Treatment of hemophilia A and methods for estimating bleeding risk and their use

Abstract

This disclosure provides a method for estimating the bleeding risk of a person with hemophilia A, as well as a computer-based system that includes and implements an Repeat Time to Event (RTTE) model.

Description

[Technical Field]

[0001] Reference to electronically submitted sequence listings The contents of the electronically submitted sequence listing XML file (name: 744527_SA9-504-3_ST26.xml; size: 33,139 bytes; and creation date: October 20, 2023) are incorporated herein by reference in their entirety.

[0002] Cross-reference of related applications This application claims the interests of U.S. Provisional Patent Application No. 63 / 482,631 filed on 1 February 2023; No. 63 / 485,658 filed on 17 February 2023; and No. 63 / 592,100 filed on 20 October 2023, each of which is incorporated by reference in whole for all purposes. [Background technology]

[0003] While plasma-derived and recombinant coagulation factor products enable hemophilia patients to live longer and healthier lives, hemophilia remains one of the most expensive and complex conditions to manage. Due to its complexity, the treatment of hemophilia A with FVIII replacement therapy requires a special treatment management process for physicians, pharmacies, and patients. Clinicians often assess lifestyle, psychosocial requirements, and home environment when evaluating the patient's or caregiver's ability to provide adequate care.

[0004] The currently recommended standard of care includes regular administration of FVIII (routine prophylaxis) to minimize the number of bleeding episodes. While routine prophylaxis is associated with improved long-term outcomes, it is a demanding regimen limited by the need for frequent intravenous (IV) administration. See Manco-Johnson et al., N Engl J Med. 357(6):535-44 (2007). Although FVIII products with extended half-lives have reduced the frequency of prophylactic FVIII administration, the most currently available FVIII products interact with endogenous von Willebrand factor (VWF) and have comparable circulating half-lives, matching the upper limit of the half-life of rFVIII variants due to the half-life of endogenous VWF. See, for example, Pipe et al., Blood. 128(16):2007-16 (2016). [Overview of the project] [Means for solving the problem]

[0005] This specification discloses, in particular, a method for assessing the bleeding risk of subjects with hemophilia A, a computer-based system including an Repeat Time to Event (RTTE) model, and the use thereof for estimating the bleeding risk of subjects with hemophilia A. In some embodiments, the RTTE model includes a treatment effect as a covariate. In some embodiments, the treatment effect is prophylactic treatment or on-demand treatment. In some embodiments, the RTTE model is RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2]. In some embodiments, estimating the bleeding risk is equivalent to estimating the risk of a bleeding event occurring.

[0006] A particular aspect of the present disclosure relates to a method for estimating bleeding risk, comprising: receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an RTTE model [B]; calculating bleeding risk using the RTTE model [B] and the received information by the computer program; and transmitting the calculated bleeding risk information (b) by the software-based system for output of bleeding risk information.

[0007] A particular aspect of the present disclosure relates to a method for estimating bleeding risk, comprising: receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an RTTE model [B]; calculating bleeding risk using the RTTE model [C] and the received information by the computer program; and transmitting the calculated bleeding risk information (b) by the software-based system for output of bleeding risk information.

[0008] A particular aspect of the present disclosure relates to a method for estimating bleeding risk, comprising: receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an RTTE model [B]; calculating bleeding risk using the RTTE model [D1] and the received information by the computer program; and transmitting the calculated bleeding risk information (b) by the software-based system for output of bleeding risk information.

[0009] A particular aspect of the present disclosure relates to a method for estimating bleeding risk, comprising: receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an RTTE model [B]; calculating bleeding risk using the RTTE model [D2] and the received information by the computer program; and transmitting the calculated bleeding risk information (b) by the software-based system for output of bleeding risk information.

[0010] A particular aspect of the present disclosure is a method for estimating bleeding risk, comprising: receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information by a processing device to a software-based system, the software-based system being programmed to implement an RTTE model [B] for calculating bleeding risk; receiving bleeding risk information calculated using the transmitted information in (b) and the RTTE model [B] from the software-based system; and transmitting bleeding risk information in (c) to output bleeding risk information by one or more electronic devices.

[0011] A particular aspect of the present disclosure is a method for estimating bleeding risk, comprising: receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information by a processing device to a software-based system, the software-based system being programmed to implement an RTTE model [C] for calculating bleeding risk; receiving bleeding risk information calculated using the transmitted information in (b) and the RTTE model [C] from the software-based system; and transmitting the bleeding risk information in (c) to output bleeding risk information by one or more electronic devices.

[0012] Certain aspects of the present disclosure include receiving FVIII activity information by one or more electronic devices, and transmitting FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement a RTTE model [D1] to calculate a bleeding risk, transmitting, receiving from the software-based system the transmitted information of (b) and bleeding risk information calculated using the RTTE model [D1], and transmitting the bleeding risk information of (c) by one or more electronic devices to output the bleeding risk information. The present disclosure relates to a method for estimating a bleeding risk.

[0013] Certain aspects of the present disclosure include receiving FVIII activity information by one or more electronic devices, and transmitting FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement a RTTE model [D2] to calculate a bleeding risk, transmitting, receiving from the software-based system the transmitted information of (b) and bleeding risk information calculated using the RTTE model [D2], and transmitting the bleeding risk information of (c) by one or more electronic devices to output the bleeding risk information. The present disclosure relates to a method for estimating a bleeding risk.

[0014] Certain aspects of the present disclosure relate to a method for estimating the bleeding risk of an individual subject, the method comprising: receiving personalized subject information, including the subject's weight, by a software-based system; receiving desired treatment outcome information, including FVIII activity levels, by a software-based system; applying an RTTE model to the subject based on the personalized information and / or the desired treatment outcome information; estimating the bleeding risk of the individual subject using the RTTE model; and transmitting the estimated bleeding risk information of (d) for output of bleeding risk information by the software-based system. In some embodiments, the desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A]. In some embodiments, the desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A']. In some embodiments, the desired treatment outcome information is provided by the individual subject. In some embodiments, the individual subject has severe hemophilia A. In some embodiments, the desired treatment outcome information is provided by a healthcare professional. In some embodiments, the RTTE model is RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2].

[0015] Certain aspects of this disclosure relate to a method for treating a subject having hemophilia A, comprising: identifying a subject having hemophilia A; estimating the subject's bleeding risk when treated with 50 IU / kg efanesoctocog alfa approximately once a week; estimating the subject's bleeding risk if the subject has an FVIII activity level of at least 10 IU / dL; and treating the subject with 50 IU / kg efanesoctocog alfa approximately once a week if the subject has a lower estimated bleeding risk with efanesoctocog alfa therapy than the estimated bleeding risk if the subject has an FVIII activity level of at least 10 IU / dL. In some embodiments, the bleeding risk estimation is calculated using an RTTE model. In some embodiments, the RTTE model is RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2].

[0016] Certain aspects of the present disclosure relate to a method for estimating the bleeding risk of an individual subject, comprising: (a) receiving, by a software-based system, information regarding the individual subject, the system being programmed to: (i) execute a one-compartment efanesoctocog alfa popPK model for calculating FVIII activity information, the efanesoctocog alfa popPK model including body weight as a covariate and not including the level of VWF or the level of hematocrit as a covariate; and (ii) execute an RTTE model for estimating the bleeding risk using the FVIII activity information; (b) calculating, by the software-based system, an estimated bleeding risk using the efanesoctocog alfa popPK model, the RTTE model, and the received information; and (c) transmitting, by the software-based system, the calculated bleeding risk information of (b) for output of bleeding risk information. In some embodiments, the RTTE model is RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2]. In some embodiments, the individual subject has severe hemophilia A.

[0017] Specific aspects of the present disclosure are methods for estimating bleeding risk, comprising: (a) receiving information about individual subjects by one or more electronic devices; and (b) transmitting the information about individual subjects to a software-based system by a processing device, the software-based system being programmed to run a one-compartment efanesoctocog alpha popPK model for calculating FVIII activity information, wherein the efanesoctocog alpha popPK model includes body weight as a covariate and does not include VWF levels or hematocrit levels as covariates; and (ii) an RTTE model for estimating bleeding risk using the FVIII activity information; (c) receiving an estimated bleeding risk from the software-based system using the efanesoctocog alpha popPK model, the RTTE model, and the received information; and (d) transmitting the bleeding risk information from (c) to output bleeding risk information by one or more electronic devices. In some embodiments, the RTTE model is RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2]. In some embodiments, the individual subjects have severe hemophilia A.

[0018] In some embodiments, FVIII activity information is provided by a popPK model. In some embodiments, the popPK model is the efanesoctocog alfa popPK model. In some embodiments, the efanesoctocog alfa popPK model includes body weight as a covariate, but does not include VWF levels or hematocrit levels as covariates. In some embodiments, the popPK model is the efanesoctocog alfa popPK model [A]. In some embodiments, the popPK model is the efanesoctocog alfa popPK model [A'].

[0019] In some embodiments, the FVIII activity information provided pertains to individual subjects.

[0020] In some embodiments, the subject has severe hemophilia A.

[0021] In some embodiments, the subject is under 6 years old. In some embodiments, the subject is at least 6 years old. In some embodiments, the subject is under 12 years old. In some embodiments, the subject is at least 12 years old. In some embodiments, the subject is at least 18 years old. In some embodiments, the subject is under 18 years old.

[0022] In some embodiments, the provided FVIII activity information is the estimated FVIII activity level of the target population. In some embodiments, the provided FVIII activity information pertains to a hypothetical or hypothetical subject. In some embodiments, the provided FVIII activity information is the target FVIII activity level. In some embodiments, the provided FVIII activity information is a variable FVIII activity level over time. In some embodiments, the subject had, or has, an FVIII activity level of at least 10 IU / dL.

[0023] Certain aspects of the present disclosure relate to data processing devices, or systems including a processor configured to implement the efanesoctocogalpha RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2]. In some embodiments, the data processing devices, or systems are further configured to implement a one-compartment efanesoctocogalpha popPK model that includes body weight as a covariate, and the efanesoctocogalpha popPK model does not include VWF levels or hematocrit levels as covariates. In some embodiments, the popPK model is efanesoctocogalpha popPK model [A]. In some embodiments, the popPK model is efanesoctocogalpha popPK model [A'].

[0024] In some embodiments, the data processing device, device, or system includes a smartphone, tablet computer, personal digital assistant, handheld computer, laptop computer, or smartwatch.

[0025] Certain aspects of this disclosure relate to a computer program which, when the program is executed by a computer, includes instructions causing the computer to implement the efanesoctocog alfa RTTE model [B]. In some embodiments, the RTTE model is RTTE model [C]. In some embodiments, the RTTE model is RTTE model [D1]. In some embodiments, the RTTE model is RTTE model [D2]. In some embodiments, the RTTE model uses a PK profile generated from a popPK model (e.g., levels of FVIII activity over time) as input. In some embodiments, the popPK model is the efanesoctocog alfa popPK model. In some embodiments, the efanesoctocog alfa popPK model is a one-compartment efanesoctocog alfa popPK model that includes body weight as a covariate, and the efanesoctocog alfa popPK model does not include levels of VWF or hematocrit as covariates. In some embodiments, the popPK model is the efanesoctocog alfa popPK model [A]. In some embodiments, the popPK model is the ephanesoctocogalphapopPK model [A'].

[0026] Certain aspects of this disclosure relate to computer-readable media that, when executed by a computer, contains instructions causing the computer to perform any of the methods disclosed herein.

[0027] Certain aspects of this disclosure relate to a method for treating hemophilia A in a human subject requiring treatment for hemophilia A, comprising intravenous administration of ephanesoctocog alfa at a dose of approximately 25 IU / kg to approximately 50 IU / kg every approximately 4 to approximately 14 days, wherein the human subject is under 6 years of age and weighs 15 to 20 kg.

[0028] Certain aspects of this disclosure relate to a method for treating hemophilia A in a human subject requiring treatment for hemophilia A, comprising intravenous administration of ephanesoctocog alfa at a dose of approximately 25 IU / kg to approximately 50 IU / kg every approximately 4 to approximately 14 days, wherein the human subject is between 6 and 12 years of age and weighs 30 to 35 kg.

[0029] In some embodiments, efanesoctocog alfa is administered at a dose of approximately 25 IU / kg every four days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 30 IU / kg every seven days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every seven days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every fourteen days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 25 IU / kg every four days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 30 IU / kg every seven days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every seven days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every 14 days for at least approximately 52 weeks.

[0030] Certain aspects of this disclosure relate to a pharmaceutical composition comprising efanesoctocog alfa for use in treating hemophilia A in human subjects requiring treatment for hemophilia A, wherein efanesoctocog alfa is administered intravenously to the subject at a dose of approximately 25 IU / kg to approximately 50 IU / kg every approximately 4 to approximately 14 days during the period, and the human subject is under 6 years of age and weighs 15 to 20 kg.

[0031] Certain aspects of this disclosure relate to a pharmaceutical composition comprising efanesoctocog alfa for use in treating hemophilia A in human subjects requiring treatment for hemophilia A, wherein efanesoctocog alfa is administered intravenously to the subject at a dose of approximately 25 IU / kg to approximately 50 IU / kg every approximately 4 to approximately 14 days during the period, and the human subject is between 6 and under 12 years of age and weighs 30 to 35 kg.

[0032] In some embodiments, efanesoctocog alfa is administered at a dose of approximately 25 IU / kg every four days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 30 IU / kg every seven days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every seven days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every fourteen days. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 25 IU / kg every four days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 30 IU / kg every seven days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every seven days for at least approximately 52 weeks. In some embodiments, efanesoctocog alfa is administered at a dose of approximately 50 IU / kg every 14 days for at least approximately 52 weeks.

[0033] Certain aspects of this disclosure relate to a method for reducing the risk of a human subject requiring treatment for severe hemophilia A having trauma-free bleeding over a period of 52 weeks, reducing the risk to less than 50%, the method comprising intravenous administration of ephanesoctocog alfa at doses of approximately 25 IU / kg to approximately 50 IU / kg at intervals of approximately 4 to approximately 14 days during this period, the human subject being between 6 and 12 years of age and weighing 30 to 35 kg, thereby reducing the risk to less than 50%.

[0034] Certain aspects of this disclosure relate to a method for reducing the probability that a human subject requiring treatment for severe hemophilia A will have trauma-free bleeding within the next 52 weeks, reducing the risk to less than 50%, the method comprising intravenous administration of ephanesoctocog alfa at doses of approximately 25 IU / kg to approximately 50 IU / kg at intervals of approximately 4 to approximately 14 days during this period, the human subject being between 6 and 12 years of age and weighing 30 to 35 kg, thereby reducing the probability to less than 50%.

[0035] In some embodiments, the risk is reduced to a probability of less than 40%. In some embodiments, the risk is reduced to a probability of less than 30%. In some embodiments, bleeding not caused by trauma is considered spontaneous bleeding. As used herein, “spontaneous bleeding” is bleeding that occurs in the absence of known contributing factors such as clear trauma or preceding strenuous activity. In some embodiments, efanesoctocog alfa is administered at a dose of about 25 IU / kg approximately every 4 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 30 IU / kg approximately every 7 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 50 IU / kg approximately every 7 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 50 IU / kg approximately every 14 days during the period.

[0036] In certain aspects of this disclosure, the present disclosure relates to a pharmaceutical composition comprising efanesoctocog alfa for use in reducing the risk of trauma-free bleeding in a human subject having severe hemophilia A in need over a period of 52 weeks, wherein the risk is reduced to less than 50%, and the subject is administered intravenously to the subject at a dose of about 25 IU / kg to about 50 IU / kg approximately every 4 to about 14 days during the period, the human subject is between 6 and under 12 years of age, the subject weighs 30 to 35 kg, and the subject is administered intravenously, thereby reducing the probability to less than 50%.

[0037] In certain aspects of this disclosure, the present disclosure relates to a pharmaceutical composition comprising efanesoctocog alfa for use in reducing the risk of trauma-free bleeding in a human subject having severe hemophilia A in need over a 52-week period, wherein the risk is reduced to less than 50%, and the subject is administered intravenously at a dose of about 25 IU / kg to about 50 IU / kg of efanesoctocog alfa about every 4 to 14 days during the period, the human subject is between 6 and 12 years of age, the subject weighs 30 to 35 kg, and the probability is reduced to less than 50%.

[0038] In some embodiments, the risk is reduced to a probability of less than 40%. In some embodiments, the risk is reduced to a probability of less than 30%. In some embodiments, bleeding not caused by trauma is considered spontaneous bleeding. In some embodiments, efanesoctocog alfa is administered at a dose of about 25 IU / kg approximately every 4 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 30 IU / kg approximately every 7 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 50 IU / kg approximately every 7 days during the period. In some embodiments, efanesoctocog alfa is administered at a dose of about 50 IU / kg approximately every 14 days during the period. [Brief explanation of the drawing]

[0039] [Figure 1] This figure shows a visual representation of a software-based system that can be used in the manner disclosed herein. [Figure 2] This figure shows a visual representation of an exemplary network-based system that can be used according to the methods disclosed herein. [Figure 3] This figure shows a schematic diagram of an exemplary computing system 400. [Figure 4]This graph shows steady-state FVIII activity simulated over time in patients aged 12 years and older using the ephanesoctocog alfa population PK model [A] (50 IU / kg), based on clinical data obtained from a one-step coagulation assay. The FVIII activity observed from clinical data is also shown. The solid line represents the simulated median FVIII activity (IU / dL). The dashed lines represent the simulated 5th and 95th percentiles. [Figure 5A] This graph shows the baseline-adjusted FVIII activity time profile from the adult / adult study XTEND-1 (EFC16293, NCT04161495) on day 1. Data are shown for all patients. LLOQ = 1 IU / dL [Figure 5B] This graph shows the baseline-adjusted FVIII activity time profiles from the adult / adult study XTEND-1 at day 1 (solid line) and week 26 (dashed line). Data are shown only for patients with continuous arms. [Figure 6] This figure shows the correlations between four continuous covariates at baseline. WTKGB is baseline weight (median 78.3, excluding EFC16295). BH is baseline race (median 43, excluding EFC16295). BVWF is baseline VWF (median 112, excluding EFC16295). [Figures 7A-7B] These graphs show the population prediction (PRED) (Figure 7A) and individual prediction (IPRED) (Figure 7B) paired with the observed values ​​(DV; data values) of one-stage FVIII activity using the ephanesoctocogalpha population PK model [A]. The black line is the unified line. The gray line is the Loess smoothed line. For the population prediction in Figure 7A, R² = 0.92. For the individual prediction in Figure 7B, R² = 0.97. [Figure 8]This figure shows a set of graphs demonstrating the performance of the ephanesoctocogalpha population PK model [A] using visual predictive checks (VPCs). White circles represent observed data. Solid lines represent the model simulation median. Dashed lines represent the simulated 5th and 95th percentiles of the model. Shading around each dashed line represents the 90% CI around the simulated 5th and 95th percentiles. Shading around each solid line represents the 90% CI around the simulation median. [Figure 9A-9B] This figure demonstrates PRED (Figure 9A) and IPRED (Figure 9B) vs. DV for surgery using the Efanesoctocog-Alpha population PK model [A]. The gray line is the unified line. R² is shown as the black line, which is the regression line between observed and predicted values. [Figure 10] This is a set of graphs showing the distribution of time to steady-state Ctrough, Cmaxss, and 40 IU / dL FVIII activity levels across body weight. [Figure 11] This is a set of graphs showing the distribution of time to steady-state Ctrough, Cmaxss, and 40 IU / dL FVIII activity levels across different age groups in non-Asian and Asian populations. [Figure 12] This graph shows the time-course OSC FVIII activity for major surgery and massive bleeding in patients aged 6 years and under. The solid gray line represents the simulated median for a 30 IU / kg dose. The gray line with a circle represents the simulated median for a 50 IU / kg dose. The solid black lines represent the simulated 5th and 95th percentiles. The dashed lines show OSC activity at 80 IU / kg and 40 IU / kg. [Figure 13] This graph shows the simulated FVIII activity of ephanesoctocog alfa at doses of 50 IU / kg followed by 30 IU / kg, administered every three days up to day 14, in a hypothetical adult and adolescent population. [Figure 14]This graph shows the simulated OSC FVIII activity over time for all age groups. [Figure 15] This graph shows Kaplan-Meier plots for patients in arms A and B of the XTEND-1 trial who did not experience bleeding events. [Figure 16] This is a set of graphs showing the Kaplan-Meier visual prediction test for the final RTTE model of the preventive arm in the XTEND-1 trial. [Figure 17] This graph shows the probability of a bleeding event over one year in a typical patient, as generated by the RTTE model. [Figure 18] This figure shows a set of histograms illustrating the baseline distribution of covariates in patients in the pediatric BIVV001 RTTE analysis dataset. In each graph, the median is shown as a solid vertical line on the left, and the mean is shown as a dotted vertical line on the right. [Figure 19] This is the correlation matrix of covariates at baseline for patients in the pediatric BIVV001 RTTE analysis dataset. In this plot, all covariates (continuous and categorical) are treated as continuous. The correlation coefficients, presented in the lower left of the matrix, are also visualized as ellipses in the upper right, where the width of the ellipse corresponds to the absolute value of the correlation coefficient, and the slope of the ellipse indicates a positive or negative correlation. Three ellipses representing negative values ​​are further distinguished by dark borders. The correlation between each pair of variables is calculated using all individuals with observations of both variables. [Figure 20] This is a set of graphs showing the Kaplan-Meier visual prediction check of the final pediatric RTTE model applied to the pediatric BIVV001 RTTE dataset (n=74). Solid and dashed lines represent the Kaplan-Meier point estimates and 95% confidence intervals for the observed data, respectively, and the shaded areas represent the 95% confidence intervals for the Kaplan-Meier point estimates based on 400 iteration simulations. [Figure 21] This figure shows a set of graphs comparing predicted FVIII activity with observed FVIII activity for the EFC16295 and LTS16294 studies. Triangulated lines are unified lines, and dotted lines are loosess smoothed lines. [Figure 22] This is a set of graphs showing conditionally weighted residuals (CWRES) versus time and CWRES versus PRED. The gray lines are Loess smoothing lines. [Figure 23] This is a set of graphs showing individual weighted residuals (IWRES) versus time and IWRES versus IPRED. The gray lines are Loess smoothing lines. [Figure 24] This graph shows the Visual Prediction Check (VPC) of the final popPK model from the EFC16295 trial. [Figure 25] This is a set of graphs showing predicted versus observed FVIII activity during and after surgery (postoperative follow-up). Lines with triangles are unified lines, and dotted lines are Loess smoothed lines. [Figure 26] This graph shows steady-state AUCtau, Cmax, and Cmin using baseline VWF for evaluation of endogenous / exogenous factors. The gray line is the Loess smoothing line. White circles represent children over 1 year old but under 6 years old. Black circles represent children 6 years old and over but under 12 years old. [Figure 27] This graph shows steady-state AUCtau, Cmax, and Cmin using baseline body weight for evaluation of endogenous / exogenous factors. The gray line is a Loess smoothed line. White circles represent children over 1 year old but under 6 years old. Black circles represent children 6 years old and over but under 12 years old. [Figure 28] This graph shows steady-state AUCtau, Cmax, and Cmin using baseline hematocrit for evaluation of endogenous / exogenous factors. The gray line is the Loess smoothed line. White circles represent children over 1 year old but under 6 years old. Black circles represent children 6 years old and over but under 12 years old. [Modes for carrying out the invention]

[0040] With the emergence of extended half-life replacement products, therapeutic goals have expanded beyond targeting low annual bleeding rates (ABRs) to include long-term outcomes associated with high, sustained plasma FVIII activity levels, such as long-term joint protection. Ephanesoctocog alfa circulates independently of endogenous von Willebrand factor (VWF) and provides high, sustained FVIII activity (see, for example, Chhabra, et al. Blood. 2020;135(17):1484-1496, Konkle et al., N Engl J Med 2020;383:1018-1027, and von Drygalski et al., N Engl J Med 2023;388:310-318 (referring to efanesoctocog alfa as BIVV001) (the entirety of each of these is incorporated herein by reference for all purposes)).

[0041] This disclosure provides, in particular, a treatment method and a software-based system for estimating individual subject efanesoctocog alfa PK information. In some embodiments, the software-based system applies an efanesoctocog alfa population PK model [A] to estimate dose information for subjects receiving efanesoctocog alfa as FVIII replacement therapy.

[0042] This disclosure also provides, among other things, therapeutic methods and software-based systems for estimating or quantifying the risk of bleeding associated with different levels of FVIII activity, for example, over time. In some embodiments, the software-based system may use an ephanesoctocog alfa population pharmacokinetic model when quantifying the risk of bleeding with high sustained FVIII activity compared to standard care.

[0043] definition The term “approximately” is used herein to mean roughly, broadly, before or after, or within that range. When the term “approximately” is used in conjunction with a numerical range, it modifies the range by extending the upper and lower boundaries of the stated numerical value. Generally, the term “approximately” can modify numerical values ​​above and below the stated value by, for example, 10 percent, or by a higher or lower variance. In some embodiments, the term indicates a deviation of ±10%, ±5%, ±4%, ±3%, ±2%, ±1%, ±0.9%, ±0.8%, ±0.7%, ±0.6%, ±0.5%, ±0.4%, ±0.3%, ±0.2%, ±0.1%, ±0.05%, or ±0.01% from the stated numerical value. In some embodiments, “approximately” indicates a deviation of ±10% from the stated numerical value. In some embodiments, “approximately” indicates a deviation of ±5% from the stated numerical value. In some embodiments, “approximately” indicates a deviation of ±4% from the stated numerical value. In some embodiments, "approximately" indicates a deviation of ±3% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±2% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±1% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.9% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.8% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.7% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.6% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.5% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.4% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.3% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.1% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.05% from the indicated value. In some embodiments, "approximately" indicates a deviation of ±0.01% from the indicated value.

[0044] Whenever an aspect is described herein in the language of “including,” it is understood that other similar aspects are also provided, described in the terms “consisting of” and / or “essentially consisting of.”

[0045] As used herein in the context of hemophilia A, the term “prophylactic treatment” refers to the prior administration of therapy for the treatment of hemophilia A, such treatment intended to prevent or reduce the severity of one or more symptoms of hemophilia A, e.g., bleeding episodes such as one or more sudden bleeding episodes, and / or joint injury. To prevent or reduce the progression of such symptoms, e.g., bleeding episodes and joint disease, patients with hemophilia A may receive regular infusions of clotting factors (e.g., ephanesoctocog alfa) as part of a prophylactic treatment regimen.

[0046] The terms "on-demand treatment" or "on-the-spot treatment" refer to the administration of FVIII replacement therapy (such as ephanesoctocog alfa) "as needed" in response to symptoms of hemophilia A, such as a bleeding episode (e.g., a sudden bleeding episode or a traumatic bleeding episode), or before an activity that may cause bleeding. In some embodiments, on-demand treatment may be administered to subjects when bleeding begins, such as after an injury, or when bleeding is anticipated, such as before surgery. In some embodiments, on-demand treatment may be administered before activities that increase the risk of bleeding, such as contact sports. In some embodiments, on-demand treatment may be administered to subjects receiving prophylactic treatment, for example, to treat a bleeding episode or when a supplemental FVIII replacement protein dose is administered before strenuous activity. In some embodiments, on-demand treatment is given as a single dose. In some embodiments, on-demand treatment is given as a first dose followed by one or more additional doses. In some embodiments, the on-demand regimen is for the perioperative management of bleeding.

[0047] In some embodiments, a bleeding episode begins with the first sign of bleeding and ends 72 hours after the last treatment of the bleeding, and any symptom of bleeding at the same site, or an injection within 72 hours, is considered the same bleeding episode. See Blanchette V. (2006) Haemophilia 12:124-7. In some embodiments, any injection to treat a bleeding episode is given more than 72 hours after a preceding injection and is considered the first injection to treat a new bleeding episode at the same site. In some embodiments, any bleeding at different sites is considered a separate bleeding episode, regardless of the time elapsed since the last injection.

[0048] The methods provided herein can be applied to subjects requiring prophylactic or on-demand treatment. In some embodiments, subjects requiring prophylactic or on-demand treatment are those suffering from hemorrhagic arthritis, muscle bleeding, oral bleed, bleeding, intramuscular bleeding, oral hemorrhage, trauma, head trauma, gastrointestinal bleeding, intracranial hemorrhage, intraperitoneal hemorrhage, intrathoracic hemorrhage, fracture, central nervous system bleeding, hemorrhage in the retropharyngeal space, hemorrhage in the retroperitoneal space, or hemorrhage in the iliopsoas sheath. In some embodiments, subjects require treatment for surgery, including, for example, surgical prophylaxis or perioperative management. In some embodiments, the surgery may be a minor or major surgery. Exemplary surgical treatments include tooth extraction, tonsillectomy, inguinal hernia incision, synovectomy, craniotomy, osteosynthesis, trauma surgery, intracranial surgery, intraperitoneal surgery, intrathoracic surgery, joint replacement surgery (e.g., total knee replacement, hip replacement, etc.), cardiac surgery, and cesarean section.

[0049] As used herein in the context of hemophilia A, “to treat” and “to treat” include, for example, reducing the severity of hemophilia A; improving one or more symptoms associated with hemophilia A; providing a beneficial effect to a person having hemophilia A without necessarily curing hemophilia A; and / or preventing one or more symptoms associated with hemophilia A.

[0050] In some embodiments, treatment for hemophilia A includes the prevention of one or more symptoms of hemophilia A (e.g., spontaneous bleeding). In some embodiments, treatment for hemophilia A includes reducing the likelihood of bleeding episodes / events or reducing the severity of bleeding episodes / events. In some embodiments, treatment is prophylactic. In some embodiments, treatment is on-demand. In some embodiments, treatment includes reducing the frequency of one or more symptoms of hemophilia A, such as spontaneous or uncontrolled bleeding episodes.

[0051] As used herein, the term “perioperative management” means the use of efanesoctocog alfa before, during, or after a surgical procedure, such as surgery. Use for “perioperative management” of one or more bleeding episodes includes preoperative (i.e., pre-operative), intraoperative (i.e., intra-operative), or postoperative (i.e., post-operative) surgical prophylaxis to prevent one or more bleeding or bleeding episodes, or to reduce or inhibit preoperative, intraoperative, and postoperative idiopathic and / or uncontrolled bleeding episodes.

[0052] As used herein, “baseline” plasma FVIII activity level is the lowest measured plasma FVIII activity level in a subject before dose administration. In some embodiments, activity above baseline before administration can be considered residual FVIII activity from a previous treatment, which can be decayed over time using the half-life of the previous treatment and subtracted from PK data after efanesoctocog alfa administration.

[0053] The terms “patient” and “subject” are used interchangeably herein and refer to human beings. A subject may include, for example, an individual diagnosed with hemophilia A and susceptible to idiopathic and / or uncontrolled bleeding episodes. A subject may also include an individual at risk of one or more uncontrolled bleeding episodes prior to certain activities, such as surgery, sports activities, or any strenuous activity. In some embodiments, a subject has a baseline FVIII activity of less than 0.5%, less than 1%, less than 2%, less than 2.5%, less than 3%, or less than 4%. In some embodiments, a subject has severe hemophilia A, defined as an endogenous FVIII activity of less than 1 IU / dL (less than 1%). In some embodiments, a subject has no coagulation disorders other than hemophilia A.

[0054] As used herein, the terms “ELNN polypeptide” and “ELNN” are synonymous and refer to an elongated polypeptide containing a substantially non-repeating sequence (e.g., polypeptide motif) that is not naturally occurring and consists mainly of small hydrophilic amino acids, having a low degree of secondary or tertiary structure under physiological conditions, or having a sequence that does not have secondary or tertiary structure. ELNN polypeptides include unstructured hydrophilic polypeptides containing a repeating motif of six natural amino acids (G, A, P, E, S, and / or T). In some embodiments, ELNN polypeptides contain multiple motifs of the six natural amino acids (G, A, P, E, S, T), the motifs being the same or a combination of different motifs. In some embodiments, when ELNN polypeptides are linked to proteins including T cell engagers disclosed herein, they can confer certain desirable pharmacokinetic, physicochemical, and pharmaceutical properties. Such desirable properties may include, but are not limited to, improvements in pharmacokinetic parameters and solubility properties, as well as improvements in therapeutic index. ELNN polypeptides are known in the art, and a non-limiting description and examples of ELNN polypeptides known as XTEN polypeptides are available in Schellenberger et al., (2009) Nat Biotechnol 27(12):1186-90; Brandl et al., (2020) Journal of Controlled Release 327:186-197; and Radon et al., (2021) Advanced Functional Materials 31, 2101633 (pages 1-33) (the full contents of each of these are incorporated herein by reference).

[0055] As used herein, “software-based system” means an algorithm or set of algorithms that can be implemented by one or more processing devices. A software-based system may be implemented by software including, but not limited to, firmware, resident software, microcode, etc., and may take the form of one or more computer program products accessible from one or more computer-enabled or computer-readable media that provide program code for use by or in connection with a computer or any instruction execution system. The software may be implemented across several processing devices that collectively constitute a software-based system. For the purposes of this description, a computer-enabled or computer-readable medium may be any device that can house, store, communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system (or apparatus or device) or a propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer diskettes, random-access memory (RAM), read-only memory (ROM), rigid magnetic disks, and optical discs including compact disc read-only memory (CD-ROM), compact disc read / write (CD-R / W), and DVDs. Non-limiting examples of software-based systems include network-based systems and web-based systems.

[0056] As used herein, the term “processing device” refers to a data processing system suitable for storing and / or executing program code for implementing a software-based system, and may include at least one processor directly or indirectly coupled to a memory element via a system bus or other interface. The processor, i.e., the electronic circuitry that executes the instructions constituting the program code, may be instantiated by a microprocessor, microcontroller, multicore processor, array of processors, or vector processor, and may be embodied across one or more physical devices. The memory element may include local memory used during the actual execution of the program code, mass storage, cloud storage, and cache memory providing temporary storage for at least some of the program code to reduce the number of times the code must be retrieved from mass storage during execution. Input / output or I / O devices (including, but not limited to, keyboards, displays, pointing devices, touchscreens, audio, etc.) may be coupled to the system directly or via an intervening I / O controller. Network adapters may also be coupled to the system via an intervening private or public network to allow the processing device to be coupled to other processing devices or remote printers or storage devices. Modems, cable modems, and Ethernet cards are just a few of the types of network adapters currently available. The processing device may also be a shared data processing system, such as a network-based (e.g., web-based) server system accessible via a network such as the Internet, which can access and execute program code to implement a software-based system.

[0057] Description of Ephanesoctocogalpha Ephanesoctocog alfa is listed in Chhabra et al. Blood 2020;135(17):1484-1496, Konkle et al., N Engl J Med 2020;383:1018-1027, von Drygalski et al., N Engl J Med 2023;388:310-318, and the International Nonproprietary Names for Pharmaceutical Substances (INN) WHO Drug Information, 2019, Vol.33, No.4, pp.828-30, the entire contents of each are incorporated herein by reference. Ephanesoctocog alfa temporarily replaces the deficient FVIII necessary for effective hemostasis in patients with FVIII deficiency. Ephanesoctocogalpha comprises a first polypeptide containing the amino acid sequence of SEQ ID NO: 3, covalently bonded to a second polypeptide containing the amino acid sequence of SEQ ID NO: 6, with the first and second polypeptides covalently bonded to each other via disulfide bonds. Ephanesoctocogalpha can be produced, for example, by recombinant DNA technology in human fetal kidney (HEK) cell lines. For example, the cell lines can express rFVIIIFc-ELNN polypeptide (SEQ ID NO: 1), rVWF-ELNN-Fc polypeptide (SEQ ID NO: 4), and soluble PACE enzyme. Non-limiting examples of nucleotide sequences encoding rFVIIIFc-ELNN polypeptide (SEQ ID NO: 2) and rVWF-ELNN-Fc polypeptide polypeptide (SEQ ID NO: 5) can be found in Table A below. The amino acid sequences of rFVIIIFc-ELNN polypeptide (SEQ ID NO: 3) without the signal peptide and rVWF-ELNN-Fc polypeptide (SEQ ID NO: 6) without the signal peptide or the D1D2 portion of VWF can be found in Table A below.

[0058] In some embodiments for subjects receiving prophylactic treatment with efanesoctocog alfa, the methods disclosed herein may be used to determine individualized subject information (e.g., the subject's plasma FVIII levels at a particular time point or set of time points). Based on this individualized subject information, the dose and / or dose interval of efanesoctocog alfa can be adjusted to achieve individualized treatment goals, such as a minimum plasma FVIII activity level (e.g., trough).

[0059] In some embodiments, if bleeding in a subject receiving on-demand treatment with efanesoctocog alfa is not controlled or is insufficiently controlled after administration of efanesoctocog alfa at the initial recommended dose and dose interval, individual subject dose information can be determined using the method disclosed herein. Based on this individual subject dose information, the dose and / or dose interval of efanesoctocog alfa can be adjusted to achieve improved bleeding control. In some embodiments, the dose and / or dose interval are adjusted to achieve a desired estimated bleeding risk.

[0060] In some embodiments, the minimum FVIII level between the target doses can be estimated using the methods disclosed herein.

[0061] In some embodiments, if clinically indicated, the plasma of the subject may be monitored for FVIII activity levels, for example, using a one-step coagulation assay, to confirm that appropriate FVIII levels are achieved and maintained. FVIII activity can be measured by any known method in the art.

[0062] The aPTT test is a performance index that measures the efficacy of the “endogenous” coagulation pathway (also known as the contact-activated pathway) and the general coagulation pathway. This test is commonly used to measure the coagulation activity of commercially available recombinant coagulation factors, such as FVIII. It is typically used in conjunction with prothrombin time (PT) to measure the exogenous pathway. (See, for example, Kamal et al., Mayo Clin Proc., 82(7):864-873 (2007)). In some embodiments, the aPTT assay uses actin FSL as the reagent. In some embodiments, the aPTT assay does not use Actin FS as the reagent. In some embodiments, the assay is used to test aPTT, where FVIII activity is measured using Dade® Actin® FSL Activated PTT Reagent (Siemens Health Care Diagnostics) on a BCS® XP analyzer (Siemens Health Care Diagnostics).

[0063] In some embodiments, the aPTT assay may also be used to evaluate the potency of a chimeric polypeptide before administration to a subject (Hubbard AR, et al. J Thromb Haemost 11:988-9 (2013)). In some embodiments, the aPTT assay may be further used either before or after administration to a subject in conjunction with any of the assays described herein.

[0064] In some embodiments, the models provided herein provide FVIII activity calculations and information corresponding to the activity measured by the aPTT test. For example, the ephanesoctocog alfa popPK model [A] provides FVIII activity calculations and information corresponding to the activity measured by the aPTT test.

[0065] Target PK and drug administration information We developed the efanesoctocog alfa popPK model using pooled FVIII activity data from Phase 1 / 2a and Phase 3 trials in adult, adolescent, and pediatric patients with hemophilia A, where PK information (FVIII activity) was well characterized by a one-compartment-based structural model with linear exclusion. In the popPK model, clearance (CL) and central compartment (V) volume were body weight-dependent, while Asian race was identified as a significant covariate for CL. The efanesoctocog alfa popPK model [A] is represented as follows:

number

[0066] The abbreviations for the ephanesoctocog alfa popPK model [A] (also referred to herein as the “popPK model [A]”) are as follows: CL, clearance from the central compartment; TVCL, typical clearance estimate; WT, body weight (kg); Asian, indicator for Asian race (0 for non-Asian, 1 for Asian); η1, variability relative to CL; V, volume of the central compartment; TVV, typical volume estimate; η2, variability relative to V; k, removal rate from the central compartment; rate, rate of infusion; A1, amount of one-step (OS) FVIII activity in the central compartment; C, one-step (OS) FVIII activity in the central compartment.

[0067] A version of the popPK model [A] that includes parameters useful for patients of all ages may be described as the popPK model [A'].

number

[0068] In some embodiments, the EfanesoctocogAlpha model [A] is used to obtain PK information for subjects under 6 years of age. In some embodiments, the EfanesoctocogAlpha model [A] is used to obtain PK information for subjects under 12 years of age. In some embodiments, the EfanesoctocogAlpha model [A] is used to obtain PK information for subjects at least 12 years of age. In some embodiments, the EfanesoctocogAlpha model [A] is used to obtain PK information for subjects at least 18 years of age.

[0069] In some embodiments, the ephanesoctocogalpha model [A'] is used to obtain PK information for subjects under 6 years of age. In some embodiments, the ephanesoctocogalpha model [A'] is used to obtain PK information for subjects under 12 years of age. In some embodiments, the ephanesoctocogalpha model [A'] is used to obtain PK information for subjects at least 12 years of age. In some embodiments, the ephanesoctocogalpha model [A'] is used to obtain PK information for subjects at least 18 years of age.

[0070] In some embodiments, PK information is used in the RTTE model provided herein.

[0071] In some embodiments, the efanesoctocog alfa model [A] is used to obtain dose information for subjects under 6 years of age. In some embodiments, the efanesoctocog alfa model [A] is used to obtain dose information for subjects under 12 years of age. In some embodiments, the efanesoctocog alfa model [A] is used to obtain dose information for subjects at least 12 years of age. In some embodiments, the efanesoctocog alfa model [A] is used to obtain dose information for subjects at least 18 years of age.

[0072] In some embodiments, the efanesoctocog alfa model [A'] is used to obtain dose information for subjects under 6 years of age. In some embodiments, the efanesoctocog alfa model [A'] is used to obtain dose information for subjects under 12 years of age. In some embodiments, the efanesoctocog alfa model [A'] is used to obtain dose information for subjects at least 12 years of age. In some embodiments, the efanesoctocog alfa model [A'] is used to obtain dose information for subjects at least 18 years of age.

[0073] Some embodiments involve administering a certain dose of efanesoctocog alfa to a human subject in need at a dosing interval, wherein the dose and / or dosing interval is determined using the subject's weight and / or self-reported race, but not using the subject's VWF or hematocrit value. The Disclosure provides a method for administering a certain dose of efanesoctocog alfa to a human subject in need at a dosing interval, wherein the dose and / or dosing interval is determined by applying the efanesoctocog alfa model [A] disclosed herein. In some embodiments, the efanesoctocog model [A'] is used.

[0074] In some embodiments, the therapeutically effective dose of ephanesoctocog alfa is approximately 50 IU / kg. In some embodiments, subjects are administered a dose of approximately 50 IU / kg once a week. In some embodiments, subjects are administered a dose of approximately 50 IU / kg once every 7 days. In some embodiments, subjects are administered an initial dose of approximately 50 IU / kg, followed by either 50 IU / kg or 30 IU / kg every 2-3 days as needed.

[0075] In some embodiments, the methods disclosed herein are applied to determine the individualized interval prophylaxis for the subject. As used herein, the term “individualized interval prophylaxis” means the use of efanesoctocog alfa for an individualized dose and / or dosing interval or frequency to prevent or inhibit the occurrence of one or more idiopathic and / or uncontrolled bleeding or bleeding episodes, or to reduce the frequency of one or more idiopathic and / or uncontrolled bleeding or bleeding episodes.

[0076] In some embodiments, the therapeutic goal of the subject includes achieving high FVIII plasma activity levels and / or high trough levels. As used herein, “trough level” in a hemophilia subject is the lowest concentration measurement achieved by factor therapy, e.g., efanesoctocog alfa therapy, before the next dose is administered. The methods disclosed herein can be used to determine the dosing information of a subject to achieve a specific FVIII plasma activity level and / or trough level. Administration of efanesoctocog alfa has been shown to achieve high FVIII plasma activity levels and / or high trough levels without issue in hemophilia A subjects.

[0077] In some embodiments, administration of efanesoctocog alfa results in an FVIII activity level of 40% or higher in a subject for about 1, 2, 3, or 4 days. In some embodiments, administration of efanesoctocog alfa results in a higher FVIII activity level in a subject for about 1, 2, 3, or 4 days. In some embodiments, administration of efanesoctocog alfa results in an FVIII activity level of at least 40% in a subject for at least 3 days. In some embodiments, administration of efanesoctocog alfa results in an FVIII activity level of at least 50% in a subject for about 4 days. In some embodiments, the efanesoctocog alfa model [A] is used to determine the dosing information for individual subjects in order to achieve an FVIII activity level of at least 40% in a subject for about 1, 2, 3, or 4 days.

[0078] Estimation of bleeding risk This specification includes an repeated time to event (RTTE) model for evaluating the bleeding risk in subjects with hemophilia A. In some embodiments, the therapeutic effect (e.g., prevention or on-demand treatment) is a covariate of the base hazards in the RTTE model.

[0079] In the XTEND-1 Phase 3 trial, bleeding events were recorded either on-demand or prophylactically (i.e., based on treatment efficacy) in subjects treated with efanesoctocog alfa. Using information on the subjects' bleeding profiles and the effect of FVIII activity (from efanesoctocog alfa administration), exemplary RTTE models were developed to identify demographic or disease history parameters that influence bleeding profiles.

[0080] An RTTE model [B] for estimating or predicting bleeding risk is provided herein. In some embodiments, the bleeding risk relates to subjects receiving FVIII replacement therapy. In some embodiments, the bleeding risk relates to subjects receiving FVIII replacement therapy with ephanesoctocog alfa. The RTTE model [B] is expressed as follows:

number

[0081] The abbreviations for the RTTE model [B] are as follows: h: hazard function of the Weibull distribution; t: time; λ: base hazard constant; γ: shape parameter of the base hazard; β: constant of the FVIII effect; C: FVIII activity; α: shape parameter of the FVIII effect; η: individual random effect. η varies with mean 0 and coefficient of variation 114%. Additional information regarding the RTTE model [B] is provided in Example 2.

[0082] In some embodiments, η=0, and the RTTE model [C] is obtained. The RTTE model [C] is expressed as follows:

number

[0083] The abbreviations for the RTTE model [C] are as follows: h: hazard function of the Weibull distribution; t: time; λ: base hazard constant; γ: shape parameter of the base hazard; β: constant of the FVIII effect; C: FVIII activity; α: shape parameter of the FVIII effect.

[0084] The instantaneous hazards from time 0 using the final RTTE model parameters are shown in Model / Equations [D] and [D1].

number

[0085] Abbreviations in equations [D], [D1], and [D2]: h, hazard function of the Weibull distribution; t, time in units of time; C pred (t), predicted FVIII activity at time t; the arm is the therapeutic effect, with arm 1 if the subject is receiving on-demand treatment and arm 0 if the subject is receiving prophylactic treatment. Equation [D1] includes parameters for patients at least 12 years old. Equation [D2] includes parameters for patients under 12 years old.

[0086] In some embodiments, the RTTE model includes therapeutic effect as a covariate. In some embodiments, the therapeutic effect is prevention. In some embodiments, the therapeutic effect is on-demand treatment. In some embodiments, the method includes the use of the RTTE model [B]. In some embodiments, η = 0.

[0087] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of individual subjects.

[0088] In some embodiments, the RTTE model [C] is used to estimate the bleeding risk of individual subjects.

[0089] In some embodiments, the RTTE model [D] is used to estimate the bleeding risk of individual subjects.

[0090] In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of individual subjects. In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of individual subjects who are at least 12 years old. In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of individual subjects who are at least 18 years old.

[0091] In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of individual subjects. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of individual subjects under 12 years of age. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of individual subjects who are at least 6 years of age but under 12 years of age. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of individual subjects who are 6 years of age or younger.

[0092] In some embodiments, the RTTE model [B] is used to estimate the probability that a subject will have a bleeding event at a specific time (t). In some embodiments, the RTTE model [C] is used to estimate the probability that a subject will have a bleeding event within a certain period of time, for example, one month, three months, six months, one year, two years, or longer. In some embodiments, the RTTE model [B] is used to estimate the probability that a subject will have a bleeding event within one year.

[0093] In some embodiments, the FVIII activity (C) in the RTTE model [B] is determined using the popPK model [A] disclosed herein. In some embodiments, the popPK model [A] and the RTTE model [B] can be used sequentially to estimate the bleeding risk of the subject. In some embodiments, the risk of a bleeding event in a subject is predicted using the subject's weight and / or self-reported race, but not using the subject's VWF or hematocrit value.

[0094] In some embodiments, the FVIII activity (C) in the RTTE model [B] is not determined using the popPK model [A] disclosed herein.

[0095] In some embodiments, the FVIII activity (C) in the RTTE model [B] is provided by the subject. In some embodiments, the FVIII activity (C) is a selected FVIII activity level. In some embodiments, the FVIII activity (C) is a target FVIII activity level. In some embodiments, the target FVIII activity level is a variable FVIII activity level. In some embodiments, the target FVIII activity level is a constant FVIII activity level. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of an individualized subject at a selected FVIII activity level. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of an individualized subject at a target FVIII activity level.

[0096] In some embodiments, a desired treatment outcome is used in the RTTE model to estimate the bleeding risk. In some embodiments, the desired treatment outcome is provided by individual subjects.

[0097] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with high sustained FVIII activity. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with FVIII activity >40 IU / dL. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with FVIII activity >40 IU / dL for approximately 1, 2, 3, or 4 days. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with FVIII activity >10 IU / dL. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with FVIII activity >10 IU / dL for approximately 1, 2, 3, 4, 5, 6, or 7 days. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of subjects with a trough concentration of FVIII activity >10 IU / dL.

[0098] In some embodiments, the RTTE model [B] is used to determine the probability that a subject will be bleeding-free for one year with efanesoctocog alfa treatment. In some embodiments, efanesoctocog alfa treatment comprises efanesoctocog alfa once a week at a dose of approximately 50 IU / kg.

[0099] In some embodiments, bleeding risk information generated by the RTTE model [B] is used at least in part to select a treatment for hemophilia A subjects (e.g., a specific hemophilia A treatment and / or dose plan). Risk information generated by the RTTE model [B] with efanesoctocog alfa treatment can be compared with risk information using other hemophilia A treatments. Other hemophilia A treatments include, for example, treatment with continuous infusion regimens (CIRs), treatment with efanesoctocog alfa, treatment with recombinant coagulation factor substitutes other than efanesoctocog alfa, treatment with bispecific antibodies that bind to activated coagulation factor IX and coagulation factor X (e.g., emicizumab), or gene therapy for hemophilia A (e.g., baroctocogen roxaparvovec).

[0100] In some embodiments, disclosed herein are methods for treating subjects having hemophilia A, the methods comprising: a) identifying a subject having hemophilia A; b) estimating the bleeding risk of the subject if treated with 50 IU / kg efanesoctocog alfa approximately once per week; c) estimating the bleeding risk of the subject if treated with alternative hemophilia A therapy maintaining an FVIII activity level of at least 10 IU / dL; and d) treating the subject with 50 IU / kg efanesoctocog alfa approximately once per week if the estimated bleeding risk with efanesoctocog alfa therapy is lower than the estimated bleeding risk with alternative hemophilia A therapy.

[0101] In some embodiments, the RTTE model [B] can be used without inputting individual FVIII activity data.

[0102] In some embodiments, the RTTE model [B] can be used to estimate the bleeding risk in the target population. In some embodiments, the estimated bleeding risk is based on the population mean or median, rather than being individualized for a specific subject.

[0103] In some embodiments, the RTTE model [B] can be used to estimate the hypothetical or hypothetical bleeding risk.

[0104] In some embodiments, bleeding risk information generated by the RTTE model [B] is used to determine the additional benefit of efanesoctocog alfa treatment in individual subjects.

[0105] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of individual subjects.

[0106] Method, system, and storage medium for estimating individual bleeding risk information for a target subject. Methods for estimating (e.g., calculating, determining, or providing) the bleeding risk from efanesoctocog alfa treatment for individual subjects are included herein, the methods comprising (a) receiving subject information and / or desired treatment outcome information by a software-based system including a computer program programmed to operate on an efanesoctocog alfa RTTE model (e.g., efanesoctocog alfa RTTE model [B]), and b) calculating individual bleeding risk information for the subjects. In some embodiments, the program is also programmed to operate on an efanesoctocog alfa popPK model (e.g., efanesoctocog alfa popPK model [A]). One or more of the above steps may be performed using one or more of a software-based system, a network-based system, a computing system, or various combinations of the aforementioned systems. For example, an exemplary network-based system can be used to obtain estimated individual bleeding risk information for subjects.

[0107] In some embodiments, the methods disclosed herein further include receiving subject information by a software-based system. In some embodiments, the subject information includes age, self-reported race, and / or weight. In some embodiments, the subject information includes diagnostic (baseline) FVIII level, PK determination, time of PK sampling, medication history if PK samples are taken from multiple doses, actual dose, FVIII activity level, etc.

[0108] In some embodiments, the output information includes the estimated bleeding risk. In some embodiments, the output information includes the estimated bleeding risk at a specific time (t). In some embodiments, the output information includes the estimated risk of a bleeding event occurring. In some embodiments, the output information includes the estimated risk of a bleeding event occurring within one year. In some embodiments, the output information includes the estimated annual bleeding rate (ABR).

[0109] The system may comply with patient privacy laws. In some embodiments, the system may be encrypted, for example, with SSL. In some embodiments, the input information may be anonymized.

[0110] In some embodiments, the system includes a user help function.

[0111] In some embodiments, the method may be performed by, for example, the subject, a physician, a nurse, or another healthcare professional. In some embodiments, the method is performed by the subject.

[0112] Some embodiments include a computer-readable storage medium that, when executed by a processor, stores instructions causing the processor to perform one or more steps of the above method.

[0113] Some embodiments include a system comprising a processor and memory, the memory storing instructions that, when executed by the processor, cause the processor to perform one of the methods described above.

[0114] Users of the system or computer-readable storage medium may be, for example, the subject or caregiver, or a physician, nurse, or other medical professional.

[0115] In some embodiments, the subject information input to the system includes weight. In some embodiments, the subject information input to the system is self-reported race. In some embodiments, the subject information input to the system is FVIII activity level. In some embodiments, the subject information input to the system is target FVIII activity level. In some embodiments, the subject information input to the system is therapeutic effect (prevention or on-demand treatment). In some embodiments, the methods disclosed herein include electronic devices. Electronic devices may include, but are not limited to, devices having a processor and memory for executing and storing instructions. Electronic devices may also include a display and one or more computer input devices such as a keyboard, mouse, pad, touchscreen, microphone and / or joystick. In some embodiments, electronic devices are general-purpose computing devices and data communication devices such as digital pens, smartphones, smartwatches, tablet computers, personal digital assistants, handheld computers, laptop computers, point-of-sale devices, scanners, cameras and fax machines. Electronic devices may also have multiple processors and multiple shared or separate memory components. For example, electronic devices may be a clustered computing environment or server farm.

[0116] Alternatively, the electronic device may be a specialized data acquisition device, computing device, and communication device, such as a point-of-care (POC) device, that can receive target demographic information including vital signs and / or blood, including age and weight, characterizing values ​​including self-reported race. Blood characteristic values ​​may be received by the electronic device via a data communication channel, manual input, and / or by a diagnostic process performed by the electronic device. Diagnostic processes performed on a target blood sample in the device may include ultrasound measurements, impedance measurements, conductivity measurements, and / or optical measurements. The electronic device may be further configured to receive, detect, record, and / or transmit additional target information, including diagnostic (baseline) FVIII level, PK determination, PK sampling time, medication history if the PK sample is taken from multiple doses, actual dose, FVIII activity level, or therapeutic effect (prevention or on-demand treatment). The electronic device communicates with one or more network-based (e.g., web-based) application programs via one or more networks, such as the Internet. Similar to the electronic device, the network-based (e.g., web-based) application programs may be implemented using general-purpose computers, servers, or other devices capable of providing data to the electronic device. The electronic device can receive personalized subject ephanesoctocogalpha PK information from network-based (e.g., web-based) servers and programs. In some embodiments, the electronic device can assist in calculating the estimated bleeding risk for personalized subjects, populations, or other sources.

[0117] The methods and systems described herein may be implemented in or via a mobile device. Examples of mobile devices include navigation devices, mobile phones, smartphones, smartwatches, tablets, mobile personal digital information processing terminals, laptops, palmtops, netbooks, pagers, e-readers, and music players. These devices may include, apart from other components, storage media such as flash memory, buffers, RAM, ROM, and one or more computing devices. Computing devices associated with a mobile device may be adapted to execute program code, methods, and instructions stored therein. As another example, a mobile device may be configured to execute instructions in cooperation with other devices. A mobile device may communicate with a base station configured to connect to a server and execute program code. A mobile device may also communicate via a peer-to-peer network, a mesh network, or other communication network. The program code may be stored in a storage medium associated with the server and executed by a computing device embedded in the server. A base station may include a computing device and a storage medium. The storage medium may store program code and instructions executed by the computing device associated with the base station. In some embodiments, the methods and systems described herein relate to kits for collecting target information. Different embodiments of the kit may include different components, but an exemplary kit includes a diagnostic device such as processing and / or computing elements for acquiring information from a target, and a transmitting element for transmitting the target information to a computer device via a wired or wireless connection. The transmitting element in the kit may be configured to transmit the target information in real time when the device is in use, or the diagnostic information may be transmitted in response to commands from the user or provider. Any of the kit components, such as the main unit, may be configured as a hands-free unit or a handheld unit during use.

[0118] Exemplary computing environment for the disclosed methods and systems The various modeling techniques, dosage calculations, and estimations described herein may be implemented by software, firmware, hardware, or a combination thereof. Figure 1 shows an exemplary software-based computer system 1900 in which embodiments or parts thereof may be implemented as computer-readable code. In another embodiment, for efanesoctocog alfa, the modeling disclosed in the embodiments herein may be implemented in system 1900.

[0119] The computer system 1900 includes one or more processors, such as processor 1904. Processor 1904 is connected to a communication infrastructure 1906 (e.g., a bus or network).

[0120] The computer system 1900 includes main memory 1908, preferably random access memory (RAM), and may also include secondary memory 1910. According to the embodiment, user interface data may be stored in the main memory 1908, for example, but not limited to. The main memory 1908 may include, for example, a cache and / or static RAM and / or dynamic RAM. The secondary memory 1910 may include, for example, a hard disk drive and / or a removable storage drive. The removable storage drive 1914 may include a floppy disk drive, magnetic tape drive, optical disk drive, flash memory, etc. The removable storage drive 1914 reads from and / or writes to the removable storage unit 1916 in a well-known manner. The removable storage unit 1916 may include a floppy disk, magnetic tape, optical disk, etc., which is read from and written to by the removable storage drive 1914. As will be understood by those skilled in the art, the removable storage unit 1916 includes a computer-readable storage medium storing computer software and / or data.

[0121] The computer system 1900 may also include a display interface 1902. The display interface 1902 may be adapted to communicate with a display unit 1930. The display unit 1930 may include a computer monitor or similar means for displaying graphics, text, and other data received from the main memory 1908 via the communication infrastructure 1906. In an alternative embodiment, the secondary memory 1910 may include other similar means for enabling computer programs or other instructions to be loaded into the computer system 1900. Such means may include, for example, a removable storage unit 1922 and interface 1920. Examples of such means may include a program cartridge and cartridge interface, a removable memory chip (e.g., EPROM or PROM) and associated sockets, and other removable storage units 1922 and interfaces 1920 that enable the transfer of software and data from the removable storage unit 1922 to the computer system 1900.

[0122] The computer system 1900 may also include a communication interface 1924. The communication interface 1924 enables the transfer of software and data between the computer system 1900 and external devices. The communication interface 1924 may include a modem, a network interface (such as an Ethernet card or WiFi), a communication port, a PCMCIA slot and card, etc. The software and data transferred via the communication interface 1924 are in the form of signals that may be electronic, electromagnetic, optical, or other signals that can be received by the communication interface 1924. These signals are provided to the communication interface 1924 via a communication path 1926. The communication path 1926 carries the signals and may be implemented using wired or cable, optical fiber, telephone line, cellular link, WiFi, Bluetooth, RF link, or other communication channel.

[0123] In this specification, the term “computer-readable storage medium” is used generally to refer to non-temporary storage media such as the removable storage unit 1916, the removable storage unit 1922, and the hard disk installed in the hard disk drive 1912. The computer-readable storage medium may also refer to one or more memories, such as the main memory 1908 and the secondary memory 1910, which may be memory semiconductors (e.g., DRAM). These computer program products are means for providing software to the computer system 1900.

[0124] The computer program (also called computer control logic) is stored in main memory 1908 and / or secondary memory 1910. The computer program may also be received via the communication interface 1924 and stored in main memory 1908 and / or secondary memory 1910. When such a computer program is executed, it enables the computer system 1900 to implement the embodiments described herein. In particular, when the computer program is executed, it enables the processor 1904 to implement the processes of this disclosure, such as the specific methods described above. Thus, such a computer program represents a controller of the computer system 1900. If the embodiment uses software, the software is stored in the computer program product and can be loaded into the computer system 1900 using a removable storage drive 1914, interface 1920, or hard drive 1912.

[0125] Embodiments may relate to computer program products that include software stored on any computer-readable medium or distributed across several such mediums. When such software is executed on one or more processing devices, it causes the processing devices to operate as described herein. Embodiments may use any computer-usable or computer-readable medium. Examples of computer-readable storage mediums include, but are not limited to, non-temporary primary storage devices (e.g., any type of random-access memory) and non-temporary secondary storage devices (e.g., hard drives, floppy disks, CD-ROMs, ZIP disks, tapes, magnetic storage devices, and optical storage devices, MEMS, nanotechnology storage devices, etc.). Other computer-readable mediums include communication mediums (e.g., wired and wireless communication networks, local area networks, wide area networks, intranets, etc.).

[0126] Non-exclusive examples of software-based systems include network-based systems and web-based systems.

[0127] Figure 3 shows an example computing device 400 and an example mobile computing device 450 that can be used to implement the technology described herein. A software-based system, such as the one described in Figure 1, may be implemented in the computing device 400 or the mobile computing device 450. The computing device 400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The mobile computing device 450 is intended to represent various forms of mobile devices, such as personal digital assistants, tablets, mobile phones, smartphones, and other similar computing devices. The components, their connections and relationships, and their functions shown herein are intended to be illustrative only and are not intended to limit the implementations of the invention described herein and / or claimed herein.

[0128] The computing device 400 includes a processor 402, memory 404, storage device 406, a high-speed interface 408 connected to memory 404 and multiple high-speed expansion ports 410, and a low-speed interface 412 connected to low-speed expansion port 414 and storage device 406. Each of the processor 402, memory 404, storage device 406, high-speed interface 408, high-speed expansion ports 410, and low-speed interface 412 is interconnected using various buses and can be mounted on a common motherboard or in other ways as appropriate. The processor 402 processes instructions for execution within the computing device 400, including instructions stored in memory 404 or storage device 406, and can transmit and display graphical or other information of a GUI to an external input / output device such as a display 416 coupled to the high-speed interface 408. In other implementations, multiple processors and / or multiple buses can be used as appropriate, along with multiple memories and multiple types of memory. Multiple computing devices can also be connected, each providing a portion of the required operation (e.g., as a server bank, a cluster of blade servers, or a multiprocessor system).

[0129] Memory 404 stores information within the computing device 400. In some implementations, memory 404 is one or more volatile memory units. In some implementations, memory 404 is one or more non-volatile memory units. Memory 404 may also be another form of computer-readable medium, such as a magnetic disk or an optical disk.

[0130] The storage device 406 can provide large-capacity storage to the computing device 400. In some implementations, the storage device 406 may be or include a computer-readable medium such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, flash memory or other similar solid-state memory device, or an array of devices including a storage area network or other configuration. The computer program product may be clearly embodied in an information carrier. The computer program product may also include instructions that, when executed, perform one or more of the methods described above. The computer program product may also be clearly embodied in a computer-readable or machine-readable medium such as memory 404, the storage device 406, or memory on the processor 402.

[0131] The high-speed interface 408 manages bandwidth-intensive operations of the computing device 400, while the low-speed interface 412 manages bandwidth-intensive operations. Such function assignments are illustrative only. In some implementations, the high-speed interface 408 is coupled to memory 404, a display 416 (e.g., through a graphics processor or accelerator), and a high-speed expansion port 410 that can accept various expansion cards (not shown). In this implementation, the low-speed interface 412 is coupled to the storage device 406 and the low-speed expansion port 414. The low-speed expansion port 414 may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), but can be coupled to one or more input / output devices such as a keyboard, pointing device, scanner, or networking device such as a switch or router via a network adapter.

[0132] The computing device 400 can be implemented in numerous different forms, as shown in the figure. For example, it can be implemented multiple times as a standard server 420 or within a group of such servers. Furthermore, it can be implemented in a personal computer such as a laptop computer 422. It can also be implemented as part of a rack server system 424. Alternatively, components from the computing device 400 can be combined with other components in a mobile device (not shown), such as a mobile computing device 450. Each such device may include one or more computing devices 400 and mobile computing devices 450, and the entire system may consist of multiple computing devices communicating with each other.

[0133] The mobile computing device 450 includes, among other components, an input / output device such as a processor 452, memory 464, and a display 454, a communication interface 466, and a transceiver 468. The mobile computing device 450 may also be provided with storage devices such as a microdrive or other devices to provide additional storage. Each of the processor 452, memory 464, display 454, communication interface 466, and transceiver 468 is interconnected using various buses, and some of the components can be mounted on a common motherboard or in other ways as appropriate.

[0134] The processor 452 can execute instructions, including those stored in memory 464, within the mobile computing device 450. The processor 452 may be implemented as a chipset of a chip including several separate analog and digital processors. The processor 452 can be provided for, for example, controlling the user interface, applications run by the mobile computing device 450, and coordinating other components of the mobile computing device 450, such as wireless communication by the mobile computing device 450.

[0135] The processor 452 can communicate with the user via a control interface 458 and a display interface 456 coupled to the display 454. The display 454 may be, for example, a TFT (Thin Film Transistor Liquid Crystal) display, an OLED (Organic Light Emitting Diode) display, or other suitable display technology. The display interface 456 may include suitable circuitry for driving the display 454 to present graphical and other information to the user. The control interface 458 can receive commands from the user and translate them for submission to the processor 452. In addition, an external interface 462 can provide communication with the processor 452 to enable short-range communication between the mobile computing device 450 and other devices. The external interface 462 may provide, for example, wired communication in some implementations or wireless communication in other implementations, and multiple interfaces may be used.

[0136] Memory 464 stores information within the mobile computing device 450. Memory 464 may be implemented as one or more computer-readable media, one or more volatile memory units, or one or more non-volatile memory units. Extended memory 474 may also be provided to and connected to the mobile computing device 450 via an extended interface 472, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Extended memory 474 may provide additional storage space to the mobile computing device 450 or store applications or other information of the mobile computing device 450. Specifically, extended memory 474 may include instructions for performing or supplementing the above processes and may also include secure information. For example, extended memory 474 may be provided as a security module for the mobile computing device 450 and can be programmed with instructions that enable the secure use of the mobile computing device 450. Furthermore, a secure application may be provided via a SIMM card, along with additional information such as placing identification information on the SIMM card in a hack-proof manner.

[0137] The memory may include, for example, flash memory and / or NVRAM memory (non-volatile random access memory), as described below. In some implementations, the computer program product is explicitly embodied in the information carrier. When executed, the computer program product includes instructions that perform one or more of the methods described above. The computer program product may be a computer-readable or machine-readable medium such as memory 464, extended memory 474, or memory on the processor 452. In some implementations, the computer program product may be received as a propagated signal, for example, via a transceiver 468 or an external interface 462.

[0138] The mobile computing device 450 can communicate wirelessly via a communication interface 466, which may include digital signal processing circuits as needed. The communication interface 466 can provide communication under various forms or protocols, such as GSM voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Message Service), or MMS messaging (Multimedia Messaging Service), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), PDC (Personal Digital Cellular), WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS (General Packet Radio Service). Such communication may, for example, be conducted via a transceiver 468 using radio frequencies. In addition, short-range communication may be conducted using Bluetooth, WiFi, or other such transceivers (not shown). Furthermore, a GPS (Global Positioning System) receiver module 470 can provide the mobile computing device 450 with additional navigation and location-related radio data, which may be used as appropriate by applications running on the mobile computing device 450.

[0139] The mobile computing device 450 can also communicate via voice using an audio codec 460 that can receive voice information from a user and convert it into usable digital information. Similarly, the audio codec 460 can generate audible sounds for the user, for example, through a speaker in the handset of the mobile computing device 450. Such sounds may include sounds from voice calls, recorded sounds (e.g., voice messages, music files, etc.), and sounds generated by applications running on the mobile computing device 450.

[0140] The mobile computing device 450 can be implemented in numerous different forms, as shown in the figure. For example, it can be implemented as a mobile phone 480. It can also be implemented as part of a smartphone 482, a personal digital assistant, a tablet, or other similar mobile device.

[0141] Various implementations of the systems and technologies described herein may be realized as digital electronic circuits, integrated circuits, specially designed ASICs (application-specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementation forms may include implementations into one or more computer programs that are executable and / or interpretable on a programmable system comprising a storage system, at least one programmable processor which may be dedicated or general-purpose and coupled to receive data and instructions from at least one input device and at least one output device, and to transmit data and instructions.

[0142] These computer programs (also called programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, apparatus, and / or device (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including machine-readable medium that receives machine instructions as machine-readable signals. The term machine-readable signal refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0143] To provide user interaction, the systems and technologies described herein can be implemented on a computer having a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor), a keyboard, a pointing device (e.g., a mouse or trackball), and / or a touchscreen or other user interface, thereby enabling the user to provide input to the computer and the software-based system implemented on the computer. Other types of devices can be used similarly to provide user interaction; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic, voice, or tactile input.

[0144] The systems and technologies described herein may be implemented in a computing system that includes a backend component (e.g., as a data server), a middleware component (e.g., an application server), or a frontend component (e.g., a client computer having a graphical user interface or web browser that allows a user to interact with the implementation of the systems and technologies described herein), or in any combination of such backend, middleware, or frontend components. The components of the system may be interconnected by digital data communications (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the internet.

[0145] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via a communication network. The client-server relationship arises from computer programs running on each computer that have a client-server relationship with each other.

[0146] This Disclosure Embodiment This disclosure includes, but is not limited to, the following exemplary embodiments:

[0147] E1. A method for estimating bleeding risk, comprising: (a) receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an repeated time to event (RTTE) model [B]; (b) calculating bleeding risk using the RTTE model [B] and the received information by the computer program; and (c) transmitting the calculated bleeding risk information from (b) by the software-based system for output of bleeding risk information.

[0148] E2. A method for estimating bleeding risk, comprising: (a) receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices; (b) transmitting coagulation factor VIII (FVIII) information by a processing device to a software-based system, the software-based system being programmed to perform an iteration time to event (RTTE) model [B] for calculating bleeding risk; (c) receiving bleeding risk information from the software-based system calculated using the information transmitted in (b) and the RTTE model [B]; and (d) transmitting the bleeding risk information in (c) by one or more electronic devices to output bleeding risk information.

[0149] E3. The method according to Embodiment 1 or 2, wherein calculating the bleeding risk is equivalent to estimating the risk of a bleeding event occurring.

[0150] The method according to any one of Embodiments 1 to 3, wherein E4.FVIII activity information is provided by the popPK model.

[0151] The method according to Embodiment 4, wherein the E5.popPK model is the ephanesoctocogalphapopPK model.

[0152] E6. The method according to Embodiment 5, wherein the efanesoctocog alfa popPK model includes body weight as a covariate, and the efanesoctocog alfa popPK model does not include VWF levels or hematocrit levels as covariates.

[0153] The method according to Embodiment 5, wherein the E7.popPK model is the ephanesoctocogalphapopPK model [A].

[0154] The method according to Embodiment 5, wherein the E8.popPK model is the ephanesoctocogalphapopPK model [A'].

[0155] E9. The method according to any one of Embodiments 1 to 8, wherein the FVIII activity information provided pertains to individual subjects.

[0156] E10. The method according to any one of Embodiments 1 to 3, wherein the provided FVIII activity information is a target FVIII activity level.

[0157] E11. The method according to Embodiment 10, wherein the target FVIII activity level is a variable FVIII activity level.

[0158] E12. The method according to any one of Embodiments 1 to 8, wherein the FVIII activity information provided is the estimated FVIII activity level of the target population.

[0159] E13. The method according to any one of Embodiments 1 to 8, wherein the FVIII activity information provided relates to a hypothetical or hypothetical subject.

[0160] E14. The method according to any one of Embodiments 1 to 13, wherein the RTTE model is RTTE model [C].

[0161] E15. The method according to any one of Embodiments 1 to 13, wherein the RTTE model is RTTE model [D].

[0162] E16. The method according to any one of Embodiments 1 to 13, wherein the RTTE model is RTTE model [D1].

[0163] The method according to any one of Embodiments 1 to 13, wherein E17.RTTE model is RTTE model [D2].

[0164] E18. A method for estimating the bleeding risk of an individual subject, comprising: a) receiving individualized subject information, including the subject's weight, by a software-based system; b) receiving desired treatment outcome information, including FVIII level, by a software-based system; c) applying an iteration-to-event (RTTE) model to the subject based on the individualized information and / or desired treatment outcome information; d) estimating the bleeding risk of an individual subject using the RTTE model; and e) transmitting the estimated bleeding risk information from (d) for output of bleeding risk information by a software-based system.

[0165] E19. The method according to Embodiment 18, wherein the RTTE model includes therapeutic effect as a covariate.

[0166] E20. The method according to Embodiment 19, wherein the therapeutic effect is preventive treatment or on-demand treatment.

[0167] The method according to any one of embodiments 18 to 20, wherein E21.RTTE model is RTTE model [B].

[0168] The method according to any one of embodiments 18 to 20, wherein E22.RTTE model is RTTE model[C].

[0169] The method according to any one of embodiments 18 to 20, wherein E23.RTTE model is RTTE model [D].

[0170] The method according to any one of embodiments 18 to 20, wherein E24.RTTE model is RTTE model [D1].

[0171] The method according to any one of embodiments 18 to 20, wherein E25.RTTE model is RTTE model [D2].

[0172] E26. The method according to any one of Embodiments 18 to 25, wherein desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A].

[0173] E27. The method according to any one of Embodiments 18 to 25, wherein desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A'].

[0174] E28. The method according to any one of Embodiments 18 to 25, wherein desired treatment outcome information is provided by individual subjects.

[0175] E29. The method according to any one of embodiments 18 to 25, wherein desired treatment outcome information is provided by a healthcare professional.

[0176] E30. A method for treating a subject with hemophilia A, comprising: a) identifying a subject with hemophilia A; b) estimating the bleeding risk of the subject when treated with 50 IU / kg efanesoctocog alfa approximately once a week; c) estimating the bleeding risk of the subject if the subject has an FVIII activity level of at least 10 IU / dL; and d) treating the subject with 50 IU / kg efanesoctocog alfa approximately once a week if the subject has a lower estimated bleeding risk with efanesoctocog alfa therapy than the estimated bleeding risk if the subject has an FVIII activity level of at least 10 IU / dL.

[0177] E31. The method according to Embodiment 30, wherein an repeated time to event (RTTE) model is used to estimate the bleeding risk of efanesoctocog alfa therapy and / or the bleeding risk of the subject when the subject has an FVIII activity level of at least 10 IU / dL.

[0178] The method according to Embodiment 31, wherein E32.RTTE model is RTTE model [B].

[0179] The method according to Embodiment 31, wherein E33.RTTE model is RTTE model[C].

[0180] The method according to Embodiment 31, wherein E34.RTTE model is RTTE model [D].

[0181] The method according to Embodiment 31, wherein E35.RTTE model is RTTE model [D1].

[0182] The method according to Embodiment 31, wherein E36.RTTE model is RTTE model [D2].

[0183] E37. A method for estimating the bleeding risk of an individual subject, comprising: (a) receiving information about the individual subject by a software-based system, the system being programmed to run an iterative time to event (RTTE) model for estimating the bleeding risk of an individual subject, the system being

[0184] E38. A method for estimating bleeding risk, comprising (a) receiving information about individual subjects by one or more electronic devices, and (b) transmitting information about individual subjects to a software-based system by a processing device, the software-based system being (i) a one-compartment efanesoctocog alfa popPK model for calculating coagulation factor VIII (FVIII) activity information, wherein the efanesoctocog alfa popPK model includes body weight as a covariate, and the efanesoctocog alfa popPK model includes VWF level or hematocrit as a covariate. A method for estimating bleeding risk, comprising: (ii) transmitting a one-compartment ephanesoctocog alpha popPK model that does not include the level of ret; and (ii) transmitting an iterative time to event (RTTE) model that estimates bleeding risk using FVIII activity information; (ii) transmitting an estimated bleeding risk from a software-based system using the ephanesoctocog alpha popPK model, the RTTE model, and the received information; and (d) transmitting the bleeding risk information from (c) to output bleeding risk information by one or more electronic devices.

[0185] The method according to Embodiment 37 or 38, wherein E39.RTTE model is RTTE model [B].

[0186] The method according to Embodiment 37 or 38, wherein E40.RTTE model is RTTE model[C].

[0187] E41. The method according to Embodiment 37 or 38, wherein the RTTE model is RTTE model [D].

[0188] The method according to Embodiment 37 or 38, wherein E42.RTTE model is RTTE model [D1].

[0189] The method according to Embodiment 37 or 38, wherein E43.RTTE model is RTTE model [D2].

[0190] E44. The method according to any one of embodiments 38 to 43, wherein the efanesoctocogalphapopPK model is the efanesoctocogalphapopPK model [A].

[0191] E45. The method according to any one of embodiments 38 to 43, wherein the efanesoctocogalphapopPK model is efanesoctocogalphapopPK model [A'].

[0192] E46. The method according to any one of Embodiments 1 to 45, wherein the subject is at least 12 years of age.

[0193] E47. The method according to any one of Embodiments 1 to 45, wherein the subject is at least 18 years of age.

[0194] E48. The method according to any one of Embodiments 1 to 45, wherein the subject is under 12 years of age.

[0195] E49. The method according to any one of Embodiments 1 to 45, wherein the subject is under 6 years of age.

[0196] E50. The method according to any one of Embodiments 1 to 45, wherein the target age is 6 to 12 years old.

[0197] E51. A data processing device, device, or system including a processor configured to implement the EfanesoctocogalphaRTTE model.

[0198] E52. The data processing device, device, or system according to Embodiment 51, wherein the RTTE model is RTTE model [B].

[0199] E53. The data processing device, device, or system according to Embodiment 51, wherein the RTTE model is RTTE model [C].

[0200] E54. The data processing device, device, or system according to Embodiment 51, wherein the RTTE model is RTTE model [D].

[0201] E55. The data processing device, device, or system according to Embodiment 51, wherein the RTTE model is RTTE model [D1].

[0202] E56. The data processing device, device, or system according to Embodiment 51, wherein the RTTE model is RTTE model [D2].

[0203] E57. A data processing device or system, any one of embodiments 51 to 56, which is also configured to perform a one-compartment efanesoctocogalphapopPK model that includes body weight as a covariate, wherein the efanesoctocogalphapopPK model does not include VWF levels or hematocrit levels as covariates.

[0204] E58. A data processing device, device, or system according to any one of embodiments 51 to 57, wherein the EfanesoctocogalphapopPK model is EfanesoctocogalphapopPK model [A].

[0205] E59. A data processing device, device, or system according to any one of embodiments 51 to 57, wherein the efanesoctocogalphapopPK model is efanesoctocogalphapopPK model [A'].

[0206] E60. A data processing device, device, or system according to any one of embodiments 51 to 59, including a smartphone, tablet computer, personal digital assistant, handheld computer, laptop computer, or smartwatch.

[0207] E61. A data processing device, device, or system according to any one of embodiments 51 to 59, including a smartphone.

[0208] E62. A computer program which, when executed by a computer, includes instructions that cause the computer to perform an Iteration Time to an Ephanesoctocogalpha event (RTTE) model.

[0209] The computer program according to Embodiment 62, wherein the E63.RTTE model is RTTE model [B].

[0210] The computer program according to Embodiment 62, wherein the E64.RTTE model is the RTTE model [C].

[0211] The computer program according to Embodiment 62, wherein the E65.RTTE model is RTTE model [D].

[0212] The computer program according to Embodiment 62, wherein the E66.RTTE model is the RTTE model [D1].

[0213] The computer program according to Embodiment 62, wherein the E67.RTTE model is the RTTE model [D2].

[0214] A computer program in any one of embodiments 62 to 67, wherein the E68.RTTE model uses a PK profile generated from the popPK model as input.

[0215] The computer program according to Embodiment 68, wherein the E69.popPK model is the EfanesoctocogalphapopPK model.

[0216] E70. The computer program according to Embodiment 69, wherein the efanesoctocogalphapopPK model is a one-compartment efanesoctocogalphapopPK model that includes body weight as a covariate, and the efanesoctocogalphapopPK model does not include VWF levels or hematocrit levels as covariates.

[0217] A computer program according to any one of embodiments 68 to 70, wherein the E71.popPK model is the EfanesoctocogalphapopPK model [A].

[0218] A computer program according to any one of embodiments 68 to 70, wherein the E72.popPK model is the EfanesoctocogalphapopPK model[A'].

[0219] E73. Computer-readable media comprising, when executed by a computer, instructions causing the computer to perform the method according to any one of embodiments 1 to 50.

[0220] E74. A method for estimating bleeding risk, comprising: (a) receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an repeated time to event (RTTE) model [D2]; (b) calculating bleeding risk using the RTTE model [D2] and the received information by the computer program; and (c) transmitting the calculated bleeding risk information from (b) by the software-based system for output of bleeding risk information.

[0221] E75. A method for estimating bleeding risk, comprising: (a) receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices; (b) transmitting coagulation factor VIII (FVIII) information by a processing device to a software-based system, the software-based system being programmed to perform an iteration time to event (RTTE) model [D2] for calculating bleeding risk; (c) receiving bleeding risk information from the software-based system calculated using the information transmitted in (b) and the RTTE model [D2]; and (d) transmitting the bleeding risk information in (c) by one or more electronic devices to output bleeding risk information.

[0222] E76. The method according to Embodiment 74 or 75, wherein calculating the bleeding risk is equivalent to estimating the risk of a bleeding event occurring.

[0223] E77.FVIII The method according to any one of embodiments 74 to 76, wherein activity information is provided by the popPK model.

[0224] The method according to Embodiment 77, wherein the E78.popPK model is the ephanesoctocogalphapopPK model.

[0225] The method according to Embodiment 78, wherein the E79.popPK model is the ephanesoctocogalphapopPK model [A].

[0226] The method according to Embodiment 78, wherein the E80.popPK model is the ephanesoctocogalphapopPK model [A'].

[0227] E81. The method according to any one of embodiments 74 to 80, wherein the FVIII activity information provided pertains to individual subjects.

[0228] E82. The method according to any one of Embodiments 74 to 80, wherein the provided FVIII activity information is a target FVIII activity level.

[0229] E83. The method according to Embodiment 82, wherein the target FVIII activity level is a variable FVIII activity level.

[0230] E84. The method according to any one of Embodiments 74 to 80, wherein the FVIII activity information provided is the estimated FVIII activity level of the population in question.

[0231] E85. The method according to any one of Embodiments 74 to 80, wherein the FVIII activity information provided relates to a hypothetical or hypothetical subject.

[0232] E86. A method for estimating the bleeding risk of an individual subject, comprising: a) receiving individualized subject information, including the subject's weight, by a software-based system; b) receiving desired treatment outcome information, including FVIII level, by a software-based system; c) applying an iteration-to-event (RTTE) model [D2] to the subject based on the individualized information and / or desired treatment outcome information; d) estimating the bleeding risk of an individual subject using the RTTE model [D2]; and e) transmitting the estimated bleeding risk information from (d) for output of bleeding risk information by a software-based system.

[0233] E87. The method according to Embodiment 86, wherein desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A].

[0234] E88. The method according to Embodiment 86, wherein desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A'].

[0235] E89. The method according to any one of embodiments 86 to 88, wherein desired treatment outcome information is provided by individual subjects.

[0236] E90. The method according to any one of embodiments 86 to 88, wherein desired treatment outcome information is provided by a healthcare professional.

[0237] E91. A method for treating a subject having hemophilia A, comprising: a) identifying a subject having hemophilia A; b) estimating the bleeding risk of the subject if treated with 50 IU / kg efanesoctocog alfa approximately once weekly; c) estimating the bleeding risk of the subject if the subject had an FVIII activity level of at least 10 IU / dL; and d) treating the subject with 50 IU / kg efanesoctocog alfa approximately once weekly if the subject has a lower estimated bleeding risk with efanesoctocog alfa therapy than the estimated bleeding risk if the subject had an FVIII activity level of at least 10 IU / dL, wherein an repeated time to event (RTTE) model [D2] is used to estimate the bleeding risk with efanesoctocog alfa therapy and / or the bleeding risk of the subject if the subject had an FVIII activity level of at least 10 IU / dL.

[0238] E92. A method for estimating the bleeding risk of an individual subject, comprising: (a) receiving information about the individual subject by a software-based system, the system being programmed to run (i) a one-compartment ephanesoctocog alfa popPK model [A'] for calculating coagulation factor VIII (FVIII) activity information; and (ii) an iteration-to-event (RTTE) model [D2] for estimating the bleeding risk using the FVIII activity information; (b) the software-based system calculating the estimated bleeding risk using the ephanesoctocog alfa popPK model [Q], the RTTE model [D2], and the received information; and (c) the software-based system transmitting the calculated bleeding risk information from (b) for output of bleeding risk information.

[0239] E93. A method for estimating bleeding risk, comprising: (a) receiving information about individual subjects by one or more electronic devices; (b) transmitting the information about individual subjects to a software-based system by a processing device, the software-based system being programmed to run (i) a one-compartment ephanesoctocog alfa popPK model [A'] for calculating coagulation factor VIII (FVIII) activity information; and (ii) an iteration-to-event (RTTE) model [D2] for estimating bleeding risk using the FVIII activity information; (c) receiving an estimated bleeding risk from the software-based system using the ephanesoctocog alfa popPK model [Q], the RTTE model [D2], and the received information; and (d) transmitting the bleeding risk information from (c) to output bleeding risk information by one or more electronic devices.

[0240] E94. The method according to any one of embodiments 91 to 93, wherein the subject is under 12 years of age.

[0241] E95. The method according to any one of embodiments 91 to 93, wherein the subject is under 6 years of age.

[0242] E96. The method according to any one of embodiments 91 to 93, wherein the target age is 6 to 12 years old.

[0243] E97. A data processing device, device, or system including a processor configured to implement the EfanesoctocogalphaRTTE model [D2].

[0244] E98. A data processing device, device, or system according to Embodiment 97, including a smartphone, tablet computer, personal digital assistant, handheld computer, laptop computer, or smartwatch.

[0245] E99. A data processing device, device, or system according to any one of embodiments 97 to 98, including a smartphone.

[0246] E100. A computer program which, when executed by a computer, includes instructions causing the computer to perform an Iteration Time to an Ephanesoctocogalpha event (RTTE) model [D2].

[0247] E101. Computer-readable media comprising instructions, when executed by a computer, causing the computer to perform the method described in any one of embodiments 74 to 96.

[0248] E102. A pharmaceutical composition comprising efanesoctocog alfa for use in human subjects with severe hemophilia A requiring treatment to reduce the risk of trauma-free bleeding over a 52-week period, wherein the risk is reduced to less than 30%, and the subject is administered intravenously at a dose of approximately 25 IU / kg to approximately 50 IU / kg of efanesoctocog alfa approximately every 4 to 14 days during the period, the human subject is between 6 and 12 years of age, the subject weighs 30 to 35 kg, and the subject is administered intravenously, thereby reducing the probability to less than 30%.

[0249] E103. A pharmaceutical composition comprising efanesoctocog alfa for use in human subjects with severe hemophilia A requiring treatment to reduce the risk of trauma-free bleeding over a 52-week period, wherein the risk is reduced to less than 30%, and during the period, approximately 25 IU / kg to approximately 50 IU / kg of efanesoctocog alfa is administered intravenously to the subject approximately every 4 to 14 days, the human subject is between 6 and 12 years of age, the subject weighs 30 to 35 kg, and the probability is reduced to less than 30%.

[0250] E104. A pharmaceutical composition according to any one of embodiments 102 to 103, wherein bleeding not caused by trauma is spontaneous bleeding.

[0251] E105. The pharmaceutical composition according to any one of Embodiments 102 to 104, wherein efanesoctocog alfa is administered at a dose of about 25 IU / kg every about 4 days during the period.

[0252] E106. The pharmaceutical composition according to any one of Embodiments 102 to 104, wherein efanesoctocog alfa is administered at a dose of about 30 IU / kg every about 7 days during the period.

[0253] E107. The pharmaceutical composition according to any one of Embodiments 102 to 104, wherein efanesoctocog alfa is administered at a dose of about 50 IU / kg every about 7 days during the period.

[0254] E108. The pharmaceutical composition according to any one of Embodiments 102 to 104, wherein efanesoctocog alfa is administered at a dose of about 50 IU / kg every about 14 days during the period.

[0255] Although the present disclosure has been described in detail above, it will be more clearly understood by referring to the following examples. The following examples are included in this specification for the sole purpose of illustration and are not intended to limit the present disclosure. All patents and publications mentioned in this specification are expressly incorporated by reference.

Examples

[0256] Example 1. Recurrence Time to Event (RTTE) Model for Characterizing the Bleeding Risk of Severe Hemophilia A Patients Treated with Efanesoctocog Alfa The XTEND-1 (EFC16293, NCT04161495) trial was a phase 3 open-label, multicenter trial of efanesoctocog alfa in previously treated adult and adolescent (12 years and older) patients with severe hemophilia A. Some patients had received FVIII prophylaxis prior to the trial and were enrolled in Arm A, where they received weekly prophylaxis of 50 IU / kg for 52 weeks. Patients who had received on-demand treatment prior to the XTEND-1 trial entered Arm B, where they received 26 weeks of on-demand prophylaxis of 50 IU / kg followed by 26 weeks of weekly prophylaxis.

[0257] During the XTEND-1 trial, treated bleeding events were recorded in patients who received efanesoctocog alfa during the prophylactic and on-demand treatment periods. Using these data, we developed an efanesoctocog alfa repeat time to event (RTTE) model as a means of quantitatively characterizing the dependence of bleeding event occurrence on base hazards and FVIII activity during efanesoctocog alfa treatment in patients with severe hemophilia A. Although the RTTE model was developed for efanesoctocog alfa, it is also useful for evaluating bleeding risk for existing FVIII alternatives (e.g., recombinant FVIII, fusion proteins containing recombinant FVIII and Fc, and PEGylated FVIII) and hypothetical or desired FVIII activity levels.

[0258] The RTTE model was developed using NONMEM (v.7.4.4 or later). Table 1 shows an overview of the RTTE model development process.

[0259] [Table 1]

[0260] During model development, models with different hazard distributions were tested to estimate the base hazards. After the base hazards were identified, the FVIII activity effect was evaluated as a proportional hazards effect relative to the base hazards. FVIII activity was predicted using individual model-based pharmacokinetic parameters from a population pharmacokinetic (popPK) model (i.e., popPK model [A]) using phase 1 and phase 3 clinical data together with efanesoctocog alfa. Potential covariate effects were tested against the base hazards using stepwise covariate modeling in PsN (v.5.2.6).

[0261] After stepwise covariate modeling and model refinement, treatment efficacy (weekly prophylaxis and on-demand treatment) was selected as a covariate for the baseline hazards. The treatment efficacy relative to the baseline hazards showed that the hazard (λ) for bleeding events was approximately 9 times higher with on-demand treatment than with prophylaxis.

[0262] Among the various base and FVIII effect models evaluated, both the Weibull function as a base hazard and the power model for the effect on FVIII activity provided the lowest statistically significant objective function values:

number

[0263] The abbreviations for Model [B] are as follows: h: hazard function of the Weibull distribution; t: time; λ: base hazard constant; γ: shape parameter of the base hazard; β: constant of the FVIII effect; C: FVIII activity (drug plasma concentration); α: shape parameter of the FVIII effect; η: individual random effect.

[0264] The appropriateness of the model was assessed by comparing the proportion of patients without bleeding events observed in XTEND-1 with the proportion predicted by the RTTE model, using other aspects such as the Kaplan-Meier (KM) visual prediction test, the statistical significance of identified covariates, and the accuracy of parameter estimations. Parameter estimates by RSE (%) and 95% confidence intervals (CI) from SIR are shown in Table 2, confirming that the parameter estimates fall within the 95% CI.

[0265] [Table 2]

[0266] The parameter estimates from the final RTTE model [B] are given by λ = 0.00247h -1 This indicates that the Weibull-based hazard, with a shape parameter γ of 1.02, is slowly increasing over time because γ > 1. For the power model of the FVIII activity effect, β = -1.73 (IU / dL) -1 The estimates of α=0.247 indicate that the hazard (hazard ratio [HR]=3.76) was 3.76 times higher for 1 IU / dL of FVIII activity compared to 10 IU / dL of FVIII activity. The hazard for on-demand treatment was approximately 9.12 times higher than the hazard for prophylactic treatment (HR=e2.21). Finally, including treatment effect as a covariate to the baseline hazard, the %CV of IIV relative to the baseline hazard decreased from 148% (run201) to 114% (run213).

[0267] Model evaluation results: Using the final RTTE model [B], the probability of no bleeding for one year in a typical 78.3 kg patient was simulated for weekly efanesoctocog alfa 50 IU / kg prophylaxis and a theoretical continuous infusion regimen (e.g., targeting a stable level of FVIII activity of 10–20 IU / dL) using typical parameters and parameter uncertainties of the RTTE model. Annualized bleeding rates for weekly efanesoctocog alfa 50 IU / kg prophylaxis, along with IIV from the RTTE model, were simulated using a hypothetical population of 1000 adult / adolescent patients in which inter-individual variability in pharmacokinetics (IIV) was incorporated using a popPK model.

[0268] Observed data showed that 362 bleeding events occurred during the study, with 94 during prophylaxis (86 during the 52-week prophylaxis period in arm A [n=133] and 8 during the 6-month prophylaxis period in arm B [n=26]) and 268 during on-demand treatment (during the first 6 months in arm B [n=26]). One bleeding event occurred before the initiation of efanesoctocog alfa treatment and was not included in the RTTE model development. Observed data show a clear difference in the proportion of patients without bleeding events between the on-demand treatment arm and the prophylaxis arm. The proportion of patients without bleeding events was higher in the prophylaxis group than in the on-demand treatment group, and the time to bleeding events was longer in the prophylaxis group than in the on-demand treatment group (see Figure 15).

[0269] Figure 16 shows the Kaplan-Meier (KM) visual prediction of the final RTTE model [B] for the prevention arm in the XTEND-1 trial. The solid line represents the KM observed, and the dashed line represents the 95% confidence interval around the KM. The gray band indicates the visual prediction check at the 95% prediction interval. RTTE, time to event. The KM visual prediction check of the final model (see Figure 16) provided a good explanation for the observed KM survival and demonstrated that the model was able to capture the general trends in the data as well as their variability.

[0270] Annual bleeding rate (ABR) and bleeding-related outcome simulations: Simulations were performed using the final RTTE model to predict bleeding events in a virtual population of 1000 adult and adolescent patients for 52 weeks with a prophylactic regimen of 50 IU / kg QW. Individual simulated ABRs were calculated from the simulations. The simulated mean (SD) ABR was 0.71 (1.50), and the median ABR was 0 (Q1 - Q3: 0 - 1). This was in good agreement with the ABR observed for Arm A of the XTEND-1 trial, where the mean (SD) ABR was 0.71 (1.43) and the median ABR was 0. (See Table 3). The similarity between the simulated and observed ABRs indicates that the final RTTE model can predict the ABR of the population prophylactically treated with 50 IU / kg QW of BIVV001.

[0271] 64.1% of virtual patients had 0 bleeding episodes in the simulation, which was similar to the percentage in Arm A of the XTEND-1 trial where 64.7% (86 out of 133) of patients had 0 bleeding episodes during the efficacy period. (See Table 3). Thus, the final RTTE model was also able to adequately predict the proportion of patients without bleeding in the population prophylactically dosed with 50 IU / kg QW of efanesoctocog alfa.

[0272]

Table 3

[0273] Probability of first bleeding in a one-year simulation: Using an RTTE model simulation, the probability of no bleeding within one year in a typical 78.3 kg patient was determined using different efanesoctocog alfa dosing regimens. The evaluated dose regimens for efanesoctocog alfa included 50 IU / kg QW efanesoctocog alfa (i.e., prophylaxis) and continuous infusion regimens (CIRs) of 10, 12, and 15 IU / kg. In this simulation, CIRs were infused over one week. For example, in the case of a 78.3 kg patient with a 10 IU / kg continuous infusion, 783 IU were infused over one week and continued for 52 weeks. The simulation results of the RTTE model are shown in Table 4.

[0274] [Table 4]

[0275] As shown in Table 4, the probability of no bleeding over one year for the weekly prophylactic regimen of 50 IU / kg of efanesoctocog alfa was 71% (95% CI: 50%–83%). The probability of no bleeding with the weekly efanesoctocog alfa 50 IU / kg prophylactic regimen was 23%–35% higher than that of the 10 IU / kg CIR, and it maintained stable FVIII activity in the range of 10.8–16.1 IU / dL. Neither CIR provided a significant amount of time with >40 IU / dL FVIII during the administration interval. The probability of no bleeding with weekly prophylactic efanesoctocog alfa 50 IU / kg and continuous infusion of 10 IU / kg is shown in Figure 17.

[0276] Conclusion: Bleeding events in adults and adolescents treated with IV ephanesoctocog alfa were well characterized by the Weibull base hazard and RTTE model [B] with the effect of FVIII activity on base hazards modeled by the power model. Treatment efficacy (on-demand or prophylactic) was identified as a statistically significant covariate to base hazards, indicating that the bleeding hazard was 9.12 times higher with on-demand treatment compared to prophylactic treatment.

[0277] Simulations of the 50 IU / kg QW prophylaxis regimen in a hypothetical adult and adolescent population showed that the proportion of patients with simulated ABR and zero bleeding was comparable to that observed in Arm A of the XTEND-1 trial. Simulations of the 50 IU / kg QW prophylaxis regimen in typical patients predicted a 29% (17%–50%) probability of first bleeding (95% CI) at one year. The probability of first bleeding within one year was approximately 23%–35% lower with the 50 IU / kg QW prophylaxis regimen compared to maintaining stable FVIII activity in the range of 16.1 IU / dL–10.8 IU / dL compared to CIR. Thus, RTTE model simulations of weekly 50 IU / kg efanesoctocog alfa prophylaxis estimated a twice as high probability of no bleeding at one year compared to stable FVIII activity of 11–16 IU / dL achieved by CIR. These results further support the benefit of sustained normal to near-normal FVIII activity (>40 IU / dL) achieved by the 50 IU / kg QW prophylaxis regimen in hemophilia A subjects.

[0278] Example 2. Population pharmacokinetic (popPK) model for characterizing ephanesoctocog factor VIII (FVIII) activity levels in patients with severe hemophilia A. Once-weekly efanesoctocog alfa provided high sustained FVIII activity in the normal to near-normal range for most weeks, demonstrating superior bleeding protection compared to previous FVIII prophylaxis. FVIII activity data were collected from five clinical studies (Phase 1 / 2a single-dose and repeated-dose studies in adults [NCT03205163 and EudraCT 2018-001535-51, respectively], Phase 3 studies in adults and adolescents 12 years and older [XTEND-1, EFC16293, NCT04161495], and children 1 year and older but under 12 years [XTEND-Kids, NCT04759131], as well as a Phase 3 long-term extension study [XTEND-ed, NCT04644575]). To characterize FVIII activity after efanesoctocog alfa administration, identify endogenous and exogenous factors influencing pharmacokinetics (PK), and evaluate PK fluctuations, we developed a popPK model.

[0279] FVIII activity levels used to develop the popPK model were measured by a one-step coagulation assay from 3054 blood samples from 199 adults and adolescents and 61 children who received efanesoctocog alfa in the above study. Body weight and VWF levels ranged from 12.5 kg to 133 kg and 40 IU / dL to 339 IU / dL, respectively. Using a one-compartment model with linear elimination, FVIII activity was characterized using the estimated allometric body weight effect on clearance (CL) and central compartment volume (V), and the dependence of CL and V on body size was explained. The efanesoctocog alfa popPK model is shown as equation [A] above.

[0280] Baseline VWF, baseline race, racial (Caucasian and Asian), hepatitis C virus and human immunodeficiency virus status, and blood type (A, B, O) were tested for statistical significance in covariate analyses. Using the final popPK model, various dose regimens were simulated in a hypothetical population of adult and adolescent patients generated using baseline weight distributions from the Phase 1 / 2a study and XTEND-1.

[0281] result:

[0282] [Table 5]

[0283] The final popPK model described FVIII activity profiles over time, captured inter-individual variability in FVIII activity, and accurately estimated moderate inter-individual variability in CL and V (Table 5). The allometric index of the body weight effect showed that CL and V increased with body weight, and were excreted more rapidly overall at lower body weights. Asian race was identified as a statistically significant covariate for CL (P<0.001). Asian CL was 10.4% lower than non-Asian CL. Baseline VWF levels were not identified as a statistically significant covariate in the final popPK model, consistent with previous studies demonstrating that efanesoctocog alfa PK is VWF-independent. Blood type was not identified as a statistically significant covariate in the final popPK model.

[0284] Figure 4 shows the simulated steady-state FVIII activity over time for efanesoctocog alfa and predicted population and individual predicted versus observed FVIII activity in the final popPK model, illustrating FVIII activity >40 IU / dL 3-4 days post-administration. The final popPK model shows that a once-weekly efanesoctocog alfa (50 IU / kg) prophylactic regimen resulted in steady-state FVIII activity >10 IU / dL in the majority of adult and adolescent patients, regardless of weight and race. troughThe study demonstrated that the time to reach 40 IU / dL FVIII activity was 3-4 days. Simulations for perioperative management during major surgery and treatment of massive bleeding showed that a loading dose of 50 IU / kg, followed by 30 IU / kg loading doses every 3 days during the postoperative period, met the World Federation of Hemophilia guidelines for peak FVIII activity (>50 IU / dL~80 IU / dL) for most adults and adolescents. Similarly, for minor surgery and treatment of moderate to mild bleeding, a single dose of 50 IU / kg of efanesoctocog alfa resulted in peak FVIII activity that met these guidelines.

[0285] Conclusion: A linear one-compartment popPK model was able to adequately characterize FVIII activity in patients with severe hemophilia A. CL and V were weight-dependent, and Asian race was identified as a covariate of CL; however, the limited influence of weight and Asian race on FVIII exposure was not considered clinically significant. PopPK simulations demonstrated that in most adults and adolescents, weekly 50 IU / kg efanesoctocog alfa achieved sustained FVIII activity in the normal to near-normal range (≥40 IU / dL) for 3-4 days, achieving ≥10 IU / dL on day 7. PopPK simulations also supported phase 3 dose regimens selected for routine prophylaxis, bleeding management, and perioperative management. Individual clearance of efanesoctocog alfa was independent of baseline VWF in adults and adolescents.

[0286] Further details on the development of the popPK model Data available from Phase 3 studies in adults, adolescents, and children using efanesoctocog alfa were incorporated into the development of a population pharmacokinetic (popPK) model. This included complete data from adult and adolescent studies, as well as partial data from pediatric and long-term safety studies.

[0287] [Table 6]

[0288] Table 6 Definitions: a) Number of exposures to efanesoctocog alfa in each study for popPK model development; total N=260, including 199 adult and adolescent patients and 61 pediatric patients in EFC15295; b) Only patients who underwent surgery in the LTS16294 study (3 patients) are included in popPK model development; c) In EFC16293, 17 patients are in a continuous arm, and patients skipped doses on day 7 of week 1 and day 7 of week 26 to allow estimation of the terminal phase half-life by collecting one-step coagulation (OSC) FVIII active samples up to day 15 after doses on day 1 and week 26.

[0289] In the Phase 1 popPK analysis, OSC FVIII activity data were described using a 1-CMT model with body weight as a covariate for CL and V, and hematocrit level as a covariate for V. In the final popPK analysis, the 1-CMT model was selected as the structural model describing the OSC FVIII activity profile, with the base model including the WT effect. Further covariate screening was performed with the base popPK model.

[0290] Body weight (kg) (WT) or other continuous covariates were scaled to the median baseline WT (median baseline value) in adults and adolescents (78.3 kg) to evaluate them as covariate effects in adults or contrast growth effects in children.

[0291] For example, in the case of covariate effects for adults and contrast growth effects for children, CL = TVCL × (WT 時間-変動 / 78.3) CLexp ×(exp(ETA1)). A similar method was used for volume.

[0292] Dataset: One popPK dataset for observed data is based on the adult / adult study EFC16293. Figure 5 shows baseline-adjusted FVIII activity time profiles. Day 1 (baseline) is shown for all patients (Figure 5A). Day 1 (baseline) and week 26 are shown for patients in a continuous arm (Figure 5B). The FVIII activity time profiles follow a typical one-compartment (linearly decreasing on a logarithmic scale) kinetic pattern. FVIII activity shows the mean half-life of efanesoctocog alfa at 47.8 hours.

[0293] Figure 6 shows the correlations between the four continuous covariates at baseline. The median baseline weight (WTKGB) was 78.3, excluding the EFC16295 study. Baseline race (BH) had a median of 43 (also excluding the EFC16295 study). Baseline volume-weight-fat (BVWF) had a median of 112 (also excluding the EFC16295 study). Age was not tested as a covariate because it appeared to correlate with WT. Race and VWF were tested as covariates and were found not to be significant.

[0294] Table 7 shows the distribution of categorical covariates such as blood type, race, HIV status, and HCV status. The data in Table 7 includes all studies (n=260). Black (1.92%) and other races (3.08%) account for approximately 5% of patients. Regarding blood type, blood types A and O are more common, accounting for 29.62% and 36.15% of patients, respectively. Blood type B is present in less than 10% of patients (9.23%). Blood type AB is present in less than 5% of patients (3.85%). HCV and HIV-positive patients are elderly, while pediatric patients are not HCV or HIV-positive. There are two patients under the age of 2 years. Of the presented categorical factors, only HCV status, HIV status, blood type (A, B, and O), and race (Caucasian and Asian) were tested as covariates.

[0295] [Table 7]

[0296] Table 8 provides further details regarding the covariate model.

[0297] [Table 8]

[0298] OSC activity is defined as the concentration (C) in the central compartment. All parameters of the base covariate model and the final covariate model were estimated with acceptable accuracy. Adding the weight effect reduced the instrumental variable estimates (IIV) for CL and V, while adding the Asian race effect for CL reduced the IIV for CL. The WT effect indices for CL and V are acceptable when compared to the simple allometric indices. An effect of Asian race was observed in CL, with Asian clearance being 10.4% lower than that of non-Asians of the same weight.

[0299] The PK parameters incorporating the covariates into the final popPK model are shown below:

number

[0300] Figures 7A and 7B show the population prediction (PRED) and individual prediction (IPRED) for DV, respectively. This demonstrates that both the population model and the individual model can describe PK data across age categories.

[0301] Figure 8 shows the visual predictive check (VPC) of the final popPK model. The VPC for each study indicates that the majority of the observed FVIII activity data were within the predictive range [5th–95th percentile]. For the purpose of the VPC, one unique patient from LTS16294 was considered in EFC16293.

[0302] Figures 9A and 9B show the surgical population prediction (PRED) and individual prediction (IPRED) vs. DV, respectively. Data from 19 patients with EFC16293, EFC16295, and LTS16294 during the surgical time frame are included. This model functions quite well when describing the PK data collected during surgery and after ad hoc surgical dosing.

[0303] Figure 10 shows the steady-state C across the entire population by baseline body weight (kg). trough C maxss and the distribution of time to 40 IU / dL FVIII activity. Figure 11 shows the steady-state C trough C maxss and the distribution of time to 40 IU / dL across non-Asian and Asian populations for all age groups. Steady-state FVIII activity C max C trough and time to 40 IU / dL FVIII activity increase with increasing body weight and are higher in Asians compared to non-Asians. However, regardless of body weight and race, the 50 IU / kg QW prophylaxis regimen showed that a steady-state C trough above 10 IU / dL and time to 40 IU / dL FVIII activity of 3 - 4 days were achieved in the majority of the adult and adolescent (age ≥ 12 years) population. The 50 IU / kg QW prophylaxis regimen also showed that a steady-state C trough above 5 IU / dL and time to 40 IU / dL FVIII activity of 2 - 3 days were achieved in the majority of the pediatric (age < 12 years) population.

[0304] Major surgery and massive bleeding: The model was analyzed for major surgery and massive bleeding. Major surgery and massive bleeding were classified based on the criteria listed in Table 9.

[0305]

Table 9

[0306] For major surgery and bleeding, the simulations were based on a single-dose drug regimen at 50 IU / kg with additional doses of 30 or 50 IU / kg every 2-3 days as needed. Therefore, 50 IU / kg Q2D, 50 IU / kg Q3D, 30 IU / kg Q2D, and 30 IU / kg Q3D are possible combinations of drug regimens following a preoperative dose of 50 IU / kg (QXD is every X days). These same simulations can be applied to both major surgery and massive bleeding, as they both involve the same drug combinations.

[0307] Figure 12 shows simulated OSC FVIII activity over time for massive bleeding and major surgery in subjects under 6 years of age. Throughout the entire surgical period, over 95% of patients 6 years of age and older met the criteria for major surgery, and over 80% of patients under 6 years of age met the surgical criteria.

[0308] Figure 13 shows simulated FVIII activity in a hypothetical adult and adolescent population, starting with a 50 IU / kg dose of efanesoctocog alfa followed by 30 IU / kg every three days up to day 14. The simulation indicated that an initial dose of 50 IU / kg, followed by 30 IU / kg every three days up to day 14, should be sufficient for perioperative management during major surgery and for treating massive bleeding. Over 95% of adult and adolescent patients were predicted to meet the World Federation of Hemophilia (WFH) guidelines for peak FVIII activity (over 80-100 IU / dL preoperatively or on the day of massive bleeding) (Srivastava A, et al. Haemophilia. 2020; 26 Suppl 6: 1-158). Furthermore, other dosing regimens, such as an initial dose of 50 IU / kg followed by 50 or 30 IU / kg every two or three days, were simulated, and these additional dosing regimens were also predicted to meet the WFH peak FVIII guidelines. Thus, the simulations showed that an initial dose of 50 IU / kg, followed by initial doses of 50 IU / kg or 30 IU / kg every two or three days, met the WFH peak FVIII guidelines in the majority (over 95%) of adult and adolescent patients being managed during treatment for major surgery and massive bleeding.

[0309] Minor surgery and mild or moderate bleeding: Figure 14 shows simulated OSC FVIII activity over time for all age groups. Over 95% of patients in all age groups meet the criteria for peak FVIII > 50 IU / dL after preoperative administration for minor surgery. Similarly, over 95% of patients in all age groups meet the criteria for peak FVIII > 40 IU / dL, which is necessary for managing mild / moderate bleeding. With an additional dose of 30 or 50 IU / kg every 2 or 3 days, over 95% of patients in all age groups meet the criteria for peak FVIII > 50 IU / dL.

[0310] Conclusion: Based on this data, the one-compartment (1-CMT) model describes OSC FVIII activity data fairly well in adults, adolescents, and children. Weight effects (for CL and V) were included in the base model, and the Asian race effect (for CL) was identified as a statistically significant covariate. Simulations for various weights show that a fixed regimen of 50 IU / kg QW provides high FVIII activity in adult, adolescent, and pediatric populations, regardless of weight and race. Simulations using this model also supported and demonstrated potential efanesoctocog alfa dosing schemes for surgical and bleeding scenarios.

[0311] Example 3. Repeated time to event (RTTE) analysis of ephanesoctocog alfa in pediatric patients with severe hemophilia A. The XTEND-KIDS (EFC16295, NCT04759131) study was a phase 3 open-label, multicenter study of the safety, efficacy, and pharmacokinetics of efanesoctocog alfa in pediatric patients under 12 years of age with previously treated severe hemophilia A. This example includes a description of the RTTE (bleeding) analysis of efanesoctocog alfa in pediatric patients under 6 years of age and 6 to under 12 years of age with severe hemophilia A from the EFC16295 study. All pediatric bleeding events were used to evaluate the model performance of a previously developed RTTE model in adult / adolescent patients (Model [B], see Example 1). As described above, this previous RTTE model was characterized by the Weibull baseline hazard, from which the effect of OSC FVIII activity on the baseline hazard was modeled in power relationships.

[0312] This analysis included 1) evaluating the adult / adult RTTE model using pediatric data to assess its ability to predict the annual bleeding rate (ABR) observed in study EFC16295, and 2) establishing a pediatric RTTE model by reestimating model parameters using only pediatric data. Using the pediatric RTTE model, observed and simulated ABRs in the pediatric population were compared. Potential covariate-parameter relationships were also evaluated to assess whether additional factors in the pediatric population may influence bleeding risk and ultimately bleeding event rates compared to adults and adolescents.

[0313] data: We analyzed bleeding event data from 74 patients from the EFC16295 study. This patient group includes the "Pediatric BIVV001 RTTE Analysis Dataset." Key features of the Pediatric BIVV001 RTTE Analysis Dataset are shown in Table 10. Overall, 63 bleeding events were recorded for all patients, 17 from patients under 6 years of age and 46 from patients between 6 and 12 years of age.

[0314] [Table 10]

[0315] Of the 63 bleeding events, 11 (17.5%) were spontaneous bleeding events, 30 (47.6%) were traumatic bleeding events, and 22 (34.9%) were of an unknown type. Of the 63 bleeding events, 42 (66.7%) were joint bleeding events, and 21 (33.3%) were non-joint bleeding events.

[0316] Table 11 shows the baseline patient characteristics of the pediatric BIVV001 RTTE analysis dataset, broken down by age category and overall. Baseline weight (WT) ranged from 11.4 kg to 66.5 kg, with a median weight of 22 kg. The age of patients in the dataset ranged from 1.4 years to 11 years, with a median age of 5 years.

[0317] The age of initial prevention ranged from 0 years (receiving prevention immediately after birth) to 5 years, with a median of 1 year. This indicates that 50% of patients began some type of FVIII prophylactic treatment at a very early age, resulting in a highly skewed distribution. Similarly, the distribution of the number of target joints was also highly skewed, as 72 / 74 (97%) of patients recorded 0 target joints (no target joints of bleeding concern). The Hemophilia Joint Health Score (HJHS) ranged from 0 to 24, with a median of 0. Baseline values ​​were used for all covariates. Baseline was defined as the last available measurement before the first dose of the study drug.

[0318] [Table 11]

[0319] Covariate analysis: To investigate whether additional factors explain potential differences in bleeding risk between pediatric and adult / adult patients, we explored significant covariate parameter relationships against a fixed adult / adult RTTE model. To evaluate the effects of other factors (e.g., age, bleeding history) on bleeding risk in pediatric patients, we assessed potential covariate parameter relationships using a stepwise covariate model building procedure (SCM) with reduced adaptability (ASR) against a model with fixed adult / adult parameters. Notably, covariates from the adult / adult RTTE model (on-demand treatment arm) are not relevant to the current analysis. In the pediatric trial EFC16295, efanesoctocog alfa was administered only as prophylactic treatment.

[0320] Histograms of baseline covariates for patients in the pediatric BIVV001 RTTE analysis dataset (Figure 18) show signs of a skewed distribution for several disease history factors, including age at first prevention 12 months prior to BIVV001 treatment and HJHS, number of target joints, and bleeding history.

[0321] To evaluate covariate correlations, Figure 19 shows the correlation matrix of covariates at baseline for patients in the pediatric BIVV001 RTTE analysis dataset. As expected, strong correlations were observed for body weight (WT) and both age and age group (i.e., age continuous and age category). A strong correlation (>0.8) was also found between target joint number and bleeding history. For covariates with a correlation coefficient >0.7, only age category (<6 years; 6 years to under 12 years) and bleeding history were included in the covariate model analysis.

[0322] To assess whether other factors could explain the observed differences, we used the SCM procedure to explore significant covariate parameter relationships for the RTTE model with fixed parameters for the adult / adult estimates, but no significant covariate parameter relationships were identified.

[0323] Re-estimation of the RTTE model To potentially improve the fit of previous RTTE models to the pediatric population, we re-estimated the previous RTTE models on the pediatric ephanesoctocog alfa RTTE dataset. The model parameters from the adult-adult model and those re-estimated with the pediatric data are shown together in Table 12. Pediatric patients are characterized by a much lower baseline scale parameter λ and a shape parameter γ<1, resulting in a baseline hazard that decreases over time. Pediatric patients also feature shallower drug effects. Notably, for the re-estimated RTTE models, the baseline scale parameter estimate λ was obtained with higher uncertainty (>50%) than the adult / adult dataset, likely reflecting the smaller number of patients and events in the pediatric vs. adult / adult dataset.

[0324] [Table 12]

[0325] A second covariate screening was performed on the re-estimated pediatric RTTE model to investigate whether it could be further improved. Covariates evaluated included age group and bleeding history. Quality control (QC) checks were performed to enhance the reliability of the reported results. No significant covariate-parameter relationships were identified in the re-estimated RTTE model. Therefore, the re-estimated RTTE model was considered the final pediatric RTTE model.

[0326] Final Pediatric RTTE Model The final pediatric RTTE model has the following characteristics: • Baseline hazard model: Weibull with scale parameter λ and shape parameter γ. • Drug effect model: Output with parameters β and α • Covariate model: (None). • IIV model: IIV exponential model with respect to λ.

[0327] Table 12 shows the parameter estimates for the final RTTE model. The parameter estimates from the final RTTE model indicate that the Weibull-based hazard, with λ = 0.0000429 1 / h and shape parameter γ = 0.694, decreases with time after γ < 1. The parameter estimates also suggest that for the power model of the FVIII activity effect, the estimates β = -0.00535 (dL / IU) and α = 1.27 indicate that the hazard ratio is 1.1 times higher for FVIII activity at 1 IU / dL compared to FVIII activity at 10 IU / dL.

[0328] The following RTTE model [D2] shows the final RTTE model, including parameter estimates for patients under 12 years of age:

number

[0329] The predictive performance of the adult / adult RTTE model was also evaluated in pediatric hemophilia A patients using the Visual Predictive Test (VPC), i.e., by keeping the model parameters fixed to the estimates for the adult / adult population. The VPC of the final RTTE model showed that the model adequately described the data in the complete pediatric dataset (see Figure 20). The VPC of the final RTTE model also adequately described the data in both age groups (data not shown).

[0330] Final pediatric RTTE model trial: Probability of first bleeding in a one-year simulation: Using the final pediatric BIVV001 RTTE model, the probability of first bleeding at one year was predicted for a range of BIVV001 prophylactic dosing regimens in typical pediatric patients in both age groups. Typical patient weights for those under 6 years and those 6 to under 12 years were 18 kg and 32.8 kg, respectively, corresponding to the median observed values ​​in trial EFC16295. Using 300 SIR samples from the RTTE parameter set, the uncertainty of the final RTTE model parameters was included in these simulations. The results for the selected dosing regimens are shown in Tables 13 and 14. Continuous infusion was considered as the dose infused over one week, e.g., the dose infused for a continuous infusion of 10 IU / kg. For an 18 kg patient, 180 IU was infused over one week for 52 weeks, and for a 32.8 kg patient, 328 IU was infused over one week for 52 weeks.

[0331] [Table 13]

[0332] As shown in Table 13, the results showed that in a typical pediatric patient under 6 years of age and weighing 18 kg who received prophylactic 50 IU / kg QW for one year, the probability (95% CI) of having the first bleeding episode within one year was 28% (14% to 44%), and the steady-state trough concentration was C trough It was predicted that the probabilities of first bleeding at 1 year were 38% to 28% compared to a 10-IU / dL continuous infusion regimen in a typical pediatric patient under 6 years of age weighing 18 kg, with high sustained FVIII activity achieved by the 50 IU / kg QW regimen.

[0333] [Table 14]

[0334] As shown in Table 14, the results for a typical pediatric patient aged 6 to under 12 years, weighing 32.8 kg, who received prophylactic 50 IU / kg QW for one year, showed steady-state C trough The predicted probability (95% CI) of having the first bleeding episode within one year was 26% (13%–41%) for time > 10 IU / dL (percentage of administration interval) and time > 40 IU / dL (percentage of administration interval) (8.14 IU / dL, 92%, and 40%, respectively). The probability of the first bleeding episode at one year was shown to be reduced from 37% to 26% by the high sustained FVIII activity achieved with the 50 IU / kg QW regimen compared to a continuous infusion regimen of 10–15 IU / kg in a typical pediatric patient under 6–12 years of age, weighing 32.8 kg.

[0335] The results of this simulation demonstrate the benefit of achieving normal to near-normal FVIII activity (>40 IU / dL) after a 50 IU / kg QW regimen, which was covered by 32% and 40% of the time in pediatric patients under 6 years and 6-12 years, respectively, compared to 0% of the time after continuous infusion of 10-15 IU / kg in both age groups.

[0336] Annual bleeding rate (ABR): Simulations were performed using the final pediatric RTTE model in hypothetical populations of 10,000 children under 6 years of age and 10,000 children aged 6 to under 12 years treated with a 50 IU / kg QW prophylactic regimen of BIVV001 for up to 1 year (52 weeks). Individual simulated ABRs were calculated from the simulations. The hypothetical populations under 6 years of age and 6 to under 12 years of age had mean (SD) body weights of 16.2 kg (4.73 kg) and 35.5 kg (13.6 kg), respectively. Descriptive statistics for the simulated ABRs are shown in Table 15.

[0337] The simulated mean ABR for the entire pediatric population was 0.74, with a median ABR of 0 (Q1-Q3: 0-1). This is in good agreement with the observed ABR in trial EFC16295, where the mean (95% CI) ABR was 0.89 (0.56, 1.42) and the median ABR was 0. In the under 6 years and 6–12 years age groups, the simulated mean ABRs were 0.76 and 0.70, respectively, within the 95% CIs of the observed mean ABRs of 0.48 (0.30; 0.77) and 1.33 (0.64; 2.76), respectively. In the simulation, 63.6% and 65.6% of the hypothetical patients under 6 years old and between 6 and 12 years old, respectively, had no bleeding episodes. This is similar to the rates in the EFC16295 trial, where 63.2% (24 out of 38) and 63.8% (23 out of 36) of patients in the same age group had no bleeding episodes during the effective period.

[0338] [Table 15]

[0339] Sensitivity analysis was performed in the cohort of children aged 6 to under 12 years, excluding participants with an outlier in the number of treated bleeding events. The mean ABR estimated from the negative binomial model decreased to 0.75 (95% CI: 0.41–1.40) in the 6–under 12 year cohort and to 0.61 (95% CI: 0.42–0.90) overall.

[0340] These simulations demonstrate that the performance of the final pediatric RTTE model was acceptable and can be used to predict the first-year bleeding profile and ABR of the pediatric population (under 12 years of age) listed in 50 IU / kg QW BIVV001 prophylactically.

[0341] Conclusion: Bleeding events in pediatric hemophilia A patients treated with BIVV001 were well characterized by the final pediatric RTTE model [Model D2] with Weibull baseline hazards, from which the effect of OSC FVIII activity on baseline hazards was modeled in power relationships. Diagnostic plots showed that the proportion of pediatric patients experiencing bleeding events over time was generally well explained by the adult / adult RTTE model, but the time to first bleeding was slightly underpredictable in younger patients (<6 years), i.e., bleeding events in this age group were predicted to occur slightly earlier than observed. Covariate exploration performed on the RTTE model with fixed parameters for adult / adult estimates using the SCM procedure did not identify significant covariate parameter relationships. Fitting of pediatric data to the adult / adult RTTE model was improved by reestimating model parameters on the pediatric ephanesoctocogalpha RTTE dataset. Additional covariate exploration on the reestimated RTTE model did not yield further model improvements, so Model [D2] was considered the final pediatric RTTE model.

[0342] The final pediatric RTTE model successfully captured bleeding time (events 1-5) in the pediatric population. While the observed time to first bleeding was still at the upper end of the predictive interval for younger patients (<6 years), this model was better suited to explaining the ABR in this age group, where the simulated mean ABR based on the adult / adult RTTE model was 0.76 vs. 1.32, compared to the observed mean (95% CI) ABR = 0.48 (0.30; 0.77).

[0343] Therefore, bleeding events in pediatric hemophilia A patients treated with ephanesoctocog alfa were well characterized by the RTTE model with Weibull baseline hazards, from which the effect of OSC FVIII activity on baseline hazards was modeled in a force relationship.

[0344] Example 4. Application of the Population Pharmacokinetic (popPK) Model to Pediatric Patients The objective of the following analysis was to describe FVIII activity from the EFC16295 study using empirical Bayesian estimates (EBEs) of PK parameters based on the popPK model (Model [A]) described in Example 2. This study included only pediatric patients under 12 years of age. Descriptive statistics of patient latent covariates (baseline values) from the EFC16295 study included in the MAP Bayesian analysis are summarized in Tables 16 and 17.

[0345] [Table 16]

[0346] [Table 17]

[0347] Baseline weight in pediatric patients (1.4 years to under 12 years) from the EFC16295 study ranged from 11.4 kg to 66.5 kg. There were 2, 36, and 36 patients in the age ranges of <2 years, 2 years ≤ age < 6 years, and 6 years ≤ age < 12 years, respectively.

[0348] Baseline VWF in pediatric patients (from EFC16295) was lower than that in adult and adolescent patients across all other studies.

[0349] In EFC16295, approximately 74% of patients were white, 11% Asian, 4% Black, 5% other, and 5% did not report their race. Regarding blood type distribution, approximately 28% of patients had blood type A, approximately 35% had blood type O, approximately 11% had blood type B, approximately 3% had blood type AB, and the remaining approximately 23% had an unknown blood type. Patients in EFC16295 were neither HCV-positive nor HIV-positive.

[0350] Application of the PopPK model described in Example 2 to EFC16295 Using the popPK model (model [A]), FVIII activity data from EFC16295 was described with the MAXEVAL=0 option in NONMEM. The parameterization in the popPK model is as follows:

number

[0351] Model performance was evaluated based on goodness-of-fit (GOF), visual predictive inspections (VPCs), and quality criteria.

[0352] PopPK models [A] and [A'] are useful for subjects of all ages.

[0353] As shown in Figure 21, the population-predicted OSC FVIII activity (PRED) versus observed (DV) data suggest the adequacy of the previous popPK model describing FVIII activity versus time from the EFC16295 and LTS16294 studies. Individual predicted OSC FVIII activity (PRED) versus observed (DV) plots show that the inter-individual variability model was able to explain the predictive variability.

[0354] Figures 22 and 23 show the residual fluctuations and predicted FVIII activity over time. Both CWRES and IWRES are well / uniformly distributed around 0 when plotted over time over the entire duration, or when plotted together with PRED and IPRED, respectively.

[0355] The performance of the final popPK model was evaluated using EFC16295 trial data with VPC. The VPC graph is shown in Figure 24. The VPC results showed that the majority of the observed FVIII activity was within the predicted range [5th to 95th percentile], with only a few FVIII activity data points outside the predicted percentile range [5th to 95th percentile].

[0356] Furthermore, Table 18 shows the quality criteria summarized for the EFC16295 study. Overall, the MPE as a measure of bias was <10% for PRED and <2% for IPRED, while the RMSE was <36.2% for PRED and <28.4% for IPRED. Both MPE and RMSE were considered acceptable for model performance in explaining the FVIII activity of EFC16295.

[0357] [Table 18]

[0358] Final PopPK model performance regarding perioperative FVIII activity Thirty-two patients underwent surgery during efanesoctocog alfa treatment (from EFC16295 and LTS16294). Following the protocol, these patients received ad hoc efanesoctocog alfa doses during preoperative treatment and postoperative follow-up, and PK samples were collected to assess patient FVIII activity during and after surgical treatment. Figure 25 shows plots of predicted population (PRED) vs. observed population and individual predicted population (IPRED) vs. observed FVIII activity for FVIII activity assessed during the perioperative period. As can be seen from Figure 25, both PRED and IPRED reasonably represented the FVIII activity data, with R2 values ​​of 0.78 and 0.88, respectively. The adult-adult patients who underwent surgery in Figure 25 are from the LTS16294 study.

[0359] EFC16295 Post-event endogenous or exogenous factor assessment After validating the final popPK model using GOF, VPC, and reference quality criteria, we explored post-exposure parameters using endogenous and exogenous factors selected for the EFC16295 study. Steady-state AUCtau (AUC0-168h), C max and C minThis was used as the FVIII exposure parameter generated at week 26 for 73 patients in EFC16295. One patient did not continue until week 26, and therefore, the patient's steady-state exposure could not be calculated.

[0360] Steady-state AUC(AUC) tauss ), C max (C maxss ) and C min (C minss When plotted together with baseline VWF (BVWF) and baseline hematocrit (BH), as shown in Figures 26-28, no particular trends were observed for either BVWF or BH in both younger and older children. As expected, the weight effect already incorporated into the popPK model for CL and V parameters did not increase with increasing weight. tauss , C max and C minss An increasing trend was observed. This is consistent with the predicted trend that FVIII exposure increases with weight gain.

[0361] Similarly, steady-state FVIII exposure parameters were evaluated using categorical factors such as blood type A and blood type O. No significant trends were observed for steady-state exposure using blood type A or blood type O.

[0362] Other categorical factors were not used in the assessment due to the limited diversity among patients who possessed those factors. For example, the EFC16295 study did not include any patients with HCV or HIV infection. Similarly, in the case of blood type B, 8 out of 73 patients had blood type B, while only 2 out of 73 patients had blood type AB.

[0363] Post-PK parameters Using the post-PK parameter, C max and C min (C troughExposure parameters such as (also known as) were derived by simulating the FVIII activity profile of each individual over time for the actual dose administered. The simulation was performed to determine the day 1 and steady state C of patients with EFC16295. max and C min The following was derived. FVIII activity was simulated up to the dose at week 26, and the steady state C max and C min Of the 74 patients in EFC16295, 73 received medication for more than 26 weeks and were included in the steady-state calculation.

[0364] Table 19 shows C after administration on day 1, stratified by age group. max and C min This section presents a comparison of summary statistics regarding C, predicted from PopPK analysis. max and C min Across both age groups, the C was NCA-derived or observed in EFC16295. max and C min It was equivalent to [this].

[0365] Table 20 shows the mean (SD) values ​​of PK parameters obtained from the ex-post assessment of group PK and non-compartmental analysis (NCA). This indicates that for PK parameters obtained from both group PK and NCA, CL and V increase with decreasing age, while t1 / 2 decreases. Overall, the mean and SD of t1 / 2 were nearly equivalent between group PK and NCA for each age group, but CL and V were lower than the NCA estimates compared to PopPK for each age group.

[0366] These results demonstrate that the popPK model predicts FVIII profiles over time, individual PK and exposure parameters, and inter-patient variability quite well.

[0367] [Table 19]

[0368] [Table 20]

[0369] Time to FVIII activity The time to steady-state FVIII activity increases as the FVIII activity threshold decreases from 150 IU / dL to 1 IU / dL. This was observed in both pediatric age groups, as shown in Table 21.

[0370] [Table 21]

[0371] For pediatric patients aged 6 years or older and <12 years, the median time to FVIII activity was 324 hours (13.5 days), 172 hours (7.17 days), and 78.4 hours (3.27 days) for FVIII activity thresholds of 1 IU / dL, 10 IU / dL, and 40 IU / dL, respectively.

[0372] For pediatric patients under 6 years of age, the median time to FVIII activity was 284 hours (11.8 days), 147 hours (6.13 days), and 64.8 hours (2.70 days) for FVIII activity thresholds of 1 IU / dL, 10 IU / dL, and 40 IU / dL, respectively.

[0373] Post-hoc simulations predicted that eight patients aged 6 years or older and three patients under 6 years of age would have peak FVIII activity exceeding 150 IU / dL at steady state.

[0374] Table 22 shows descriptive statistics on the time to FVIII activity on day 1 in patients in the PK group for both pediatric age groups.

[0375] [Table 22]

[0376] Conclusion: Based on GOF, VPC, and quality criteria, the popPK model was deemed appropriate for characterizing FVIII activity data from pediatric studies. Furthermore, this model was also able to describe FVIII activity data collected during ad hoc administration in perioperative management in pediatric and long-term safety studies. The influence of selected endogenous and exogenous factors on FVIII activity in pediatric patients was evaluated by comparing posterior estimates of FVIII activity exposure.

[0377] Consistent with previous findings, since the weight effect was included in CL and V in the popPK model, post-hoc steady-state FVIII activity exposure in children was significantly varied by weight alone. All other evaluated factors, such as baseline VWF, baseline hematocrit, and blood types A and O, did not have an apparent effect on pediatric FVIII activity based on available data. FVIII activity in pediatric and long-term safety studies has been well explained by previous popPK models, and the effect of weight on exposure was the sole cause of apparent FVIII variability across the pediatric population.

[0378] Using the MAP Bayesian method, further FVIII activity data for pediatric patients in the EFC16295 study were well characterized by the popPK model previously developed for adult / adolescent patients, as described in Example 2.

[0379] [Table 23]

[0380] [Table 24]

[0381] [Table 25]

[0382] [Table 26]

[0383] Table 27

[0384] Table 28

[0385] Table 29

Claims

1. A method for estimating bleeding risk, (a) Receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an event time-to-event (RTTE) model [B], (b) Using the RTTE model [B] and the received information, the computer program calculates the bleeding risk, (c) The software-based system transmits the calculated bleeding risk information in (b) for outputting the bleeding risk information. Methods that include...

2. A method for estimating bleeding risk, (a) Receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices, (b) The processing device transmits the coagulation factor VIII (FVIII) information to a software-based system, which is programmed to perform an iteration time to event (RTTE) model [B] to calculate the bleeding risk, (c) Receiving from the software-based system the transmitted information in (b) and bleeding risk information calculated using the RTTE model [B], (d) Transmitting the bleeding risk information in (c) in order to output the bleeding risk information by one or more electronic devices. Methods that include...

3. The method according to claim 1 or 2, wherein calculating the bleeding risk is equivalent to estimating the risk of a bleeding event occurring.

4. The method according to any one of claims 1 to 3, wherein the FVIII activity information is provided by the popPK model.

5. The method according to claim 4, wherein the popPK model is the efanesoctocogalpha popPK model.

6. The method according to claim 5, wherein the efanesoctocogalphapopPK model includes body weight as a covariate, and the efanesoctocogalphapopPK model does not include VWF level or hematocrit level as a covariate.

7. The method according to claim 5, wherein the popPK model is the ephanesoctocogalpha popPK model [A].

8. The method according to claim 5, wherein the popPK model is the ephanesoctocogalpha popPK model [A'].

9. The method according to any one of claims 1 to 8, wherein the FVIII activity information provided pertains to individual subjects.

10. The method according to any one of claims 1 to 3, wherein the FVIII activity information provided is a target FVIII activity level.

11. The method according to claim 10, wherein the target FVIII activity level is a variable FVIII activity level.

12. The method according to any one of claims 1 to 8, wherein the FVIII activity information provided is the estimated FVIII activity level of the target population.

13. The method according to any one of claims 1 to 8, wherein the FVIII activity information provided relates to a virtual or hypothetical object.

14. The method according to any one of claims 1 to 13, wherein the RTTE model is the RTTE model [C].

15. The method according to any one of claims 1 to 13, wherein the RTTE model is the RTTE model [D].

16. The method according to any one of claims 1 to 13, wherein the RTTE model is the RTTE model [D1].

17. The method according to any one of claims 1 to 13, wherein the RTTE model is the RTTE model [D2].

18. A method for estimating the bleeding risk of individual subjects, a) Receiving personalized subject information, including the subject's weight, through a software-based system, b) The software-based system receives desired treatment outcome information, including FVIII level, c) Applying an event time to event (RTTE) model to the subject based on the personalized information and / or the desired treatment outcome information, d) Estimating the bleeding risk of each individual subject using the RTTE model, e) The software-based system transmits the estimated bleeding risk information in (d) for outputting the bleeding risk information. Methods that include...

19. The method according to claim 18, wherein the RTTE model includes therapeutic effect as a covariate.

20. The method according to claim 19, wherein the therapeutic effect is preventive treatment or on-demand treatment.

21. The method according to any one of claims 18 to 20, wherein the RTTE model is the RTTE model [B].

22. The method according to any one of claims 18 to 20, wherein the RTTE model is the RTTE model [C].

23. The method according to any one of claims 18 to 20, wherein the RTTE model is the RTTE model [D].

24. The method according to any one of claims 18 to 20, wherein the RTTE model is the RTTE model [D1].

25. The method according to any one of claims 18 to 20, wherein the RTTE model is the RTTE model [D2].

26. The method according to any one of claims 18 to 25, wherein the desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A].

27. The method according to any one of claims 18 to 25, wherein the desired treatment outcome information is provided by the ephanesoctocog alfa popPK model [A'].

28. The method according to any one of claims 18 to 25, wherein the desired treatment outcome information is provided by the individual subjects.

29. The method according to any one of claims 18 to 25, wherein the desired treatment outcome information is provided by a healthcare professional.

30. A method for treating a subject with hemophilia A, (a) Identifying subjects with hemophilia A, (b) To estimate the bleeding risk of the subject when treated with 50 IU / kg ephanesoctocog alfa approximately once a week, (c) If the subject has an FVIII activity level of at least 10 IU / dL, estimate the bleeding risk of the subject. (d) If the estimated bleeding risk of the subject with efanesoctocog alfa therapy is lower than the estimated bleeding risk of the subject having an FVIII activity level of at least 10 IU / dL, the subject shall be treated with efanesoctocog alfa at 50 IU / kg approximately once a week, Methods that include...

31. A method for estimating the bleeding risk of individual subjects, (a) Receiving information about individual objects through a software-based system, the system is (i) A one-compartment efanesoctocogalphapopPK model for calculating coagulation factor VIII (FVIII) activity information, wherein the efanesoctocogalphapopPK model includes body weight as a covariate, and the efanesoctocogalphapopPK model does not include VWF level or hematocrit level as a covariate; and (ii) An iteration time to event (RTTE) model that estimates bleeding risk using the FVIII activity information described above. It is programmed to execute, receive, (b) Using the software-based system, calculate the estimated bleeding risk using the ephanesoctocogalphapopPK model, the RTTE model, and the received information, (c) The software-based system transmits the calculated bleeding risk information in (b) for outputting the bleeding risk information. Methods that include...

32. A method for estimating bleeding risk, (a) Receiving information about individual objects through one or more electronic devices, (b) The processing device transmits information about individual objects to a software-based system, the software-based system (i) A one-compartment efanesoctocogalphapopPK model for calculating coagulation factor VIII (FVIII) activity information, wherein the efanesoctocogalphapopPK model includes body weight as a covariate, and the efanesoctocogalphapopPK model does not include VWF level or hematocrit level as a covariate; and (ii) An iteration time to event (RTTE) model that estimates bleeding risk using the FVIII activity information described above. It is programmed to perform the following actions: sending, (c) Obtaining an estimated bleeding risk from the software-based system using the ephanesoctocog alfa popPK model, the RTTE model, and the received information, (d) Transmitting the bleeding risk information in (c) in order to output the bleeding risk information by one or more electronic devices. Methods that include...

33. The method according to any one of claims 1 to 32, wherein the subject is at least 12 years old.

34. The method according to any one of claims 1 to 32, wherein the subject is at least 18 years old.

35. The method according to any one of claims 1 to 32, wherein the subject is under 12 years of age.

36. The method according to any one of claims 1 to 32, wherein the subject is under 6 years of age.

37. The method according to any one of claims 1 to 32, wherein the subject is 6 to 12 years of age.

38. A data processing device, device, or system including a processor configured to implement the EfanesoctocogalphaRTTE model.

39. A computer program, which, when executed by a computer, includes instructions causing the computer to implement an iteration time-to-event (RTTE) model.

40. A computer-readable medium comprising, when executed by a computer, an instruction causing the computer to perform the method according to any one of claims 1 to 37.

41. A method for estimating bleeding risk, (a) Receiving coagulation factor VIII (FVIII) activity information by a software-based system including a computer program programmed to perform an event-time-to-event (RTTE) model [D2], (b) Using the computer program, calculate the bleeding risk using the RTTE model [D2] and the received information, (c) The software-based system transmits the calculated bleeding risk information in (b) for outputting the bleeding risk information. Methods that include...

42. A method for estimating bleeding risk, (a) Receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices, (b) The processing device transmits the coagulation factor VIII (FVIII) information to a software-based system, which is programmed to perform an iteration time to event (RTTE) model [D2] to calculate the bleeding risk, (c) Receiving from the software-based system the transmitted information in (b) and bleeding risk information calculated using the RTTE model [D2], (d) Transmitting the bleeding risk information in (c) in order to output the bleeding risk information by one or more electronic devices. Methods that include...

43. A method for estimating the bleeding risk of individual subjects, (a) Receiving personalized subject information, including the subject's weight, through a software-based system, (b) The software-based system receives desired treatment outcome information, including FVIII level, (c) Applying an iteration time to event (RTTE) model [D2] to the subject based on the personalized information and / or the desired treatment outcome information, (d) Estimating the bleeding risk of each individual subject using the RTTE model [D2], (e) The software-based system transmits the estimated bleeding risk information in (d) for outputting the bleeding risk information. Methods that include...

44. A method for treating a subject with hemophilia A, (a) Identifying subjects with hemophilia A, (b) To estimate the bleeding risk of the subject when treated with 50 IU / kg ephanesoctocog alfa approximately once a week, (c) If the subject has an FVIII activity level of at least 10 IU / dL, estimate the bleeding risk of the subject. (d) If the estimated bleeding risk of the subject with efanesoctocog alfa therapy is lower than the estimated bleeding risk of the subject having an FVIII activity level of at least 10 IU / dL, then the subject shall be treated with efanesoctocog alfa at 50 IU / kg approximately once a week. Includes, To estimate the bleeding risk of efanesoctocog alfa therapy and / or the bleeding risk of the subject when the subject has an FVIII activity level of at least 10 IU / dL, the repeat time to event (RTTE) model [D2] is used. method.

45. A method for estimating the bleeding risk of individual subjects, (a) Receiving information about individual objects through a software-based system, the system is (i) A one-compartment ephanesoctocogalphapopPK model [A'] for calculating coagulation factor VIII (FVIII) activity information; and (ii) A time-to-event (RTTE) model that estimates bleeding risk using the FVIII activity information [D2] It is programmed to execute, receive, (b) The software-based system calculates the estimated bleeding risk using the ephanesoctocog alfa popPK model [Q], the RTTE model [D2], and the received information, (c) The software-based system transmits the calculated bleeding risk information in (b) for outputting the bleeding risk information. Methods that include...

46. A method for estimating bleeding risk, (a) Receiving information about individual objects through one or more electronic devices, (b) The processing device transmits information about individual objects to a software-based system, the software-based system (i) A one-compartment ephanesoctocogalphapopPK model [A'] for calculating coagulation factor VIII (FVIII) activity information; and (ii) A time-to-event (RTTE) model that estimates bleeding risk using the FVIII activity information [D2] It is programmed to perform the following actions: sending, (c) Receiving an estimated bleeding risk from the software-based system using the ephanesoctocog alfa popPK popPK model [Q], the RTTE model [D2], and the received information, (d) Transmitting the bleeding risk information in (c) in order to output the bleeding risk information by one or more electronic devices. Methods that include...

47. A data processing device, device, or system including a processor configured to implement the EfanesoctocogalphaRTTE model [D2].

48. A computer program, which, when executed by a computer, includes instructions causing the computer to perform an iteration time to an ephanesoctocogalpha event (RTTE) model [D2].

49. A computer-readable medium comprising, when executed by a computer, an instruction causing the computer to perform the method described in any one of claims 41 to 46.

50. A pharmaceutical composition comprising efanesoctocog alfa for use in reducing the risk of trauma-free bleeding in human subjects with severe hemophilia A over a period of 52 weeks, wherein the risk is reduced to less than 30%, and during the period, efanesoctocog alfa is administered intravenously to the subject at a dose of about 25 IU / kg to about 50 IU / kg every about 4 to about 14 days, the human subject is between 6 and 12 years of age, the subject weighs 30 to 35 kg, and the intravenous administration reduces the probability to less than 30%.

51. A pharmaceutical composition comprising efanesoctocog alfa for use in reducing the risk of trauma-free bleeding in human subjects with severe hemophilia A over a period of 52 weeks, wherein the risk is reduced to less than 30%, and during the period, efanesoctocog alfa is administered intravenously to the subject at a dose of about 25 IU / kg to about 50 IU / kg every about 4 to about 14 days, the human subject is between 6 and 12 years of age, and the subject weighs 30 to 35 kg, thereby reducing the probability to less than 30%.

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