Systems and methods for modeling thrombin-antithrombin

Abstract

The present disclosure provides methods and systems for modeling, simulating, and treating hemophilia. The methods and systems disclosed herein include a quantitative systems pharmacology (QSP) model for predicting thrombin generation characteristics in people with hemophilia A and people with hemophilia B in relation to antithrombin (AT) decline.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application No. 63 / 421,912, filed November 2, 2022, and U.S. Provisional Application No. 63 / 431,186, filed December 8, 2022, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to a quantitative systems pharmacology (QSP) model of hemostatic dynamics in human subjects. [Background technology]

[0003] In the setting of low AT, it remains difficult to predict the contribution of thrombin activity and factor VIII equivalence, especially in reactions involving α2-macroglobulin. Accurate prediction of thrombin activity in the setting of low AT is needed to account for the contribution of α2-macroglobulin in the human body. Summary of the Invention [Means for solving the problem]

[0004] This disclosure provides a quantitative systems pharmacology (QSP) model for predicting or validating thrombin generation characteristics in PwHA and PwHB in relation to AT lowering. To better understand the hemostatic equivalence of fitusiran prophylaxis (i.e., AT lowering), a QSP model was developed to integrate clinical and nonclinical data on the coagulation pathway to predict the outcomes of multiple in vitro coagulation assays, including thrombin generation assays (TGA), and to provide a mechanistic understanding of the readouts of these assays.

[0005] The QSP model represents reported steady-state levels of coagulation factors and accessory proteins in plasma from healthy individuals and from patients with high blood pressure (PwHA) and high blood pressure (PwHB). In addition, the model describes the effect of α2-macroglobulin, a thrombin regulator whose effect increases with decreased AT levels, to predict the effect of fitusiran on thrombin generation and assess factor equivalence.

[0006] By accounting for the influence of α2-macroglobulin, the QSP model provides a mechanistic representation of thrombin generation in the setting of decreased AT that is consistent with TGA data from donor-derived spiked plasma and clinical TGA data from both PwHA and PwHB. VP analysis of fitusiran prophylaxis provided hemostatic equivalence with FVIII in a representative population of severe PwHA, with a target therapeutic range of 15-35% AT resulting in a thrombin peak profile equivalent to 10-20 IU / kg FVIII. Future applications of the QSP model include evaluation of other therapeutic agents for PwHA and PwHB as well as extension to describe additional coagulation tests.

[0007] In one aspect, the present disclosure provides a method for generating a quantitative systems pharmacology model for predicting thrombin levels in plasma of a subject, the method comprising: (a) providing a plurality of relationships and / or parameters characterizing time-dependent antithrombin (AT) levels in the plasma of a subject in response to an agent that targets AT in the plasma of the subject; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the subject's plasma; (c) determining multiple rate constants for multiple relationships and / or parameters that characterize the time-dependent AT level in the plasma of the subject in response to an agent that targets antithrombin in the plasma of the subject; (d) determining the rate constant of a relationship or parameter that characterizes the α2-macroglobulin level in the subject's plasma; and / or (e) programming a computational system with (i) a plurality of rate constants of a plurality of relationships and / or parameters characterizing the time-dependent AT level in the plasma of the subject in response to an agent that targets antithrombin in the plasma of the subject, and (ii) a rate constant of a relationship or parameter characterizing the α2-macroglobulin level in the plasma of the subject. whereby the computational system is programmed to (i) solve a system of equations under a defined set of pharmacological conditions, the system of equations including a plurality of relationships and / or parameters characterizing the time dependence of AT levels in the subject's plasma in response to a drug targeting antithrombin in the subject's plasma and a plurality of relationships and / or parameters characterizing α2-macroglobulin levels in the subject's plasma, and (ii) output the thrombin levels in the subject's plasma after administration of the drug targeting antithrombin in the subject's plasma under the defined set of pharmacological conditions.

[0008] In some embodiments, the agent that targets antithrombin in the plasma of the subject is an siRNA therapeutic. In some embodiments, the siRNA therapeutic is fitusiran.

[0009] In some embodiments, the AT level is less than 35%. In some embodiments, the method calculates the formation and degradation of α2-macroglobulin-thrombin complexes. In some embodiments, the α2-macroglobulin has a concentration of about 3 to about 6 μM.

[0010] In one aspect, the present disclosure provides a computer-implemented method for modeling and simulating thrombin levels in plasma of a subject, the method comprising one or more of the following steps: obtaining a quantitative systems pharmacology (QSP) model of thrombin levels in the subject's plasma, the QSP model configured to represent the results of a thrombin generation assay (TGA) in response to plasma levels of antithrombin (AT), α2-macroglobulin, and a pharmaceutical agent targeting antithrombin in the subject's plasma; determining parameters affecting peak thrombin as indicated by TGA; assigning parameters influencing peak thrombin to the virtual patient population; and / or Processing the virtual patient population using the QSP model to provide processed data, the processed data comprising an amount of the drug targeting antithrombin in the subject's plasma.

[0011] In some embodiments, the AT level is less than 35%.

[0012] In some embodiments, the method calculates the formation and breakdown of α2-macroglobulin-thrombin complexes.

[0013] In some embodiments, the method further includes displaying the processed data.

[0014] In some embodiments, the method further includes determining pharmacokinetic parameters of the agent targeting antithrombin in the subject's plasma, determining pharmacokinetic parameters of one or more additional therapeutic agents, and / or processing the pharmacokinetic parameters of the agent targeting antithrombin in the subject's plasma and the pharmacokinetic parameters of the one or more additional therapeutic agents to determine the efficacy of a combination of the agent targeting antithrombin and the one or more additional therapeutic agents in the subject's plasma.

[0015] In some embodiments, the agent that targets antithrombin in the plasma of the subject is an RNAi therapeutic. In some embodiments, the RNAi therapeutic is an siRNA therapeutic. In some embodiments, the siRNA therapeutic is fitusiran.

[0016] In one aspect, the present disclosure provides a computer-implemented method for determining factor VIII equivalence in a subject, the method comprising one or more of the following steps: obtaining a quantitative systems pharmacology (QSP) model of thrombin levels in the subject's plasma, the QSP model configured to represent the interaction of plasma levels of antithrombin (AT), α2-macroglobulin, and thrombin; determining parameters influencing a thrombin generation assay; and / or processing the QSP model to provide processed data, the processed data indicative of factor VIII equivalence in the subject.

[0017] In some embodiments, the plasma level of antithrombin is a time-dependent variable, hi some embodiments, the plasma level of antithrombin is less than 35%.

[0018] In some embodiments, the formation and degradation of thrombin-antithrombin (T-AT) is calculated.

[0019] In some embodiments, the formation and breakdown of the α2-macroglobulin-thrombin complex is calculated.

[0020] In some embodiments, the results of a thrombin generation assay (TGA) in response to the interaction of plasma levels of antithrombin (AT), α2-macroglobulin, and thrombin are modeled.

[0021] In some embodiments, peak thrombin as indicated by TGA is used to determine Factor VIII equivalence. In some embodiments, the subject is administered an siRNA therapeutic. In some embodiments, the siRNA therapeutic is fitusiran.

[0022] In one aspect, the present disclosure provides a method of achieving about 10% to about 50% factor VIII equivalence in a hemophilia patient in need thereof, comprising subcutaneously administering to the patient a prophylactically effective amount of fitusiran to achieve an AT level of 10-35% in the patient.

[0023] In some embodiments, the patient is a patient with hemophilia A or hemophilia B. In some embodiments, the patient is a patient with hemophilia A with or without inhibitors or a patient with hemophilia B with or without inhibitors.

[0024] In some embodiments, the methods involve achieving about 20% to about 40% Factor VIII equivalence.

[0025] In some embodiments, the prophylactically effective amount of fitusiran is selected from about 1.25 mg, about 2.5 mg, about 5 mg, about 25 mg, about 30 mg, about 50 mg, and about 80 mg.

[0026] In some embodiments, a prophylactically effective amount of fitusiran is administered about once every month (or about every four weeks) or about once every two months (or about once every eight weeks).

[0027] In some embodiments, the prophylactically effective amount of fitusiran is about 50 mg administered about every month (or about every four weeks) or about once every two months (or about eight weeks).

[0028] In some embodiments, the prophylactically effective amount of fitusiran is about 20 mg administered about every month (or about every four weeks) or about once every two months (or about eight weeks).

[0029] In one aspect, the present disclosure provides a method of treating hemophilia in a patient with or without an inhibitor, the method comprising one or more of the following steps: (a) providing a time-dependent antithrombin level in the patient's plasma; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the patient's plasma; (c) programming the computing system with one or more rate constants of the relationships or parameters characterizing the thrombin levels; (d) determining hemostatic equivalence with factor VIII in the patient; and / or (e) administering a therapeutic agent to the patient.

[0030] In some embodiments, the therapeutic agent is supplemental Factor VIII. In some embodiments, the therapeutic agent is fitusiran.

[0031] In some embodiments, hemostatic equivalence with Factor VIII is determined by modeling the interaction of plasma levels of antithrombin (AT), α2-macroglobulin, and thrombin. In some embodiments, the time-dependent antithrombin level is less than 35%.

[0032] In one aspect, the present disclosure provides a method for determining hemostatic equivalence with factor VIII in a subject being treated for hemophilia A or B, the method comprising one or more of the following steps: (a) providing a time-dependent antithrombin level in the plasma of a subject; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the subject's plasma; (c) programming the computing system with one or more rate constants of the relationships or parameters characterizing the thrombin levels; and / or (d) determining hemostatic equivalence with factor VIII in the subject.

[0033] In some embodiments, the method further comprises administering supplemental Factor VIII to the subject. In some embodiments, the method further comprises adjusting the dosage of fitusiran to the subject.

[0034] In one aspect, the present disclosure provides a method of achieving about 10% to about 50% factor VIII equivalence in a hemophilia patient in need thereof, comprising subcutaneously administering to the patient a prophylactically effective amount of fitusiran to achieve about 10% to about 50% factor VIII equivalence in the patient. In some embodiments, the method comprises achieving about 20% to about 40% factor VIII equivalence.

[0035] In some embodiments, the model does not consider the PK / PD of fitusiran. Instead, time-dependent AT levels are provided.

[0036] As used herein, the term "about" generally means within 10%, 5%, 1%, or 0.5% of a given value or range. Alternatively, the term "about" means within an acceptable standard error of the mean as considered by one of ordinary skill in the art.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and are not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0038] Other features and advantages of the invention will become apparent from the following detailed description and drawings, and from the claims.

[0039] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. [Brief explanation of the drawings]

[0040] [Figure 1] 1 is a flowchart of an exemplary method for generating a QSP model of the present disclosure. [Figure 2] 1 is a flowchart of an exemplary method for using the QSP model of the present disclosure. [Figure 3] FIG. 1 is a process diagram depicting the species and reactions involved in modeling and simulation of hemostasis in connection with treatment with fitusiran, according to some embodiments of the present disclosure. [Figure 4] 1 is a biological process map depicting the roles of antithrombin, thrombin, and α2-macroglobulin, according to some embodiments of the present disclosure. [Figure 5A] FIG. 1 is a plot of plasma thrombin concentration (nM) over time comparing thrombin levels predicted by the QSP model disclosed herein (black line) with previously collected clinical data at 100% antithrombin levels and 100% supplemental Factor VIII (Advate) levels. [Figure 5B] FIG. 1 is a plot of plasma thrombin concentration (nM) over time comparing thrombin levels predicted by the QSP model disclosed herein (black line) with previously collected clinical data at 20% antithrombin levels and 50% supplemental Factor VIII (Advate) levels. [Figure 5C] Plot of α2M-IIa complex concentration (y-axis) against supplementary factor VIII (Advate) levels (x-axis), comparing α2M-IIa complex levels (black data points) predicted by the QSP model disclosed herein with previously collected clinical data at 100% antithrombin levels. [Figure 5D] Plot of α2M-IIa complex concentration (y-axis) against supplementary factor VIII (Advate) levels (x-axis), comparing α2M-IIa complex levels (black data points) predicted by the QSP model disclosed herein with previously collected clinical data at 20% antithrombin levels. [Figure 5E]Plot of α2M-IIa complex concentration (y-axis) against supplementary factor VIII (Advate) levels (x-axis), comparing α2M-IIa complex levels (black data points) predicted by the QSP model disclosed herein with previously collected clinical data at 10% antithrombin levels. [Figure 5F] FIG. 1 is a plot of α2M-IIa complex concentration (y-axis) against supplemental factor VIII (Advate) levels (x-axis), comparing α2M-IIa complex levels (black data points) predicted by the QSP model disclosed herein with previously collected clinical data at 5% antithrombin levels. [Figure 5G] Plot of plasma thrombin concentration (nM) over time, comparing thrombin levels predicted by a previous model of the coagulation cascade without α2-macroglobulin (black line) with previously collected clinical data at 100% antithrombin levels and 100% supplemental factor VIII (Advate) levels. [Figure 5H] Plot of plasma thrombin concentration (nM) over time, comparing thrombin levels predicted by a previous model of the coagulation cascade without α2-macroglobulin (black line) with previously collected clinical data at 20% antithrombin levels and 50% supplemental factor VIII (Advate) levels. [Figure 6] 1 is a plot of peak thrombin levels (nM) for patients with a range of HEMA severity (residual factor VIII levels) observed in clinical data at various AT% ranges (x-axis). [Figure 7A] 1 is a plot depicting the overall predictive performance of the QSP model disclosed herein, plotting predicted peak thrombin concentration (y-axis) over AT% (x-axis). [Figure 7B]1 is a plot showing the overall predictive performance of the QSP model disclosed herein, plotting the peak thrombin concentrations observed in clinical trials (y-axis) against the peak thrombin concentrations predicted by the QSP model disclosed herein (x-axis), with the dotted line representing the correlation between observed and predicted peak thrombin concentrations. [Figure 8] Box plot of peak thrombin concentrations (nM) predicted by the QSP model disclosed herein at 100% AT (1), about 10-20% AT (2), about 10-25% AT (3), and about 20-25% AT (4) for three doses of supplemental Factor VIII. The predicted peak thrombin concentrations correspond to TGA assays performed on AT and Factor VIII doubly depleted plasma spiked with different levels of AT and / or Factor VIII. [Figure 9A] 9B, 9C, 9D, and 9E. A heat map of peak thrombin (nM) distribution plotted against % antithrombin (y-axis) and supplemental Factor VIII dose (x-axis). Quadrants of the plot are indicated and correspond to Figures 9B, 9C, 9D, and 9E. [Figure 9B] FIG. 9B is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 5% antithrombin and 0% supplemental factor VIII (Advate) levels; FIG. 9B corresponds to the "1" designation in the heatmap of FIG. 9A. [Figure 9C] FIG. 9C is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 5% antithrombin and 100% supplemental factor VIII (Advate) levels; FIG. 9C corresponds to the "2" designation in the heatmap of FIG. 9A. [Figure 9D]FIG. 9D is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 100% antithrombin and 0% adjuvant factor VIII (Advate) levels; FIG. 9D corresponds to the "3" designation in the heatmap of FIG. 9A. [Figure 9E] FIG. 9E is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 100% antithrombin and 100% supplemental factor VIII (Advate) levels; FIG. 9E corresponds to the "4" designation on the heatmap in FIG. 9A. [Figure 10A] Heat map of peak thrombin (nM) distribution plotted against % antithrombin (y-axis) and supplemental Factor VIII dose (x-axis), labeled "1," "2," "3," and "4," corresponding to Figures 10B, 10C, 10D, and 10E. [Figure 10B] FIG. 10B is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 20% antithrombin and 5% supplemental factor VIII (Advate) levels; FIG. 10B corresponds to the "1" designation in the heatmap of FIG. 10A. [Figure 10C] FIG. 10C is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 24% antithrombin and 50% supplemental factor VIII (Advate) levels; FIG. 10C corresponds to the "2" designation in the heatmap of FIG. 10A. [Figure 10D] FIG. 10D is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 40% antithrombin and 20% supplemental factor VIII (Advate) levels; FIG. 10D corresponds to the "4" designation in the heatmap of FIG. 10A. [Figure 10E] FIG. 10E is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 30% antithrombin and 50% supplemental factor VIII (Advate) levels; FIG. 10E corresponds to the "3" designation in the heatmap of FIG. 10A. [Figure 11A] Heat map of peak thrombin (nM) distribution plotted against % antithrombin (y-axis) and supplemental Factor VIII dose (x-axis), labeled "5," "6," "7," and "8," corresponding to Figures 11B, 11C, 11D, and 11E. [Figure 11B] FIG. 11B is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 12% antithrombin and 100% supplemental factor VIII (Advate) levels; FIG. 11B corresponds to the "5" designation in the heatmap of FIG. 11A. [Figure 11C]FIG. 11C is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 100% antithrombin and 50% supplemental factor VIII (Advate) levels; FIG. 11C corresponds to the "6" designation in the heatmap of FIG. 11A. [Figure 11D] FIG. 11D is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 34% antithrombin and 50% supplemental factor VIII (Advate) levels; FIG. 11D corresponds to the "7" designation in the heatmap of FIG. 11A. [Figure 11E] FIG. 11E is a plot of simulated thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by the QSP model disclosed herein (black line) with AT and factor VIII doubly depleted plasma spiked with different levels of AT and / or factor VIII at 12% antithrombin and 5% supplemental factor VIII (Advate). FIG. 11E corresponds to the "8" designation in the heatmap of FIG. 11A. [Figure 12] Plot of simulated activated partial thromboplastin time (aPTT) (seconds) in plasma comparing aPTT predicted by the QSP model disclosed herein with previously collected clinical data and data from the literature for healthy subjects (left), hemophilia A subjects (center), and warfarin-treated subjects (right). [Figure 13A] Plot of simulated activated partial thromboplastin time (aPTT) (seconds) in plasma plotted against supplemental factor VIII % (x-axis) for a simulated patient with 0.1% residual factor VIII, with 5%, 10%, 20% and 100% AT plotted. [Figure 13B]FIG. 1 is a plot of data relating to activated partial thromboplastin time (aPTT) (seconds) in plasma plotted against % supplemental Factor VIII added (x-axis) for clinical plasma samples, with 5%, 10%, 20% and 100% AT plotted. [Figure 14] Plot of peak thrombin (nM) data from the literature for hemophilia A patients (circles) and healthy volunteers (triangles). The x-axis shows, from left to right, data for controls with 50% residual AT; controls with 10% residual AT; 50% residual AT; 10% residual AT; and healthy volunteers. [Figure 15] 1 is a table of peak thrombin (mean ± standard deviation) for a simulated hypothetical population (n=1000) with severe hemophilia A (0.1% residual factor VIII). [Figure 16] FIG. 1 is a schematic diagram of an exemplary computer system used to implement the QSP model of the present disclosure. [Figure 17] The expanded structural formula, chemical formula, and molecular weight of phytosilane are shown. [Figure 18] 1 is a plot of predicted peak thrombin (nM) based on QSP model simulations for therapeutic antithrombin levels of 15-35% and FVIII activity of 20-40% with fitusiran in people with hemophilia A. DETAILED DESCRIPTION OF THE INVENTION

[0041] Aspects of the present disclosure provide methods for modeling hemostatic equivalence for thrombin generation in PwHA and PwHB in relation to fitusiran prophylaxis, particularly antithrombin (AT) reduction. In some embodiments, the QSP model simulates the association and dissociation of AT and thrombin, α2-macroglobulin and thrombin, and thrombin and fibrinogen.

[0042] Use of the QSP model described herein can provide various types of information regarding the interactions between fitusiran, thrombin, antithrombin, and α2-macroglobulin that may be impractical or impossible to obtain clinically. For example, the QSP model can provide as input a simulation of a subject's in vivo conditions. The in vivo conditions may include, but are not limited to, fitusiran plasma pharmacokinetics (PK), fitusiran liver PK, AT synthesis in the liver, AT synthesis in plasma, synthesis of coagulation factors and coagulation proteins in plasma, and disappearance of coagulation factors and coagulation proteins in plasma. The QSP model can provide a simulated plasma sample from the subject. The simulated plasma sample may include parameters for AT concentration, factor II concentration, factor V concentration, factor VII concentration, factor VIII concentration, factor IX concentration, factor X concentration, factor XI concentration, factor XII concentration, α2-macroglobulin concentration, protein S concentration, protein C concentration, thrombomodulin concentration, and prekallikrein concentration. The QSP model described herein can perform a simulated thrombin generation assay (TGA) based on the subject's simulated plasma sample. The QSP model can perform a simulated activated partial thromboplastin time (aPTT) test on a simulated plasma sample from a subject. The QSP model can provide as output the predicted peak height in the thrombin generation assay, the area under the curve (AUC) in the thrombin generation assay, and the lag time in the thrombin generation assay in samples from subjects administered fitusiran and / or supplemental factor VIII.

[0043] The QSP model inputs may further include a therapeutic intervention for treating PwHA and / or PxHB, e.g., a specific dose and / or dosing regimen of fitusiran with or without co-clotting factors. Thus, the QSP model may provide a quantitative relationship between the therapeutic intervention (e.g., the dose or dosing regimen of fitusiran with or without co-clotting factors) and biomarkers, e.g., peak thrombin and / or AT decline, that provide useful clinical targets for treating patients. This quantitative relationship may be used to aid in determining in-human dosing of the therapeutic intervention for HEMA and / or HEMB.

[0044] In some embodiments, the QSP model can be used to evaluate the effectiveness of therapeutic interventions for patients with deficiencies in coagulation factor function. In some embodiments, the coagulation factor is factor VIII or factor IX. In some embodiments, the therapeutic intervention includes administration of an RNAi therapeutic. In some embodiments, the RNAi therapeutic is an siRNA therapeutic. In some embodiments, the RNAi therapeutic is fitusiran. In some embodiments, the therapeutic intervention targets antithrombin (AT) to enhance thrombin generation (TG) and restore hemostatic balance in patients with HEMA or HEMA with or without inhibitors. The QSP model can simulate the effect of any of these therapeutic interventions in relation to AT reduction. In some embodiments, the QSP model can be used to determine the appropriate human dosage or dosing regimen of the therapeutic intervention. The therapeutic intervention can restore hemostatic balance in people with HEMA and / or HEMB. The QSP model enables these determinations without the need for additional human testing, thereby providing information that may be impractical or impossible to obtain clinically. In some embodiments, the QSP model can be implemented with virtual populations to perform virtual clinical trials to evaluate the efficacy of therapeutic interventions. The present disclosure finds that such technology can facilitate the development of new and more effective treatment modalities to restore hemostatic balance in people with HEMA and / or HEMB.Accordingly, some aspects provide a computer-implemented method for modeling thrombin-antithrombin and thrombin-α2-macroglobulin interactions following administration of fitusiran and / or accessory coagulation factors, the method comprising, inter alia, deriving a quantitative systems pharmacology (QSP) model representing thrombin-antithrombin and thrombin-α2-macroglobulin interactions, including a mechanism by which fitusiran targets and reduces antithrombin levels; determining thrombin level descriptors; assigning the thrombin level descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, the processed data including the concentration of at least one biomarker (e.g., results of a simulated thrombin generation assay and / or activated partial thromboplastin time). In some embodiments, the method further comprises displaying the processed data; determining therapeutic intervention data based on the administration of one or more administered therapeutic agents; and processing the therapeutic intervention data and the virtual patient population with the QSP model to determine the effectiveness of the administered one or more therapeutic agents. In some embodiments, administering therapeutic agents includes administering fitusiran and / or an accessory clotting factor, such as factor VIII.

[0045] In some embodiments, the present disclosure provides methods for modeling the hemostatic equivalence of fitusiran prophylaxis with respect to thrombin generation in severe hemophilia A or severe hemophilia B, particularly in relation to antithrombin (AT) decline. In some embodiments, the present disclosure provides methods for modeling the hemostatic equivalence of fitusiran prophylaxis with respect to thrombin generation in moderate hemophilia A or moderate hemophilia B, particularly in relation to antithrombin (AT) decline. In some embodiments, the present disclosure provides methods for modeling the hemostatic equivalence of fitusiran prophylaxis with respect to thrombin generation in mild hemophilia A or mild hemophilia B, particularly in relation to antithrombin (AT) decline.

[0046] In some embodiments, the QSP model represents the binding / unbinding between thrombin and antithrombin. The QSP model can simulate the conversion of unbound thrombin and antithrombin to a thrombin-antithrombin complex.

[0047] In some embodiments, the QSP model includes the plasma concentration of α2-macroglobulin, the rate of degradation or clearance of α2-macroglobulin, the rate of synthesis of α2-macroglobulin, the rate of binding of α2-macroglobulin to thrombin, the K D These parameters include, but are not limited to, the rate of dissociation of α2-macroglobulin and thrombin, the catalytic action of S2238 by thrombin, and the catalytic action of S2238 by the α2-globulin-thrombin complex.

[0048] In some embodiments, the method further includes using the processed data to determine the change in concentration of at least one biomarker over time. In some embodiments, the QSP model includes multiple differential equations that describe one or more biological responses.

[0049] hemophilia Hemophilia A (HEMA) is an X-linked recessive bleeding disorder caused by a deficiency in the activity of clotting factor VIII. The disorder is clinically heterogeneous, with varying severity depending on the plasma level of clotting factor VIII: mild, with levels of about 6% to about 30% of normal factor VIII levels; moderate, with levels of about 2% to about 5% of normal factor VIII levels; and severe, with levels of less than about 1% of normal factor VIII levels. Patients with mild hemophilia may bleed excessively only after trauma or surgery, whereas those with severe hemophilia may experience an average of 20 to 30 spontaneous or excessive bleeding episodes per year, especially after minor trauma, especially into joints and muscles. These symptoms are substantially different from those of bleeding disorders due to platelet deficiency or von Willebrand disease, in which mucosal bleeding predominates. The characteristics of the disease and its causes are described, for example, in Mannucci PM, Tuddenham EG. The hemophilias--from royal genes to gene therapy. N Engl J Med. 2001 Jun 7;344(23):1773-9, which is incorporated herein by reference in its entirety.

[0050] The severity and frequency of bleeding in hemophilia A are inversely proportional to the amount of residual factor VIII in the plasma: less than 1% factor VIII causes severe bleeding, 2% to 6% causes moderate bleeding, and 6% to 30% causes mild bleeding. The proportions of severe, moderate, and mild cases are approximately 50%, 10%, and 40%, respectively. Joints may be affected, resulting in swelling, pain, loss of function, and degenerative arthritis. Similarly, muscle bleeding can lead to entrapment, necrosis, contracture, and neurological deficits. Hematuria may occur and may be painless. Intracranial bleeding can occur even after minor head trauma and can lead to severe complications. Bleeding from lacerated tongues or lips can be persistent.

[0051] Hemophilia B due to factor IX deficiency is phenotypically equivalent to hemophilia A, which, as noted above, results from a deficiency of clotting factor VIII. Classic laboratory findings in hemophilia B include a prolonged activated partial thromboplastin time (aPTT) and a normal prothrombin time (PT).

[0052] Fitzsillan The structure of fitusiran is provided herein. Fitusiran is a synthetically modified, double-stranded, small interfering RNA (siRNA) oligonucleotide that covalently binds to a tri-antennary N-acetyl-galactosamine (GalNAc) ligand that targets AT3 mRNA in the liver, thereby inhibiting antithrombin synthesis. See, e.g., Pasi, supra. Antithrombin is encoded by the SERPINC1 gene. The nucleosides in each chain of fitusiran are linked by either 3'-5' phosphodiester or phosphorothioate linkages to form the sugar-phosphate backbone of the oligonucleotide.

[0053] The fitusiran dosage weights described herein refer to the weight of fitusiran free acid (active moiety), while administration of fitusiran to a patient herein refers to administration of fitusiran sodium (drug substance) provided in a pharmaceutically suitable aqueous solution (e.g., phosphate buffered saline at physiological pH).

[0054] The sense and antisense strands of Phytusilan contain 21 and 23 nucleotides, respectively. The 3' end of the sense strand is conjugated to a GalNAc-containing moiety (also called L96) via a phosphodiester bond. The sense strand contains two consecutive phosphorothioate linkages at its 5' end. The antisense strand contains four phosphorothioate linkages, two at the 3' end and two at the 5' end. The 21 nucleotides of the sense strand hybridize with the complementary 21 nucleotides of the antisense strand, forming 21 nucleotide base pairs and a two-base overhang at the 3' end of the antisense strand. See also U.S. Pat. Nos. 9,127,274, 11,091,759, U.S. Patent Application Publication No. 2020 / 0163987A1, and WO 2019 / 014187. These references describe fitusiran and its use in methods of treating hemophilia. The entire contents of each of these references are expressly incorporated herein by reference.

[0055] The two nucleotide chains of phytosillane are shown below: Sense strand: 5'Gf-ps-Gm-ps-Uf-Um-Af-Am-Cf-Am-Cf-Cf-Af-Um-Uf-Um-Af-Cm-Uf-Um-Cf-Am-Af-L96 3' (SEQ ID NO: 1), and Antisense strand: 5'Um-ps-Uf-ps-Gm-Af-Am-Gf-Um-Af-Am-Af-Um-Gm-Gm-Uf-Gm-Uf-Um-Af-Am-Cf-Cm-ps-Am-ps-Gm 3' (SEQ ID NO: 2), During the ceremony, Af = 2'-fluoroadenosine (i.e., 2'-deoxy-2'-fluoroadenosine) Cf = 2'-fluorocytidine (i.e., 2'-deoxy-2'-fluorocytidine) Gf = 2'-fluoroguanosine (i.e., 2'-deoxy-2'-fluoroguanosine) Uf = 2'-fluorouridine (i.e., 2'-deoxy-2'-fluorouridine) Am = 2'-O-methyladenosine Cm = 2'-O-methylcytidine Gm = 2'-O-methylguanosine Um = 2'-O-methyluridine "-" (hyphen) = 3'-5' phosphodiester bond sodium salt "-ps-" = 3'-5' phosphorothioate bond sodium salt and L96 is represented by the following formula: [ka] It has.

[0056] The expanded structural formula, molecular formula and molecular weight of phytusirane are shown in Figure 17.

[0057] The structure of fitusirane is shown in the diagram below: [ka] (wherein X is O) It can also be written using

[0058] Fitusiran can inhibit hepatic production of antithrombin (AT). In its role as an anticoagulant, AT regulates hemostasis by either directly targeting thrombin production or by inactivating uncomplexed FXa, which therefore reduces thrombin production (Quinsey et al., Int J Biochem Cell Biol. (2004) 36(3):386-9). Fitusiran can be used to treat individuals with impaired hemostasis. For example, fitusiran can be used to treat patients with hemophilia A or B, with or without inhibitors, for routine prophylaxis to prevent or reduce the frequency of bleeding episodes. In certain embodiments, fitusiran is used to treat, for example, adult and adolescent patients (ages 12 and older) with hemophilia A or B (congenital factor VIII deficiency or congenital factor IX deficiency), with or without inhibitors.

[0059] A patient with hemophilia A or B with inhibitors is one who has developed alloantibodies to a factor (e.g., factor VIII for patients with hemophilia A or factor IX for patients with hemophilia B) to which the patient previously received therapy. Patients with hemophilia A or B with inhibitors may become refractory to replacement factor therapy. A patient without inhibitors is one who does not have such alloantibodies.

[0060] As used herein, a patient with "hemophilia A or hemophilia B, with or without inhibitors" refers to 1) a patient with hemophilia A with inhibitors, or 2) a patient with hemophilia B with inhibitors, 3) a patient with hemophilia A without inhibitors, or 4) a patient with hemophilia B without inhibitors. As used herein, patient refers to a human patient. Patient can also refer to a human subject.

[0061] The method involves administering a prophylactically effective amount of fitusiran to a hemophilia patient (e.g., a hemophilia A or B patient with or without inhibitors) in need thereof. A "prophylactically effective amount" refers to the amount of fitusiran that promotes a hemophilia A or B patient, with or without inhibitors, to achieve a desired clinical endpoint, such as a reduction in annualized bleed rate (ABR), annualized joint bleed rate (AjBR), annualized spontaneous bleeding rate (AsBR), or frequency of bleeding episodes. As used herein in reference to fitusiran, the terms "treat," "treating," or "treatment" include prophylactic treatment of the disease and refer to the achievement of a desired clinical endpoint. The terms "prevention" and "prophylactic treatment" are used interchangeably herein.

[0062] In some embodiments, the prophylactically effective amount of fitusiran is about 20 to about 80 mg (e.g., about 20 mg, about 25 mg, about 30 mg, about 40 mg, about 50 mg, or about 80 mg) of fitusiran. In some embodiments, the prophylactically effective amount of fitusiran is about 1 to about 30 mg (e.g., about 1.25 mg, about 2.5 mg, about 5 mg, about 10 mg, about 20 mg, or about 30 mg) of fitusiran.

[0063] The fitusiran dosage weights described herein refer to the weight of fitusiran free acid (active moiety), while administration of fitusiran to a patient herein refers to administration of fitusiran sodium (drug substance) provided in a pharmaceutically appropriate aqueous solution (e.g., phosphate-buffered saline at physiological pH). For example, about 100 mg / mL of fitusiran means about 100 mg of fitusiran free acid per mL (equivalent to about 106 mg of fitusiran sodium drug substance). Unless otherwise indicated, fitusiran weights recited in this disclosure are the weight of fitusiran free acid (active moiety).

[0064] A prophylactically effective amount of fitusiran can be delivered about once every month (or about every four weeks) or about once every two months (or about every eight weeks).

[0065] AT measurement can be performed by well-established methods, including both kinetic and chromogenic assays. AT activity (%) in plasma samples is calculated relative to WHO reference plasma. 100% AT level is defined as 1 unit of antithrombin activity in 1 mL of reference plasma sample. AT levels range from approximately 80% to approximately 120% in the general population.

[0066] It has been observed that the risk of arterial thrombotic events among patients receiving fitusiran may increase with AT levels <10%. To reduce the risk of vascular thrombotic events while maintaining a favorable benefit-risk balance for fitusiran-treated patients, patients, such as adult patients (18 years of age or older) or adolescent patients (12-17 years of age, inclusive), may begin fitusiran therapy by subcutaneously injecting 50 mg of fitusiran every two months (or every eight weeks). Patients' AT levels may be monitored periodically (e.g., every 1, 2, 3, 4, 5, 6, 7, or 8 weeks or every 1, 2, 3, 4, 5, or 6 months). If a patient has two AT measurements below 15% (e.g., below 10%), the patient will discontinue fitusiran treatment. In some embodiments, if the first AT level is below 15%, the patient may have another AT activity level sample taken within one month (e.g., within one or two weeks). If this result is less than 15%, this is considered a second AT of less than 15%. Patients receiving fitusiran at a dose of 50 mg Q2M who have two or more (e.g., two) AT activity levels less than 15% may discontinue fitusiran.

[0067] However, if a 50 mg / Q2M (or Q8W) patient has two AT measurements above 25% (e.g., above 35%), the patient will have their dosing regimen titrated up. The patient may receive 50 mg of fitusiran monthly (or every four weeks); if a patient has two AT measurements above 25% (e.g., above 35%) under the 50 mg / QM (Q4W) regimen, the patient may receive 80 mg of fitusiran monthly (or every four weeks). In some cases, a 50 mg / Q2M (or Q8W) patient may receive 80 mg of fitusiran every two months (or every eight weeks); if a patient has two AT measurements of greater than 25% (e.g., greater than 35%) under the 80 mg / Q2M (or Q8W) regimen, the patient may receive 50 mg of fitusiran monthly (or every four weeks); if the patient has two AT measurements of greater than 25% (e.g., greater than 35%) under the 50 mg / QM (or Q4W) regimen, the patient will receive 80 mg of fitusiran monthly (or every four weeks). In some cases, a 50 mg / Q2M (or Q8W) patient may receive 80 mg of fitusiran every two months (or every eight weeks); if a patient has two AT measurements of greater than 25% (e.g., greater than 35%) under the 80 mg / Q2M (or Q8W) regimen, the patient may receive 80 mg of fitusiran monthly (or every four weeks).

[0068] Patients who discontinue fitusiran after having two or more (e.g., two) AT activity levels below 15% when receiving fitusiran at a dose of 50 mg Q2M may receive fitusiran at a dose of 20 mg Q2M once their AT levels have recovered to 22% or greater. Patients receiving fitusiran at a dose of 2.5 mg Q2M who have two or more (e.g., two) AT activity levels below 15% may discontinue fitusiran treatment. If a patient receiving fitusiran at a dose of 20 mg Q2M (or Q8W) has two AT measurements above 25% (e.g., above 35%), they may receive fitusiran at 20 mg Q2M or Q4W.

[0069] In the above-described titration regimen, AT measurements for dose determination are performed during steady state (SS) of AT activity, i.e., after the patient's AT level has stabilized (in the low AT activity range) after fitusiran treatment. SS is typically achieved after two or three doses of fitusiran. AT measurements for dose determination are performed at appropriate intervals (e.g., every four or eight weeks).

[0070] In the above titration regimen, a starting dose of 50 mg Q2M of fitusiran is included as an illustrative example. For example, the starting dose of fitusiran can be 50 mg Q2M, 20 mg Q2M, 20 mg Q2M, or 10 mg QM. Thereafter, dose escalation and de-escalation can be carried out from each starting dose accordingly. For example, a starting dose of 20 mg Q2M of fitusiran can be sequentially escalated to 20 mg Q2M, 50 mg Q2M, 50 mg Q2M, or 80 mg QM, optionally in that order, or can be de-escalated to 10 mg QM.

[0071] An AT level of 10-35% (e.g., 10-25%, 15-35%, or 15-25%) is targeted to reduce the risk of vascular thrombotic events while maintaining a favorable benefit-risk balance for patients regularly using fitusiran. Therefore, as long as a patient achieves this targeted AT level, there is no need for the patient to receive a higher or more frequent dose of fitusiran. That is, the patient can remain on their current treatment regimen (i.e., maintenance regimen). For example, once the desired AT level is achieved, the patient can be treated with subcutaneous doses of fitusiran (e.g., 40-90 mg per dose) at intervals of, for example, every 1, 2, 3, 4, 5, 6, 7, or 8 weeks, or every 1, 2, 3, 4, 5, or 6 months. In some embodiments, if a patient achieves two AT measurements of 35% or less while receiving 50 mg Q2M, they will remain on this dosing regimen without the need for further titration of the dose or dosing frequency. As another example, if a patient receives 80 mg Q2M or 50 mg QM and two AT measurements fall below 35%, they will remain on this dosing regimen without needing to further titrate the dose or dosing frequency (e.g., to 80 mg QM). However, fitusiran treatment should be discontinued if the patient has two or more (e.g., two) AT measurements below 15% (e.g., below 10%) as a risk mitigation measure for vascular thrombotic events. Alternatively, the patient may resume treatment with a lower dose of fitusiran after AT levels return to above 15%, e.g., 22% or above.

[0072] A detailed description of fitusiran and its uses can be found, for example, in WO 2022 / 120292, which is incorporated herein by reference in its entirety.

[0073] QSP Modeling QSP modeling, in the context of this disclosure, is a mechanistic approach that integrates clinical and nonclinical data on the coagulation pathway to predict the results of multiple in vitro coagulation assays, including thrombin generation assays (TGAs), and to mechanistically understand the readouts of these assays and the hemostatic equivalence (i.e., AT reduction) of fitusiran prophylaxis. The QSP model disclosed herein also models the coagulation cascade and represents reported steady-state levels of coagulation factors and accessory proteins in the plasma of healthy individuals and people with hemophilia A (PwHA) and hemophilia B (PwHB). In some embodiments, the model also includes parameters describing the influence of α2-macroglobulin, a key regulator of thrombin whose influence is amplified by reduced AT levels, to provide predictions regarding the impact of fitusiran on thrombin generation.

[0074] To this end, the QSP model disclosed herein describes properties specific to fitusiran, antithrombin, thrombin, antithrombin degradation, thrombin-antithrombin complex, α2-macroglobulin, α2-macroglobulin-thrombin complex, fibrinogen, and fibrin and fibrin degradation products. Examples of such properties include the plasma concentration of α2-macroglobulin, the rate of degradation or clearance of α2-macroglobulin, the rate of synthesis of α2-macroglobulin, the rate of binding of α2-macroglobulin to thrombin, and the K of α2-macroglobulin to thrombin. D These include, but are not limited to, the dissociation rate of α2-macroglobulin and thrombin, the catalytic action of S238 by thrombin, and the catalytic action of S2238 by the α2-macroglobulin-thrombin complex.

[0075] The QSP model disclosed herein takes into account, among other things, peak thrombin levels in plasma samples from subjects administered fitusiran and / or supplemental factor VIII, predicted peak height in the thrombin generation assay, area under the curve (AUC) in the thrombin generation assay, and lag time in the thrombin generation assay.

[0076] In some embodiments, the QSP model disclosed herein predicts peak thrombin levels, predicted peak height in a thrombin generation assay, area under the curve (AUC) in a thrombin generation assay, and lag time after a subject receives fitusiran and / or supplemental factor VIII by considering some or all of the following factors as inputs: AT synthesis in plasma, synthesis of coagulation factors and coagulation proteins in plasma, and disappearance of coagulation factors and coagulation proteins in plasma. In some embodiments, the subject's simulated plasma sample considers some or all of the following factors: AT concentration, Factor II concentration, Factor V concentration, Factor VII concentration, Factor VIII concentration, Factor IX concentration, Factor X concentration, Factor XI concentration, Factor XII concentration, α2-macroglobulin concentration, Protein S concentration, Protein C concentration, thrombomodulin concentration, and prekallikrein concentration.

[0077] flowchart 1 presents a flowchart for an exemplary method for generating a QSP model for predicting peak thrombin levels, predicted peak height in a thrombin generation assay, area under the curve (AUC) in a thrombin generation assay, and lag time after a subject receives fitusiran and / or supplemental factor VIII. The method is represented by reference numeral 100 and begins with operation 102, in which a biochemical process map is generated. Biochemical process maps for the QSP model disclosed herein are shown, for example, in FIGS. 3 and 4.

[0078] Pharmacologically relevant species of the QSP model are identified in operation 104. Pharmacologically relevant species of the QSP model disclosed herein are provided, for example, in Table 1 below.

[0079] [Table 1]

[0080] Once the in vivo simulation, simulated plasma sample, and in vitro simulation and associated species have been identified, the computer system receives a set of relationships that describe the pharmacokinetics, pharmacodynamics, and / or responses of the species in the subject and simulated plasma sample. Exemplary responses between species in the QSP models disclosed herein are provided, for example, in Table 2 below.

[0081] [Table 2]

[0082] These reactions and relationships between species may be described by one or more equations, and the QSP model is described in operation 106 using appropriate governing mathematical formulas. Equations for the QSP models disclosed herein are provided, for example, in Table 3 below.

[0083] [Table 3]

[0084] After developing a model diagram of the QSP model and a set of differential equations that describe the responses between parameters reflected in the model, the QSP model may be parameterized in operation 108. For example, the model parameters defined in Table 4 below may be set to initial values ​​based on either literature data, clinical data, known mathematical relationships between other parameters, or those obtained through further calibration steps as described herein. Table 4 below provides the model parameters, their initial values, and the source of the initial values.

[0085] [Table 4]

[0086] These relationships may include rate constants, equilibrium constants, concentrations of one or more species, etc. In certain embodiments, one or more of these relationships provides the rate of accumulation or depletion of a species due to a particular physical phenomenon (e.g., synthesis, decomposition, or reaction within a compartment). In some embodiments, one or more of the relationships is the ratio of concentrations of two or more species or the ratio of products of these species (e.g., equilibrium constants or partition coefficients). In certain embodiments, the computer system obtains parameters such as rate constants of these relationships. Examples of sources of these parameters and methods for determining them are provided below.

[0087] Using the received relationships, the computer system generates an equation system using the rate constants, species concentrations, and any other components of the relationships that can be used by the computational system to implement the QSP model. See operation 110. In certain embodiments, this operation includes organizing the information from the set of relationships into vectors, matrices, tensors, specified data structures, and / or other constructs that the computer system can use to calculate the time-dependent concentrations of one or more species over a defined period of time. The equation system is generally a computer-usable representation of equations or other mathematics that characterize the species within a compartment. In certain embodiments, the equation system includes formulas representing one or more differential equations for in vivo simulations, simulated plasma samples, and in vitro simulations.

[0088] Once provided with the formula system, the computer system programs a particular computational system with the formula system in executable form. See operation 112. In some cases, the computer system used to generate the QSP model is the same as the particular computational system programmed to execute the model. In other cases, the two systems are physically or logically distinct. The programming of operation 112 enables the computational system to execute the QSP model when provided with appropriate initial conditions (pharmacological conditions) or other information.

[0089] Receiving instructions or data in acts 106, 108, 110, and / or 112 refers to actions by or for a computer system that generates a QSP model. These actions may include entering and / or storing information in memory accessible by a processor responsible for programming the computing system with the instructions and data that make up the QSP model. A human user may be indirectly responsible for causing instructions and / or data to be sent to portions of the computing system that may be used to program the QSP model.

[0090] 2 presents a flowchart of an exemplary method of using a QSP model to predict peak thrombin levels, predicted peak height in a thrombin generation assay, area under the curve (AUC) in a thrombin generation assay, and lag time after a subject receives fitusiran and / or supplemental factor VIII. The method, designated by reference numeral 200, begins with operation 202, in which a computational system used to execute the QSP model is accessed or otherwise made available for execution. In certain embodiments, the QSP model is generated using a method following the process of FIG. 1 and followed by execution as shown in FIG. 2. Nevertheless, the computational system is programmed with equations that represent concentrations and / or reaction parameters involving, for example, fitusiran, factor VIII, and antithrombin in a simulated plasma sample of the subject.

[0091] If a QSP model is available, the computational system can receive and / or input various data and / or commands necessary to run the model to predict peak thrombin levels, predicted peak height in a thrombin generation assay, area under the curve (AUC) in a thrombin generation assay, and lag time after a subject receives fitusiran, and / or supplemental factor VIII, and / or the PK / PD of fitusiran, and / or supplemental factor VIII. For example, the computational system can receive and / or input characteristics specific to a particular simulated patient population. See operation 204. Examples of such characteristics include biochemical properties of enzymes and substrates, including thrombin, antithrombin, α2-macroglobulin, and fibrinogen, such as enzyme and substrate synthesis and / or degradation rates, enzyme and substrate binding rates, and enzyme and substrate dissociation rates. This information can be used to generate binding rates, cleavage rates, catalytic rates, K values, and K values ​​for the simulated patient or simulated patient population. D It can be provided in a variety of forms.

[0092] In operation 206, the computing system receives or inputs the condition of the subject to whom fitusiran and / or supplemental factor VIII will be administered. In some embodiments, the subject is known to have hemophilia A or hemophilia B. In some embodiments, these inputs include information such as the mass of the subject's characteristics and the subject's disease state. Intrinsic parameters may be defined for each simulated patient or simulated patient population.

[0093] The computing system further receives or inputs one or more pharmacological conditions related to the administration of fitusiran and / or supplemental factor VIII to the subject. Such pharmacological conditions may also be referred to as extrinsic parameters. As described herein, such parameters relate to the treatment of the subject and may include various details about how fitusiran and / or supplemental factor VIII are administered to the subject, such as the dosages in the treatment regimen.

[0094] With the intrinsic and extrinsic parameters available, the QSP model is ready to be executed. Execution is shown in operation 208 and involves performing various mathematical or numerical operations on the data and / or commands received via operations 106, 108, 110, 112, 202, 204, and 206. The mathematical or numerical operations are performed, for example, by the following instructions to solve the system of equations as generated in operation 110:

[0095] During or after execution, the computing system outputs values ​​related to the peak thrombin level, the predicted peak height in the thrombin generation assay, the area under the curve (AUC) in the thrombin generation assay, and the lag time after the subject has been administered fitusiran and / or supplemental factor VIII. See operation 210. These values ​​may be time-dependent representations of thrombin levels in the subject's plasma, or may be values ​​that affect thrombin levels or downstream coagulation parameters in the subject's plasma. In certain embodiments, the values ​​are PD or PK parameters of fitusiran and / or supplemental factor VIII. In certain embodiments, the values ​​are target therapeutic ranges for fitusiran and / or supplemental factor VIII doses.

[0096] Components of the QSP Model thrombin Thrombin is a serine protease, an enzyme encoded by the F2 gene in humans. During the coagulation process, the thrombin precursor, prothrombin (coagulation factor II), is proteolytically cleaved to form thrombin. Thus, thrombin acts as a serine protease, converting soluble fibrinogen into insoluble strands of fibrin and facilitating the coagulation process. Thrombin also catalyzes many reactions related to coagulation, such as converting factor XI to XIa, factor VIII to VIIIa, factor V to Va, and factor XIII to XIIIa, and stimulating platelet aggregation. The molecular weight of prothrombin is approximately 72 kDa. The catalytic domain is released from prothrombin fragment 1.2, generating the active enzyme thrombin, which has a molecular weight of 36 kDa. Prothrombin consists of four domains: an N-terminal Gla domain, two kringle domains, and a C-terminal trypsin-like serine protease domain. Prothrombin is converted to active thrombin by proteolysis of an internal peptide bond, exposing a new N-terminal Ile-NH3.

[0097] Antithrombin. Antithrombin (AT), a small glycoprotein, is a plasma protease inhibitor and a member of the serpin superfamily. This protein inhibits thrombin and other activated serine proteases of the coagulation system, regulating the blood coagulation cascade. The antithrombin protein contains two functional domains: a heparin-binding domain at the N-terminus of the mature protein and a reactive site domain at the C-terminus. AT is a 432-amino acid protein produced by the liver. It contains three disulfide bonds and a total of four potential glycosylation sites. α-Antithrombin is the major form of antithrombin found in plasma, with oligosaccharides occupying each of its four glycosylation sites. A single glycosylation site remains consistently unoccupied in the minor form of antithrombin, β-Antithrombin.

[0098] The physiological target proteases of antithrombin are those of the contact activation pathway (formerly known as the intrinsic pathway), namely, activated forms of factor X (Xa), factor IX (IXa), factor XI (XIIa), and more frequently, factor II (thrombin) (IIa), as well as activated forms of factor VII (VIIa) from the tissue factor pathway (formerly known as the extrinsic pathway). Antithrombin also inactivates kallikrein and plasmin, which are also involved in blood clotting. However, it also inactivates certain other serine proteases not involved in clotting, such as trypsin, and the C1s subunit of the enzyme C1, which is involved in the classical complement pathway.

[0099] Protease inactivation by AT results from trapping the protease in an equimolar complex with antithrombin, such that the active site of the protease enzyme, e.g., thrombin, is inaccessible to its normal substrate. Formation of an antithrombin-protease complex, e.g., an antithrombin-thrombin complex, involves the interaction between the protease and a specific reactive peptide bond within antithrombin. In the case of human antithrombin, this bond is between arginine (arg)393 and serine (ser)394.

[0100] Protease enzymes that interact with antithrombin can be trapped in an inactive antithrombin-protease complex as a result of their attack on the reactive bond. Without wishing to be bound by any particular theory, attack of a similar bond in a normal protease substrate results in rapid proteolytic cleavage of the substrate, but attacking the antithrombin reactive bond activates antithrombin and traps the enzyme at an intermediate stage in the proteolytic process. After associating with antithrombin, thrombin can cleave the reactive bond in antithrombin over time, causing the inactive antithrombin-thrombin complex to dissociate, although this can take more than three days.

[0101] Supplementary factor VIII Factor VIII, also known as antihemophilic factor (AHF), is a blood clotting protein. In humans, factor VIII is encoded by the F8 gene. Defects in this gene result in hemophilia A, a recessive X-linked clotting disorder. Factor VIII is produced in the sinusoidal cells of the liver and in endothelial cells outside the liver throughout the body. This protein circulates in the bloodstream in an inactive form bound to another molecule called von Willebrand factor until an injury occurs, such as an injury to a blood vessel. In response to injury, clotting factor VIII is activated and dissociates from von Willebrand factor. The activated protein interacts with another clotting factor called factor IX. This interaction can trigger a series of additional chemical reactions that result in the formation of a blood clot.

[0102] Supplemental factor VIII can be used as a medication for HEMA patients to prevent and control bleeding episodes after injury. Supplemental factor VIII can also be used as a medication for maintaining hemostasis (i.e., perioperative management) in HEMA patients undergoing surgery. Factor VIII replacement therapy is generally required for patients with mild to moderate hemophilia A who do not adequately respond to desmopressin or for patients with moderate to severe HemA and factor VIII levels less than 5% of normal. Cofactor VIII is effective in managing spontaneous or traumatic bleeding episodes (e.g., erythrocytosis, IM hematoma, soft tissue bleeding) or acute bleeding events (e.g., GI, retroperitoneal, tonsillar, ocular) in HemA patients. Supplemental factor VIII can also be used for routine prophylaxis (i.e., administration at regular intervals) to prevent or reduce the frequency of bleeding events. Such prophylaxis is considered the current standard of care for HEMA patients. Factor VIII prophylaxis reduces the frequency of spontaneous musculoskeletal bleeding, preserves joint function, and improves quality of life in patients with HEMA.

[0103] Alpha-2 macroglobulin α2-macroglobulin is a thrombin regulator and may influence the effect of fitusiran on thrombin generation. α2-macroglobulin is a 720 kDa plasma protein found in blood. It is primarily produced by the liver and synthesized locally by macrophages, fibroblasts, and adrenal cortical cells. In humans, it is encoded by the A2M gene. α2-macroglobulin acts as an antiprotease and can inactivate various proteinases. It functions as an inhibitor of fibrinolysis by inhibiting plasmin and kallikrein. It functions as an inhibitor of coagulation by inhibiting thrombin. α2-macroglobulin can also bind to numerous growth factors and cytokines, such as platelet-derived growth factor, basic fibroblast growth factor, TGF-β, insulin, and IL-1β, and therefore may act as a carrier protein.

[0104] α2-macroglobulin inhibits by steric hindrance. This mechanism involves protease cleavage of the bait region, specifically the segment of α2-macroglobulin that is susceptible to proteolytic cleavage, which initiates a conformational change such that αM collapses around the protease. In the resulting α2-macroglobulin-protease complex, the active site of the protease is sterically shielded, thus substantially reducing access to the protein substrate. α2-macroglobulin can inactivate a variety of proteinases (including serine, cysteine, aspartic, and metalloproteinases), such as thrombin.

[0105] Thrombin generation assay The thrombin generation assay (TGA) or thrombin generation test (TGT) is a type of comprehensive coagulation assay (GCA) and coagulation test that can be used to assess clotting and thrombotic risk. It is based on the potential of plasma to generate thrombin over time after activation of coagulation by the addition of phospholipids, tissue factor, and calcium. TGA results can be output as a thrombogram or thrombin generation curve using computer software with calculation of thrombogram parameters. TGA can be performed, for example, using semi-automated calibrated thrombograms (CATs) or fully automated methods such as the ST Genesia system. TGA was first used as a manual assay in the 1950s and has since become increasingly automated.

[0106] In some embodiments, the QSP model disclosed herein performs a simulated TGA on a simulated plasma sample. In some embodiments, the QSP model's simulated TGA receives as input parameters and values ​​including, but not limited to, AT concentration, factor II concentration, factor V concentration, factor VII concentration, factor VIII concentration, factor IX concentration, factor X concentration, factor XI concentration, factor XII concentration, α2-macroglobulin concentration, protein S concentration, protein C concentration, thrombomodulin concentration, and prekallikrein concentration. In some embodiments, the QSP model's simulated TGA is performed on a simulated sample from a healthy individual. In some embodiments, the QSP model's simulated TGA is performed on a simulated sample from an individual with hemophilia A. In some embodiments, the QSP model's simulated TGA is performed on a simulated sample from an individual with hemophilia B. In some embodiments, the QSP model's simulated TGA is performed on a simulated sample from an individual with hemophilia A or B after treatment with fitusiran. In some embodiments, a simulated TGA of the QSP model is performed on a simulated sample of an individual with hemophilia A or B after treatment with fitusiran and adjunctive factor VIII.

[0107] Reaction Details The QSP models disclosed herein use various relationships and other details related to model species and reactions that affect the concentrations of model species, e.g., in simulated in vivo conditions of a subject, simulated plasma samples of a subject, and simulated in vitro assays of the subject plasma sample. For example, a QSP model may be based on, among other things, the association or dissociation of the thrombin-antithrombin complex, the rate of synthesis of α2-macroglobulin, the rate of binding of α2-macroglobulin to thrombin, or the mechanism of cleavage of fibrinogen by thrombin. In certain embodiments, reactions are modeled using zero-, first-, and second-order mass action relationships.

[0108] Examples of relationships that can be used in a QSP model follow: In certain embodiments, a QSP model employs any one or more of these relationships: In certain embodiments, a QSP model employs any two or more of these relationships:

[0109] Representative QSP model formulas in computational systems As discussed, the QSP model of the present disclosure executes instructions that represent mathematical expressions characterizing one or more species in, for example, a simulated in vivo condition of interest, a simulated plasma sample of interest, and a simulated in vitro assay of the plasma sample. The mathematical expressions are provided as a series of equations describing the relationships and amounts of species in the simulated in vivo condition of interest, the simulated plasma sample of interest, and the simulated in vitro assay of the plasma sample. In some implementations, the simulated in vivo condition of interest, the simulated plasma sample of interest, and the simulated in vitro assay of the plasma sample each have one or more separate mathematical expressions, one for each species considered in the modeled condition. These expressions correspond to governing relationships, such as reaction phenomena described herein. The mathematical expressions may include all information sufficient to computationally represent or predict the time-varying concentrations of components of interest in the simulated in vivo condition of interest, the simulated plasma sample of interest, and the simulated in vitro assay of the plasma sample.

[0110] For example, a subject's simulated plasma sample can have mathematical formulas that represent AT decline, factor VIII, and antithrombin concentrations. Similarly, the QSP model disclosed herein can predict the results of a simulated thrombin generation assay (TGA) or activated partial thromboplastin time (aPTT) assay, including peak thrombin, AUC, and lag time, based on mathematical formulas that represent AT decline, factor VIII, and antithrombin concentrations in the simulated plasma sample.

[0111] In some embodiments, the mathematical expressions are differential equations that provide time-dependent representations of the species of interest in the simulated in vivo conditions of the subject, the simulated plasma sample of the subject, and the simulated in vitro assay of the subject plasma sample. The differential equations may include vectors and / or matrices of rate constants or other parameters that affect the concentration or amount of the species of interest. In some embodiments, the individual differential equations and / or other mathematical representations of the species of interest in the simulated in vivo conditions of the subject, the simulated plasma sample of the subject, and the simulated in vitro assay of the subject plasma sample are typically solved simultaneously by numerical means to provide time-dependent values ​​for each of the species in the model.

[0112] To solve for the time-dependent concentrations of species in the model, in addition to rate constants and other information regarding the reaction between the species and the simulated assay output (e.g., via differential equations programmed into the computational system), a set of subject-specific parameters, e.g., for a simulated patient or simulated patient population, can be included in the model. These include intrinsic and extrinsic parameters. Intrinsic parameters are parameters that are specific to the subject and outside the control of the physician or clinician treating the subject. Extrinsic parameters are parameters that are under the control of the physician or clinician. Examples of intrinsic parameters include the mass of the subject and subject-specific characteristics of the plasma sample. Examples of extrinsic parameters include the dose of one or more administered therapeutic agents (e.g., AT lowering or supplemental factor VIII), the administration frequency of one or more administered therapeutic agents, other medications administered simultaneously with one or more administered therapeutic agents, etc.

[0113] The rate constants and other parameters (e.g., contained in the ordinary differential equations of the numerical solution) programmed into the QSP models disclosed herein can be obtained from a variety of sources, including literature references, clinical data, and experimental calibration. Calibration can be performed in vitro or in vivo.

[0114] Context for the Disclosed Computational Embodiments Certain embodiments disclosed herein relate to systems for generating and / or using QSP models. Certain embodiments disclosed herein relate to methods for generating and / or using QSP models implemented on such systems. A system for generating a QSP model can be configured to analyze data to calibrate a formula or relationship used to describe the hemostatic equivalence of fitusiran prophylaxis (i.e., AT lowering) in a subject. In such calibration, the system can determine rate constants or other parameter values ​​characterizing peak thrombin levels in a subject after treatment with fitusiran and / or supplemental factor VIII. A system for generating a QSP model can also be configured to receive data and instructions, such as program code, that describe physical processes in a subject's plasma. In this manner, a QSP model is generated or programmed on such a system. A programmed system for using a QSP model can be configured to (i) receive inputs, such as pharmacological conditions characterizing a subject, and (ii) execute instructions to determine the hemostatic equivalence of fitusiran prophylaxis (i.e., AT lowering) in the subject's plasma. To this end, the system can calculate the time-dependent concentration of antithrombin in the subject's plasma.

[0115] Many types of computing systems having any of a variety of computer architectures can be employed as the disclosed systems for implementing QSP models and algorithms for generating and / or calibrating such models. For example, the systems may include software components running on one or more general-purpose processors or specially designed processors such as programmable logic devices (e.g., field programmable gate arrays (FPGAs)). Furthermore, the systems may be implemented on a single device or distributed across multiple devices. The functionality of the computational elements may be combined together or further divided into multiple sub-modules.

[0116] In some embodiments, the code executed during the generation or execution of a QSP model on a suitably programmed system may be embodied in the form of a software element that can be stored on a non-volatile storage medium (optical disk, flash storage device, mobile hard disk, etc.) containing a number of instructions for creating a computing device (personal computer, server, network appliance, etc.).

[0117] At one level, software elements are implemented as a series of commands created by a programmer / developer. However, modular software that can be executed by computer hardware is executable code committed to memory using "machine code" or "native instructions" selected from the specific machine instruction set designed into the hardware processor. The machine instruction set, or native instruction set, is known to and inherently built into the hardware processor. It is the "language" by which system and application software communicate with the hardware processor. Each native instruction is a distinct piece of code recognized by the processing architecture and may specify specific registers for arithmetic, addressing, or control functions, specific memory locations or offsets, and specific addressing modes to be used to interpret operands. These simple native instructions are combined to build more complex operations, which are executed sequentially or as directed by control flow instructions.

[0118] The interrelationship between executable software instructions and a hardware processor is structural. In other words, the instructions themselves are a series of symbols or numbers. They do not inherently convey any information. By design, it is the processor that is pre-configured to interpret the symbols / numbers, which gives meaning to the instructions.

[0119] The models used herein may be configured to run on a single machine at a single location, on multiple machines at a single location, or on multiple machines at multiple locations. When multiple machines are employed, the individual machines may be adapted for their specific tasks. For example, operations requiring large blocks of code and / or significant processing power may be performed on large and / or stationary machines. Such operations may be implemented on hardware remote from the location where the sample is acquired or the data is entered, for example, on a server or server farm connected by a network to a field device that captures the sample image. Less computationally intensive operations may be implemented on portable or mobile devices used in the field for clinical evaluation.

[0120] Additionally, certain embodiments relate to tangible and / or non-transitory computer-readable media or computer program products that contain program instructions and / or data (including data structures) for performing various computer-implemented operations. Examples of computer-readable media include, but are not limited to, semiconductor memory devices, phase-change devices, magnetic media such as disk drives and magnetic tape, optical media such as CDs, magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as read-only memory devices (ROM) and random access memory (RAM). Computer-readable media may be directly controlled by an end user, or the media may be indirectly controlled by an end user. Examples of directly controlled media include media located at a user's premises and / or media that are not shared with other entities. Examples of indirectly controlled media include media that are indirectly accessible to a user via an external network and / or via a service that provides shared resources, such as the "cloud." Examples of program instructions include both machine code, such as that generated by a compiler, and files containing higher-level code that can be executed by a computer using an interpreter.

[0121] In various embodiments, data or information employed in the disclosed methods and devices is provided in electronic format. Such data or information may include pharmacological conditions associated with administration of fitusiran and / or supplemental factor VIII to a subject, intrinsic characteristics of the subject, model parameters such as kinetic constants, PK / PD results, etc. As used herein, data or other information provided in electronic format is available for machine storage and machine-to-machine transmission. Traditionally, data in electronic format is provided digitally and can be stored as bits and / or bytes in various data structures, lists, databases, etc. Data may be embodied electronically, optically, etc.

[0122] In certain embodiments, the QSP model may be viewed as a form of application software that interfaces with users and system software, respectively. System software typically interfaces with computer hardware and associated memory. In certain embodiments, system software includes operating system software and / or firmware and any middleware and drivers installed on the system. System software is task-independent and provides the basic functionality of the computer. In contrast, modules and other application software are used to accomplish specific tasks. Each native instruction of a module is stored in a memory device and represented by a numerical value.

[0123] An exemplary computer system 1600 is shown in FIG. 16. As shown, computer system 1600 includes an input / output subsystem 1602, which may implement an interface for interacting with a human user and / or other computer systems, depending on the application. Embodiments of the present invention may be implemented in program code on system 1600, with I / O subsystem 1602 used to receive program statements and / or data entered from a human user (e.g., via a GUI or keyboard) and display them to the user. I / O subsystem 1602 may include, for example, a keyboard, mouse, graphical user interface, touch screen, or other interface for input, such as an LED or other flat screen display, or other interface for output.

[0124] The program code may be stored in a non-transitory medium, such as persistent storage 1612, memory 1612, or both. One or more processors 1604 read the program code from the one or more non-transitory media and execute the code to enable the computer system to perform the methods performed by embodiments herein, such as methods involved in generating or using a QSP model as described herein. Those skilled in the art will understand that a processor can accept source code, such as statements for performing training and / or modeling operations, and interpret or compile the source code into machine code understandable at the processor's hardware gate level. Bus 1605 couples I / O subsystem 1602, processor 1604, peripheral devices 1606, communication interface 1608, memory 1610, and persistent storage 1612.

[0125] Treatment method The present disclosure provides methods of treating HEMA and / or HEMB comprising administering one or more therapeutic compositions, in some embodiments, the therapeutic compositions include, for example, fitusiran and / or supplemental factor VIII.

[0126] The present disclosure also provides a method for administering an effective amount of fitusiran to a subject to achieve about 10% to about 50% factor VIII equivalence. As used herein, the term "factor VIII equivalence" refers to a level of factor VIII equivalence that results in similar clotting ability as measured by a thrombin generation assay metric. To reduce the risk of vascular thrombotic events while maintaining a favorable risk-benefit balance for fitusiran-treated patients, the target factor VIII equivalence is 10% to 50% (e.g., 20% to 40%). Therefore, as long as a patient achieves this targeted factor VIII equivalence, the patient does not need to receive higher fitusiran doses, more frequent administration, or adjunctive therapy.

[0127] In some embodiments, the disclosure features a method of lowering AT concentration in plasma of a subject, comprising administering one or more therapeutic compositions as described herein, e.g., fitusiran and / or supplemental factor VIII.

[0128] In some embodiments, the concentration of AT in the subject's plasma is reduced by 30% to 35%, 35% to 40%, 40% to 45%, 45% to 50%, 50% to 55%, 55% to 60%, 60% to 65%, 65% to 70%, 70% to 75%, 75% to 80%, 80% to 85%, 85% to 90%, 90% to 95%, or 95% to 99% of the AT concentration found in the subject prior to administration of the composition.

[0129] In some embodiments, the disclosure features a method of increasing thrombin concentration in the plasma of a subject, comprising administering one or more therapeutic compositions described herein, e.g., fitusiran and / or supplemental factor VIII.

[0130] In some embodiments, treatment according to the methods disclosed herein results in an improvement, stabilization, or slowing of changes in HEMA or HEMB symptoms. In some embodiments, treatment according to the methods disclosed herein results in a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or greater reduction in annualized episodes of spontaneous or excessive bleeding.

[0131] In some embodiments, the effectiveness of treatment is measured by an improvement or slowing of the progression of HEMA or HEMB symptoms. In some embodiments, the effectiveness of treatment is measured by a reduction in excessive bleeding following trauma or surgery. In some embodiments, the effectiveness of treatment is measured by the prevention of hematuria in a subject.

[0132] In some embodiments, the effectiveness of the treatment is measured by a clinical laboratory finding including a reduction in activated partial thromboplastin time (aPTT). In some embodiments, the aPTT is reduced by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more than 90%. In some embodiments, the effectiveness of the treatment is measured by a clinical laboratory finding including a reduction in prothrombin time (PT). In some embodiments, the PT is reduced by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more than 90%.

[0133] In some embodiments, the QSP model disclosed herein may be used to adjust the dosing regimen.

[0134] Evaluating the sensitivity of peak thrombin levels to model parameters as disclosed herein can facilitate the development of new therapeutic modalities that can target different aspects of hemostasis in patients with HEMA or HEMB. For example, the results of AT reduction and peak thrombin prediction by the QSP model described herein can provide insight into the most effective combination of fitusiran and / or accessory clotting factors for therapeutic intervention for patients with HEMA or HEMB. In some embodiments, the results of AT reduction and peak thrombin prediction by the QSP model described herein can inform the selection of a dosing regimen for fitusiran and / or accessory clotting factors for patients with HEMA or HEMB. In some embodiments, the results of AT reduction and peak thrombin prediction by the QSP model described herein can inform adjustment of the dosing regimen for fitusiran and / or accessory clotting factors for patients with HEMA or HEMB.

[0135] Patients taking fitusiran regularly should be monitored for hemostatic parameters, such as coagulation parameters (D-dimer, prothrombin fragments 1+2, and fibrinogen), and signs and symptoms of vascular thrombotic events. Such signs and symptoms may include, but are not limited to, severe or persistent headache, headache accompanied by nausea and vomiting, chest pain and / or tightness, hemoptysis, dyspnea, abdominal pain, fainting or loss of consciousness, swelling or pain in the arms or legs, vision problems, weakness and / or sensory impairment, and changes in speech. Evaluation of signs and symptoms potentially consistent with vascular thrombosis should include appropriate imaging studies, if applicable. Magnetic resonance imaging venogram (MRV) or computed tomography venogram (CTV) is recommended for the diagnosis of cerebral venous sinus thrombosis.

[0136] If a patient develops thrombosis while taking fitusiran, AT reversal may be administered in combination with replacement factors or BPA and appropriate anticoagulation. AT reversal should follow labeled product recommendations for the prevention of perioperative thrombosis in AT-deficient patients, and patient doses should be individualized to target 80-120% AT activity. The use of plasma-derived AT may be preferred over recombinant AT given its longer half-life.

[0137] Bleeding events in patients taking fitusiran regularly can be managed by on-demand administration of replacement factors (recombinant or plasma-derived factor VIII or IX) or BPA (e.g., fresh frozen plasma (FFP); rFVIIa; and aPCC). The amount of factor or BPA must be reduced in patients taking fitusiran regularly to prevent vascular thrombosis. See, e.g., WO 2019 / 014187. [Example]

[0138] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

[0139] Example 1: Thrombin Generation Assay (TGA) Simulation To validate the QSP model disclosed herein, model-based predictions of peak thrombin (nM) were compared with thrombin generation assay (TGA) results from human plasma. TGA simulations were performed across various percentages of AT and FVIII (Figures 5A and 5B). These results demonstrate that, by including the α2-macroglobulin reaction, the QSP model disclosed herein accurately characterized thrombin kinetics in thrombin generation assays at different antithrombin levels. Figures 5G and 5H are plots of thrombin concentration (nM) in plasma over time, comparing thrombin levels predicted by a previous model of the coagulation cascade that did not include α2-macroglobulin (black line) with previously collected clinical data at 100% antithrombin and 100% supplemental factor VIII (Advate) levels (Figure 5G) and 20% antithrombin and 50% supplemental factor VIII (Figure 5H). As shown in Figure 5H, a model of the coagulation cascade that did not include α2-macroglobulin (black line) did not accurately simulate thrombin generation at lower antithrombin levels (e.g., 20% AT).

[0140] Figures 5C-5F are plots of α2M-IIa complex concentration (y-axis) against supplemental factor VIII (Advate) levels (x-axis). The α2M-IIa complex levels (plotted lines) predicted by the QSP model disclosed herein are compared with previously collected clinical data (data points with error bars) at 100% AT (Figure 5C), 20% AT (Figure 5D), 10% AT (Figure 5E), and 5% AT (Figure 5F). These results suggest that the QSP model accurately represents thrombin complex formation with α2-macroglobulin even at different levels of antithrombin and factor VIII.

[0141] Figure 6 shows the peak thrombin levels (nM) of patients with various HEMA severities (residual factor VIII levels) observed in clinical data at different AT% ranges (x-axis). Figures 7A-7B show the overall predictive performance of the QSP model.

[0142] These simulations reproduced experimental TGA data from AT- and FVIII-doubly depleted plasma spiked with different levels of AT and / or FVIII. In these in vitro experiments, factor VIII (FVIII) and AT-doubly depleted plasma was generated from congenitally FVIII-deficient plasma by immunodepleting AT using an anti-AT antibody, and recombinant AT protein was added to achieve different levels of AT. Factor VIII was spiked to replicate supplemental factor VIII doses of 0 IU / kg, 10 IU / kg, and 20 IU / kg. AT was added in defined amounts corresponding to 100% AT, 10-20% AT, 10-25% AT, and 20-25% AT. TGA was performed on spiked plasma samples. As shown in Figure 8, the predictions of the QSP model (boxplots) are consistent with the TGA data (ranges) for the three factor VIII doses and each of the four ranges of AT% at each factor VIII dose.

[0143] We next compared the model predictions with spike-in TGA results for human plasma over a wider range of AT percentages and factor VIII doses. Figure 9A shows a heat map of predicted peak thrombin (nM) at various AT% (y-axis) and factor VIII doses (x-axis). Labels "1," "2," "3," and "4" correspond to the relative extremes of the AT% and supplemental factor VIII dose ranges. The QSP model was used to predict peak thrombin, and TGA was performed on AT-depleted plasma over these ranges. Figure 9B shows the predicted addition and laboratory dosing results for 5% AT and 0% supplemental factor VIII. Figure 9C shows the predicted addition and laboratory dosing results for 5% AT and 100% supplemental factor VIII. Figure 9D shows the predicted addition and laboratory dosing results for 100% AT and 0% supplemental factor VIII. Figure 9E shows the predicted addition and laboratory dosing results for 100% AT and 100% supplemental Factor VIII. For each condition, the QSP model predicted results correspond to the laboratory TGA results, demonstrating that the QSP model accurately predicts peak thrombin (nM) across a wide range of conditions.

[0144] Next, we compared the model predictions with spike-in TGA results for human plasma at intermediate ranges of AT percentage and factor VIII dose. Figure 10A shows a heat map of predicted peak thrombin (nM) at various AT% (y-axis) and factor VIII dose (x-axis). Labels "1," "2," "3," and "4" correspond to intermediate ranges of AT% and supplemental factor VIII dose, which overlap with the target therapeutic window for clinical treatment of HEMA and HEMB patients. The QSP model was used to predict peak thrombin within these ranges, and TGA was performed on AT-depleted plasma within these ranges. Figure 10B shows the predicted dosing results and laboratory dosing results for 20% AT and 5% supplemental factor VIII. Figure 10C shows the predicted dosing results and laboratory dosing results for 24% AT and 50% supplemental factor VIII. Figure 10D shows the predicted dosing results and laboratory dosing results for 40% AT and 20% supplemental factor VIII. Figure 10E shows the predicted addition and laboratory dosing results for 30% AT and 50% supplemental Factor VIII. For each condition, the QSP model predictions were in good agreement with the laboratory TGA results, demonstrating that the QSP model accurately predicts peak thrombin (nM) over an intermediate range of AT% and supplemental Factor VIII doses that overlap with the target therapeutic window for clinical treatment of HEMA and HEMB patients.

[0145] Next, we compared the model predictions with the results of dosing TGA on human plasma for a further range of AT percentages and factor VIII doses. Figure 11A shows a heat map of predicted peak thrombin (nM) at various AT% (y-axis) and factor VIII doses (x-axis). Labels "5," "6," "7," and "8" correspond to further ranges of AT% and supplemental factor VIII doses, some of which overlap with the target therapeutic window for clinical treatment of HEMA and HEMB patients. We used the QSP model to predict peak thrombin in these ranges, and performed TGA on AT-depleted plasma in these ranges. Figure 11B shows the predicted dosing results and laboratory dosing results for 12% AT and 100% supplemental factor VIII. Figure 11C shows the predicted dosing results and laboratory dosing results for 100% AT and 50% supplemental factor VIII. Figure 11D shows the predicted dosing results and laboratory dosing results for 34% AT and 50% supplemental factor VIII. Figure 11E shows the predicted addition and laboratory dosing results for 12% AT and 5% supplemental Factor VIII. For each condition, the QSP model predictions correspond to the laboratory TGA results, demonstrating that the QSP model accurately predicts peak thrombin (nM) across the mid-range of AT% and supplemental Factor VIII dose.

[0146] Example 2: Activated Partial Thromboplastin Time (aPTT) Assay Simulation To further validate the QSP model disclosed herein, predictions of aPTT results based on the model were compared with aPTT results from clinical trial data and results previously reported in the literature. As shown in Figure 12, aPTT results were predicted for healthy subjects, HEMA patients, and non-HEMA patients treated with warfarin for other bleeding disorders as a validation exercise. The QSP model adequately predicted aPTT results for these three groups, and the results corresponded to the clinical trial data and previously reported data from the literature. The results demonstrate that the QSP model accurately describes multiple aspects of the coagulation pathway.

[0147] Next, aPTT results were predicted across a range of AT percentages and supplemental factor VIII percentages, and the QSP model predictions were compared to laboratory aPTT results for AT- and factor VIII-doubly depleted plasma spiked with different levels of AT and / or factor VIII, as described in Example 1. Figure 13A shows the results of the QSP model prediction, and Figure 13B shows the laboratory aPTT results for the spiked plasma. These results demonstrate that the QSP model adequately predicts aPTT results across a wide range of AT percentages and supplemental factor VIII percentages.

[0148] Finally, the QSP model-predicted effect of AT reduction on peak thrombin using the QSP model disclosed herein was compared with the results reported by Livnat et al. (Livnat T, et al. Thrombin generation in plasma of patients with heemophilia A and B with inhibitors: Effects of bypassing agents and antithrombin reduction. Blood Cells Mol Dis. 2020 May;82:102416). Figure 14 is a plot reproduced from that published report, showing peak thrombin (nM) for control 50% residual AT, control 10% residual AT, 50% residual AT, 10% residual AT, and healthy volunteers. For patients with 10% residual AT, Livnat et al. reported a peak thrombin (nM) of approximately 35% compared to healthy volunteers, while the QSP model predicted a peak thrombin (nM) of 30-45% compared to healthy volunteers. For patients with 50% residual AT, Livnat et al. reported a peak thrombin (nM) of approximately 17% compared to healthy volunteers, and the QSP model predicted a peak thrombin (nM) of 10–16% compared to healthy volunteers.

[0149] A simulated virtual population (n = 1000) of patients with severe hemophilia A (0.1% residual factor VIII) was also tested. The virtual population was generated based on calibration of the QSP model to individual patient pharmacokinetic, antithrombin, and thrombin generation assay data from completed fitusiran clinical trials. The virtual population was applied to simulate peak thrombin associated with AT decline or adjunctive factor VIII. Figure 15 shows peak thrombin (mean ± standard deviation) in the simulated virtual population. Figure 18 is a plot of predicted peak thrombin (nM) based on the results of a simulated virtual population generated based on calibration of the QSP model from patient data from clinical trials. The plot shows predicted peak thrombin (nM) at 15% and 35% therapeutic antithrombin levels in people with hemophilia A, along with predicted peak thrombin (nM) at 20% and 40% fitusiran and FVIII activity. The targeted therapeutic antithrombin range for fitusiran prophylactic treatment at steady state is 15-35%. Based on this modeling analysis and the results of the simulated hypothetical population presented in Figure 18, the peak thrombin generated at 15-35% antithrombin was comparable to the peak thrombin observed at 20-40% factor VIII activity in people with hemophilia A. These results demonstrate that the QSP model disclosed herein predicts peak thrombin levels (nM) consistent with clinical data from previously published literature.

[0150] Other embodiments While the present invention has been described with reference to its detailed description, it should be understood that the foregoing description is for illustrative purposes only and is not intended to limit the scope of the invention as defined by the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

1. 1. A method of generating a quantitative systems pharmacology model for predicting thrombin levels in plasma of a subject, comprising: (a) providing a plurality of relationships and / or parameters characterizing time-dependent antithrombin (AT) levels in plasma of a subject in response to an agent targeting antithrombin in the plasma of the subject; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the plasma of the subject; and (c) determining a plurality of rate constants of the plurality of relationships and / or parameters that characterize the time-dependent AT level in the plasma of the subject in response to a drug that targets antithrombin in the plasma of the subject; (d) determining the rate constant of said relationship or said parameter characterizing the α2-macroglobulin level in said plasma of said subject; (e) programming a computing system with (i) the rate constants of the relationships and / or parameters characterizing a time-dependent AT level in the plasma of a subject in response to a drug that targets antithrombin in the plasma of the subject, and (ii) the rate constants of the relationships or parameters characterizing an α2-macroglobulin level in the plasma of the subject; whereby the computing system is programmed to: (i) solve an equation system under a defined set of pharmacological conditions, the equation system comprising the plurality of relationships and / or parameters characterizing a time-dependent AT level in the plasma of the subject and the plurality of relationships and / or parameters characterizing an α2-macroglobulin level in the plasma of the subject in response to an agent targeting antithrombin in the plasma of the subject; and (ii) output a thrombin level in the plasma of the subject after administration of the agent targeting antithrombin in the plasma of the subject under the defined set of pharmacological conditions.

2. 10. The method of claim 1, wherein the agent targeting antithrombin in the plasma of the subject is an siRNA therapeutic.

3. 3. The method of claim 2, wherein the siRNA therapeutic is fitusiran.

4. The method of any one of claims 1 to 3, wherein the AT level is less than 35%.

5. The method according to any one of claims 1 to 4, wherein the formation and degradation of α2-macroglobulin-thrombin complexes is calculated.

6. The method of any one of claims 1 to 5, wherein the α2-macroglobulin has a concentration of about 3 to about 6 μM.

7. 1. A computer-implemented method for modeling and simulating thrombin levels in plasma of a subject, comprising: obtaining a quantitative systems pharmacology (QSP) model of thrombin levels in plasma of a subject, the QSP model configured to represent results of a thrombin generation assay (TGA) in response to plasma levels of antithrombin (AT), α2-macroglobulin, and a pharmaceutical agent targeting antithrombin in the plasma of the subject; determining parameters affecting peak thrombin as indicated by said TGA; assigning said parameters influencing peak thrombin to a virtual patient population; processing the virtual patient population using the QSP model to provide processed data, the processed data comprising an amount of the agent targeting antithrombin in the plasma of the subject; 20. A computer-implemented method comprising:

8. 8. The computer-implemented method of claim 7, wherein the plasma level of AT is less than 35%.

9. 9. A computer-implemented method according to claim 7 or 8, for calculating the formation and breakdown of α2-macroglobulin-thrombin complexes.

10. The computer-implemented method of any one of claims 7 to 9, further comprising displaying the processed data.

11. determining pharmacokinetic parameters of the agent targeting antithrombin in the plasma of the subject; determining pharmacokinetic parameters of one or more additional therapeutic agents; processing the pharmacokinetic parameters of the drug targeting antithrombin in the plasma of the subject and the pharmacokinetic parameters of the one or more additional therapeutic agents to determine the efficacy of a combination of the drug targeting antithrombin in the plasma of the subject and one or more additional therapeutic agents; The computer-implemented method of any one of claims 7 to 10, further comprising:

12. 12. The computer-implemented method of claim 7, wherein the agent that targets antithrombin in the plasma of the subject is an RNAi therapeutic agent.

13. 13. The method of claim 12, wherein the RNAi therapeutic is an siRNA therapeutic.

14. 13. The method of claim 12, wherein the siRNA therapeutic is fitusiran.

15. 1. A computer-implemented method for determining factor VIII equivalence in a subject, comprising: obtaining a quantitative systems pharmacology (QSP) model of thrombin levels in the plasma of a subject, the QSP model configured to represent the interaction of plasma levels of antithrombin (AT), α2-macroglobulin, and thrombin; Determining parameters that affect the thrombin generation assay; processing the QSP model to provide processed data, the processed data being indicative of factor VIII equivalence in the subject; and 20. A computer-implemented method comprising:

16. 16. The computer-implemented method of claim 15, wherein the plasma level of antithrombin is a time-dependent variable.

17. 17. The method of claim 15 or 16, wherein the plasma level of antithrombin is less than 35%.

18. 18. The computer-implemented method of any one of claims 15 to 17, wherein the formation and degradation of thrombin-antithrombin (T-AT) is calculated.

19. The computer-implemented method of any one of claims 15 to 18, wherein the formation and breakdown of α2-macroglobulin-thrombin complexes is calculated.

20. 20. The computer-implemented method of any one of claims 15 to 19, wherein the results of a thrombin generation assay (TGA) in response to said interaction of plasma levels of antithrombin (AT), α2-macroglobulin and thrombin are modeled.

21. 20. The computer-implemented method of any one of claims 15 to 19, wherein the peak thrombin indicated by TGA is used to determine factor VIII equivalence.

22. 22. The computer-implemented method of any one of claims 15 to 21, wherein the subject is administered an siRNA therapeutic.

23. 23. The method of claim 22, wherein the siRNA therapeutic is fitusiran.

24. 1. A method of achieving about 10% to about 50% factor VIII equivalence in a hemophilia patient in need thereof, comprising subcutaneously administering to said patient a prophylactically effective amount of fitusiran to achieve an AT level of 10-35% in said patient.

25. 25. The method of claim 24, wherein the patient is a hemophilia A or hemophilia B patient.

26. 26. The method of claim 25, wherein the patient is a hemophilia A patient with or without inhibitors or a hemophilia B patient with or without inhibitors.

27. 27. The method of any one of claims 24 to 26, comprising achieving a Factor VIII equivalence of about 20% to about 40%.

28. 28. The method according to any one of claims 24 to 27, wherein the prophylactically effective amount of fitusiran is selected from about 1.25 mg, about 2.5 mg, about 5 mg, about 25 mg, about 30 mg, about 50 mg and about 80 mg.

29. 29. The method of any one of claims 24 to 28, wherein the prophylactically effective amount of fitusiran is administered about once every month (or about every four weeks) or about once every two months (or about once every eight weeks).

30. 30. The method of any one of claims 24 to 29, wherein the prophylactically effective amount of fitusiran is about 50 mg administered about once every month (or about every four weeks) or about once every two months (or about every eight weeks).

31. 30. The method of any one of claims 24 to 29, wherein the prophylactically effective amount of fitusiran is about 20 mg administered about once every month (or about every four weeks) or about once every two months (or about every eight weeks).

32. 1. A method of treating hemophilia in a patient with or without an inhibitor, comprising: (a) providing a time-dependent antithrombin level in the patient's plasma; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the plasma of the patient; and (c) programming a computing system with one or more rate constants of said relationship or said parameters that characterize thrombin levels; (d) determining hemostatic equivalence with factor VIII in said patient; (e) administering a therapeutic agent to said patient; A method comprising:

33. 33. The method of claim 32, wherein the therapeutic agent is supplemental factor VIII.

34. 33. The method of claim 32, wherein the therapeutic agent is fitusiran.

35. The method according to any one of claims 32 to 34, wherein the hemostatic equivalence with Factor VIII is determined by modelling the interaction of plasma levels of antithrombin (AT), α2-macroglobulin and thrombin.

36. The method according to any one of claims 32 to 35, wherein the time-dependent antithrombin level is less than 35%.

37. 1. A method for determining hemostatic equivalence with factor VIII in a subject being treated for hemophilia A or B, comprising: (a) providing a time-dependent antithrombin level in the plasma of said subject; (b) providing a plurality of relationships and / or parameters characterizing the α2-macroglobulin level in the plasma of the subject; and (c) programming a computing system with one or more rate constants of said relationship or said parameters that characterize thrombin levels; (d) determining hemostatic equivalence with factor VIII in said subject; A method comprising:

38. 38. The method of claim 37, further comprising administering supplemental Factor VIII to the subject.

39. 38. The method of claim 37, further comprising adjusting the dosage of fitusiran to the subject.

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