediated thrombocytopenic purpura (iTTP) and congenital thrombotic thrombocytopenic purpura (cTTP).

Some aspects provide a computer-implemented method for modeling the interaction of ADAMTS13 and VWF, comprising: obtaining a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 and VWF , the model includes the mechanism by which ADAMTS13 cleaves oversized VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; identifies disease-predictive descriptors; assigns these disease-predictive descriptors to virtual patient populations; and uses the QSP model The virtual patient population is processed to provide processed data, wherein the processed data includes concentrations of at least one biomarker.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer-implemented method for modeling the interaction of ADAMTS13 and the Win Weber factor (VWF), the method comprising: obtaining a quantitative system representing the interaction of ADAMTS13 and VWF Pharmacology (QSP) model that includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; identifies disease-predicting descriptors; assigns these disease-predicting descriptors to virtual patient populations and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises concentrations of at least one biomarker.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method for modeling the interaction of ADAMTS13 and VWF Mechanisms of ULVWF) multimers and their inhibition by extracellular hemoglobin; determining disease predictive descriptors; assigning these disease predictive descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises the concentration of at least one biomarker.

Some aspects provide a computer-implemented method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra warm Weber factor (ULVWF) multimers, the method comprising: determining the drug for a virtual patient population. Pharmacokinetic parameters of the administered drug; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; using quantitative systems pharmacology ( The virtual patient population was processed to obtain processed data using a QSP model that simulates the interaction of Win Weber factor (VWF) with ADAMTS13, including the mechanism by which ADAMTS13 cleaves the uncleaved ULVWF multimers and extracellular its inhibition by hemoglobin, and the processed data comprises the concentration of at least one biomarker; and using the processed data to obtain the effect of the administered drug in reducing the concentration of the uncleaved ULVWF multimers An indicator of this effectiveness.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer-implemented method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-high temperature Weber factor (ULVWF) multimers, the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population Patient population; the virtual patient population was processed using a quantitative systems pharmacology (QSP) model to obtain processed data, wherein the QSP model simulates the interaction of Win Weber factor (VWF) with ADAMTS13, including the way ADAMTS13 cleaves the uncleaved mechanism of ULVWF multimers and their inhibition by extracellular hemoglobin, and the processed data includes the concentration of at least one biomarker; and use of the processed data to obtain the administered drug in reducing the uncleaved An indicator of this effectiveness in terms of this concentration of ULVWF multimer.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method of determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra large Weber factor (ULVWF) multimers, the method comprising: determining drug metabolism of the administered drug for a virtual patient population kinetic parameters; determine disease prediction descriptors for the virtual patient population; assign the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; process the virtual patient using a quantitative systems pharmacology (QSP) model population to obtain processed data in which the QSP model mimics the interaction of Win Weber factor (VWF) with ADAMTS13, including the mechanism by which ADAMTS13 cleaved the uncleaved ULVWF multimers and its inhibition by extracellular hemoglobin, and The processed data includes the concentration of at least one biomarker; and the processed data is used to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of the uncleaved ULVWF multimers.

Some aspects provide a computer-implemented method for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultra-warm Weber factor (ULVWF) multimers, the method comprising: providing a virtual patient the population determines pharmacokinetic parameters of the administered drug, wherein the pharmacokinetic parameters include frequency of non-compliance with the dosing regimen; determines disease prediction descriptors for the virtual patient population; The virtual patient population was assigned the clinical parameters and the disease prediction descriptors; the virtual patient population was processed using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 and VWF to obtain processed data, wherein the QSP model included The mechanism by which ADAMTS13 cleaved ultra large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin, and the processed data included the concentration of the uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments and use the processed data to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer implementation for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-high-temperature Weber factor (ULVWF) multimers A method comprising: determining pharmacokinetic parameters of rADAMTS13 for a virtual patient population, wherein the pharmacokinetic parameters include frequency of non-compliance with the dosing regimen; determining a disease prediction descriptor for the virtual patient population; applying the The pharmacokinetic parameters and the disease prediction descriptors were assigned to the virtual patient population; the virtual patient population was processed using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF to obtain processed data, wherein The QSP model includes the mechanism by which ADAMTS13 cleaves oversized VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin, and the processed data include the concentrations of the uncleaved ULVWF multimers or the cleaved Win Weber one of the concentrations of factor fragments; and using the processed data to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultrawarm Weber factor (ULVWF) multimers, the method comprising: determining the pharmacokinetics of rADAMTS13 for a virtual patient population determining a disease prediction descriptor for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the A virtual patient population; the virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the method by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers. mechanism and its inhibition by extracellular hemoglobin, and the processed data includes either the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Wein-Weber factor fragment; and the use of the processed data to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers.

Some aspects provide a computer-implemented method for determining the concentration of a Win Weber factor (VWF) multimer in response to administration of ADAMTS13, the method comprising: determining the pharmacokinetics of administered ADAMTS13 for a virtual patient population determine disease prediction descriptors for the virtual patient population; assign the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; use quantitative systems pharmacology representing the interaction of ADAMTS13 with VWF ( QSP) model processing the virtual patient population to obtain processed data, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; and determined based on the processed data This concentration of VWF multimer.

introduction

Aspects of the present application provide methods and apparatus for modeling the interaction of ADAMTS13 with VWF. In particular, aspects of the present application provide a quantitative systems pharmacology (QSP) model for modeling the interaction of ADAMTS13 with VWF. In some embodiments, the QSP model mimics the mechanism by which ADAMTS13 cleaves oversized VWF (ULVWF) multimers and the inhibition of this effect by extracellular hemoglobin (Hb). QSP models can include pharmacokinetic (PK) and pharmacodynamic (PD) components.

Using the QSP model described in this application can provide various types of information on the interaction of VWF with ADAMTS13 that may not be practical or possible to obtain clinically. For example, a QSP model can provide a patient's biomarker content (eg, ULVWF concentration, cleaved VWF fragment concentration, platelet count, or lactate dehydrogenase (LDH) concentration) as output. The QSP model may receive as input specific doses and/or dosing regimens of therapeutic interventions for the treatment of ADAMTS13 inhibition and/or deficiency. Thus, the QSP model can provide a correlation between therapeutic interventions (eg, doses or dosing regimens of therapeutic interventions) and biomarkers that provide clinical targets for use in treating patients (eg, SCD patients experiencing VOCs, cTTP or iTTP patients). Quantitative Relationship. This quantitative relationship can be used to help determine human doses for therapeutic intervention of SCD, cTTP, iTTP.

In some embodiments, the QSP model can be used to assess the efficacy of therapeutic interventions in patients with reduced or suboptimal ADAMTS13 performance. In some embodiments, the therapeutic intervention comprises administering a recombinant form of ADAMTS13 (rADAMTS13). In some embodiments, the therapeutic intervention comprises plasmapheresis of donor plasma with healthy levels of endogenous ADAMTS13. In some embodiments, the therapeutic intervention comprises administering frozen plasma with healthy levels of endogenous ADAMTS13. The replacement of donor plasma and the administration of frozen plasma increased the concentration of endogenous ADAMTS13 in recipient patients. For example, donor plasma contains variable amounts of endogenous ADAMTS13. Plasma exchange and administration of frozen plasma are the current standard of care for cTTP and iTTP, respectively. The QSP model can simulate the effect of any of these therapeutic interventions on the VWF-ADAMTS13 interaction.

In some embodiments, QSP models can be used to determine appropriate human doses or dosing regimens for therapeutic interventions. Therapeutic interventions can be used to modulate constancy in VOC by improving the VWF-ADAMTS13 interaction. The QSP model enables such determinations without further human testing, thereby providing information that may be impractical or impossible to obtain clinically. In some embodiments, the QSP model can be implemented with a virtual population to perform a virtual clinical trial to assess the effects of therapeutic interventions. The inventors have recognized that such techniques may facilitate the development of novel and more effective therapeutic modalities for ADAMTS13 inhibition and/or deficiency.

Accordingly, some aspects provide a computer-implemented method for modeling the interaction of ADAMTS13 and VWF, comprising: obtaining a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 and VWF, the The model includes the mechanism by which ADAMTS13 cleaves oversized VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; determines disease prediction descriptors; assigns these disease prediction descriptors to virtual patient populations; and uses the QSP model to process the Virtual patient populations to provide processed data, wherein the processed data includes at least one biomarker (e.g., ULVWF multimers including stretched or globular ULVWF multimers, cleavage of stretched or globular VWF fragments VWF fragments, lactate dehydrogenase and/or platelet cells). In some embodiments, the method further includes displaying the processed data.

In some embodiments, the method further comprises determining pharmacokinetic parameters; assigning the pharmacokinetic parameters to a virtual patient population; determining therapeutic intervention data based on the administration of the administered drug; and processing with a QSP model Therapeutic intervention data and virtual patient populations to determine the effectiveness of administered drugs. In some embodiments, the administration of the administered drug comprises the administration of endogenous and/or recombinant ADAMTS13. In some embodiments, the administration of endogenous ADAMTS13 comprises plasma exchange with plasma having endogenous ADAMTS13.

In some embodiments, the QSP model represents the interaction of ADAMTS13 with stretched and globular VWF multimers. The QSP model can simulate the conversion of spherical VWF multimers to stretched VWF multimers.

In some embodiments, the QSP model includes the binding affinity of extracellular hemoglobin to ULVWF multimers.

In some embodiments, the method further comprises using the processed data to determine whether the concentration of the at least one biomarker is below a first threshold value or above a second threshold value. In some embodiments, the method further comprises using the processed data to determine the duration for which the concentration of the at least one biomarker is below a first threshold value or the duration for which the concentration of the at least one biomarker is above a second threshold value . In some embodiments, the method further comprises using the processed data to determine changes in the concentration of at least one biomarker over time.

In some embodiments, the QSP model includes a plurality of differential equations representing one or more biological responses.

In some embodiments, the pharmacokinetic parameters comprise one or more biographical characteristics (eg, height, weight, age, or gender) of the patient to whom the administered drug is administered that is indicative of how the administered drug is administered at least one of) the one or more parameters affected.

In some embodiments, the disease prediction descriptor includes one or more parameters that characterize the concentration of ADAMTS13 in the patient. Patients may include patients with sickle cell disease, patients with congenital thrombotic thrombocytopenic purpura (cTTP), or patients with immune-mediated thrombotic thrombocytopenic purpura (iTTP) .

In some embodiments, the virtual patient population includes a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more variables that define one or more characteristics of the virtual patient. Pharmacokinetic parameters and disease prediction descriptors can be assigned to one or more variables for each dataset.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer-implemented method for modeling the interaction of ADAMTS13 and the Win Weber factor (VWF), the method comprising: obtaining a quantitative system representing the interaction of ADAMTS13 and VWF Pharmacology (QSP) model that includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; identifies disease-predicting descriptors; assigns these disease-predicting descriptors to virtual patient populations and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises concentrations of at least one biomarker.

Some aspects provide a non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for modeling A computer-implemented method for the interaction of ADAMTS13 with Win-Weber factor (VWF), the method comprising: obtaining a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, the model including the cleavage of ultra-large VWF (ULVWF) by ADAMTS13 the mechanism of multimers and their inhibition by extracellular hemoglobin; determining disease predictive descriptors; assigning the disease predictive descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data includes the concentration of at least one biomarker.

Some aspects provide a computer-implemented method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra warm Weber factor (ULVWF) multimers, the method comprising: determining the drug for a virtual patient population. Pharmacokinetic parameters of the administered drug; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; using quantitative systems pharmacology ( The virtual patient population was processed to obtain processed data using a QSP model that simulates the interaction of Win Weber factor (VWF) with ADAMTS13, including the mechanism by which ADAMTS13 cleaves the uncleaved ULVWF multimers and extracellular its inhibition by hemoglobin, and the processed data includes the concentration of at least one biomarker (eg, uncleaved ULVWF multimers, cleaved VWF fragments, lactate dehydrogenase, and/or platelet cells); and use of the processed data to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of uncleaved ULVWF multimers. In some embodiments, the method further includes displaying the processed data.

In some embodiments, the administered drug comprises endogenous and/or recombinant ADAMTS13. In some embodiments, the administered drug comprises plasma (eg, frozen plasma) of the donor patient.

In some embodiments, the QSP model represents the interaction of ADAMTS13 with stretched and globular VWF multimers. The QSP model can simulate the conversion of spherical VWF multimers to stretched VWF multimers. In some embodiments, the QSP model includes the binding affinity of extracellular hemoglobin to ULVWF multimers.

In some embodiments, an indicator of the effectiveness of the administered drug is obtained at least in part by comparing the processed data to known data indicative of a threshold concentration of at least one biomarker. Known data may include biomarker quantities for untreated individuals with sickle cell disease, congenital thrombotic thrombocytopenic purpura, and/or immune-mediated thrombotic thrombocytopenic purpura without sickle cell disease Quantities of biomarkers in individuals with congenital thrombocytopenic purpura disease, congenital thrombotic thrombocytopenic purpura disease, or immune-mediated thrombotic thrombocytopenic purpura disease, and/or in individuals with sickle cell disease in remission.

In some embodiments, the method further comprises using the processed data to determine whether the concentration of the at least one biomarker is below a first threshold value or above a second threshold value. In some embodiments, the method further comprises using the processed data to determine the duration for which the concentration of the at least one biomarker is below a first threshold value or the duration for which the concentration of the at least one biomarker is above a second threshold value . In some embodiments, the method further comprises using the processed data to determine changes in the concentration of at least one biomarker over time.

In some embodiments, the QSP model includes a plurality of differential equations representing one or more biological responses.

In some embodiments, the pharmacokinetic parameter comprises one or more life history characteristics (at least one of height, weight, age, or gender) of the patient to whom the administered drug is administered that is indicative of how the administered drug is administered. ) affects one or more parameters.

In some embodiments, the disease prediction descriptor comprises a defined patient (eg, a patient with sickle cell disease, a patient with congenital thrombotic thrombocytopenic purpura (cTTP), and/or with immune-mediated embolism) One or more parameters that characterize the concentration of ADAMTS13 in patients with idiopathic thrombocytopenic purpura (iTTP).

In some embodiments, the virtual patient population includes a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more variables that define one or more characteristics of the virtual patient. Pharmacokinetic parameters and disease prediction descriptors can be assigned to one or more variables for each dataset.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer-implemented method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-high temperature Weber factor (ULVWF) multimers, the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population Patient population; the virtual patient population was processed using a quantitative systems pharmacology (QSP) model to obtain processed data, wherein the QSP model simulates the interaction of Win Weber factor (VWF) with ADAMTS13, including the way ADAMTS13 cleaves the uncleaved Mechanisms of ULVWF multimers and their inhibition by extracellular hemoglobin, and the processed data includes the concentration of at least one biomarker; and use the processed data to obtain the effect of the administered drug on reducing the poly(uncleaved ULVWF) level An indicator of this effectiveness in terms of this concentration of aggregates.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method of determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra large Weber factor (ULVWF) multimers, the method comprising: determining drug metabolism of the administered drug for a virtual patient population kinetic parameters; determine disease prediction descriptors for the virtual patient population; assign the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; process the virtual patient using a quantitative systems pharmacology (QSP) model population to obtain processed data in which the QSP model mimics the interaction of Win Weber factor (VWF) with ADAMTS13, including the mechanism by which ADAMTS13 cleaved the uncleaved ULVWF multimers and its inhibition by extracellular hemoglobin, and The processed data includes the concentration of at least one biomarker; and the processed data is used to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of uncleaved ULVWF multimers.

Some aspects provide a computer-implemented method for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultra-warm Weber factor (ULVWF) multimers, the method comprising: providing a virtual patient the population determines pharmacokinetic parameters of the administered drug, wherein the pharmacokinetic parameters include frequency of non-compliance with the dosing regimen; determines disease prediction descriptors for the virtual patient population; The virtual patient population was assigned the clinical parameters and the disease prediction descriptors; the virtual patient population was processed using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 and VWF to obtain processed data, wherein the QSP model included The mechanism by which ADAMTS13 cleaved ultra large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin, and the processed data included the concentration of the uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments and use the processed data to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of uncleaved ULVWF multimers. The method may further include displaying the processed data.

In some embodiments, the processed data includes the frequency with which the amount of uncleaved ULVWF fragments exceeds a threshold value. In some embodiments, the processed data includes the percentage that the concentration of uncleaved ULVWF fragments exceeds a threshold value. In some embodiments, using the processed data to determine the impact of the frequency of non-compliance includes comparing the processed data to known data.

In some embodiments, the administered drug comprises endogenous and/or recombinant ADAMTS13. In some embodiments, the administered drug comprises plasma (eg, frozen plasma) of the donor patient.

In some embodiments, the QSP model represents the interaction of ADAMTS13 with stretched and globular VWF multimers. In some embodiments, the QSP model simulates the conversion of spherical VWF multimers to stretched VWF multimers. In some embodiments, the QSP model includes the binding affinity of extracellular hemoglobin to ULVWF multimers.

In some embodiments, the method further comprises determining whether one of the concentration of the uncleaved ULVWF multimer or the concentration of the cleaved Win-Weber factor fragment is below a first threshold value or above a second threshold value. In some embodiments, the method further comprises determining the duration for which one of the concentration of uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments is below a first threshold value, or uncleaved ULVWF Either the concentration of the multimer or the concentration of the cleaved Win-Weber factor fragment is above the second threshold value. In some embodiments, the method further comprises determining a change in one of the concentration of uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments.

In some embodiments, the QSP model includes a plurality of differential equations representing one or more biological responses.

In some embodiments, the pharmacokinetic parameter comprises one or more life history characteristics (at least one of height, weight, age, or gender) of the patient to whom the administered drug is administered that is indicative of how the administered drug is administered. ) affects one or more parameters.

In some embodiments, the disease prediction descriptor comprises a defined patient (eg, a patient with sickle cell disease, a patient with congenital thrombotic thrombocytopenic purpura (cTTP), and/or immune-mediated thrombotic platelets) One or more parameters characterizing the concentration of ADAMTS13 in inductive purpura (iTTP) patients).

In some embodiments, the virtual patient population includes a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more variables that define one or more characteristics of the virtual patient. Pharmacokinetic parameters and disease prediction descriptors can be assigned to one or more variables for each dataset.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer implementation for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-high-temperature Weber factor (ULVWF) multimers A method, the method comprising: determining, for a virtual patient population, pharmacokinetic parameters of the administered drug, wherein the pharmacokinetic parameters include frequency of non-compliance with the dosing regimen; determining a disease prediction for the virtual patient population descriptors; assigning the pharmacokinetic parameters and the disease-predicting descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF to obtain Processed data, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves oversized VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin, and the processed data includes the concentration of the uncleaved ULVWF multimers or one of the concentrations of cleaved Win-Weber factor fragments; and the processed data were used to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of uncleaved ULVWF multimers.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method of determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultra-warm Weber factor (ULVWF) multimers, the method comprising: determining the administered drug for a virtual patient population pharmacokinetic parameters of a drug, wherein the pharmacokinetic parameters include frequency of non-compliance with the dosing regimen; determining disease prediction descriptors for the virtual patient population; predicting the pharmacokinetic parameters and the disease Descriptors were assigned to the virtual patient population; the virtual patient population was processed using a Quantitative Systems Pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model included the way ADAMTS13 cleaved ultra-large VWF (ULVWF) to obtain processed data ) the mechanism of multimers and their inhibition by extracellular hemoglobin, and the processed data comprise either the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Wein-Weber factor fragment; and using The data were processed to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of uncleaved ULVWF multimers.

Some aspects provide a computer-implemented method for determining the concentration of a Win Weber factor (VWF) multimer in response to administration of ADAMTS13, the method comprising: determining the pharmacokinetics of administered ADAMTS13 for a virtual patient population determine disease prediction descriptors for the virtual patient population; assign the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; use quantitative systems pharmacology representing the interaction of ADAMTS13 with VWF ( QSP) model processing the virtual patient population to obtain processed data, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; and determined based on the processed data This concentration of VWF multimer.

In some embodiments, the administration of ADAMTS13 comprises administration of recombinant ADAMTS13. In some embodiments, administration of ADAMTS13 comprises administration of plasma (eg, frozen plasma) of the donor patient.

In some embodiments, wherein the VWF multimers comprise one of uncleaved oversized VWF multimers or cleaved VWF fragments. In some embodiments, the QSP model represents the interaction of ADAMTS13 with stretched and globular VWF multimers. The QSP model can simulate the conversion of spherical VWF multimers to stretched VWF multimers. In some embodiments, the QSP model includes the binding affinity of extracellular hemoglobin to ULVWF multimers.

In some embodiments, the method further comprises determining whether the concentration of the VWF multimer is below a first threshold value or above a second threshold value. The method can include determining the duration for which the concentration of the VWF multimer is below a first threshold value or the duration for which the concentration of at least one biomarker is above a second threshold value. In some embodiments, the method further comprises using a QSP model to determine changes in the concentration of VWF multimers over time.

In some embodiments, the QSP model includes a plurality of differential equations representing one or more biological responses.

In some embodiments, the pharmacokinetic parameter comprises one or more life history characteristics (at least one of height, weight, age, or gender) of the patient to whom the administered drug is administered that is indicative of how the administered drug is administered. ) affects one or more parameters. In some embodiments, the disease prediction descriptor comprises a defined patient (eg, a patient with sickle cell disease, a patient with congenital thrombotic thrombocytopenic purpura (cTTP), and/or with immune-mediated embolism) One or more parameters that characterize the concentration of ADAMTS13 in patients with idiopathic thrombocytopenic purpura (iTTP).

In some embodiments, the virtual patient population includes a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more variables that define one or more characteristics of the virtual patient. Pharmacokinetic parameters and disease prediction descriptors can be assigned to one or more variables for each dataset.

Some aspects provide a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor , the instructions cause the at least one computer hardware processor to execute a computer-implemented method for determining a concentration of a Win Weber factor (VWF) multimer in response to administration of ADAMTS13, the method comprising: determining for a virtual patient population Pharmacokinetic parameters of ADAMTS13 administered; determine disease prediction descriptors for the virtual patient population; assign the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; express ADAMTS13 and VWF using Quantitative Systems Pharmacology (QSP) model of interaction of the virtual patient population was processed to obtain processed data, wherein the QSP model included the mechanism by which ADAMTS13 cleaved ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin ; and the concentration of VWF multimer is determined based on the processed data.

Some aspects provide at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for A computer-implemented method of determining the concentration of a Win Weber factor (VWF) multimer in response to administration of ADAMTS13, the method comprising: determining for a virtual patient population a pharmacokinetic parameter of the administered ADAMTS13; for the virtual patient Population determines disease prediction descriptors; assigns the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; processes the virtual patient using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF Population to obtain processed data, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; and the concentration of VWF multimers is determined based on the processed data .

Biological overview

According to some aspects of the present application, the apparatus and methods described herein can be used to simulate the interaction between VWF and ADAMTS13. That is, the QSP model described herein may represent the mechanism by which ADAMTS13 cleaves ULVWF and its inhibition by extracellular hemoglobin.

Win-Weber factor (VWF) is an adhesive and polysaccharide protein that plays an important role in maintaining hemostatic balance. VWF promotes platelet aggregation and clot formation at sites of endothelial injury. However, the trigger for VOC events in SCD patients is the accumulation of high concentrations of uncleaved ultra-ultra-warm Weber factor (ULVWF) multimers in the patient's plasma, to which extracellular hemoglobin (Hb) binds, causing excessive coagulation.

To prevent unnecessary coagulation, VWF is negatively regulated by the catalytic enzyme "disintegrin and metalloproteinase 13 with thrombospondin type 1 motif" (ADAMTS13). The VWF cleaving protease ADAMTS13 is a plasma zinc metalloprotease that cleaves VWF in the A2 domain. ADAMTS13 plays a role in primary hemostasis by regulating the platelet tethering and hemostatic ability of VWF. Thus, ADAMST13-mediated cleavage of VWF helps maintain the balance between normal hemostatic function and abnormal platelet aggregation leading to thrombosis. A severe lack of ADAMTS13 activity (<5% of normal) results in the persistence and accumulation of hyperactive UL VWF multimers in the circulation. ULVWF multimers accumulate in plasma and can lead to unwanted platelet aggregation and widespread microvascular thrombosis.

Cleavage of ULVWF by ADAMTS13 is inhibited by extracellular Hb, which can also be represented by the QSP model. Extracellular hemoglobin interacts with VWF, binds to the ADAMTS13 cleavage site on the A2 domain of VWF, and blocks ADAMTS13 cleavage of VWF.

Figure 1 shows a schematic diagram 100 of ADAMTS13 cleavage of ULVWF into smaller VWF polymer fragments and inhibition of this effect by hemoglobin (Hb) at the vascular endothelium. Schematic 100 illustrates the mechanism of action employed in the pharmacodynamic models described herein.

ULVWF multimers are secreted from endothelial cells at the vascular endothelium. These VWF multimers play important roles in cell adhesion and prothrombotic complications. Schematic 100 shows stretched and spherical ULVWF present in vivo. Shear force (eg, due to blood flow) induces expansion of spherical ULVWF into stretched ULVWF, which can be cleaved into VWF fragments by ADAMTS13. As used herein, active VWF refers to uncleaved VWF in stretched form (ie, ULVWF). In the stretched form, the binding site of ULVWF is exposed for adhesion to platelets, causing coagulation, and binding of Hb and ADAMTS13.

As shown in schematic 100, ADAMTS13 can cleave stretched ULVWF into VWF fragments (plasma VWF). However, absence or inhibition of extracellular Hb or ADAMTS13 limited ADAMTS13 cleavage of stretched ULVWF. Extracellular Hb (free Hb in schematic 100) binds to stretched ULVWF, preventing cleavage of any ADAMTS13 present. Hb-bound ULVWF accumulates, leading to coagulation. The accumulation of ULVWF can lead to thrombus generation and/or blockage of the microcirculation caused by the accumulation of sickle cells, which can lead to VOCs.

Sickle cell disease (SCD) is an inherited hemoglobin disease caused by a single recessive single point mutation in the β-globin chain of adult hemoglobin. During hypoxic conditions, deoxygenation triggers the sickling of red blood cells, causing excessive extracellular hemoglobin release. SCD is characterized by the onset of chronic hemolytic anemia and vaso-occlusive painful events leading to progressive tissue ischemia and multiple organ damage.

An important pathophysiological factor is the presence of high concentrations of uncleaved VWF multimers in plasma from SCD patients. Compared to healthy individuals, the plasma of SCD patients (both clinically asymptomatic and with acute pain crises) exhibited minimal or no deficiency of ADAMTS13 activity, but higher concentrations of VWF and especially ULVWF multimers, and thus ADAMTS13 Activity is relatively deficient relative to its substrate.

Thrombotic thrombocytopenic purpura (TTP) is a condition that causes blood clots (thrombosis) to form in small blood vessels throughout the body. Serious medical problems can result if these clots block blood vessels and restrict blood flow to organs such as the brain, kidneys, and heart. In patients with TTP, thrombosis can also be caused by high concentrations of uncleaved VWF multimers in plasma. Thrombotic thrombocytopenic purpura includes immune-mediated thrombotic thrombocytopenic purpura (iTTP) and congenital thrombotic thrombocytopenic purpura (cTTP).

A recombinant ("r") form of ADAMTS13 ("rADAMTS13") can be used to help regulate ADAMTS13 production and/or function in vivo. That is, rADAMTS13 can be administered to patients with irregular production of ADAMTS13 or extracellular Hb or inhibition of ADAMTS13 function. Additionally or alternatively, the level of endogenous ADAMTS13 in a patient can be increased by plasmapheresis and/or administration of frozen plasma of donor plasma with healthy levels of naturally occurring ADAMTS13. Plasma exchange is generally performed in an intensive care unit. As will be described further herein, the QSP model represents the molecular interactions of components in the ADAMTS13-VWF interaction, eg as shown in schematic 100, in which ADAMTS13 cleaves ULVWF multimers into smaller VWF fragments and cleaves ULVWF multimers into smaller VWF fragments by extracellular hemoglobin (Hb) binding to ULVWF multimer inhibits this effect.

Quantitative Systems Pharmacology Model Development

As described herein, the inventors have developed a QSP model for modeling the interaction between ADAMTS13 and VWF. The QSP model may include a plurality of differential equations with parameters reflecting the interaction between ADAMTS13 and VWF. Parameters can be parameterized and calibrated using biological data in the literature and clinical data from one or more clinical trials. The QSP model can be verified by comparing the QSP model output to known data.

The development of a QSP model can include several steps starting with the development of a model diagram. A model map can be developed based on studies of the biological mechanism of ADAMTS13 cleavage of ULVWF into smaller VWF fragments and its inhibition by Hb.

After the model graph is developed, the QSP model can be formulated by determining a series of mathematical equations representing the model graph. In particular, the series of mathematical equations may include a plurality of differential equations representing the cleavage of ULVWF by ADAMTS13 and its inhibition by Hb.

The formulation model can then be parameterized. For example, the values of the model parameters described further herein can be estimated based on literature and clinical data. Parametric models can be calibrated with one or more datasets. For example, the parameterized values of the model can be refitted based on the in vitro and/or in vivo data used to calibrate the model. The calibrated model can then be validated against additional data not used in the calibration.

Validated models can be tested via simulations. For example, simulations of clinical trials in which a therapeutic intervention is administered to a patient can be performed and the effect of the therapeutic intervention on the interaction of ADAMTS13 and VWF can be observed. For example, test doses and/or dosing regimens can be input into a QSP model to assess the efficacy of doses and/or dosing regimens in treating patients, such as SCD patients under VOC.

biological process diagram

The development of a QSP model can begin with the development of a model diagram. Figure 2 shows a biological process diagram representing the molecular interactions of components in the ADAMTS13-VWF interaction. A model map can be developed based on studies of the biological mechanism of ADAMTS13 cleavage of ULVWF into smaller VWF fragments and its inhibition by Hb. As used throughout this invention, hemoglobin (Hb) is defined as extracellular Hb. ADAMTS13 and rADAMTS13 refer to the endogenous and recombinant forms, respectively. VWF begins as ULVWF when it is initially produced by the vascular endothelium. Once it has been cleaved to form a single VWF monomer, it is referred to as a VWF fragment.

As shown in FIG. 2, the process diagram 200 includes both ULVWF (VG) in a spherical state and ULVWF (VS) in a stretched state. The shear stress exhibited on the spherical ULVWF (eg, due to blood flow) expands the spherical ULVWF into a stretched ULVWF.

Stretched ULVWF can be acted upon by three different products (ADAMTS13 in endogenous or recombinant form, extracellular Hb or thrombospondin-1). First, stretched ULVWF can be cleaved into smaller fragments by ADAMTS13 in endogenous or recombinant form. Cleavage of ULVWF produces VWF fragments, as shown in FIG. 2 . The rate at which this occurs depends on the binding constant between stretched ULVWF and ADAMTS13 (Kd _VWF-ADAM ), which can be parameterized, calibrated, and validated during model development. Cleavage of ULVWF by ADAMTS13 (either in endogenous or recombinant form) can also be based on the turnover number of VWF by ADAMTS13 (or the maximum number of substrate molecules converted to product per active site per unit time), which can be determined during model development Parameterized, calibrated and verified.

When ULVWF unfolds into a stretched form, the binding site of ULVWF is exposed. Therefore, extracellular hemoglobin (f-Hb) and thrombospondin (TSP-1) may additionally act to stretch ULVWF. Specifically, extracellular hemoglobin and thrombospondin inhibit the cleavage of ULVWF by binding to binding sites that stretch ULVWF. The binding of extracellular hemoglobin to stretched VWF can be based on the binding constant between stretched ULVWF and extracellular hemoglobin (Kd _VWF-HB ), which can be parameterized, calibrated, and validated during model development. Binding of thrombospondin to stretched VWF can be based on the binding constant between stretched ULVWF and thrombospondin (Kd _VWF-TSP ), which can be parameterized, calibrated, and validated during model development.

Process diagram 200 further represents the import of endogenous or recombinant ADAMSTS13 into a region of the patient's body that contains stretched ULVWF. The input concentrations of ADAMTS13 for a given patient can be output from the pharmacokinetic model of the QSP model, described further herein, including the central volume ( _Vc ) and the peripheral volume ( _Vp ).

3 shows a model diagram 300 illustrating a QSP model for modeling the interaction between ADAMTS 13 and VWF, according to some non-limiting embodiments. As shown in FIG. 3 , the QSP model may include a number of individual models, including a pharmacokinetic (PK) model 310 , a pharmacodynamic (PD) model 320 , and a clinical outcome model 330 . PK models may provide PK parameters for use in one or more PD models, such as describing how patient characteristics (eg, height, weight, gender, age, etc.) affect the drug administered to the patient (eg, affect drug concentration in the patient's bloodstream) ). One or more PD models can illustrate the interaction between VWF and ADAMTS13, as well as other components as described herein, such as thrombospondin and extracellular hemoglobin.

The QSP model shown in FIG. 3 further includes a clinical outcome model 330 . The clinical outcome model may receive output from the PD model 320 (eg, the concentration of one or more biomarkers, such as active VWF, VWF fragment, platelet count, and/or LDH concentration). The measured results may include the level of active VWF (eg, concentration), the level of VWF fragment (eg, concentration), the determination of whether the level of active VWF is below a threshold value and its duration and/or whether the level of VWF fragment is above the threshold value. Determination of limits and their duration.

In some embodiments, the QSP model can be configured to model the interaction between VWF and ADAMTS13 (either in endogenous or recombinant form). The QSP model can also take into account the inhibition of ADAMTS13 cleavage of VWF by extracellular hemoglobin and/or thrombospondin. For example, a model may include parameters as described herein that represent binding constants between VWF and ADAMTS13, thrombospondin, and hemoglobin.

In some embodiments, the QSP model can represent the interaction of ADAMTS13 with both stretched and spherical forms of ULVWF. The QSP model can simulate the conversion of spherical VWF multimers to stretched VWF multimers. For example, a QSP model may include one or more equations representing the conversion of spherical VWF to stretched VWF.

In some embodiments, the QSP model is used in a computer-implemented method of simulating SCD, cTTP and/or iTTP therapy. For example, the various PK, PD and clinical outcome models described herein can be used to assess the effectiveness of novel or existing treatment modalities for SCD, cTTP and/or iTTP. In some embodiments, only some of the individual models may be utilized when implementing the QSP models in a computer-implemented method. For example, in some embodiments, the QSP model can be implemented without the use of a PK model to better understand biomarker behavior in the absence of any therapeutic intervention. Thus, as used herein, quantitative systems pharmacology (QSP) models should be understood to encompass any combination of the PK, PD, and clinical outcome models described herein.

PK Model

According to some aspects, the QSP model includes a PK model for providing PK parameters to the PD model. An example PK model 310 is shown in FIG. 3 . A PK model can describe how a drug is absorbed and distributed by a particular patient, more specifically, the rate and extent of distribution of the drug to different tissues and the rate of elimination of the drug. A PK model can be modeled as a series of differential equations that describe the systemic delivery of a drug.

As shown in Figure 3, PK model 300 is a two-compartment PK model with subcutaneous (SC) reservoirs. Specifically, the PK model can be divided into central and peripheral compartments. The central compartment is composed of plasma and tissue where drug distribution occurs more rapidly, while the peripheral compartment is composed of tissue and plasma where drug distribution occurs more slowly. The inventors have understood that the use of a PK model with multiple compartments allows for non-uniformity of drug distribution.

PK models can be used to model the PK behavior of drugs in patients. For example, in some embodiments, PK models are used to model the PK behavior of existing treatment modalities such as rADAMTS13 and/or plasma exchange and/or administration of frozen plasma to increase endogenous ADAMTS13 levels. In some embodiments, PK models can be used to model the PK behavior of novel and/or previously untested drugs. For example, the rate of absorption (ka) and bioavailability (F) of the drug to be modeled can be input into a PK model, and the predicted concentration of the drug in a patient can be output for input into a PD model.

Although a two-compartment PK model is used in the embodiment shown in Figure 3, in other embodiments, a single-compartment PK model with subcutaneous (SC) reservoirs or a non-compartmental PK model can be used.

PD Model

According to some aspects, the QSP model includes one or more PD models for modeling the interaction between ADAMTS13 and VWF. Specifically, the PD model reflects the cleavage of ULVWF by endogenous or recombinant ADAMTS13 and its inhibition by extracellular hemoglobin. Table 2 gives a list of variables used in the QSP model.

surface 1 : QSP List of species of substances in the model Substance type name unit illustrate ADAM nM Amount of ADAMTS13 rADAM nM The amount of rADAMTS13 Hb nM Amount of Hb (extracellular) VS nM Amount of VWF monomer units in stretched form VS_ADAM nM Amount of VWF monomeric units in stretched form bound to ADAMTS13 VS_Hb nM Amount of VWF monomer unit in stretched form bound to Hb (extracellular) VS_rADAM nM Amount of VWF monomer units in stretched form bound to rADAMTS13 VS_Frag nM Amount of fragmented VWF monomer derived from VS VG nM Amount of VWF monomer unit in spherical form VG_ADAM nM Amount of VWF monomer units in spherical form bound to ADAMTS13 VG_Hb nM Amount of VWF monomer units in spherical form bound to Hb (extracellular) VG_rADAM nM Amount of VWF monomer units in spherical form bound to rADAMTS13 VG_Frag nM Amount of fragmented VWF monomer derived from VG

As described herein, ADAMTS13 in endogenous or recombinant form cleaves ULVWF into VWF fragments to prevent ULVWF accumulation, which can lead to the appearance of sickled red blood cells (RBCs) due to hypoxia that accompanies ULVWF accumulation and is A blockage occurs in the circulation. However, cleavage is inhibited by deficiency or inhibition of ADAMTS13 and/or binding to excess extracellular hemoglobin that stretches ULVWF. These relationships and responses are reflected in Tables 2a and 2b below. Table 2a gives the list of responses represented by the QSP model.

surface 2a : list of reactions in the model involving in stretched form VWF monomer( VS )Reaction: Binding/Uncoupling between ADAM and VS: VS+ADAM ↔ VS_ADAM (R1) Binding/uncoupling between Hb and VS: VS + Hb ↔ VS_Hb (R2) Cleavage of VS by ADAM VS_ADAM ↔ VS_Frag + ADAM (R3) Binding/uncoupling between rADAM and VS: VS+rADAM ↔ VS_rADAM (R4) Cleavage of VS by rADAM VS_rADAM ↔ VS_Frag + rADAM (R5) involving spherical form VWF monomer( VG )Reaction: Associating/disengaging between ADAM and VG: VG+ADAM ↔ VG_ADAM (R6) Binding/uncoupling between Hb and VG: VG + Hb ↔ VG_Hb (R7) Cleavage of VG by ADAM VG_ADAM ↔ VG_Frag + ADAM (R8) Binding/uncoupling between rADAM and VG: VG + rADAM ↔ VG_rADAM (R9) Cleavage of VG by rADAM VG_rADAM ↔ VG_Frag + rADAM (R10)

Table 2b : List of governing equations in the model

The QSP model is limited to considering VWF, Hb and ADAMTS13 (both endogenous and recombinant forms). Other proteins such as thrombospondin 1 and binding globulin that could affect the levels of these proteins were not included. The inventors have recognized that the inclusion of certain proteins in the QSP model, such as thrombospondin 1 and binding globulin, can make the model unstable and inaccurate. Excluding such proteins from the QSP model can improve the overall accuracy of the model.

The QSP model is based on the assumption that recombinant rADAMTS13 behaves the same as endogenous ADAMTS13, and the same model parameter values apply to both types of ADAMTS13. The microvessels of the circulatory system are most sensitive to thrombosis and vascular occlusion. The model assumes this area as the dominant site of action.

The QSP model takes into account ULVWF in both stretched and spherical forms. The QSP model simulates and therefore includes parameters for the transformation of ULVWF from spherical to tensile form by expansion due to shear forces. Steady state transformation was applied to model transformation of ULVWF from spherical to stretched form. The same degradation rate parameters are assumed for VWF and its complexes in both stretched and spherical forms.

The inventors have recognized that the underlying molecular mechanism represented by the QSP model, namely the competitive binding of ADAMTS13 and Hb to VWF, may be applicable to mimic patients and their treatment in the context of SCD or TTP.

。QSP模型將Hb與VWF之間的結合表示為一對一關系。發明人已排除QSP模型顧及多個血紅蛋白對一個VWF之協同性而導致較不準確模型的可能性。 Equation E11 in Table 2b above represents the binding affinity between Hb and VWF

. The QSP model represents the binding between Hb and VWF as a one-to-one relationship. The inventors have ruled out the possibility that the QSP model takes into account the cooperativity of multiple hemoglobins to a VWF resulting in a less accurate model.

The model tracks the states of individual monomeric units of VWF as unbound, ADAMTS13 bound, Hb bound and cleaved products. The model distinguishes between stretched and spherical forms of VWF. The blood flow (velocity gradient in the vessel) provides shear forces to induce stretching of the VWF. The microvessels of the circulatory system experience the highest shear rates.

All association/dissociation and proteolytic reactions are based on mass action, ie, first-order reactions of the reactants. The same binding/disengagement reactions occurred in both stretched and spherical forms, but with different reaction parameters (Table 2a). VWF monomer units bound to ADAMTS13 can undergo proteolysis, resulting in cleaved products. However, it has been reported that the proteolytic rate of VWF in globular form is negligible because the cleavage site in the A2 domain is in a folded form and inaccessible to ADAMTS13.

clinical outcome model

As described herein, the QSP model can further include a clinical outcome model. Literature is available comparing ADAMTS13 and VWF levels in healthy control individuals with SCD patients in the asymptomatic state and with pain crisis. The study found that the VWF Ristocetin cofactor activity (VWF: RCo) was significantly higher in SCD patients: 73 U in healthy controls, asymptomatic SCD patients, and SCD patients with pain crisis, respectively /mL, 143 U/mL and 172 U/mL. VWF:RCo is a measure of the ability of VWF to bind platelet glycoprotein Ib (GPIb). This binding requires the A1 domain of VWF to be in a stretched form. Other literature sources also report a significant correlation between the percentage of high molecular weight VWF multimers and VWF:RCo.

The model assumes that VWF:RCo represents "active VWF" leading to thrombosis and vascular occlusion and is used as a measure of clinical outcome. In the model, the amount of VWF:RCo is represented by the amount of VWF in stretched form with and without bound Hb (active VWF = VS + VS_Hb, names are defined in Table 1). VWF bound to ADAMTS13 was excluded as it is expected that once bound it would be negligible due to rapid proteolysis. As another measure of clinical outcome, using the model predictions that patients in VOCs can maintain active VWF levels below levels found in SCD remission.

Thus, the clinical outcome model defines "active VWF" as uncleaved VWF in a stretched form with binding sites exposed for adhesion to platelets and binding to Hb and ADAMTS13. Active VWF is a biomarker that can be output by a clinical outcome model.

The clinical outcome model may also output the determination that the active VWF concentration is below the threshold level and/or the duration of the active VWF concentration below the threshold level. In some embodiments, the threshold level of active VWF may be the concentration of active VWF in an SCD patient in remission (eg, as may be obtained from clinical data). In some embodiments, the clinical outcome model can output a determination that the concentration of cleaved VWF fragments is above a threshold level and/or the duration of time that the concentration of cleaved VWF fragments is above a threshold level. The threshold level of cleaved VWF fragments can be determined based on the concentration of cleaved VWF fragments in SCD patients in remission (eg, as can be obtained from clinical data).

Quantitative Systems Pharmacology Model Parameterization

After developing a model diagram of the QSP model and a system of differential equations representing the responses between the parameters reflected in the model, the QSP model can be parameterized. For example, the parameters of the models defined in Table 3 below can be set to initial values based on literature data, clinical data, known mathematical relationships between other parameters, or obtained through further calibration steps described herein. Table 3 below gives the model parameters, their initial values and the source of the initial values.

surface 3 : list of model parameters parameter value unit illustrate source PD Model parameters KD _{VS_ADAM} 10 nM Binding constant between VS and ADAM (assumed to be the same for rADAM) literature KD _{VS_Hb} 915/457.5 nM Binding constant between VS and Hb In vitro/in vivo calibrated kon _{VS_ADAM} 0.36 nM-1hr-1 ADAM vs VS kon (assuming same for rADAM) literature kon _{VS_Hb} 0.36 nM-1hr-1 The kon of Hb and VS literature koff _{VS_ADAM} 3.6 1/hr koff of the VS-ADAM complex (assumed to be the same for rADAM) Calculated according to the definition KD*kon koff _{VS_Hb} 164.7 1/hr koff of VS-Hb complex Calculated according to the definition KD*kon _Kcats 2.34783 1/hr ADAMTS13 kcat for VWF in stretched form literature kdeg _ADAM 0.115525 1/hr ADAMTS13 kdeg literature kdeg _Hb 0.693147 1/hr kdeg of Hb Calibrated to within the protein half-life range kdeg _VWF 0.462098 1/hr kdeg of VWF literature ksyn _ADAM 0.0405376 nM/hr ksyn of ADAMTS13 calibrated ksyn _Hb 2365/77 nM/hr ksyn of Hb calibrated ksyn _VWF 4.24206 nM/hr ksyn of VWF calibrated VS _Frac 1/0.025 - Fraction of stretched VWF in total VWF In vitro, assuming full stretch/in vivo, calibrated since literature KD _{VG_ADAM} 80 nM Binding constant between VG and ADAM (assumed to be the same for rADAM) literature KD _{VG_Hb} 20587.5 nM Binding constant between VG and Hb calibrated kon _{VG_ADAM} 0.36 nM-hr-1 The kon of ADAM and VG (assuming the same for rADAM) literature kon _{VG_Hb} 0.36 nM-1hr-1 The kon of Hb and VG literature koff _{VG_ADAM} 28.8 1/hr koff between VG and ADAM (assuming the same for rADAM) Calculated according to the definition KD*kon koff _{VG_Hb} 164.7 1/hr koff of VG-Hb complex Calculated according to the definition KD*kon kcat _G 0 1/hr ADAMTS13 kcat for globular form of VWF literature PK Model parameters CL 0.044457 L/hr clearance rate calibrated with clinical data CLD 0.0791412 L/hr Volume transfer rate between center and periphery calibrated with clinical data Vc 3.126 L central volume calibrated with clinical data Vp 1.57057 L Peripheral volume calibrated with clinical data physicochemical parameters MW _{VWF monomer} 220000 Da MW of VWF monomer-polymer unit literature MW _ADAM 190000 Da MW of ADAMTS13 and rADAMTS13 literature MW _Hb 64458 Da MW of hemoglobin (in tetrameric form) literature

Binding between ADAMTS13 and VWF has been described as a "molecular zipper" interaction between multiple domains of the two proteins that creates Tyrl605 and Met1606 cleavage sites in the A2 domain. The reported dissociation constant KD between ADAMTS13 and VWF _ADAMTS13-VWF is 80 nM when VWF is in a globular conformation (between ADAMTS13 TSP5-CUB domain and VWF D4-CK domain) and is in a stretched conformation in VWF Formed at 10 nM (between the A2 domain of VWF and the spacer domain of ADAMTS13).

The following data were obtained from the literature for the binding between Hb and VWF: Hb binds directly to VWF via the A1 domain, KD (Hb-VWF) dissociation constant is about 15,000 nM, and Hb binds directly to VWF via the A2 domain, KD ( Hb-VWF) dissociation constant is about 183 nM. Validation of the model described herein demonstrated the ability of the model to accurately reproduce both in vitro and in vivo data representing outcomes for healthy individuals, SCD patients in remission, and SCD patients undergoing VOC.

As shown in Table 3, some parameters were estimated from literature values. However, the binding affinity between VWF in stretched form (active VWF) and Hb, the binding affinity between VWF in spherical form and Hb, and the fraction of VWF in stretched form (active VWF) are not directly obtained. From the literature, but adjusted in further calibration steps based on clinical data. In some cases, the binding affinity between Hb and stretched VWF (active VWF) is the only model parameter that requires changing in vitro values to match in vivo data.

Quantitative Systems Pharmacology Model Calibration and Validation

As described herein, after parameterizing the developed model, the QSP model can be calibrated and validated. For example, QSP models can be calibrated using known data (eg, in vitro data, in vivo data). The initial values of the parameters set when parameterizing the QSP model can be adjusted based on calibration. The calibrated parameters can then be verified against data not used in the calibration.

Calibrated with in vitro data QSP Model

First, the QSP model was calibrated using in vitro data obtained from the analysis described herein. Table 7 shows an overview of the assay setup to measure the proteolytic activity of rADAMTS13 in the presence of Hb in the presence and absence of preincubation. The study utilized flow-based analysis to create shear stress conditions to unfold the full-length VWF substrate, allowing ADAMTS13 binding and ADAMTS13-mediated cleavage. The study assumes that the full length VWF is fully stretched - that is, all monomer units are available for the reaction. Literature data has shown that cleavage levels in flow-based assays closely match those using pre-denatured full-length VWF and small peptides derived from the VWF-A2 domain.

surface 4 : Measured in the presence and absence of pre-incubation rADAMTS13 Overview of the assay setup for proteolytic activity in the presence of hemoglobin setup/sample (i) Pre-cultivation (ii) Direct Breeding testing sample control sample testing sample control sample rVWF 3 IU/ml platelets 300×103 cells/µl hemoglobin 0.5 - 10 mg/mL 0 mg/mL Vortex Assay Buffer (Vortex Assay Buffer; VAB) to make up to 45 μL .1 - 1 mg/mL 0 mg/ML VAB make up to 45 μL volume Make up to 45 μL Make up to 45 μL Pre-incubation under constant vortex 30 minutes at 2500 rpm No preincubation - subsequent addition of purified recombinant ADAMTS13 ADAMTS13 15 μL 1 - 16 U/mL (i.e. 0.25 - 4 U/ml ec) (final concentration) 15 μL 1 -8 U/mL (i.e. .25 - 2 U/ml ec) (final concentration) volume 60 μL Incubation under constant vortex 60 minutes at 2500 rpm reaction stopped 10mM EDTA

Figures 4A-4C show a comparison of the cleaved VWF concentrations predicted by the QSP model of Figure 3 to the cleaved VWF concentrations from in vitro data over a range of rADAMTS13 and Hb levels under direct incubation settings. Cleaved product data are reported as % relative to the amount of cleaved product at Hb=0. The cleavage product was measured as the amount of dimeric 176 kDa VWF cleavage fragment. The data show that the % cleavage product decreases monotonically with increasing Hb amount in all cases, and decreases monotonically with decreasing rADATMTS13 amount for almost all contents. As shown in Figure 4A, the data point at rADAMTS13=0.5, Hb=0.1 (indicated by 1 in Figure 4A) deviates from this trend, and the data point at rADAMTS13=0.25, Hb=0.1 (indicated by 2) shows the same Significantly greater reduction compared to other datasets at the same Hb content.

Figure 4B compares the model predictions to data using a lower literature value for the binding constant of Hb to VWF: KD _{VS_Hb} of 183 nM. This value should be used for the combination of fully stretched VWF (VS) for in vitro simulation. The Hb sensitivity from the model results was significantly greater than that shown in the direct incubation data, suggesting that the binding constant may be too strong. A 5-fold increase in KD _{VS_Hb} to 915 nM matched the direct incubation data best, as shown in Figure 4C, and the sensitivity of rADATMTS13 and Hb from the model to % cleavage products matched the direct incubation data well.

Figure 4D shows the sensitivity of the KD _{VS_Hb} fit to the data obtained under the direct incubation setting. In particular, the sensitivity of the binding constant of Hb to VWF (KD _{VS_Hb} ) reflected in the QSP model of Figure 3 is shown, according to some non-limiting examples. Figure 4E shows a model simulation of percent VWF cleavage for different binding constants of Hb to VWF, according to some non-limiting examples.

calibrated with in-vitro data QSP model validation

The parametric model can be validated against data not used in the calibration to determine that the model results substantially match known data, thereby ensuring that the QSP model can accurately model the interaction between ADAMTS13 and VWF; and providing a basis for novel existing treatment modalities. effective assessment. For example, after calibrating a QSP model with in vitro data, the calibrated model can be validated against additional data.

The validation steps described herein were performed using pre-incubated in vitro data. The amount of cleaved VWF product was measured in the same manner as described for the in vitro data used to calibrate the QSP model. The in vitro data used for validation showed that in all cases the % cleavage product decreased monotonically with increasing Hb amount. However, the effect of rADATMTS13 content on % cracked showed considerable dispersion and no clear trend could be observed.

surface 5 : Use pre-incubated in-vitro data for validation hemoglobin concentration mg/mL 0.5 1 5 10 rADAMTS13 Concentration (based on FRETS-VWF73 activity result) dimerization 1.76 kDa % ratio of cleavage products ( Hemoglobin-containing samples / Ratio of control samples without hemoglobin , by % unit ) 4 U/mL rADAMTS13 46 59 19 13 2 U/mL rADAMTS13 68 55 19 15 1 U/mL rADAMTS13 56 59 twenty three 11 0.5 U/mL rADAMTS13 69 46 28 20 0.25 U/mL rADAMTS13 65 38 28 twenty one Average percent ratio of dimeric 176 kDa cleavage products 61 51 twenty three 16

Figure 5 shows a comparison of percent VWF cleavage between model predictions and in vitro pre-incubation data, according to some non-limiting examples. In particular, Figure 5 compares the pre-incubation data with a model using calibrated KD _{VS_Hb} = 915 nM. For each Hb content, the % cleavage product averaged over the range of rADAATMTS13 was used for comparison. Trends in Hb amount yielded % cleavage product predictions consistent with the data, but the absolute % cleavage product content was lower for all Hb contents. Some of this difference can be attributed to considerable uncertainty in the measurements. Given this, the model can continue to use KD _{VS_Hb} = 915 nM as a starting point for comparison with the in vivo data.

Calibrated with in vivo data QSP Model

The QSP model may be further calibrated with one or more additional datasets. For example, QSP models can be calibrated using in vivo data reflecting the interaction of ADAMTS13 with VWF.

Table 6 summarizes ADAMTS13, Hb and VWF levels in healthy individuals, SCD patients in remission, and SCD patients in VOC. These levels were compiled from literature sources. Using the degradation rates obtained from the literature, each of these protein contents was used to estimate the corresponding synthesis rate constants under steady state conditions (ie, ksynADAM, ksynHb, and ksynVWF). These calibrated rate constants are labeled "Calibrated" in Table 3.

surface 6 : healthy, in remission SCD patient, in VOC Nakano SCD patients and TTP patient's ADAMTS13 , Hb and VWF content   ADAMTS13 ( U/μg ) hemoglobin ( U/μg ) total VWF ( μg/mL ) healthy 1.5 (Data range: 0.5 to 1.6) 50 (Data range: up to 50) 10 SCD - ease 1.5 (Data range: 0.5 to 1.6) 220 (Data range: 20-330) 20 (Data range: 10-30) SCD-VOC 1.5 (Data range: 0.5 to 1.6) 400 (Data range: up to 400) 20 (Data range: 10-30) TTP patient 0.1 50 Assumed to be the same as healthy individuals 10 Assumed to be the same as healthy individuals

For in vivo simulations, it is necessary to estimate what fraction of the total VWF is in stretched and spherical form. Literature data can be used to estimate this, with data reporting the amount of VWF bound to Hb in healthy individuals and SCD patients in remission, "active VWF" levels from healthy individuals and SCD patients in VOC, using the amount of VWF directed against VWF. Antibodies to the A1 domain were measured by enzyme linked immunosorbent assay (ELISA). Given that the A1 domain is only accessible in stretched form, it can be assumed that this measure obtained in the literature is comparable to the model definition of active VWF described herein.

6A and 6B illustrate a comparison of the fraction of VWF in the active form between model predictions and in vivo data, according to some non-limiting embodiments. In particular, Figure 6A shows a comparison of the amount of VWF bound to Hb in normal individuals and SCD patients in remission from in vivo data and model predictions. Figure 6B shows a comparison of the amount of active VWF in normal individuals and SCD patients in VOC from in vivo data and model predictions.

To match the data, the amount of VWF in stretched form was set to 2.5% of the total VWF for all conditions (normal individuals, SCD patients, TTP patients). The volume fraction measurements of human microvessels range from 3% to 5%. Except for the binding affinity between Hb and VWF, all other model parameters were the same as those obtained from in vitro calibration/validation. To match the in vivo data, KD _{VS_Hb was} reduced to half the calibrated value in vitro, 457.5 nM. One possible explanation for this reduction, representing greater apparent inhibition by Hb in vivo compared to in vitro, could be the presentation of other proteins known to have inhibitory effects in in vivo conditions, such as thrombospondin 1 (TSP-1; TSP-1). )Impact. The calibrated value of KD _{VG_Hb} = 20,588 nM is close to the 15,000 nM reported in the literature.

calibrated with in vivo data QSP model validation

The calibrated model can be re-validated using data not used in the calibration. Validation of the QSP model calibrated to in vivo data was performed using data from the Phase 1 TTP study. This study investigated the first-in-human PK and safety of rADAMTS13 in patients with congenital ADAMTS13 deficiency. Simulation results for this validation step are reported in the Results section described below.

PK Parameterization of the model

Figure 7 shows the comparison of the pharmacokinetics (PK) of different doses of rADAMTS13 (5 U/kg, 20 U/kg and 40 U/kg) between model predictions and in vivo data, according to some non-limiting examples data graph. Figure 7 shows the results of the two-compartment PK model compared to the TTP data. The fitted model parameters are shown in Table 3. The data showed dose proportionality and this was tightly captured by the model.

Virtual group development and simulation

As described herein, the QSP model can be used to model the interaction between VWF and ADAMTS13 to obtain information suitable for evaluating novel and existing treatments for SCD, iTTP and cTTP. To simulate such interactions without the need for clinical trials, virtual population and treatment simulations can be developed.

virtual group development

A virtual population for testing therapeutic interventions may include a virtual dataset that includes a plurality of "patients." Each patient may contain a subsequent dataset (eg, Patient ₁ ) and may represent an individual virtual patient in a virtual population.

Each patient in a virtual patient population can be assigned a set of PK parameters that represent variability in a particular patient's drug dynamics (eg, parameters that indicate how therapeutic intervention is affected by the patient's life history characteristics). In some embodiments, PK parameters are randomly assigned to virtual populations, and in some embodiments may be based on clinical data or general data. Example PK parameters may include weight, age, gender, height, ethnicity, and/or SCD status (eg, in remission, healthy, onset).

In some embodiments, each virtual patient in a virtual population may be assigned a disease prediction descriptor. For example, example disease prediction descriptors may include a virtual patient's propensity to develop VOCs in the absence of therapeutic intervention. For example, disease predictive descriptors can include the concentration of endogenous ADAMTS13 and/or the concentration of extracellular Hb in the patient prior to administration of any therapeutic intervention. In some embodiments, the disease prediction descriptors are determined at least in part by a simulation of a Poisson distribution informed by known data about the disease prediction descriptors.

In some embodiments, a constant disease prediction descriptor may be applied to each patient in the virtual patient population. For example, in some embodiments, baseline characteristics may apply equally to all patients in a virtual patient population.

Simulation development

Simulations of clinical trials using the QSP model can be performed. The simulation design was designed to make first-in-human dose predictions, and the sensitivity to model inputs was studied based on the variability of the literature and the uncertainty of the model parameters. The general method used for the simulation is described herein.

The QSP model described herein was used to represent 3 types of virtual patients: healthy individuals, SCD patients in remission, and SCD patients in VOC. These patients were created using the same model input parameters shown in Table 3 for all patients and the steady state conditions shown in Table 6 specific to each patient.

VOC events were created by simulation prior to initiation of treatment, as shown in Figure 8A. Beginning with SCD patients in remission at steady state, Hb levels from SCD patients in remission were stepped to levels in SCD patients in VOC 20 hours prior to initiation of treatment, as shown in Figure 8A. Figure 8B shows the model's response to this change in terms of active (ie, stretched) VWF.

From patients in VOC (time = 0 in Figure 8A), rADAMTS13 was simulated intravenously with the following dosing regimens: single dose, single dose plus half of single dose booster at 2 days and single dose plus single dose at 3 days Half the dose booster.

Clinical outcomes from the QSP model include (a) reduction in the amount of active VWF over time and (b) duration of maintaining active VWF below target levels. The target level of active VWF is that in patients in remission.

Table 7 summarizes the simulation design studies that have been performed. The range of parameter values studied corresponds to the range reported in the literature.

surface 7 : Overview of Simulation Design Research case number Parameters under study Nominal value scope 1 base case 220 μg/ml Hb in remission 400 μg/ml Hb in VOC none 2 KD _{VS_HB} 457.5 nM 180, 600 nM 3 VWF content in remission 20 μg/ml 10, 30 μg/ml 4 Activity of endogenous ADAMTS13 1.5 U/μg (equivalent to 3.5 nM) 0.5, 1 U/μg (equivalent to 1.1, 2.3 nM) 5 Mitigation and Hb content in VOC 220 μg/mL Hb in remission 400 μg/ml Hb in VOC 100, 220, 320 μg/ml Hb in remission 200, 300, 400 μg/ml Hb in VOC

Case 1 represents the base case. The three dosing regimens described above were simulated for VOC patients.

Case 2 studies the binding affinity between Hb and VWF in stretched form. Lower binding affinity indicates greater inhibition by Hb.

Case 3 studied the variability of VWF levels in patients in remission.

Case 4 studies the variability of endogenous ADAMTS13 activity in SCD patients. The activity of rADAMTS13 did not change from the nominal value of 1.5 U/μg.

Case 5 studies mitigation and variability in Hb content in VOCs. 9 is a graph showing ranges of Hb levels in patients in remission, according to some non-limiting embodiments. Figure 9 shows the range of Hb content studied. Case 5F is from the base case and represents the midpoint of Hb content in mitigation and the upper range of Hb content in VOC. The sensitivity of Hb content in mitigation was studied by Case 5 C-F-G, and the sensitivity of Hb content in VOC was studied by Case 5 A-B-C and 5 D-E-F.

Simulation results

The results from the simulations described herein can be verified. In this case, the simulation results were validated using data from the Phase 1 TTP clinical study. This study investigated the first-in-human PK and safety of rADAMTS13 in patients with congenital ADAMTS13 deficiency. Table 8 shows the key plasma ADAMTS13 PK parameters for the non-compartmental model reported in the TTP study.

surface 8. Shire 1 Expect TTP Key plasma in study ADAMTS13PK An overview of parameters. Pharmacokinetic parameters mean (standard deviation) (geometric mean) 5 U/kg (n=3) 20 U/kg (n=3) 40 U/kg (n=7) IR (μg/ml*kg/μg) 0.0181 (0.00120) [0.0180] 0.0252 (0.00912) [0.0241] 0.0247 (0.00427) [0.0244] _Cmax (μg/mL) 0.065 (0.002) [0.065] 0.338 (0.126) [0.323] 0.678 (0.103) [0.672] T _max (h) (median (min-max)) 0.25 (0.25-0.52) 0.55 (0.53-0.97) 0.30 (0.22-1.12) AUC _(0-inf) (μg*h/ml) 4.72 (1.02) [4.66] 18.4 (17.3) [18.3] 37.6 (13.5) [36.0] AUC _(0-t) ( μg*h/mL) 4.05 (0.449) [4.03] 17.3 (2.25) [17.2] 35.3 (11.6) [34.1] T _1/2 (h) 90.7 (32.9) [86.3] 59.1 (20.1) [57.1] 67.4 (13.5) [66.2] MRT _(0-inf) (h) 132.989 (58.371) [123.954] 86.105 (36.998) [81.415] 88.376 (21.586) [86.270] Cl(mL/h) 50.1 (20.1) [46.9] 53.0 (26.2) [49.2] 64.5 (24.1) [61.5] _Vss (ML) 6050 (2170) [5180] 4160 (1300) [4010] 5510 (1680) [5300]

Figure 10 is a graph showing the proportion of individuals in each treatment group with detectable rADAMTS13-mediated VWF cleavage product, as provided by the TTP Phase 1 study data. When comparing the model predictions to the data, using the small number of patient samples used in the study will produce large variability in the simulated output, depending on the value of the PK parameter selected within the reported standard deviation for each treatment, and therefore makes comparison difficult. To solve this problem, the simulation can be performed iteratively to increase the virtual patient sample size until the simulation output mean stops changing. A sample size of 50 virtual patients was found to be sufficient for this goal.

11A-11C illustrate graphs showing the pharmacokinetic profiles of virtual patients in different treatment groups predicted by the QSP model of FIG. 3, according to some non-limiting embodiments. Grey lines 1102A-C are predictions for individual virtual patients and black lines 1100A-C are measurements.

Model predictions for the % of patients with detectable cleavage products were performed with 50 dummy cohort patients. The model was identical to that developed for use with SCD patients, except that TTP-specific input conditions (protein content, Table 8) were used. Figures 12A and 12B show a comparison of model output to TTP Phase 1 study data using PK parameters from each specific treatment group. The PK curves shown in Figures 12A and 12B were used, and the detection limit of the cleavage product was adjusted to best match the data (17 pM).

Good agreement was found with the exception of the 20 U/kg treatment group. Given the small number of patients in the clinical study (eg, 3 patients in the 20 U/kg treatment group), some of this difference could be caused by errors in the PK parameters obtained from the data. This hypothesis was tested by running the same simulations but using the PK parameters obtained with the 40 U/kg data, as this data has the most patients, the comparison is shown in Figure 12B.

13A and 13B illustrate a comparison of model output of total VWF concentration in a patient with clinical data, according to some non-limiting embodiments. Figure 13A shows data as measured by VWF antigen (VWF:Ag). Figure 13B shows the model predictions represented by the total amount of VWF. Figure 13A shows little change in VWF:Ag levels at three different doses of rADAMTS13 based on the same TTP Phase 1 study data. Using VWF:Ag as a measure of total VWF, Figure 13B shows that the model predicts similar behavior. This behavior can be rationalized with a model: the majority of VWF is in an inactive (globular) form and thus the total amount is relatively insensitive to rADAMTS13 dose.

Figures 14A and 14B illustrate a comparison of model output of active VWF amount in a patient to clinical data, according to some non-limiting embodiments. In particular, Figure 14A shows data for the 40 U/kg dose of rADAMTS13, as measured by VWF ristocetin cofactor activity (VWF:RCo). Figure 14B shows model predictions expressed by the amount of activity of VWF.

It was observed that VWF:RCo levels decreased for the first 5 to 10 hours for almost all individuals, then recovered slightly in some individuals and remained at reduced levels in others. The prediction reproduces the initial lowering behavior, followed by recovery and flat line regions, as shown in Figure 14B. This behavior can also be rationalized using a model: a high initial content of rADAMTS13 causes greater proteolysis of active VWF, followed by a progressive decrease in the rate of proteolytic degradation due to rADAMTS13.

The underlying molecular mechanism of the model and the calibrated model parameters were applied to both TTP and SCD. The above results support this hypothesis and the applicability of the model to establish quantitative relationships between dose and biomarkers that may be applicable clinical indicators in the treatment of SCD patients in VOC using the mechanistic PKPD model.

The model produced a "base case" quantitative relationship between the dose of rADAMTS13 and the output of active VWF from a pharmacodynamic model. Simulations were able to predict dose effects on lysed products consistent with the clinical data of virtual patients under typical TTP conditions relative to the patient data population.

Models show that a single dose of 120 U/kg rADAMTS13, a single dose of 80 U/kg rADAMTS13 plus a booster of 40 U/kg at 3 days, or a single dose of 80 U/kg plus a booster of 40 U/kg at 2 days , to achieve the goal of maintaining the active VWF level below the remission level for five days.

Example Quantitative Systems Pharmacology Model Application

In some embodiments, the QSP models and/or virtual populations described herein can be implemented to conduct virtual clinical trials. 15A is a flowchart illustrating a computer-implemented system and method for simulating the interaction of ADAMTS 13 with VWF, according to some non-limiting embodiments.

At act 101, a QSP model for modeling the interaction between ADAMTS 13 and VWF can be established. For example, a QSP model can include one or more PK models, one or more PD models, and one or more clinical outcome models, as shown in FIG. 3 . At act 102, the QSP model may be described with suitable mathematical equations (eg, a plurality of ordinary differential equations). In some embodiments, mathematical equations can describe the responses that govern ADAMTS13-VWF interactions modeled by the QSP model, eg, as shown in Tables 2a and 2b.

At act 104, parameter estimates for parameterizing the QSP model may be obtained from literature and/or clinical data, as described herein. The parameter estimates can be applied to the QSP model to parameterize the model.

At act 106, the QSP model can be validated by comparing the simulation output from the model to literature and/or clinical data. For example, a QSP model can be applied to obtain the output of one or more biomarkers (eg, cleaved VWF fragments, stretched ULVWF, platelet count, LDH, etc.), and the output can be compared to biomarker values from clinical data to Verify the accuracy of the QSP model. As described herein, prior to validation, the QSP model can be calibrated with one or more sets of data (eg, in vitro data, in vivo data).

At act 108, virtual population development may begin by establishing the total number of virtual patients and the duration of the virtual clinical trial. For example, in some embodiments, the total number of virtual patients is 1000. The duration of a virtual clinical trial can refer to the length of time ADAMTS13-VWF interaction is observed in a patient population, including the time period during which therapeutic interventions are applied to the virtual patient population.

At acts 110-112, PK parameters and disease prediction descriptors and their associated variability may be obtained from real patient data. For example, in some embodiments, clinical data can be used to inform PK parameters and disease prediction descriptors to be applied to a virtual patient population. At act 114, virtual PK parameters and virtual disease prediction descriptors may be obtained, eg, based on PK parameters and disease prediction descriptors obtained from clinical data. At acts 116-118, virtual PK parameters and disease prediction descriptors may be randomly assigned to virtual patients in the virtual patient population.

At act 120, the QSP model may be used to simulate disease development in a virtual patient. For example, in some embodiments, a QSP model can be used to simulate the occurrence of VOCs in a virtual patient and reflect the resulting protein content of the episode. At act 122, the virtual patient disease profile may be compared to disease profiles of real individuals with SCD and/or TTP.

At act 124, the QSP model can be used to assess the effectiveness of therapeutic interventions (eg, for treating ADAMTS13 inhibition or deficiency and/or excess Hb or thrombospondin). For example, parameters indicating that a virtual patient population is being administered a dose of a drug (eg, rADAMTS13) according to a dosing regimen or have received plasmapheresis or frozen plasma administration of healthy donor plasma can be input into the QSP model.

At act 126, a virtual clinical trial may be performed. For example, the resulting effect of the drug administration applied in act 124 on the virtual patient population can be observed. In some embodiments, biomarker levels can be assessed to determine relative changes in biomarker levels resulting from administration of a therapeutic intervention. In some embodiments, the duration for which the biomarker level is observed to be above or below a threshold value. In some embodiments, virtual clinical trial data can be compared to data from real individuals.

In some embodiments, the QSP model can be used to assess the interaction of ADAMTS13 with VWF, as shown in Figure 15B. 15B illustrates an example method 1500 for modeling the interaction of ADAMTS 13 with VWF, according to some non-limiting embodiments.

Method 1500 begins at act 1502, where a QSP model representing the interaction of ADAMTS13 with VWF is obtained, eg, using any technique for developing, parameterizing, calibrating, and/or validating the QSP model described herein. QSP models may include one or more PK models, one or more PD models, and/or one or more clinical outcome models, as shown in FIG. 2 . In some embodiments, the QSP model may include a plurality of ordinary differential equations. In some embodiments, mathematical equations can describe the interaction between ADAMTS13 in endogenous and/or recombinant form and VWF, and/or other associated biomarkers, such as Hb and/or thrombospondin, for example as shown in Table 2a and Table 2b.

At act 1504, disease prediction descriptors can be obtained. For example, the disease prediction descriptors may include the virtual patient's propensity to experience VOCs or experience thrombosis. In particular, disease predictive descriptors can include concentrations of endogenous ADAMTS 13, Hb and/or thrombospondin. In some embodiments, the disease prediction descriptors are determined, at least in part, by a Boisson process informed by known data about the disease prediction descriptors.

At act 1506, disease prediction descriptors may be assigned to datasets. For example, a dataset may represent a virtual patient population to which the QSP model is applied. A virtual group may contain multiple datasets. Each dataset (eg, Patient ₁ ) may represent an individual virtual patient of a virtual population and may have one or more variables that define one or more characteristics of the virtual patient (eg, for assigning PK parameters and/or disease prediction descriptors).

At act 1508, the data set may be processed using the QSP model (eg, by inputting the data set into the QSP model) to obtain processed data. Processed data may include, for example, the virtual patient's biomarker concentrations (eg, cleaved VWF fragments, stretched ULVWF, platelet count, LDH). In some embodiments, the method further includes displaying the processed data.

Evaluate novel or existing drug treatments ADAMTS13 Lack of or inhibited effectiveness

In some embodiments, the QSP model can be used to assess the effectiveness of therapeutic interventions such as rADAMTS13, plasmapheresis or administration of frozen plasma of donor plasma including healthy levels of endogenous ADAMTS13. For example, the inventors have recognized that the QSP model provides data that may be impractical or impossible to obtain clinically. Thus, the QSP model provides for the assessment of therapeutic interventions in a cheaper and faster manner without requiring testing on human individuals.

Figure 15C illustrates an example method 1520 for determining the effectiveness of an administered drug in reducing the concentration of uncleaved supersized VWF multimers, according to some non-limiting embodiments.

Method 1520 begins at act 1522, where PK parameters for a virtual data set are obtained. As described herein, PK parameters can be used to describe the behavior of a drug in a patient. The virtual dataset may reflect the virtual patient population on which the virtual clinical trial performed by the QSP model is run. The dose and characteristics of the drug administered to each virtual patient can be reflected by the PK parameters.

At act 1524, disease prediction descriptors may be determined for the virtual dataset. In some embodiments, disease prediction descriptors may be informed by clinical data.

At act 1526, the PK parameters and disease prediction descriptors are assigned to the virtual dataset. In some embodiments, disease prediction descriptors may be specified using a Boisson process.

At act 1528, the virtual data set may be processed by the QSP model to obtain processed data. At act 1530, an indicator of the effectiveness of the administered drug can be obtained. In some embodiments, the processed data output by the QSP model may include one or more biomarker concentrations (eg, uncleaved stretched ULVWF, cleaved VWF fragments, LDH, platelet count). Biomarker concentrations can be used to determine the effectiveness of an administered drug in reducing the concentration of uncleaved stretched ULVWF. For example, a decrease in the content of uncleaved stretched ULVWF or an increase in the content of cleaved VWF fragments may indicate that the drug is effectively modulating uncleaved ULVWF content and inhibiting VOC or thrombosis. In some embodiments, biomarker levels obtained from the QSP model are compared to threshold values (eg, biomarker levels in healthy patients or patients in remission).

In some embodiments, the drug administered is ADAMTS13 in endogenous or recombinant form. ADAMTS13 can be administered in any suitable manner. For example, in some embodiments, administration may comprise exchanging the patient's plasma with that of a healthy donor to increase the amount of naturally occurring (endogenous) ADAMTS13 in the patient. In some embodiments, administering may comprise administering frozen donor plasma to the patient. For example, the donor plasma may contain endogenous ADAMTS13 such that replacement increases the concentration of endogenous ADAMTS13 in the recipient patient. In some embodiments, a recombinant form of ADAMTS13 (rADAMTS13) can be administered to a patient.

Evaluating the efficacy of combination therapy

In some embodiments, the QSP model can be used to assess the effectiveness of combination therapy for ADAMTS13 inhibition or deficiency and/or excess Hb. For example, in some embodiments, a virtual patient can be administered two or more drugs for reducing the concentration of uncleaved stretched ULVWF, and a QSP model can be used based on outputs from clinical outcome models (eg, biomarkers) concentration) to assess the effectiveness of combination therapy.

Evaluate dose effectiveness

In some embodiments, the QSP model can be used to assess the effectiveness of a specific dose of an administered drug, such as administration of rADAMTS13 or plasmapheresis in which the donor plasma includes a specific concentration of endogenous ADAMTS13. The methods described herein for assessing drug efficacy using the QSP model are equally applicable to assessing the efficacy of specific doses of drugs (eg, doses of ADAMTS13 in recombinant or endogenous form).

Assess dosing frequency and / or the effectiveness of the dosing regimen

In some embodiments, QSP models can be used to assess the effectiveness of a particular dosing frequency and/or dosing regimen (eg, assessing the manner or frequency of administering an agent). The methods described herein for assessing drug efficacy using the QSP model may be equally applicable to assessing the efficacy of dosing frequency and/or dosing regimen.

Assess the impact of non-adherence to the dosing schedule

In some embodiments, the QSP model can be used to assess the impact of non-adherence to a dosing schedule (eg, missing one or more scheduled doses). Figure 15D shows an example method 1540 for determining the effect of a dosing regimen of a non-adherent drug (ADAMTS13 in recombinant or endogenous form) on reducing the concentration of uncleaved supersized VWF multimers, according to some non-limiting embodiments .

Method 1540 begins at act 1542, where PK parameters for the virtual data set are obtained. As described herein, PK parameters can be used to describe the behavior of a drug in a patient. The virtual dataset may reflect the virtual patient population on which the virtual clinical trial performed by the QSP model is run. The dose and characteristics of the drug administered to each virtual patient can be reflected by the PK parameters. In particular, the PK parameters may reflect one or more missed agents according to method 1540.

At act 1544, disease prediction descriptors may be determined for the virtual dataset. In some embodiments, disease prediction descriptors may be informed by clinical data.

At act 1546, the PK parameters and disease prediction descriptors are assigned to the virtual dataset. In some embodiments, disease prediction descriptors may be specified using a Boisson process.

At act 1548, the virtual data set may be processed by the QSP model to obtain processed data. At act 1550, the impact of non-compliance (including frequency of non-compliance) can be determined. For example, the analog output can provide the levels of one or more biomarkers, including changes in biomarker levels over time. The simulated output can be used as described herein to determine the effect of missing one or more predetermined doses. In some embodiments, different non-compliance frequencies (eg, full compliance, 15% missed dose, 20% missed dose, etc.) can be compared to determine the effect of non-compliance on reducing the concentration of cleaved ULVWF.

result

The inventors have recognized that by providing quantitative relationships between doses and biomarkers that may provide applicable clinical targets in the treatment of SCD patients experiencing VOCs, the QSP model described herein can help clinicians determine the first use of rADAMTS13. human dose. In some embodiments, the QSP model can be used to evaluate the administration of plasmapheresis or frozen plasma to a patient. Based on information on molecular interactions of certain biomarkers that play a role in ADAMTS13-VWF interaction and their role in SCD and TTP disease, the mechanism by which ADAMTS13 cleaves ULVWF into smaller VWF fragments and the extracellular PD representation of the inhibition of this effect by hemoglobin was incorporated into the QSP model. Uncleaved Hb-bound ULVWF multimers accumulate in plasma, leading to cell adhesion and events such as thrombosis and vascular occlusion. Active VWF or uncleaved VWF in stretched form, wherein binding sites are exposed for adhesion to platelets and binding to Hb and ADAMTS13, are provided as outputs of clinical outcome models and can be determined by clinicians for any therapeutic efficacy according to the description herein. Appropriate data points are provided for appropriate doses of ADAMTS13 for intervention.

As described herein, the QSP model can be used to obtain information on the effect of doses of ADAMTS13 on VWF multimer activity. VWF multimer concentrations provide useful biomarkers in a variety of applications. Figure 15E illustrates an example method 1560 for determining the concentration of VWF multimer in response to administration of ADAMTS13, according to some non-limiting embodiments.

Method 1560 begins at act 1562, where PK parameters for the virtual data set are obtained. As described herein, PK parameters can be used to describe the behavior of a drug in a patient. The virtual dataset may reflect the virtual patient population on which the virtual clinical trial performed by the QSP model is run. The dose and characteristics of the drug administered to each virtual patient can be reflected by the PK parameters.

At act 1564, disease prediction descriptors may be determined for the virtual dataset. In some embodiments, disease prediction descriptors may be informed by clinical data.

At act 1566, the PK parameters and disease prediction descriptors are assigned to the virtual dataset. In some embodiments, disease prediction descriptors may be specified using a Boisson process.

At act 1568, the virtual data set may be processed by the QSP model to obtain processed data. At act 1570, the concentration of VWF multimers based on the processed data can be obtained. As described herein, in some embodiments, the processed data output from the QSP model may include the concentration of one or more biomarkers associated with VWF interaction, such as uncleaved stretched ULVWF, cleaved VWF fragments, platelet counts and/or LDH concentration. Thus, the model output can be used at act 1570 to obtain the concentration of VWF multimers (eg, cleaved or uncleaved VWF multimers, stretched or spherical ULVWF multimers, or the sum thereof).

The QSP model described herein was applied for first-in-human dosing prediction for treating SCD patients in VOC. The amount of active VWF predicted by the model was used as an indicator of clinical outcome: specifically, the reduction in the amount of active VWF with treatment and the duration of maintaining active VWF below the level in remission. Simulation conditions are shown in Table 7.

surface 9 : single dose administration , drug or drug only + endogenous ADAMTS13 at different doses and durations AUC Dose [U/kg] AUC (drug only) [h*U/mL) AUC (drug and endogenous ADAMTS13) [h*U/mL] 0 - infinity 0-3 days 0-5 days 0-7 days 0-14 days 20 31.5 90.8 145.3 197.7 372.3 40 63 107 166.9 222.7 402.4 80 126 139.4 210.1 272.9 462.6 120 189.1 171.8 253.2 323 522.7 160 252.1 204.1 296.3 373 582.9

Figures 16A and 16B show model results for pharmacokinetic parameters and amount of active VWF for various doses and dosing schedules of recombinant ADAMTS13, according to some non-limiting examples.

Figure 16A shows graphs 1610, 1620, 1630 showing PK simulation results for single dose, single dose plus booster at 3 days, and single dose plus booster at 2 days under base case conditions. The booster is half the amount of a single dose. Table 9 shows drug exposure at different doses and durations (drug alone or with endogenous ADAMTS13)

Figure 16B shows graphs 1640, 1650, 1660 showing model results for the amount of active VWF over time from VOC onset (-20 hours) to treatment response (beginning at 0 hours) for the 3 dosing regimens. In all cases, the reduction in active VWF levels reached a minimum level within less than 12 hours after initiation of treatment, and reached a minimum earlier at higher doses. The horizontal dashed line 1645 indicates the amount of active VWF in remission in this base case, 221 ng/ml. This level is assumed to represent the threshold at which a VOC patient returns to remission (ie, the patient has returned to remission when the amount of active VWF falls below this threshold). Applying this hypothesis, a single dose of 120 U/kg enabled VOC patients to maintain active VWF levels below remission levels for the entire 4 days from the start of treatment, compared with 80 U/kg in a single plus 3-day booster regimen. This enables patients to maintain active VWF levels below remission levels for approximately 5 days. Since the booster used half the amount of a single dose, the total amount of rADAMTS13 used in these regimens was the same, 120 U/kg.

Based on the above results, Figure 17 compares the number of days that the active VWF is below the remission level for the three dosing regimens. From a single dose of 80 U/kg, the addition of the booster roughly doubled the number of days below the relieving level. When compared on a total dose basis, the booster regimen lasted less than one day (compare 80 U/kg single dose + 2 or 3 days booster to 120 U/kg single dose regimen).

Figures 18A-24 show example results of applying the QSP model to a virtual population receiving different doses of rADAMTS13. 18A and 18B show graphs showing percent VWF cleavage over time for different doses of rADAMTS13, according to some non-limiting examples. Based on the simulation results, 40 U/kg was predicted to cause a maximum reduction of 23% in active VWF and was determined to be the minimum dose required to reduce active VWF levels to relieving levels. The 80 U/kg dose demonstrated a 32% reduction in active VWF, while the 160 U/kg dose demonstrated a 50% reduction in active VWF. As shown in Figure 18B, active VWF trough levels are expected to be between 5-15 hours from dose administration.

Figure 19 shows a graph comparing the concentration of rADAMTS13 and the concentration of active VWF over time for a dose of rADAMTS13, according to some non-limiting examples. In particular, graphs 1910 and 1920 show simulation results for a virtual patient population receiving a dose of 40 IU/kg of rADAMTS13. Graph 1910 shows rADAMTS13 concentrations over time for a virtual patient population. Plot 1920 shows active VWF over time for a virtual patient population.

Figure 20A shows a graph showing time profiles of recombinant ADAMTS13, rADAMTS13, in different types of patients and for different doses, according to some non-limiting embodiments. Specifically, Figure 20A compares simulated results of rADAMTS13 plasma concentrations over time in virtual patients receiving 40 U/kg, 80 U/kg, and 160 U/kg rADAMTS13.

Figure 20B shows the concentration of active VWF in different types of patients for different doses of recombinant ADAMTS13, according to some non-limiting examples. Specifically, Figure 20B compares various patients, including healthy patients, SCD patients in remission, and SCD patients receiving doses of 0 U/kg rADAMTS13, 40 U/kg rADAMTS13, 80 U/kg rADAMTS13, and 160 U/kg rADAMTS13, respectively. Simulation results of active VWF content in . Using the level of active VWF in SCD patients in remission as a threshold value, Figure 20B shows the effectiveness of various doses in reducing the amount of active VWF in SCD patients.

Figure 21 shows the duration for which the VWF concentration is below the relieving level for various doses of recombinant ADAMTS13, according to some non-limiting examples. In particular, Figure 21 shows simulation results for the number of days that the active VWF concentration was below the threshold for various doses of rADAMTS13. Likewise, active VWF levels in SCD patients in remission were used as thresholds. Figure 21 shows that over a dose range of 40-160 U/kg, a single dose of 120 U/kg is predicted to enable patients with VOC events to maintain active VWF levels below remission levels throughout 3-6 days. Figure 21 further shows that the dose of 40 U/kg appears to provide a small effect on reducing active VWF levels below the remission threshold after a single treatment.

Figure 22 shows a graph showing the effect of baseline total VWF on the duration of active VWF below remission levels for various doses of recombinant ADAMTS13, according to some non-limiting examples. In particular, Figure 22 compares the simulated results of the duration of active VWF levels below threshold as a function of baseline total VWF levels in virtual patients receiving various doses of rADAMTS13. Figure 22 shows that baseline VWF levels have a negligible effect on the number of days that active VWF levels are below remission levels.

23 shows a graph showing the effect of free Hb content on the duration of active VWF below remission levels, according to some non-limiting examples. In particular, Figure 23 shows simulation results for the duration of active VWF levels below threshold as a function of free Hb concentration in virtual patients receiving various doses of rADAMTS13. Elevated free hemoglobin in SCD patients. Figure 23 shows that with higher levels of free hemoglobin present during VOC, high doses of rADAMTS13 are required to maintain active VWF levels below the threshold for longer durations.

Figure 24 shows the effect of hemoglobin binding affinity to VWF on the duration of active VWF below remission levels for different doses of recombinant ADAMTS13, according to some non-limiting examples. In particular, Figure 24 shows the effect of altering the parameter KD _{VS_Hb} representing the binding affinity of Hb to VWF in the QSP model for various doses of rADAMTS13. As described herein, the binding affinity between stretched VWF and hemoglobin is the only model parameter recalibrated from literature data to match in vivo data. Figure 24 shows the range in which the KD _{VS_Hb} constant can vary with negligible impact on the QSP model.

Assessing Model Sensitivity and Results

As described herein, the QSP model can be parameterized with a number of parameters that play a role in the VWF-ADAMTS13 interaction. The parameters used in the QSP model described herein are provided in Table 3. In some embodiments, a study of the sensitivity of the model output to changes in one or more of these parameters may be performed.

25A-25C are graphs showing the sensitivity of the number of days that active VWF is below remission levels to the binding affinity constant KD _{VS_Hb} of the hemoglobin used in the model for VWF, according to some non-limiting examples. Figures 25A-C show that for all dosing regimens studied, altering the binding affinity between Hb and active VWF (KD _{VS_Hb} ) had a negligible effect on the number of days that active VWF was below remission levels.

26A-26C are graphs showing the sensitivity of the number of days active VWF below remission levels to total VWF levels, according to some non-limiting examples. Similarly, Figures 26A-26C show that for all dosing regimens studied, the effect of total VWF content on the number of days when active VWF was below remission was also insignificant.

27A-27C are graphs showing the sensitivity of the number of days for active VWF below remission levels to endogenous ADAMTS13 activity, according to some non-limiting examples. Figures 27A-27C show the sensitivity of the number of days of active VWF below remission levels to the activity of endogenous ADAMTS13. The results indicate that higher activity of endogenous ADAMTS13 requires larger doses. For example, to maintain active VWF concentrations below the threshold for 4 days or longer, single doses of 40, 80, 120 U/kg are required when ADAMTS13 activity is increased from 0.5, 1, 1.5 U/μg, respectively. Active rADAMTS13 was immobilized at a base case value of 1.5 U/μg.

28A-28G are graphs showing the sensitivity of days of active VWF below remission levels to remission Hb and vaso-occlusive crisis levels, according to some non-limiting embodiments. Figures 28A-28G show the sensitivity of days of active VWF below mitigation levels to mitigation Hb and VOC Hb levels. Case 5F is the base case described above. The dose requirement was significantly increased at the lowest remission Hb content relative to the base case (cases 5A, 5B, 5C). Among them, the dosage requirement is the highest at the Hb content in the highest VOC, case 5C. Overall, along the diagonal of Cases 5D to 5C, the dose requirement increased as the difference between VOC and Hb content in mitigation increased. 28H is a graph comparing the results of the graphs shown in FIGS. 28A-28G, according to some non-limiting embodiments.

29A and 29B are graphs showing the dose-response of the amount of hemoglobin-bound VWF and the amount of active VWF ten hours after dosing of recombinant ADAMTS13, according to some non-limiting examples. Two other potentially effective targets were also investigated, including 1) hemoglobin-bound VWF and 2) active VWF 10 hours after dosing. Ten hours was chosen because model results showed the highest reduction in active VWF after 10 hours of dosing. Figures 29A and 29B show each target for different doses (single dose administration). Table 10 summarizes the doses required to achieve relieving levels and ranges assuming an acceptable 10-40% reduction compared to the no-drug condition.

surface 10 : using hemoglobin binding VWF and activity VWF Dosage and Dosage Range Required to Achieve Relieving Levels as Effective Targets effective target Dosage required to achieve relieving content dose range ( 10-40% reduce) Hb binds VWF 65 30-130 Active VWF 55 20-130

Taken together, the model results suggest a dose range of 30-130 U/kg (eg, the common range between the two target options), which is comparable to the effective range of 40- 200 U/kg consistent.

30 is a graph showing the sensitivity of the amount of active VWF predicted by the QSP model to changes in different model parameters, according to some non-limiting embodiments.

example computing system

31 schematically depicts an illustrative computing device 1000 upon which any aspect of the present invention may be implemented, according to some non-limiting embodiments.

31 schematically shows an illustrative computer 1000 on which any aspect of the present invention may be implemented. In the embodiment shown in Figure 31, a computer 1000 includes a processing unit 1001 having one or more computer hardware processors and one or more articles of manufacture including non-transitory computer readable storage Media, such as system memory 1002, which may include, for example, volatile and/or non-volatile memory. Memory 1002 may store one or more instructions for programming processing unit 1001 to perform any of the functions described herein. In addition to system memory 1002, computer 1000 may also include other types of non-transitory computer-readable media, such as storage 1005 (eg, one or more disk drives). Storage 1005 may also store one or more applications that can be loaded into memory 1002 and/or external components (eg, software libraries) used by the applications. To perform any of the functionality described herein, processing unit 1001 may execute one or more data stored in one or more non-transitory computer-readable storage media (eg, memory 1002, storage 1005). The one or more non-transitory computer-readable storage media may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by processing unit 1001 .

Computer 1000 may have one or more input devices and/or output devices, such as devices 1006 and 1007 shown in FIG. 31 . Among other things, these devices can also be used to present a user interface. Examples of output devices that can be used to provide a user interface include a printer or display screen for visual presentation of output, and speakers or other sound producing devices for audible presentation of output. Examples of input devices that can be used in a user interface include keyboards and pointing devices, such as mice, trackpads, and digitizing tablets. As another example, input device 1007 may include a microphone for capturing audio signals, and output device 1006 may include a display screen for visually reproducing recognized text, and/or a speaker for audibly reproducing recognized text.

As shown in FIG. 31, computer 1000 may also include one or more network interfaces (eg, network interface 10010) to enable communication over various networks (eg, network 10020). Examples of networks include local area networks or wide area networks, such as corporate networks or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol, and may include wireless networks, wired networks, or fiber optic networks.

in conclusion

Having thus described several aspects of at least one embodiment, it should be appreciated that various changes, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications and improvements are intended to be within the spirit and scope of the present invention. Accordingly, the foregoing description and drawings are by way of example only.

The above-described embodiments of the invention may be implemented in any of a number of ways. For example, embodiments may be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed among multiple computers.

Furthermore, the various methods or processes outlined herein can be coded as software executable on one or more processors employing any of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and may also be compiled into executable machine language code for execution on a framework or virtual machine or Intermediate code.

In this regard, the concepts disclosed herein can be implemented as a non-transitory computer-readable medium (or computer-readable media) (eg, computer memory, one or more floppy disks) encoding one or more programs. , compact disc, optical disc, magnetic tape, flash memory, field programmable gate array, or circuit configuration in other semiconductor devices, or other non-transitory tangible computer storage media), the one or more programs are executed on a The methods of implementing the various embodiments of the invention discussed above are executed on or on multiple computers or other processors. One or more computer-readable media may be transportable such that one or more programs stored thereon may be loaded onto one or more different computers or other processors to implement the present invention as discussed above various forms.

The terms "program" or "software" are used herein to refer to any type of computer program code that can be used to program a computer or other processor to implement various aspects of the invention as discussed above or a computer-executable instruction set. Additionally, it should be understood that, according to one aspect of this embodiment, the one or more computer programs that, when executed, perform the methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion over several Various aspects of the invention can be implemented in different computers or processors.

Computer-executable instructions may take many forms, such as program modules executed by one or more computers or other devices. Generally speaking, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

Furthermore, the data structures can be stored in computer-readable media in any suitable form. For simplicity of illustration, a data structure may be shown with fields that are related by position in the data structure. Such relationships can likewise be achieved by assigning storage for the fields using locations in a computer-readable medium that express the relationship between the fields. However, any suitable mechanism may be used to establish relationships between information in the fields of the data structure, including through the use of indicators, tags, or other mechanisms for establishing relationships between data elements.

The various features and aspects of the invention may be used alone, in any combination of two or more than two, or in a variety of configurations not specifically discussed in the embodiments described in the foregoing, and are therefore not limited in their application The details and configurations of the components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

Furthermore, the concepts disclosed herein can be implemented as methods, examples of which have been provided. The actions performed as part of the method can be ordered in any suitable manner. Accordingly, embodiments may be constructed in which the actions are performed in an order different from that illustrated, which may include performing some actions concurrently, even though the actions are shown as sequential actions in the illustrative embodiments.

The terms "substantially," "approximately," and "about" may be used to mean within ±20% of a target value in some embodiments, within ±20% of a target value in some embodiments Within 10%, in some embodiments within ±5% of the target value, in some embodiments within ±2% of the target value. The terms "about" and "about" can include target values.

The use of ordinal terms such as "first," "second," "third," etc. to modify claim elements in the scope of the claim does not in itself imply that one claim element is relative to another Any priority, priority or order of claim elements or chronological order in which method actions are performed, but is merely used as a label to distinguish one claim element with a certain name from another with the same name (but using ordinal terminology) elements to distinguish those request item elements.

Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including", "comprising", "having", "containing", "involving" and variations thereof herein is meant to encompass what follows Listed items and their equivalents and additional items.

5A: Case 5B: Case 5C: Case 5D: Case 5E: Case 5F: Case 5G: The Case 100: Schematic 101: Action 102: Action 104: Action 106: Action 108: Action 110: Action 112: Action 114: Action 116: Action 118: Action 120: Action 122: Action 124: Action 126: Action 200: Process Map 300: Model Diagram 310: Pharmacokinetic (PK) Models/PK Models 320: Pharmacodynamic (PD) Models/PD Models 330: Clinical Outcomes Models 1000: Computing Devices/Computers 1001: Processing unit 1002: System Memory/Memory 1005: Storage 1006: Output devices/devices 1007: Input devices/devices 1010: Web Interface 1020: Internet 1100A: black wire 1100B: Black wire 1100C: black wire 1102A: Gray Line 1102B: Gray Line 1102C: Gray Line 1500: Method 1502: Action 1504: Action 1506: Action 1508: Action 1510: Action 1520: Method 1522: Action 1524: Action 1526: Action 1528: Action 1530: Action 1540: Method 1542: Action 1544: Action 1546: Action 1548: Action 1550: Action 1560: Method 1562: Action 1564: Action 1566: Action 1568: Action 1570: Action 1610: Figure 1620: Figure 1630: Figure 1640: Figure 1645: horizontal dotted line 1650: Figure 1660: Figure 1910: Figures 1920: Figures

Various aspects and embodiments of the present application will be described with reference to the following drawings. It should be understood that the drawings are not necessarily drawn to scale. Items that appear in multiple figures are indicated by the same reference numerals appearing in all the figures. For the sake of clarity, not every component will be labeled in every schema.

[ FIG. 1 ] A schematic diagram showing the cleavage of ULVWF into smaller VWF polymers by ADAMTS13 and the inhibition of this effect by hemoglobin (Hb).

[ FIG. 2 ] A biological process diagram showing molecular interactions (interplay) of components in the ADAMTS13-VWF interaction is shown.

[FIG. 3] shows a model diagram illustrating the QSP model used to simulate the interaction between ADAMTS13 and VWF, according to some non-limiting embodiments.

[FIG. 4A] to [FIG. 4C] show the predicted cleaved VWF concentrations from the QSP model of FIG. 3 and cleaved VWF concentrations from in vitro data over a range of rADAMTS13 and Hb levels, according to some non-limiting examples Comparison.

[FIG. 4D] shows the sensitivity of the binding constant of Hb to VWF as reflected in the QSP model of FIG. 3, according to some non-limiting examples.

[FIG. 4E] A model simulation showing percent VWF cleavage for different binding constants of Hb to VWF, according to some non-limiting examples.

[FIG. 5] shows a comparison of percent VWF cleavage between model predictions and in vitro pre-incubation data, according to some non-limiting examples.

[FIG. 6A] and [FIG. 6B] show a comparison of the fraction of VWF in active form between model predictions and in vivo data, according to some non-limiting examples.

[FIG. 7] A graph showing the comparison of the pharmacokinetic (PK) data of rADAMTS13 between model predictions and in vivo data, according to some non-limiting examples.

[FIG. 8A] and [FIG. 8B] illustrate simulations of vascular occlusive crisis (VOC) events according to some non-limiting embodiments.

[FIG. 9] is a graph showing the range of Hb levels in patients in remission, according to some non-limiting embodiments.

[FIG. 10] is a graph showing the proportion of individuals in each treatment group with detectable rADAMTS13-mediated VWF cleavage product, as provided by the TTP Phase 1 study data, according to some non-limiting examples.

[FIG. 11A] to [FIG. 11C] show graphs showing the pharmacokinetic profiles of virtual patients in different treatment groups predicted by the QSP model of FIG. 3, according to some non-limiting embodiments.

[FIG. 12A] and [FIG. 12B] show graphs comparing model output and clinical data that detect rADAMTS13-mediated cleavage of VWF, according to some non-limiting examples.

[FIG. 13A] and [FIG. 13B] show a comparison of model output of total VWF concentration in patients with clinical data, according to some non-limiting embodiments.

[FIG. 14A] and [FIG. 14B] show a comparison of model output and clinical data for the amount of active VWF in a patient, according to some non-limiting examples.

[FIG. 15A] is a flowchart illustrating a computer-implemented system and method for simulating the interaction of ADAMTS13 with VWF, according to some non-limiting embodiments.

[FIG. 15B] illustrates an example method for modeling the interaction of ADAMTS13 with VWF, according to some non-limiting embodiments.

[FIG. 15C] illustrates an exemplary method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved supersized VWF multimers, according to some non-limiting embodiments.

[FIG. 15D] shows an exemplary method for determining the effect of non-compliance with a dosing regimen of recombinant ADAMTS13 on reducing the concentration of uncleaved supersized VWF multimers, according to some non-limiting examples.

[FIG. 15E] shows an exemplary method for determining the concentration of VWF multimer in response to administration of recombinant ADAMTS13, according to some non-limiting embodiments.

[FIG. 16A] and [FIG. 16B] show model results of pharmacokinetic parameters and active VWF amount for various doses and dosing schedules of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 17] is a graph comparing the number of days when active VWF is below the remission level for three different dosing regimens, according to some non-limiting examples.

[FIG. 18A] and [FIG. 18B] show graphs showing percent VWF cleavage over time for different doses of rADAMTS13, according to some non-limiting examples.

[FIG. 19] shows a graph comparing rADAMTS13 concentration and active VWF concentration over time for a dose of rADAMTS13, according to some non-limiting examples.

[FIG. 20A] A graph showing time profiles of recombinant ADAMTS13, rADAMTS13, in different types of patients and for different doses, according to some non-limiting examples.

[FIG. 20B] shows the concentration of active VWF in different types of patients for different doses of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 21] shows the duration for which the VWF concentration is below the relieving level for various doses of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 22] A graph showing the effect of baseline total VWF on the duration of active VWF below remission levels for different doses of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 23] A graph showing the effect of free Hb content on the duration of active VWF below remission levels, according to some non-limiting examples.

[FIG. 24] shows the effect of the binding affinity of hemoglobin to VWF on the duration of active VWF below remission levels for different doses of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 25A] to [FIG. 25C] are graphs showing the sensitivity of the number of days for active VWF below remission levels to the binding affinity constant of hemoglobin to VWF used in the model, according to some non-limiting examples.

[FIG. 26A] to [FIG. 26C] are graphs showing the sensitivity of the number of days that the active VWF is below the remission level to the total VWF level, according to some non-limiting examples.

[ FIG. 27A ] to [ FIG. 27C ] are graphs showing the sensitivity of the number of days that active VWF is below remission levels to endogenous ADAMTS13 activity, according to some non-limiting examples.

[FIG. 28A] to [FIG. 28G] are graphs showing the sensitivity of days of active VWF below remission levels to remission Hb and vaso-occlusive crisis levels, according to some non-limiting embodiments.

[FIG. 28H] is a graph comparing the results of the graphs shown in FIGS. 28A-28G, according to some non-limiting embodiments.

[FIG. 29A] and [FIG. 29B] are graphs showing the dose-response of the amount of hemoglobin-bound VWF and the amount of active VWF ten hours after the dose of recombinant ADAMTS13, according to some non-limiting examples.

[FIG. 30] is a graph showing the sensitivity of the amount of active VWF predicted by the QSP model to changes in different model parameters, according to some non-limiting embodiments.

[FIG. 31] schematically depicts an illustrative computing device upon which any aspect of the present invention may be implemented, according to some non-limiting embodiments.

300: Model Diagram

310: Pharmacokinetic (PK) Models/PK Models

320: Pharmacodynamic (PD) Models/PD Models

330: Clinical Outcomes Models

Claims (100)

A computer-implemented method for modeling the interaction of ADAMTS13 with von Willebrand factor (VWF), comprising: Obtained a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, including the mechanism by which ADAMTS13 cleaved ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; Determining disease prediction descriptors; assigning these disease prediction descriptors to virtual patient populations; and The virtual patient population is processed using the QSP model to provide processed data, wherein the processed data includes concentrations of at least one biomarker. The computer-implemented method of claim 1, further comprising displaying the processed data. The computer-implemented method of claim 1, further comprising: Determination of pharmacokinetic parameters; assigning the pharmacokinetic parameters to the virtual patient population; Determining therapeutic intervention data based on the administration of the administered drug; and The therapeutic intervention data and the virtual patient population are processed with the QSP model to determine the effectiveness of the administered drug. The computer-implemented method of claim 3, wherein the administered drug comprises administration of endogenous ADAMTS13 and/or recombinant ADAMTS13. The computer-implemented method of claim 4, wherein the administration of the endogenous ADAMTS13 comprises plasma exchange with plasma having the endogenous ADAMTS13. The computer-implemented method of claim 1, wherein the QSP model represents the interaction of ADAMTS13 with stretched and spherical VWF multimers. The computer-implemented method of claim 6, wherein the QSP model simulates the conversion of spherical VWF multimers to stretched VWF multimers. The computer-implemented method of claim 1, wherein the QSP model includes the binding affinity of the extracellular hemoglobin to the ULVWF multimers. The computer-implemented method of claim 1, wherein the at least one biomarker comprises the ULVWF multimers, cleaved VWF fragments, lactate dehydrogenase and/or platelet cells. The computer-implemented method of claim 6, wherein the ULVWF multimers comprise stretched ULVWF multimers and spherical ULVWF multimers. The computer-implemented method of claim 6, wherein the cleaved VWF fragments comprise stretched cleaved VWF fragments and globular cleaved VWF fragments. The computer-implemented method of claim 1, further comprising using the processed data to determine whether the concentration of the at least one biomarker is below a first threshold value or above a second threshold value. The computer-implemented method of claim 12, further comprising using the processed data to determine the duration for which the concentration of the at least one biomarker is below the first threshold value or the concentration of the at least one biomarker is above The duration of this second threshold value. The computer-implemented method of claim 1, further comprising using the processed data to determine a change in the concentration of the at least one biomarker over time. The computer-implemented method of claim 1, wherein the QSP model includes a plurality of differential equations representing one or more biological responses. The computer-implemented method of claim 3, wherein the pharmacokinetic parameters comprise one or more biographical characteristics indicative of how the administered drug is affected by the patient to whom the administered drug is administered one or more parameters. The computer-implemented method of claim 16, wherein the one or more life history characteristics comprise at least one of height, weight, age, or gender. The computer-implemented method of claim 1, wherein the disease prediction descriptors comprise one or more parameters that characterize the concentration of ADAMTS13 in the patient. The computer-implemented method of claim 18, wherein the patient comprises a patient with sickle cell disease. The computer-implemented method of claim 18, wherein the patient comprises a patient with congenital thrombotic thrombocytopenic purpura (cTTP). The computer-implemented method of claim 18, wherein the patient comprises a patient with immune-mediated thrombotic thrombocytopenic purpura (iTTP). The computer-implemented method of claim 1, wherein the virtual patient population comprises a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more characteristics defining one or more characteristics of the virtual patient a variable. The computer-implemented method of claim 22, wherein the pharmacokinetic parameters and the disease prediction descriptors are assigned to the one or more variables of each dataset. A system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to execute for modeling ADAMTS13 A computer-implemented method of interaction with Win Weber factor (VWF), the computer-implemented method comprising: Obtained a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, including the mechanism by which ADAMTS13 cleaved ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; Determining disease prediction descriptors; assigning these disease prediction descriptors to virtual patient populations; and The virtual patient population is processed using the QSP model to provide processed data, wherein the processed data includes concentrations of at least one biomarker. A non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute instructions for modeling ADAMTS 13 and A computer-implemented method for the interaction of Win Weber factor (VWF), the computer-implemented method comprising: Obtained a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, including the mechanism by which ADAMTS13 cleaved ultra-large VWF (ULVWF) multimers and its inhibition by extracellular hemoglobin; Determining disease prediction descriptors; assigning these disease prediction descriptors to virtual patient populations; and The virtual patient population is processed using the QSP model to provide processed data, wherein the processed data includes concentrations of at least one biomarker. A computer-implemented method for determining the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra warm Weber factor (ULVWF) multimers, the computer-implemented method comprising: determining the pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed using a quantitative systems pharmacology (QSP) model to obtain processed data, wherein the QSP model simulated the interaction of Win Weber factor (VWF) with ADAMTS13, including the cleavage of the uncleaved ULVWF polymers by ADAMTS13 the mechanism of the body and its inhibition by extracellular hemoglobin, and the processed data includes the concentration of at least one biomarker; and The processed data were used to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of the uncleaved ULVWF multimers. The computer-implemented method of claim 26, further comprising displaying the processed data. The computer-implemented method of claim 26, wherein the administered drug comprises endogenous ADAMTS13 and/or recombinant ADAMTS13. The computer-implemented method of claim 28, wherein the administered drug comprises plasma of the donor patient. The computer-implemented method of claim 29, wherein the plasma comprises frozen plasma. The computer-implemented method of claim 26, wherein the QSP model represents the interaction of ADAMTS13 with stretched and spherical VWF multimers. The computer-implemented method of claim 31, wherein the QSP model simulates the conversion of spherical VWF multimers to stretched VWF multimers. The computer-implemented method of claim 26, wherein the QSP model includes the binding affinity of the extracellular hemoglobin for the ULVWF multimers. The computer-implemented method of claim 26, wherein the at least one biomarker comprises the uncleaved ULVWF multimers, cleaved VWF fragments, lactate dehydrogenase and/or platelet cells. The computer-implemented method of claim 26, wherein the effectiveness of the administered drug is obtained at least in part by comparing the processed data to known data indicative of a threshold concentration of the at least one biomarker this indicator. The computer-implemented method of claim 35, wherein the known data comprises untreated individuals with sickle cell disease, congenital thrombotic thrombocytopenic purpura, and/or immune-mediated thrombotic thrombocytopenic purpura amount of biomarkers. The computer-implemented method of claim 35, wherein the known data comprises biomarker quantities for individuals not suffering from sickle cell disease, congenital thrombotic thrombocytopenic purpura, or immune-mediated thrombotic thrombocytopenic purpura. The computer-implemented method of claim 35, wherein the known data comprises biomarker quantities of individuals with sickle cell disease in remission. The computer-implemented method of claim 26, further comprising using the processed data to determine whether the concentration of the at least one biomarker is below a first threshold value or above a second threshold value. The computer-implemented method of claim 39, further comprising using the processed data to determine the duration for which the concentration of the at least one biomarker is below the first threshold value or the concentration of the at least one biomarker is above The duration of this second threshold value. The computer-implemented method of claim 26, further comprising using the processed data to determine a change in the concentration of the at least one biomarker over time. The computer-implemented method of claim 26, wherein the QSP model includes a plurality of differential equations representing one or more biological responses. The computer-implemented method of claim 26, wherein the pharmacokinetic parameters comprise one or more parameters indicative of how the administered drug is affected by one or more life history characteristics of the patient to which the administered drug is administered parameters. The computer-implemented method of claim 43, wherein the one or more life history characteristics comprise at least one of height, weight, age, or gender. The computer-implemented method of claim 26, wherein the disease prediction descriptors comprise one or more parameters that characterize the concentration of ADAMTS13 in the patient. The computer-implemented method of claim 45, wherein the patient comprises a patient with sickle cell disease. The computer-implemented method of claim 45, wherein the patient comprises a patient with congenital thrombotic thrombocytopenic purpura (cTTP). The computer-implemented method of claim 45, wherein the patient comprises a patient with immune-mediated thrombotic thrombocytopenic purpura (iTTP). The computer-implemented method of claim 26, wherein the virtual patient population comprises a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more characteristics defining one or more characteristics of the virtual patient a variable. The computer-implemented method of claim 49, wherein the pharmacokinetic parameters and the disease prediction descriptors are assigned to the one or more variables of each dataset. A system comprising: at least one computer hardware processor; and At least one non-transitory computer-readable storage medium storing processor-executable instructions, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to execute A computer-implemented method of the effectiveness of an administered drug in reducing the concentration of uncleaved ultra-ultra warm Weber factor (ULVWF) multimers, the computer-implemented method comprising: determining the pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed using a quantitative systems pharmacology (QSP) model to obtain processed data, wherein the QSP model simulated the interaction of Win Weber factor (VWF) with ADAMTS13, including the cleavage of the uncleaved ULVWF polymers by ADAMTS13 the mechanism of the body and its inhibition by extracellular hemoglobin, and the processed data includes the concentration of at least one biomarker; and The processed data were used to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of the uncleaved ULVWF multimers. At least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute instructions for determining an administered A computer-implemented method of the effectiveness of the medicament in reducing the concentration of uncleaved ultra-ultra warm Weber factor (ULVWF) multimers, the computer-implemented method comprising: determining the pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed using a quantitative systems pharmacology (QSP) model to obtain processed data, wherein the QSP model simulated the interaction of Win Weber factor (VWF) with ADAMTS13, including the cleavage of the uncleaved ULVWF polymers by ADAMTS13 the mechanism of the body and its inhibition by extracellular hemoglobin, and the processed data includes the concentration of at least one biomarker; and The processed data were used to obtain an indicator of the effectiveness of the administered drug in reducing the concentration of the uncleaved ULVWF multimers. A computer-implemented method for determining the effect of non-compliance with a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultra-warm Weber factor (ULVWF) multimers, the computer-implemented method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include the frequency of non-compliance with the dosing regimen; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin, and the processed data comprises one of the concentration of the uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments; and The processed data were used to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers. The computer-implemented method of claim 53, wherein the processed data includes the frequency at which the amount of uncleaved ULVWF fragments exceeds a threshold value. The computer-implemented method of claim 53, wherein the processed data includes the percentage of concentrations of uncleaved ULVWF fragments that exceed a threshold value. The computer-implemented method of claim 53, wherein using the processed data to determine the effect of frequency of non-compliance with the dosing regimen comprises comparing the processed data to known data. The computer-implemented method of claim 53, further comprising displaying the processed data. The computer-implemented method of claim 53, wherein the administered drug comprises endogenous ADAMTS13 and/or recombinant ADAMTS13. The computer-implemented method of claim 58, wherein the administered drug comprises plasma of the donor patient. The computer-implemented method of claim 59, wherein the plasma comprises frozen plasma. The computer-implemented method of claim 53, wherein the QSP model represents the interaction of ADAMTS13 with stretched and spherical VWF multimers. The computer-implemented method of claim 61, wherein the QSP model simulates the conversion of spherical VWF multimers to stretched VWF multimers. The computer-implemented method of claim 53, wherein the QSP model includes the binding affinity of the extracellular hemoglobin for the ULVWF multimers. The computer-implemented method of claim 53, wherein the processed data comprises the concentration of the uncleaved ULVWF multimers. The computer-implemented method of claim 53, further comprising using the processed data to determine whether the one of the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Win-Weber factor fragments Below the first threshold value or above the second threshold value. The computer-implemented method of claim 65, further comprising using the processed data to determine the duration of: the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Win-Weber factor fragments The one of the one is below the first threshold value, or the one of the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Win-Weber factor fragments is above the second threshold value. The computer-implemented method of claim 53, further comprising using the processed data to determine the one of the concentration of the uncleaved ULVWF multimers or the concentration of the cleaved Win-Weber factor fragments along with changes over time. The computer-implemented method of claim 53, wherein the QSP model includes a plurality of differential equations representing one or more biological responses. The computer-implemented method of claim 53, wherein the pharmacokinetic parameters comprise one or more parameters indicative of how the administered drug is affected by one or more life history characteristics of the patient to which the administered drug is administered parameters. The computer-implemented method of claim 69, wherein the one or more life history characteristics comprise at least one of height, weight, age, or gender. The computer-implemented method of claim 53, wherein the disease prediction descriptors comprise one or more parameters that characterize the concentration of ADAMTS13 in the patient. The computer-implemented method of claim 71, wherein the patient comprises a patient with sickle cell disease. The computer-implemented method of claim 71, wherein the patient comprises a patient with congenital thrombotic thrombocytopenic purpura (cTTP). The computer-implemented method of claim 71, wherein the patient comprises a patient with immune-mediated thrombotic thrombocytopenic purpura (iTTP). The computer-implemented method of claim 53, wherein the virtual patient population comprises a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more characteristics defining one or more characteristics of the virtual patient a variable. The computer-implemented method of claim 75, wherein the pharmacokinetic parameters and the disease prediction descriptors are assigned to the one or more variables of each dataset. A system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to execute for determining non-compliance A computer-implemented method of reducing the effect of a dosing regimen of an administered drug on reducing the concentration of uncleaved ultra-ultra-warm Weber's factor (ULVWF) multimer, the computer-implemented method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include the frequency of non-compliance with the dosing regimen; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin, and the processed data comprises one of the concentration of the uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments; and The processed data were used to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers. At least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute instructions for determining non-compliance with A computer-implemented method of reducing the effect of a dosing regimen of an administered medicament on reducing the concentration of uncleaved ultra-ultra large Weber factor (ULVWF) multimer, the computer-implemented method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include the frequency of non-compliance with the dosing regimen; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin, and the processed data comprises one of the concentration of the uncleaved ULVWF multimers or the concentration of cleaved Win-Weber factor fragments; and The processed data were used to determine the effect of frequency of non-compliance with the dosing regimen on reducing the concentration of the uncleaved ULVWF multimers. A computer-implemented method for determining the concentration of a Win Weber factor (VWF) multimer in response to administration of ADAMTS13, the computer-implemented method comprising: Determining the pharmacokinetic parameters of administered ADAMTS13 for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin; and The concentration of VWF multimer is determined based on the processed data. The computer-implemented method of claim 79, wherein the administration of ADAMTS13 comprises administration of recombinant ADAMTS13. The computer-implemented method of claim 79, wherein the administration of ADAMTS13 comprises administration of plasma from the donor patient. The computer-implemented method of claim 81, wherein the plasma comprises frozen plasma. The computer-implemented method of claim 79, wherein the VWF multimers comprise one of uncleaved oversized VWF multimers or cleaved VWF fragments. The computer-implemented method of claim 79, wherein the QSP model represents the interaction of ADAMTS13 with stretched and spherical VWF multimers. The computer-implemented method of claim 84, wherein the QSP model simulates the conversion of spherical VWF multimers to stretched VWF multimers. The computer-implemented method of claim 79, wherein the QSP model includes the binding affinity of the extracellular hemoglobin to the ULVWF multimers. The computer-implemented method of claim 79, further comprising determining whether the concentration of VWF multimer is below a first threshold value or above a second threshold value. The computer-implemented method of claim 87, which determines the duration for which the concentration of VWF multimer is below the first threshold value or the duration for which the concentration of the at least one biomarker is above the second threshold value. The computer-implemented method of claim 79, further comprising using the QSP model to determine the change in the concentration of VWF multimers over time. The computer-implemented method of claim 79, wherein the QSP model includes a plurality of differential equations representing one or more biological responses. The computer-implemented method of claim 79, wherein the pharmacokinetic parameters comprise one or more indicators of how the administered drug is affected by one or more life history characteristics of the patient to which the administered drug is administered parameter. The computer-implemented method of claim 91, wherein the one or more life history characteristics comprise at least one of height, weight, age, or gender. The computer-implemented method of claim 79, wherein the disease prediction descriptors comprise one or more parameters that characterize the concentration of ADAMTS13 in the patient. The computer-implemented method of claim 93, wherein the patient comprises a patient with sickle cell disease. The computer-implemented method of claim 93, wherein the patient comprises a patient with congenital thrombotic thrombocytopenic purpura (cTTP). The computer-implemented method of claim 93, wherein the patient comprises a patient with immune-mediated thrombotic thrombocytopenic purpura (iTTP). The computer-implemented method of claim 79, wherein the virtual patient population comprises a plurality of data sets, each data set of the plurality of data sets representing a virtual patient and having one or more characteristics defining one or more characteristics of the virtual patient a variable. The computer-implemented method of claim 97, wherein the pharmacokinetic parameters and the disease prediction descriptors are assigned to the one or more variables of each dataset. A system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to execute instructions for determining a response to A computer-implemented method of administering ADAMTS13 concentration of Win Weber factor (VWF) multimer, the computer-implemented method comprising: Determining the pharmacokinetic parameters of administered ADAMTS13 for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin; and The concentration of VWF multimer is determined based on the processed data. At least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to execute for determining a response to ADAMTS13 A computer-implemented method of administering the concentration of Win Weber factor (VWF) multimer, the computer-implemented method comprising: Determining the pharmacokinetic parameters of administered ADAMTS13 for a virtual patient population; determining disease prediction descriptors for the virtual patient population; assigning the pharmacokinetic parameters and the disease prediction descriptors to the virtual patient population; The virtual patient population was processed to obtain processed data using a quantitative systems pharmacology (QSP) model representing the interaction of ADAMTS13 with VWF, wherein the QSP model includes the mechanism by which ADAMTS13 cleaves ultra-large VWF (ULVWF) multimers and extracellular its inhibition by hemoglobin; and The concentration of VWF multimer is determined based on the processed data.

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