Methods for treating hemophilia A and estimating bleeding risk and uses thereof

Abstract

The present disclosure provides methods for estimating the risk of bleeding in a subject having hemophilia A, and computer-based systems that include and implement a time of occurrence (RTTE) model of repetitive events.

Description

[0001] Reference to the electronically submitted sequence list

[0002] The contents of the sequence list of electronic submissions in XML file form (name: 744527_SA9-504-3_ST26.xml; size: 33,139 bytes; creation date: October 20, 2023) are incorporated herein by reference in their entirety.

[0003] Cross-references to related applications

[0004] This application claims the benefit of U.S. Provisional Application No. 63 / 482,631, filed February 1, 2023; U.S. Provisional Application No. 63 / 485,658, filed February 17, 2023; and U.S. Provisional Application No. 63 / 592,100, filed October 20, 2023; each of these applications is incorporated herein by reference in its entirety for all purposes. Background Technology

[0005] While plasma-derived and recombinant clotting factor products allow hemophilia patients to live longer and healthier lives, hemophilia remains one of the most expensive and complex conditions to manage. Due to its complexity, treatment of hemophilia A with FVIII replacement therapy requires a specialized approach to management for physicians, pharmacies, and patients. Clinicians frequently assess lifestyle, psychosocial needs, and family environment when evaluating the ability of patients or guardians to provide appropriate care.

[0006] Current recommended standards of care involve the regular administration of FVIII (routine prophylaxis) to minimize the number of bleeding episodes. Routine prophylaxis is associated with improved long-term outcomes but is a demanding protocol limited by the need for frequent intravenous (IV) administration. See Manco-Johnson et al., N Engl J Med. [New England Journal of Medicine] 357(6):535-44 (2007). FVIII products with extended half-lives reduce the frequency of FVIII administration for prophylaxis; however, most existing FVIII products interact with endogenous von Willebrand factor (VWF) and have comparable circulating half-lives consistent with the upper limit of the half-life of rFVIII variants due to the endogenous VWF half-life. See, for example, Pipe et al., Blood. [Blood] 128(16):2007-16 (2016). Summary of the Invention

[0007] This article discloses, in particular, methods for assessing the bleeding risk in subjects with hemophilia A and computer-based systems including time-of-recurrence-event (RTTE) models, and their use in estimating the bleeding risk in subjects with hemophilia A. In some embodiments, the RTTE model includes treatment effect as a covariate. In some embodiments, treatment effect is preventative treatment or on-demand treatment. In some embodiments, the RTTE model is an RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2]. In some embodiments, estimating the bleeding risk is estimating the risk of a bleeding event occurring.

[0008] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving coagulation factor VIII (FVIII) activity information by a software-based system, the software-based system including a computer program programmed to implement an RTTE model [B]; calculating the bleeding risk by the computer program using the RTTE model [B] and the received information; and transmitting the calculated bleeding risk information (b) by the software-based system to output bleeding risk information.

[0009] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving coagulation factor VIII (FVIII) activity information by a software-based system comprising a computer program programmed to implement an RTTE model [B]; calculating the bleeding risk by the computer program using the RTTE model [C] and the received information; and transmitting the calculated bleeding risk information (b) by the software-based system to output bleeding risk information.

[0010] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving coagulation factor VIII (FVIII) activity information by a software-based system, the software-based system including a computer program programmed to implement an RTTE model [B]; calculating the bleeding risk by the computer program using the RTTE model [D1] and the received information; and transmitting the calculated bleeding risk information (b) by the software-based system to output bleeding risk information.

[0011] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving coagulation factor VIII (FVIII) activity information by a software-based system comprising a computer program programmed to implement an RTTE model [B]; calculating bleeding risk by the computer program using the RTTE model [D2] and the received information; and transmitting the calculated bleeding risk information (b) by the software-based system to output bleeding risk information.

[0012] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement an RTTE model [B] to calculate the bleeding risk; receiving from the software-based system the transmitted information and the bleeding risk information calculated using the RTTE model [B]; and transmitting the bleeding risk information by one or more electronic devices (c) to output the bleeding risk information.

[0013] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement an RTTE model [C] to calculate the bleeding risk; receiving bleeding risk information calculated using (b) the transmitted information and the RTTE model [C] from the software-based system; and transmitting (c) the bleeding risk information by one or more electronic devices to output the bleeding risk information.

[0014] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement an RTTE model [D1] to calculate the bleeding risk; receiving bleeding risk information calculated using (b) the transmitted information and the RTTE model [D1] from the software-based system; and transmitting (c) the bleeding risk information by one or more electronic devices to output the bleeding risk information.

[0015] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including receiving FVIII activity information by one or more electronic devices; transmitting the FVIII information to a software-based system by a processing device, wherein the software-based system is programmed to implement an RTTE model [D2] to calculate the bleeding risk; receiving bleeding risk information calculated using (b) the transmitted information and the RTTE model [D2] from the software-based system; and transmitting (c) the bleeding risk information by one or more electronic devices to output the bleeding risk information.

[0016] Certain aspects of this disclosure relate to methods for estimating the bleeding risk of an individual subject, methods comprising receiving, by a software-based system, individualized subject information including the subject's weight; receiving, by a software-based system, expected treatment outcome information including FVIII activity levels; applying a subject-specific RTTE model based on the individualized information and / or expected treatment outcome information; estimating the individual subject's bleeding risk using the RTTE model; and transmitting (d) the estimated bleeding risk information by the software-based system to output bleeding risk information. In some embodiments, the expected treatment outcome information is provided by an Ivanin αpopPK model [A]. In some embodiments, the expected treatment outcome information is provided by an Ivanin αpopPK model [A']. In some embodiments, the expected treatment outcome information is provided by the individual subject. In some embodiments, the individual subject has severe hemophilia A. In some embodiments, the expected treatment outcome information is provided by a medical professional. In some embodiments, the RTTE model is an RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2].

[0017] Certain aspects of this disclosure relate to methods for treating subjects with hemophilia A, including identifying the subject with hemophilia A; estimating the subject's bleeding risk when treated with 50 IU / kg levonorgestrel-alpha approximately once weekly; 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 levonorgestrel-alpha approximately once weekly if the subject has a lower estimated bleeding risk under levonorgestrel-alpha therapy compared to the estimated bleeding risk when the subject has an FVIII activity level of at least 10 IU / dL. In some embodiments, the estimated bleeding risk is calculated using an RTTE model. In some embodiments, the RTTE model is an RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2].

[0018] Certain aspects of this disclosure relate to methods for estimating the bleeding risk of an individual subject, methods comprising (a) receiving information of the individual subject by a software-based system, wherein the system is programmed to implement (i) a one-compartment Evans αpopPK model to calculate FVIII activity information, wherein the Evans αpopPK model includes body weight as a covariate, and wherein the Evans αpopPK model does not include VWF levels or hematocrit levels as covariates; and (ii) an RTTE model that uses the FVIII activity information to estimate the bleeding risk, (b) calculating the estimated bleeding risk by the software-based system using the Evans αpopPK model, the RTTE model, and the received information, and (c) transmitting the calculated bleeding risk information from (b) by the software-based system to output bleeding risk information. In some embodiments, the RTTE model is an RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2]. In some embodiments, the individual subject has severe hemophilia A.

[0019] Certain aspects of this disclosure relate to methods for estimating bleeding risk, including: (a) receiving information from an individual subject by one or more electronic devices; (b) transmitting the individual subject's information to a software-based system by a processing device, wherein the software-based system is programmed to implement (i) a one-compartment Evans αpopPK model to calculate FVIII activity information, wherein the Evans αpopPK model includes body weight as a covariate and wherein the Evans αpopPK model does not include VWF levels or hematocrit levels as covariates; and (ii) an RTTE model that uses FVIII activity information to estimate bleeding risk; (c) receiving from the software-based system a bleeding risk estimated using the Evans αpopPK model, the RTTE model, and the received information; and (d) transmitting the bleeding risk information from (c) by one or more electronic devices to output bleeding risk information. In some embodiments, the RTTE model is an RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2]. In some embodiments, the individual subject has severe hemophilia A.

[0020] In some embodiments, FVIII activity information is provided by a popPK model. In some embodiments, the popPK model is an Ivanin α popPK model. In some embodiments, the Ivanin α popPK model includes body weight as a covariate, and wherein the Ivanin α popPK model does not include VWF levels or hematocrit levels as covariates. In some embodiments, the popPK model is an Ivanin α popPK model [A]. In some embodiments, the popPK model is an Ivanin α popPK model [A'].

[0021] In some embodiments, the FVIII activity information provided is specific to an individual subject.

[0022] In some embodiments, the subject has severe hemophilia A.

[0023] In some embodiments, the subject is less than 6 years old. In some embodiments, the subject is at least 6 years old. In some embodiments, the subject is less than 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 less than 18 years old.

[0024] In some embodiments, the FVIII activity information provided is an estimated FVIII activity level for a subject population. In some embodiments, the FVIII activity information provided is for a hypothetical or virtual subject. In some embodiments, the FVIII activity information provided is a target FVIII activity level. In some embodiments, the FVIII activity information provided is a variable FVIII activity level over time. In some embodiments, the subject has had or has had an FVIII activity level of at least 10 IU / dL.

[0025] Certain aspects of this disclosure relate to a data processing apparatus, device, or system including a processor configured to implement an Evans α RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2]. In some embodiments, the data processing apparatus, device, or system is further configured to implement a one-compartment Evans α popPK model that includes body weight as a covariate, wherein the Evans α popPK model does not include the level of VWF or the level of hematocrit as covariates. In some embodiments, the popPK model is an Evans α popPK model [A]. In some embodiments, the popPK model is an Evans α popPK model [A']. In some embodiments, the data processing apparatus, device, or system includes a smartphone, tablet computer, personal digital assistant, handheld computer, laptop computer, or smartwatch. In some embodiments, the data processing apparatus, device, or system includes a smartphone.

[0026] Some aspects of this disclosure relate to a computer program comprising instructions that, when executed by a computer, cause the computer to implement an Ivanin α RTTE model [B]. In some embodiments, the RTTE model is an RTTE model [C]. In some embodiments, the RTTE model is an RTTE model [D1]. In some embodiments, the RTTE model is an RTTE model [D2]. In some embodiments, the RTTE model uses a PK curve (e.g., the level of FVIII activity over time) generated from a popPK model as input. In some embodiments, the popPK model is an Ivanin α popPK model. In some embodiments, the Ivanin α popPK model is a one-compartment Ivanin α popPK model that includes body weight as a covariate, wherein the Ivanin α popPK model does not include the level of VWF or the level of hematocrit as covariates. In some embodiments, the popPK model is an Ivanin α popPK model [A]. In some embodiments, the popPK model is an Ivanin α popPK model [A'].

[0027] Certain aspects of this disclosure relate to a computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein.

[0028] Certain aspects of this disclosure relate to methods for treating hemophilia A in human subjects in need, including intravenous administration of 25 IU / kg to 50 IU / kg of levonorgestrel alpha to the subject every about 4 to about 14 days, wherein the human subject is less than 6 years old and weighs 15 to 20 kg.

[0029] Certain aspects of this disclosure relate to methods for treating hemophilia A in human subjects in need, including intravenous administration of 25 IU / kg to 50 IU / kg of levonorgestrel α to the subject every about 4 to about 14 days, wherein the human subject is 6 to <12 years old and weighs 30 to 35 kg.

[0030] In some embodiments, levonorgestrel-alpha is administered at a dose of about 25 IU / kg every about 4 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 30 IU / kg every about 7 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 7 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 14 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 25 IU / kg every about 4 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 30 IU / kg every about 7 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 7 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 14 days for at least about 52 weeks.

[0031] Certain aspects of this disclosure relate to a pharmaceutical composition comprising Evansin Alpha for the treatment of hemophilia A in human subjects in need, wherein Evansin Alpha is administered intravenously to the subject every approximately 4 to approximately 14 days at a dose of approximately 25 IU / kg to approximately 50 IU / kg, and wherein the human subject is less than 6 years of age and weighs between 15 and 20 kg.

[0032] Certain aspects of this disclosure relate to a pharmaceutical composition comprising Evansin Alpha for the treatment of hemophilia A in human subjects in need, wherein Evansin Alpha is administered intravenously to the subject every approximately 4 to approximately 14 days at a dose of approximately 25 IU / kg to approximately 50 IU / kg, and wherein the human subject is 6 to <12 years of age and weighs 30 to 35 kg.

[0033] In some embodiments, levonorgestrel-alpha is administered at a dose of about 25 IU / kg every about 4 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 30 IU / kg every about 7 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 7 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 14 days. In some embodiments, levonorgestrel-alpha is administered at a dose of about 25 IU / kg every about 4 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 30 IU / kg every about 7 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 7 days for at least about 52 weeks. In some embodiments, levonorgestrel-alpha is administered at a dose of about 50 IU / kg every about 14 days for at least about 52 weeks.

[0034] Certain aspects of this disclosure relate to methods for reducing the risk of non-traumatic bleeding in human subjects with severe hemophilia A during a 52-week period, wherein the risk is reduced to a probability of less than 50%, the method comprising administering intravenous levonorgestrel alpha of about 25 IU / kg to about 50 IU / kg to the subject every about 4 to about 14 days during this period, wherein the human subject is 6 to <12 years old and weighs 30 to 35 kg, thereby reducing the probability to less than 50%.

[0035] Certain aspects of this disclosure relate to methods for reducing the probability of non-traumatic bleeding in human subjects with severe hemophilia A over the next 52 weeks, wherein the risk is reduced to a probability of less than 50%, the method comprising administering intravenous levonorgestrel alpha of about 25 IU / kg to about 50 IU / kg to the subject every about 4 to about 14 days during this period, wherein the human subject is 6 to <12 years old and weighs 30 to 35 kg, thereby reducing the probability to less than 50%.

[0036] 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 spontaneous bleeding. As used herein, “spontaneous bleeding” is bleeding that occurs without known contributing factors, such as explicit trauma or prior strenuous activity. In some embodiments, levonorgestrel α is administered at a dose of about 25 IU / kg every about 4 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 30 IU / kg every about 7 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 50 IU / kg every about 7 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 50 IU / kg every about 14 days during this period.

[0037] Certain aspects of this disclosure relate to a pharmaceutical composition comprising Evansin Alpha for reducing the risk of non-traumatic bleeding in human subjects with severe hemophilia A over a 52-week period, wherein the risk is reduced to a probability of less than 50%, wherein Evansin Alpha is administered intravenously to the subject every approximately 4 to approximately 14 days during this period at a rate of approximately 25 IU / kg to approximately 50 IU / kg, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 50%.

[0038] Certain aspects of this disclosure relate to pharmaceutical compositions comprising Evansin Alpha for reducing the risk of non-traumatic bleeding in human subjects with severe hemophilia A over a 52-week period, wherein the risk is reduced to a probability of less than 50%, wherein Evansin Alpha is administered intravenously to the subject every approximately 4 to approximately 14 days during this period at a dose of approximately 25 IU / kg to approximately 50 IU / kg, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 50%.

[0039] 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, the bleeding not caused by trauma is spontaneous bleeding. In some embodiments, levonorgestrel α is administered at a dose of about 25 IU / kg every approximately 4 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 30 IU / kg every approximately 7 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 50 IU / kg every approximately 7 days during this period. In some embodiments, levonorgestrel α is administered at a dose of about 50 IU / kg every approximately 14 days during this period. Attached Figure Description

[0040] Figure 1 It is a visual representation of a software-based system that can be used in the methods disclosed herein.

[0041] Figure 2 It is an exemplary visual representation of a network-based system that can be used according to the methods disclosed herein.

[0042] Figure 3 A schematic diagram of an example computing system 400 is shown.

[0043] Figure 4This is a graph showing simulated steady-state FVIII activity over time in patients aged ≥12 years using an Evansin α population PK model [A] (50 IU / kg) based on clinical data derived from a phase I coagulation assay. Observed FVIII activity 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.

[0044] Figure 5A This is a graph showing the baseline-corrected FVIII activity time curves from the adult / adolescent study XTEND-1 (EFC16293, NCT04161495) on day 1. Data for all patients are shown. LLOQ = 1 IU / dL. Figure 5B This is a graph showing the baseline-corrected FVIII activity time curves from the XTEND-1 adult / adolescent study at day 1 (solid line) and week 26 (dashed line). Data from patients in the sequential group are shown only.

[0045] Figure 6 The correlations among four continuous covariates at baseline are shown. WTKGB represents baseline weight (median 78.3, excluding EFC16295). BH represents baseline ethnicity (median 43, excluding EFC16295). BVWF represents baseline VWF (median 112, excluding EFC16295).

[0046] Figure 7A and Figure 7B This demonstrates population prediction (PRED) using the Ivanin α population PK model [A]. Figure 7A ) and Individual Prediction (IPRED) Figure 7B This is a graph comparing the observed period one FVIII activity value (DV; data value). The black line is the uniform line. The gray line is the locally weighted regression (LOESS) smoothing line. Figure 7A R-value of the middle population prediction 2 =0.92. In Figure 7B R predicted by individuals 2 =0.97.

[0047] Figure 8 This is a set of graphs demonstrating the performance of the Ivan α population PK model [A] using Visual Predictive Check (VPC). Hollow circles represent the observed data. Solid lines represent the median simulated by the model. Dashed lines represent the 5th and 95th percentiles simulated by the model. The shading around each dashed line represents the 90% CI around the 5th and 95th percentiles simulated. The shading around each solid line represents the 90% CI around the median simulated.

[0048] Figure 9A and Figure 9BThis demonstrates the PRED (Paired Activated Receptor Detection) of surgery compared to DV when using the Ivanin α population PK model [A]. Figure 9A ) and IPRED ( Figure 9B The gray line is a uniform line. (R) 2 As shown by the black line, the black line is the regression line between the observed value and the predicted value.

[0049] Figure 10 This shows the steady state C 谷 C maxss A set of graphs showing the distribution of time to reach 40 IU / dL of FVIII activity level at different body weights.

[0050] Figure 11 This shows the steady state C 谷 C maxss A set of graphs showing the distribution of time to reach 40 IU / dL for FVIII activity levels in different age groups in non-Asian and Asian populations.

[0051] Figure 12 This graph shows OSC FVIII activity over time in patients aged 6 years and younger undergoing major surgery and experiencing significant bleeding. Solid gray line: Median of the simulated dose at 30 IU / kg. Circled gray line: Median of the simulated dose at 50 IU / kg. Solid black line: 5th and 95th percentiles of the simulated dose. Dashed lines indicate OSC activity at 80 IU / kg and 40 IU / kg.

[0052] Figure 13 This is a graph showing simulated FVIII activity in virtual adult and adolescent populations at a dose of 50 IU / kg levonorgestrel-alpha, followed by 30 IU / kg every 3 days until day 14.

[0053] Figure 14 This is a graph showing simulated OSC FVIII activity over time for all age groups.

[0054] Figure 15 This is a Kaplan-Meier diagram showing patients without bleeding events in groups A and B of the XTEND-1 study.

[0055] Figure 16 This is a set of figures showing the Kaplan–Meier visual prediction test of the final RTTE model of the prevention group in the XTEND-1 study.

[0056] Figure 17 This is a graph showing the probability of a bleeding event occurring in a typical patient within one year, generated by the RTTE model.

[0057] Figure 18This is a set of histograms showing the distribution of covariates at baseline in patients in the BIVV001 RTTE analysis dataset for children. In each plot, the median is shown as the solid vertical line on the left, and the mean is shown as the dashed vertical line on the right.

[0058] Figure 19 This is the correlation matrix of covariates at baseline for patients in the BIVV001 RTTE analysis dataset for children. For 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, with the width of the ellipse corresponding to the absolute value of the correlation coefficient, and the slope of the ellipse indicating whether the correlation is positive or negative. Three ellipses representing negative values ​​are further identified by darkened borders. The correlation between each pair of variables is calculated using all individuals who observed both variables simultaneously.

[0059] Figure 20 This is a set of graphs showing the Kaplan-Meier visual prediction check of the final children's RTTE model applied to the children's BIVV001 RTTE dataset (n=74). The solid and dashed lines represent the Kaplan-Meier point estimates and 95% confidence intervals for the observed data, respectively; the shaded areas represent the 95% confidence intervals for the Kaplan-Meier point estimates based on 400 repeated simulations.

[0060] Figure 21 This is a set of graphs showing the predicted FVIII activity compared to the observed FVIII activity from the EFC16295 and LTS16294 studies. The lines with triangles are uniform lines, while the dashed lines are locally weighted regression smoothing lines.

[0061] Figure 22 This is a set of graphs showing the conditionally weighted residuals (CWRES) compared to time and CWRES compared to PRED. The gray line is the locally weighted regression smoothing line.

[0062] Figure 23 This is a set of graphs showing Individual Weighted Residuals (IWRES) compared to time and IWRES compared to IPRED. The gray line is the locally weighted regression smoothing line.

[0063] Figure 24 This is a graph showing the Visual Prediction Check (VPC) of the final popPK model from the EFC16295 study.

[0064] Figure 25 This is a set of graphs showing predicted FVIII activity during and after surgery (postoperative follow-up) compared to observed FVIII activity. The lines with triangles are uniform lines, while the dashed lines are locally weighted regression smoothing lines.

[0065] Figure 26 This is a plot showing the steady-state AUCtau, Cmax, and Cmin used to assess intrinsic / extrinsic factors against the baseline VWF. The gray line is the locally weighted regression smoothing line. Hollow circles represent children aged 1 year and older but under 6 years old. Solid circles represent children aged 6 years and older but under 12 years old.

[0066] Figure 27 This is a graph showing the steady-state AUCtau, Cmax, and Cmin used to assess intrinsic / extrinsic factors against baseline weight. The gray line is a locally weighted regression smoothing line. Hollow circles represent children aged 1 year and older but under 6 years old. Solid circles represent children aged 6 years and older but under 12 years old.

[0067] Figure 28 This is a graph showing the homeostatic AUCtau, Cmax, and Cmin used to assess intrinsic / extrinsic factors against baseline hematocrit. The gray line is a locally weighted regression smoothing line. Hollow circles represent children aged 1 year and older but under 6 years. Solid circles represent children aged 6 years and older but under 12 years. Detailed Implementation

[0068] With the emergence of alternatives with extended half-lives, treatment targets are expanding beyond low annualized bleeding rates (ABR) to include long-term outcomes associated with high and sustained plasma FVIII activity levels, such as long-term joint protection. Evansin α is independent of the circulation of endogenous von Willebrand factor (VWF) and provides high and 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 Evansin α as BIVV001), the entire contents of each of these references are incorporated herein by reference for all purposes).

[0069] This disclosure provides, in particular, a treatment method and a software-based system for estimating individualized eprenin α PK information for subjects. In some embodiments, the software-based system applies an eprenin α population PK model [A] to estimate dose information for subjects receiving eprenin α as an alternative to FVIII.

[0070] This disclosure also provides, in particular, treatment methods and software-based systems for estimating or quantifying bleeding risk associated with different levels of FVIII activity (e.g., over time). In some embodiments, the software-based system may use an alpha population pharmacokinetic model of levonorgestrel when quantifying the bleeding risk in cases of high sustained FVIII activity compared to standard of care.

[0071] definition

[0072] The term "about" is used herein to mean approximately, roughly, roughly, or around. When the term "about" is used in conjunction with a numerical range, it defines the range by extending the boundaries to be above and below the stated value. Generally, the term "about" can define a value to be above and below a specified value by, for example, a variation of around 10% (higher or lower). In some embodiments, the term indicates a deviation from the indicated value 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%. In some embodiments, "about" indicates a deviation from the indicated value of ±10%. In some embodiments, "about" indicates a deviation from the indicated value of ±5%. In some embodiments, "about" indicates a deviation from the indicated value of ±4%. In some embodiments, "about" indicates a deviation from the indicated value of ±3%. In some embodiments, "about" indicates a deviation from the indicated value of ±2%. In some embodiments, "about" indicates a deviation of ±1% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.9% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.8% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.7% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.6% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.5% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.4% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.3% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.1% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.05% from the indicated value. In some embodiments, "about" indicates a deviation of ±0.01% from the indicated value.

[0073] It should be understood that wherever aspects are described in this document using the language “comprising / including”, similar aspects described in the form of “consisting of” and / or “substantially consisting of” are also provided.

[0074] As used in the context of hemophilia A, the term "prophylactic treatment" refers to the prior administration of a therapy intended for the treatment of hemophilia A, wherein such treatment aims to prevent or reduce the severity of one or more symptoms of hemophilia A, such as bleeding episodes (e.g., one or more spontaneous bleeding episodes) and / or joint damage. To prevent such symptoms (e.g., bleeding episodes and the progression of joint disease) or reduce their severity, patients with hemophilia A may receive regular infusions of clotting factors (such as Evansin Alpha) as part of a prophylactic treatment regimen.

[0075] The terms "on-demand treatment" or "episodic treatment" refer to the administration of FVIII replacement therapy (such as levonorgestrel-releasing agent alpha) "as needed" in response to symptoms of hemophilia A (e.g., bleeding episodes such as spontaneous or traumatic bleeding episodes) or before activities that may cause bleeding. In some embodiments, on-demand treatment may be given to a subject at the onset of bleeding (e.g., after injury) or when bleeding is anticipated (e.g., before surgery). In some embodiments, on-demand treatment may be given before activities that increase the risk of bleeding (e.g., contact sports). In some embodiments, on-demand treatment may be given to a subject receiving prophylactic treatment, for example, if a supplemental dose of FVIII replacement protein is administered to treat a bleeding episode or 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 protocol is used for perioperative bleeding management.

[0076] In some embodiments, a bleeding episode begins with the first sign of bleeding and ends 72 hours after the last treatment for bleeding, wherein any bleeding at the same location or injections spaced 72 hours apart are considered the same bleeding episode. See Blanchette V. (2006) Haemophilia 12:124-7. In some embodiments, any injection administered more than 72 hours after a previous injection for a bleeding episode is considered the first injection for a new bleeding episode at the same location. In some embodiments, any bleeding at different locations is considered a separate bleeding episode, regardless of the time elapsed since the last injection.

[0077] The methods described herein can be applied to subjects requiring prophylactic or episodic / on-demand treatment. In some embodiments, subjects requiring prophylactic or episodic / on-demand treatment may have hemarthrosis, muscle bleeding, oral bleeding, hemorrhage, bleeding into muscles, trauma, head trauma (trauma capitis), gastrointestinal bleeding, intracranial hemorrhage, intraperitoneal hemorrhage, intrapleural hemorrhage, fracture, central nervous system hemorrhage, retropharyngeal bleeding, retroperitoneal bleeding, and iliopsoas sheath bleeding. In some embodiments, subjects may require surgical treatment, including, for example, surgical prophylaxis or perioperative management. In some embodiments, the surgery may be minor or major. Exemplary surgeries include tooth extraction, tonsillectomy, inguinal hernia incision, synovectomy, craniotomy, bone suture, trauma surgery, intracranial surgery, intraperitoneal surgery, intrathoracic surgery, joint replacement surgery (e.g., total knee replacement, hip replacement, etc.), cardiac surgery, and cesarean section.

[0078] As used in the context of hemophilia A in this article, “treat” and “treating” include, for example, reducing the severity of hemophilia A; improving one or more symptoms associated with hemophilia A; providing a beneficial effect to a subject with hemophilia A, but not necessarily curing hemophilia A; and / or preventing one or more symptoms associated with hemophilia A.

[0079] In some embodiments, treatment of hemophilia A includes preventing one or more symptoms of hemophilia A (such as spontaneous bleeding). In some embodiments, treatment of hemophilia A includes reducing the likelihood of bleeding episodes / events or mitigating the severity of bleeding episodes / events. In some embodiments, the treatment is preventative. In some embodiments, the treatment is on-demand. In some embodiments, the treatment includes reducing the frequency of one or more symptoms of hemophilia A (e.g., spontaneous or uncontrollable bleeding episodes).

[0080] As used herein, the term “perioperative management” refers to the use of Evansin Alpha before, during, or after a surgical procedure (e.g., surgery). The use of “perioperative management” for one or more bleeding episodes includes surgical prophylaxis before (i.e., preoperative), during (i.e., intraoperative), or after (i.e., postoperative) surgery to prevent one or more bleeding episodes or to mitigate or suppress spontaneous and / or uncontrollable bleeding episodes before, during, and after surgery.

[0081] As used herein, “baseline” plasma FVIII activity level is the lowest plasma FVIII activity level measured in a subject prior to dose administration. In some embodiments, activity above baseline prior to administration may be considered as residual FVIII activity from previous treatment and may be used to account for the decay of the previous treatment’s half-life over time and subtracted from PK data following administration of Evans α.

[0082] The terms “patient” and “subject” are used interchangeably herein and refer to a person. A subject may include, for example, an individual who has been diagnosed with hemophilia A and is susceptible to spontaneous and / or uncontrollable bleeding episodes. A subject may also include an individual who is at risk of one or more uncontrollable bleeding episodes prior to some activity (e.g., surgery, sports activity, or any strenuous activity). In some embodiments, the 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, the subject has severe hemophilia A, defined as <1 IU / dL (<1%) of endogenous FVIII activity. In some embodiments, the subject has no clotting disorders other than hemophilia A.

[0083] As used herein, the terms “ELNN peptide” and “ELNN” are synonymous and refer to extended length peptides comprising a non-naturally occurring, substantially non-repetitive sequence (e.g., peptide motif) consisting primarily of small hydrophilic amino acids, which, under physiological conditions, has a low degree of secondary or tertiary structure or no secondary or tertiary structure. ELNN peptides include unstructured hydrophilic peptides comprising a repeating motif of six natural amino acids (G, A, P, E, S, and / or T). In some embodiments, ELNN peptides comprise multiple motifs of the six natural amino acids (G, A, P, E, S, T), wherein these motifs are identical or comprise combinations of different motifs. In some embodiments, ELNN peptides, when linked to proteins (including T-cell conjugates disclosed herein), may confer certain desired pharmacokinetic, physicochemical, and pharmaceutical properties. Such desired properties may include, but are not limited to, enhanced pharmacokinetic parameters and solubility characteristics, and improved therapeutic index. ELNN peptides are known in the art, and non-limiting descriptions and examples of ELNN peptides referred to as XTEN peptides are available in the following literature: 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 (pp. 1-33), the entire contents of each of these literatures are incorporated herein by reference.

[0084] As used herein, a "software-based system" refers to an algorithm or set of algorithms that can be implemented by one or more processing devices. The software-based system may be embodied in 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-usable or computer-readable media, which provide program code for use by or in conjunction with a computer or any instruction execution system. The software may be implemented across several processing devices that collectively comprise a software-based system. For the purposes of this description, a computer-usable or computer-readable medium may be any device that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system, device, or apparatus. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or device or apparatus) or a propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer floppy disks, random access memory (RAM), read-only memory (ROM), hard disks, and optical discs (including optical disc read-only memory (CD-ROM), CD-R / W, and DVD). Non-limiting examples of software-based systems include network-based systems and web-based systems.

[0085] As used herein, the term "processing device" refers to a data processing system suitable for storing and / or executing program code to implement a software-based system, and may include at least one processor directly or indirectly coupled to memory elements via a system bus or other interface. The processor (i.e., the electronic circuitry system executing the instructions constituting the program code) may be instantiated by a microprocessor, microcontroller, multi-core processor, processor array, or vector processor, and may be embodied in a single physical device or across several physical devices. Memory elements may include local memory, mass storage, cloud storage, and cache memory utilized during the actual execution of the program code, providing temporary storage for at least some of the program code to reduce the number of times 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 intermediate I / O controller. Network adapters may also be coupled to the system to enable the processing device to couple to other processing devices or remote printers or storage devices via an intermediate private or public network. Modems, cable modems, and Ethernet cards are just a few types of network adapters currently available. The processing device can 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)) that is able to access and execute program code to implement a software-based system.

[0086] Description of Evans α

[0087] Evansin α is described in the following literature: 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 International Nonproprietary Names for Pharmaceutical Substances (INN) WHO Drug Information, 2019, Vol. 33, No. 4, pp. 828-30, the entire contents of each of these literatures are hereby incorporated herein by reference. In patients with FVIII deficiency, Evansin α temporarily replaces the lost FVIII required for effective hemostasis. Evans thrombin α comprises a first polypeptide containing the amino acid sequence of SEQ ID NO:3, which is covalently bound to a second polypeptide containing the amino acid sequence of SEQ ID NO:6, wherein the first polypeptide and the second polypeptide are covalently bound to each other via disulfide bonds. Evans thrombin α can be produced, for example, in a human embryonic kidney (HEK) cell line using recombinant DNA technology. For example, the cell line can express rFVIIIFc-ELNN polypeptide (SEQ ID NO:1), rVWF-ELNN-Fc polypeptide (SEQ ID NO:4), and a soluble PACE enzyme. Non-limiting examples of nucleotide sequences encoding rFVIIIFc-ELNN polypeptide (SEQ ID NO:2) and rVWF-ELNN-Fc polypeptide (SEQ ID NO:5) can be found in Table A below. The amino acid sequences of the rFVIIIFc-ELNN polypeptide (SEQ ID NO:3) without the signal peptide and the amino acid sequences of the 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.

[0088] In some embodiments, for subjects receiving prophylactic treatment with evaporin α, the methods disclosed herein can be used to determine individualized subject information (e.g., the subject's plasma FVIII levels at a specific time point or a set of time points). Based on this individual subject information, the dose and / or dosing interval of evaporin α can be adjusted to achieve individualized treatment goals, such as minimum plasma FVIII activity levels (e.g., troughs).

[0089] In some embodiments, for subjects receiving on-demand treatment with evaporin α, if bleeding is not controlled or is unsatisfactory after administration of evaporin α at the initially recommended dose and dosing interval, the methods disclosed herein can be used to determine individual subject dosing information. Based on this individual subject dosing information, the dose and / or dosing interval of evaporin α can be adjusted to achieve improved bleeding control. In some embodiments, the dose and / or dosing interval are adjusted to achieve a desired estimated bleeding risk.

[0090] In some embodiments, the methods disclosed herein can be used to estimate the minimum FVIII level between doses for a subject.

[0091] In some embodiments, the level of FVIII activity in the subject's plasma (e.g., a phase I coagulation assay) may be monitored to confirm that adequate FVIII levels have been achieved and maintained at clinical indication. FVIII activity can be measured by any method known in the art.

[0092] The aPTT test is a performance indicator that measures the efficacy of both the "inherent" (also known as the contact activation pathway) and common coagulation pathways. This test is generally used to measure the coagulation activity of commercially available recombinant coagulation factors (e.g., FVIII). It is often used in conjunction with a measurement of prothrombin time (PT) of the extrinsic coagulation 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 a reagent. In some embodiments, the aPTT assay does not use actin FS as a reagent. In some embodiments, the aPTT is tested using the following assay, in which... XP analyzer (used by Siemens Healthcare Diagnostics) FSL-activated PTT reagent (Siemens Healthineers) was used to measure FVIII activity.

[0093] In some embodiments, the aPTT assay can also be used to assess the potency of the chimeric peptide prior to administration to the subject. (Hubbard AR et al. J Thromb Haemost [Journal of Thrombosis and Hemostasis] 11:988-9 (2013)). In some embodiments, the aPTT assay can be further used in conjunction with any of the assays described herein, either before or after administration to the subject.

[0094] In some embodiments, the models provided herein provide FVIII activity calculations and information corresponding to activities such as those measured by the aPTT test. For example, the Evans thrombin αpopPK model [A] provides FVIII activity calculations and information corresponding to activities such as those measured by the aPTT test.

[0095] Subject pharmacokinetic and dosing information

[0096] An evaporin α popPK model was developed using pooled FVIII activity data from phase 1 / 2a and 3 evaporin α studies in adults, adolescents, and children with hemophilia A. PK information (FVIII activity) was fully characterized by a one-compartmental basic structural model with linear elimination. Clearance (CL) and central compartment volume (V) in the popPK model depended on body weight, with Asian ethnicity identified as a significant covariate for CL. The evaporin α popPK model [A] is represented as follows:

[0097]

[0098] The Avan thrombin αpopPK model [A] (also referred to herein as the “popPK model [A]”) is abbreviated as: CL, central chamber clearance rate; TVCL, estimated typical clearance rate; WT, body weight (kg); ASIAN, indicator of Asian race (0 for non-Asian and 1 for Asian); η1, variability of CL; V, central chamber volume; TVV, estimated typical volume; η2, variability of V; k, central chamber elimination rate; Rate, infusion rate; A1, amount of phase I (OS) FVIII activity in the central chamber; and C, phase I (OS) FVIII activity in the central chamber.

[0099] A version of the popPK model [A] containing parameters useful for patients of all ages can be described as the popPK model [A']:

[0100]

[0101] In some embodiments, the Ivanothrombin α model [A] is used to obtain PK information in subjects younger than 6 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain PK information in subjects younger than 12 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain PK information in subjects at least 12 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain PK information in subjects at least 18 years of age.

[0102] In some embodiments, the Ivanothrombin α model [A'] is used to obtain PK information in subjects younger than 6 years of age. In some embodiments, the Ivanothrombin α model [A'] is used to obtain PK information in subjects younger than 12 years of age. In some embodiments, the Ivanothrombin α model [A'] is used to obtain PK information in subjects at least 12 years of age. In some embodiments, the Ivanothrombin α model [A'] is used to obtain PK information in subjects at least 18 years of age.

[0103] In some embodiments, PK information is used in the RTTE model provided herein.

[0104] In some embodiments, the Ivanothrombin α model [A] is used to obtain dose information for subjects younger than 6 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain dose information for subjects younger than 12 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain dose information for subjects at least 12 years of age. In some embodiments, the Ivanothrombin α model [A] is used to obtain dose information for subjects at least 18 years of age.

[0105] In some embodiments, the alpha thrombin model [A'] is used to obtain dose information for subjects younger than 6 years of age. In some embodiments, the alpha thrombin model [A'] is used to obtain dose information for subjects younger than 12 years of age. In some embodiments, the alpha thrombin model [A'] is used to obtain dose information for subjects at least 12 years of age. In some embodiments, the alpha thrombin model [A'] is used to obtain dose information for subjects at least 18 years of age.

[0106] Some embodiments include administering a dose of evaporin α to a human subject of need at predetermined dosing intervals, wherein the dose and / or dosing intervals are determined using the subject's weight and / or self-reported ethnicity rather than the subject's VWF or hematocrit level. This disclosure provides a method for administering a dose of evaporin α to a human subject of need at predetermined dosing intervals, wherein the dose and / or dosing intervals are determined by applying the evaporin α model [A] disclosed herein. In some embodiments, the evaporin model [A'] is used.

[0107] In some embodiments, the therapeutically effective dose of levonorgestrel-alpha is about 50 IU / kg. In some embodiments, a dose of about 50 IU / kg is administered to the subject once weekly. In some embodiments, a dose of about 50 IU / kg is administered to the subject once every about 7 days. In some embodiments, an initial dose of about 50 IU / kg is administered to the subject, followed by doses of 50 IU / kg or 30 IU / kg every 2-3 days as needed.

[0108] In some embodiments, the methods disclosed herein are applied to determine individualized interval prevention for subjects. As used herein, the term “individualized interval prevention” means the use of levonorgestrel α at individualized doses and / or dosing intervals or frequencies to prevent or suppress the occurrence of one or more spontaneous and / or uncontrollable bleeding or bleeding episodes, or to reduce the frequency of one or more spontaneous and / or uncontrollable bleeding or bleeding episodes.

[0109] In some embodiments, treatment goals for subjects include achieving high FVIII plasma activity levels and / or high trough levels. As used herein, a “trough level” in hemophilia subjects is a measurement of the lowest concentration achieved by factor therapy (e.g., levonorgestrel-alpha therapy) prior to administration of the next dose. The methods disclosed herein can be used to determine subject dosing information to achieve specific FVIII plasma activity levels and / or trough levels. In hemophilia A subjects, administration of levonorgestrel-alpha has been shown to successfully achieve high FVIII plasma activity levels and / or high trough levels.

[0110] In some embodiments, administration of levonorgestrel α to subjects achieves 40% or higher FVIII activity levels for approximately 1, 2, 3, or 4 days. In some embodiments, administration of levonorgestrel α to subjects achieves higher FVIII activity levels for approximately 1, 2, 3, or 4 days. In some embodiments, administration of levonorgestrel α to subjects achieves at least 40% FVIII activity levels for at least three days. In some embodiments, administration of levonorgestrel α to subjects achieves at least 50% FVIII activity levels for approximately 4 days. In some embodiments, an levonorgestrel α model [A] is used to determine individual subject dosing information to achieve at least 40% FVIII activity levels in subjects for approximately 1, 2, 3, or 4 days.

[0111] Estimating bleeding risk

[0112] This article includes a repeated event time-of-occurrence (RTTE) model for assessing the bleeding risk in subjects with hemophilia A. In some embodiments, treatment efficacy (e.g., preventative or on-demand treatment) is a covariate of the baseline risk in the RTTE model.

[0113] In the XTEND-1 Phase 3 study, bleeding events (i.e., treatment outcomes) were recorded in subjects treated with levonorgestrel-alpha (either as-needed or prophylactic). Information regarding the bleeding profile and the effect of FVIII activity (from levonorgestrel-alpha administration) was used to develop an exemplary RTTE model and to identify demographic or medical history parameters that influence the bleeding profile.

[0114] This article provides an RTTE model [B] for estimating or predicting bleeding risk. In some embodiments, the bleeding risk is for subjects receiving FVIII replacement therapy. In some embodiments, the bleeding risk is for subjects receiving FVIII replacement therapy with levonorgestrel-alpha. The RTTE model [B] is represented as follows:

[0115]

[0116] The RTTE model [B] is abbreviated as: h: hazard function of the Weibull distribution; t: time; λ: constant of the base risk; γ: shape parameter of the base risk; β: constant of the FVIII effect; C: FVIII activity; α: shape parameter of the FVIII effect; η: individual random effect. η varies with a mean of 0 and a coefficient of variation of 114%. Additional information related to the RTTE model [B] is provided in Example 2.

[0117] In some embodiments, η = 0, resulting in the RTTE model [C]. The RTTE model [C] is represented as follows:

[0118]

[0119] The RTTE model [C] is abbreviated as: h: hazard function of the Weibull distribution; t: time; λ: constant of the base risk; γ: shape parameter of the base risk; β: constant of the FVIII effect; C: FVIII activity; α: shape parameter of the FVIII effect.

[0120] The instantaneous hazards starting from time 0 using the final RTTE model parameters are shown in model / equations [D] and [D1]:

[0121]

[0122] The abbreviations for equations [D], [D1], and [D2] are: h, the danger function of the Weibull distribution; t, time in hours; C pred (t), the predicted FVIII activity at time t; ARM is the therapeutic effect such that ARM is 1 if the subject receives on-demand treatment, or 0 if the subject receives prophylactic treatment. Equation [D1] contains parameters for patients at least 12 years old. Equation [D2] contains parameters for patients less than 12 years old.

[0123] In some embodiments, the RTTE model includes treatment effect as a covariate. In some embodiments, the treatment effect is preventative. In some embodiments, the treatment effect is on-demand treatment. In some embodiments, the method includes using an RTTE model [B]. In some embodiments, η = 0.

[0124] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of an individual subject.

[0125] In some embodiments, the RTTE model [C] is used to estimate the bleeding risk of an individual subject.

[0126] In some embodiments, the RTTE model [D] is used to estimate the bleeding risk of an individual subject.

[0127] In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of an individual subject. In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of an individual subject who is at least 12 years old. In some embodiments, the RTTE model [D1] is used to estimate the bleeding risk of an individual subject who is at least 18 years old.

[0128] In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of an individual subject. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of an individual subject younger than 12 years of age. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of an individual subject older than 6 years of age but younger than 12 years of age. In some embodiments, the RTTE model [D2] is used to estimate the bleeding risk of an individual subject older than 6 years of age.

[0129] In some embodiments, the RTTE model [B] is used to estimate the probability of a subject experiencing bleeding at a specific time point (t). In some embodiments, the RTTE model [C] is used to estimate the probability of a subject experiencing a bleeding event over a period of time, such as 1 month, 3 months, 6 months, 1 year, 2 years, or longer. In some embodiments, the RTTE model [B] is used to estimate the probability of a subject experiencing a bleeding event within 1 year.

[0130] In some embodiments, 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] may be used sequentially to estimate the bleeding risk of a subject. In some embodiments, the subject's weight and / or self-reported ethnicity are used to predict the risk of bleeding events in the subject, but the subject's VWF or hematocrit level is not used.

[0131] In some embodiments, the FVIII activity (C) in the RTTE model [B] is not determined using the popPK model [A] disclosed herein.

[0132] 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 the selected FVIII activity level. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of an individualized subject at the target FVIII activity level.

[0133] In some embodiments, expected treatment outcomes are used in RTTE models to estimate bleeding risk. In some embodiments, the expected treatment outcome is provided by the individual subject.

[0134] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with sustained high FVIII activity. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with >40 IU / dL FVIII activity. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with >40 IU / dL FVIII activity lasting approximately 1, 2, 3, or 4 days. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with >10 IU / dL FVIII activity. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with >10 IU / dL FVIII activity lasting approximately 1, 2, 3, 4, 5, 6, or 7 days. In some embodiments, the RTTE model [B] is used to estimate the bleeding risk in subjects with a trough level of >10 IU / dL FVIII activity.

[0135] In some embodiments, the RTTE model [B] is used to determine the probability that a subject receiving evaporin α treatment will be free of bleeding within 1 year. In some embodiments, evaporin α treatment comprises a dose of approximately 50 IU / kg of evaporin α once weekly.

[0136] In some embodiments, the bleeding risk information generated by the RTTE model [B] is used at least in part to select treatment for subjects with hemophilia A (e.g., a specific hemophilia A treatment and / or dosage regimen). Risk information generated by the RTTE model [B] with evaporin α treatment can be compared with risk information using other hemophilia A treatments. Other hemophilia A treatments include, for example, treatment with a continuous infusion regimen (CIR), treatment with evaporin α, treatment with a recombinant coagulation factor alternative product other than evaporin α, treatment with a bispecific antibody binding activated coagulation factor IX and coagulation factor X (such as emecizumab), or gene therapy for hemophilia A (such as valoctocogene roxaparvovec).

[0137] In some embodiments, this document discloses a method for treating a subject with hemophilia A, the method comprising: a) identifying the subject with hemophilia A; b) estimating the subject's bleeding risk when treated with 50 IU / kg levonorgestrel-alpha approximately once weekly; c) estimating the subject's bleeding risk when treated with an alternative hemophilia A therapy that maintains an FVIII activity level of at least 10 IU / dL in the subject; and d) if the subject has a lower estimated bleeding risk under levonorgestrel-alpha therapy compared to the estimated bleeding risk under alternative hemophilia A therapy, then treating the subject with 50 IU / kg levonorgestrel-alpha approximately once weekly.

[0138] In some embodiments, the RTTE model can be used without inputting any individualized FVIII activity data [B].

[0139] In some embodiments, the RTTE model [B] can be used to estimate the bleeding risk of a population of subjects. In some embodiments, the estimated bleeding risk is based on the population mean or median, rather than being individualized for a specific subject.

[0140] In some embodiments, the RTTE model [B] can be used to estimate the bleeding risk of a virtual or hypothetical subject.

[0141] In some embodiments, bleeding risk information generated by the RTTE model [B] is used to determine the additional benefits of evaporin α treatment in individual subjects.

[0142] In some embodiments, the RTTE model [B] is used to estimate the bleeding risk of an individual subject.

[0143] Methods, systems, and storage media for estimating individualized bleeding risk information in subjects.

[0144] This document includes a method for estimating (e.g., calculating, determining, or providing) the bleeding risk of an individual subject receiving evaporin α treatment, the method comprising: (a) receiving subject information and / or expected treatment outcome information by a software-based system including a computer program programmed to operate in conjunction with an evaporin α RTTE model (e.g., evaporin α RTTE model [B]); and (b) calculating individualized bleeding risk information for the subject. In some embodiments, the program is also programmed to operate in conjunction with an evaporin α popPK model (e.g., evaporin α popPK model [A]). One or more of the above steps may be performed using a software-based system, a web-based system, a computational system, or a combination of the aforementioned systems. For example, an exemplary web-based system may be used to obtain estimated individualized bleeding risk information for the subject.

[0145] In some embodiments, the method disclosed herein further includes receiving subject information by a software-based system. In some embodiments, the subject information includes age, self-reported ethnicity, and / or weight. In some embodiments, the subject information includes diagnosed (baseline) FVIII level, PK assay, PK sampling time, dosing history (if PK samples were taken from multiple doses), actual dose, FVIII activity level, etc.

[0146] In some embodiments, the output information includes an estimated bleeding risk. In some embodiments, the output information includes an estimated bleeding risk at a specific time (t). In some embodiments, the output information includes an estimated risk of a bleeding event occurring. In some embodiments, the output information includes an estimated risk of a bleeding event occurring within one year. In some embodiments, the output information includes an estimated annualized bleeding rate (ABR).

[0147] The system can comply with patient privacy laws. In some embodiments, the system is encrypted, for example, using SSL encryption. In some embodiments, the input of subject information is anonymous.

[0148] In some embodiments, the system includes a user instruction function.

[0149] In some embodiments, the method may be performed by, for example, a subject, a doctor, a nurse, or other medical practitioner. In some embodiments, the method is performed by a subject.

[0150] Some embodiments include a computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause the processor to perform one or more steps of the methods described above.

[0151] Some embodiments include a system comprising a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform any of the methods described above.

[0152] Users of the system or computer-readable storage medium may be, for example, subjects or caregivers or doctors, nurses or other medical practitioners.

[0153] In some embodiments, subject information input into the system includes weight. In some embodiments, subject information input into the system is self-reported ethnicity. In some embodiments, subject information input into the system is FVIII activity level. In some embodiments, subject information input into the system is target FVIII activity level. In some embodiments, subject information input into the system is treatment effect (preventive treatment 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 for executing instructions and memory for 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 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 clustered computing environments or server farms.

[0154] Alternatively, the electronic device can be a specialized data collection, computation, and communication device, such as, for example, a real-time care (POC) device capable of receiving subject demographic information (including age, vital signs including weight, and / or blood characteristics including self-reported ethnicity). Blood characteristics can be received via data communication channels, manually entered electronically, and / or via diagnostic procedures performed via the electronic device. Diagnostic procedures performed on a subject's blood sample within the device can include ultrasound measurements, impedance measurements, conductivity measurements, and / or optical measurements. The electronic device can be further configured to receive, detect, record, and / or communicate additional subject information, including the diagnosed (baseline) FVIII level, PK assay, time of PK sampling, medication history (if the PK sample was taken from multiple doses), actual dose, FVIII activity level, or treatment effect (preventive or on-demand treatment). The electronic device communicates with one or more web-based (e.g., web-based) applications via one or more networks (such as the Internet). Similar to the electronic device, the web-based (e.g., web-based) applications can be implemented using a general-purpose computer, a server, or other devices capable of providing data to the electronic device. The electronic device can receive individualized alpha-p-thrombin (alpha-PK) information from a web-based (e.g., web-based) server and program. In some embodiments, the electronic device can assist in calculating estimated bleeding risk for individualized subjects, groups, or other sources.

[0155] The methods and systems described herein can be implemented in or via mobile devices. Mobile devices include navigation devices, mobile phones, smartphones, smartwatches, tablet computers, mobile personal digital information processing terminals, laptop computers, handheld computers, netbooks, pagers, e-book terminals, music players, etc. Among other components, these devices may include storage media (such as flash memory, buffers, RAM, ROM, and one or more computing devices). The computing device associated with the mobile device may be adapted to execute program code, methods, and instructions stored thereon. As another example, the mobile device may be configured to cooperate with other devices to execute instructions. The mobile device may communicate with a base station connected to a server and configured to execute program code. The mobile device may also communicate via peer-to-peer networks, mesh networks, or other communication networks. The program code may be stored in a storage medium associated with the server and executed by a computing device embedded in the server. The base station may include computing devices and storage media. 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 subject information. Although different embodiments of the kit may include different components, an exemplary kit includes diagnostic devices such as processing and / or computing elements for acquiring information from subjects, and transmission elements for transmitting subject information to a computer device via a wired or wireless connection. When the device is in use, the transmission elements in the kit may be configured to transmit subject information in real time, or to transmit diagnostic information upon receiving instructions from a user or provider. Any component of the kit (such as the main body) may be configured as a hands-free or handheld unit during use.

[0156] Exemplary computing environment for the disclosed methods and systems

[0157] The various modeling techniques, dose calculations, and estimates described in this article can be implemented through software, firmware, hardware, or a combination thereof. Figure 1 An example software-based computer system 1900 is illustrated, in which embodiments or portions thereof can be implemented as computer-readable code. In another embodiment, for Evans thrombin α, the modeling disclosed in the examples herein can be implemented in system 1900.

[0158] Computer system 1900 includes one or more processors (such as processor 1904). Processor 1904 is connected to communication infrastructure 1906 (e.g., bus or network).

[0159] Computer system 1900 also includes main memory 1908, preferably random access memory (RAM), and may also include auxiliary memory 1910. Depending on the implementation, user interface data may be stored in, for example, but not limited to, main memory 1908. Main memory 1908 may include, for example, cache and / or static and / or dynamic RAM. Auxiliary memory 1910 may include, for example, hard disk drives and / or removable storage drives. Removable storage drive 1914 may include floppy disk drives, magnetic tape drives, optical disk drives, flash memory, etc. Removable storage drive 1914 reads from and / or writes to removable storage unit 1916 in a well-known manner. Removable storage unit 1916 may include floppy disks, magnetic tapes, optical disks, etc., which are read from and written to by removable storage drive 1914. As those skilled in the art will understand, removable storage unit 1916 includes a computer-readable storage medium in which computer software and / or data are stored.

[0160] Computer system 1900 may also include a display interface 1902. Display interface 1902 may be adapted to communicate with display unit 1930. Display unit 1930 may include a computer monitor or similar means for displaying graphics, text, and other data received from main memory 1908 via communication infrastructure 1906. In an alternative implementation, auxiliary memory 1910 may include other similar means for allowing computer programs or other instructions to be loaded into computer system 1900. Such means may include, for example, removable storage unit 1922 and interface 1920. Examples of such means may include a program box and box interface, a removable memory chip (such as EPROM or PROM) and associated sockets, and other removable storage units 1922 and interfaces 1920 that allow software and data to be transferred from removable storage unit 1922 to computer system 1900.

[0161] Computer system 1900 may also include communication interface 1924. Communication interface 1924 allows software and data to be transferred between computer system 1900 and external devices. Communication interface 1924 may include modems, network interfaces (such as Ethernet cards or WiFi), communication ports, PCMCIA slots, and cards. Software and data transferred via communication interface 1924 are in the form of signals, which may be electronic, electromagnetic, optical, or other signals that can be received by communication interface 1924. These signals are provided to communication interface 1924 via communication path 1926. Communication path 1926 carries signals and can be implemented using wires or cables, optical fibers, telephone lines, cellular telephone links, WiFi, Bluetooth, RF links, or other communication channels.

[0162] In this document, the term "computer-readable storage medium" is generally used to refer to non-transitory storage media (such as removable storage unit 1916, removable storage unit 1922, and hard disks installed in hard disk drive 1912). Computer-readable storage media can also refer to one or more memories (such as main memory 1908 and auxiliary memory 1910), which can be memory semiconductors (e.g., DRAMs, etc.). These computer program products are means for providing software to computer system 1900.

[0163] The computer program (also referred to as computer control logic) is stored in main memory 1908 and / or auxiliary memory 1910. The computer program may also be received via communication interface 1924 and stored in main memory 1908 and / or auxiliary memory 1910. When executed, such a computer program enables computer system 1900 to implement the embodiments discussed herein. Specifically, when executed, the computer program enables processor 1904 to implement the processes disclosed herein (such as some of the methods discussed above). Therefore, such a computer program represents the controller of computer system 1900. When software is used in embodiments, the software may be stored in a computer program product and loaded into computer system 1900 using removable storage drive 1914, interface 1920, or hard disk drive 1912.

[0164] Embodiments may relate to computer program products comprising software stored on or distributed across any computer-readable medium. When such software is executed in one or more processing devices, it causes the processing devices to operate as described herein. Embodiments may utilize any computer-usable or readable medium. Examples of computer-readable storage media include, but are not limited to, non-transitory primary storage devices (e.g., any type of random access memory) and non-transitory secondary storage devices (e.g., hard disk drives, floppy disks, CD-ROMs, ZIP disks, magnetic tapes, magnetic storage devices and optical storage devices, MEMS, nanotechnology storage devices, etc.). Other computer-readable media include communication media (e.g., wired and wireless communication networks, local area networks, wide area networks, internal networks, etc.).

[0165] Non-limiting examples of software-based systems include web-based systems and web-based systems.

[0166] Figure 3 Examples of computing devices 400 and mobile computing devices 450 that can be used to implement the techniques described herein are shown. Software-based systems (such as...) Figure 1The software-based system described herein can be implemented in computing device 400 or mobile computing device 450. Computing device 400 is intended to represent various forms of digital computers (such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers). 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 shown herein, their connections and relationships, and their functions are intended to be exemplary only and are not intended to limit the implementations of the invention described and / or claimed herein.

[0167] The computing device 400 includes a processor 402, a memory 404, a storage device 406, a high-speed interface 408 connected to the memory 404 and a plurality of high-speed expansion ports 410, and a low-speed interface 412 connected to a low-speed expansion port 414 and the storage device 406. Each of the processor 402, memory 404, storage device 406, high-speed interface 408, high-speed expansion port 410, and low-speed interface 412 is interconnected using multiple buses and may be mounted on a shared motherboard or otherwise, as appropriate. The processor 402 can process instructions executed within the computing device 400 (including instructions stored in the memory 404 or on the storage device 406) to transmit and display graphical or other information for a GUI on an external input / output device, such as a display 416 coupled to the high-speed interface 408. In other embodiments, multiple processors and / or multiple buses, as well as multiple memories and multiple memory types, may be used as appropriate. Furthermore, multiple computing devices may be connected, each providing a necessary operational component (e.g., as a server library, a blade server group, or a multiple processor system).

[0168] Memory 404 stores information within computing device 400. In some implementations, memory 404 is one or more volatile memory cells. In some implementations, memory 404 is one or more non-volatile memory cells. Memory 404 may also be another form of computer-readable medium (such as a magnetic disk or optical disk).

[0169] Storage device 406 provides large-capacity storage for computing device 400. In some implementations, storage device 406 may be or contain computer-readable media, such as floppy disk devices, hard disk devices, optical disk devices, magnetic tape devices, flash memory or other similar solid-state storage devices or device arrays (including storage area networks or other configured devices). The computer program product may be tangibly embodied in an information carrier. The computer program product may also include instructions that, when executed, perform one or more methods, such as those described above. The computer program product may also be tangibly embodied in a computer-readable medium or a machine-readable medium (such as memory 404, storage device 406, or memory on processor 402).

[0170] High-speed interface 408 manages bandwidth-intensive operations of computing device 400, while low-speed interface 412 manages less bandwidth-intensive operations. This functional allocation is merely exemplary. In some implementations, high-speed interface 408 is coupled to memory 404, display 416 (e.g., via a graphics processor or accelerator), and high-speed expansion port 410, which can accept various expansion cards (not shown). In other implementations, low-speed interface 412 is coupled to storage device 406 and low-speed expansion port 414. Low-speed expansion port 414, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, Wireless Ethernet), can be coupled to one or more input / output devices (such as keyboards, clickers, scanners, or networking devices such as switches or routers) via a network adapter.

[0171] The computing device 400 can be implemented in several different forms, as shown in the figure. For example, it can be implemented as a standard server 420, or as a group of such servers multiple times. 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-mount 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 can contain one or more of the computing device 400 and the mobile computing device 450, and the entire system can consist of multiple computing devices communicating with each other.

[0172] Mobile computing device 450 includes a processor 452, memory 464, input / output devices (such as a display 454), a communication interface 466, and a transceiver 468, as well as other components. Mobile computing device 450 may also provide storage devices (such as microdrives 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 multiple buses, and some components may be mounted on a shared motherboard or otherwise mounted as appropriate.

[0173] Processor 452 can execute instructions within mobile computing device 450 (including instructions stored in memory 464). Processor 452 can be implemented as a chipset including single and multiple analog and digital processors. Processor 452 can provide, for example, coordination of other components of mobile computing device 450 (such as control of user interfaces, applications running by mobile computing device 450, and wireless communications performed by mobile computing device 450).

[0174] Processor 452 can communicate with the user via control interface 458 and display interface 456 coupled to display 454. Display 454 can be, for example, a TFT (Thin Film Transistor Liquid Crystal Display) or OLED (Organic Light Emitting Diode) display, or other suitable display technologies. Display interface 456 can contain suitable circuitry for driving display 454 to present graphics and other information to the user. Control interface 458 can receive commands from the user and translate them for submission to processor 452. Additionally, external interface 462 can provide communication with processor 452, enabling mobile computing device 450 to communicate with other devices in near-field. External interface 462 can provide, in some implementations, wired communication, or in others, wireless communication, and multiple interfaces can be used.

[0175] Memory 464 stores information within the mobile computing device 450. Memory 464 can be implemented as one or more computer-readable media, one or more volatile memory cells, or one or more non-volatile memory cells. Extended memory 474 may also be provided 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 can provide additional storage space for the mobile computing device 450, or it may store applications or other information for the mobile computing device 450. In particular, extended memory 474 may include instructions for performing or supplementing the processes described above, and may also include security information. Therefore, 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 allow secure use of the mobile computing device 450. Additionally, secure applications and other information (such as placing identification information on the SIMM card in an unbreakable manner) may be provided via a SIMM card.

[0176] The memory may include, for example, flash memory and / or NVRAM (non-volatile random access memory), as discussed below. In some implementations, the computer program product is tangibly embodied as an information carrier. The computer program product includes instructions that, when executed, perform one or more methods, such as those described above. The computer program product may be a computer-readable medium or a machine-readable medium (such as memory 464, extended memory 474, or memory on processor 452). In some implementations, the computer program product may be received, for example, via transceiver 468 or external interface 462 to transmit signals.

[0177] Mobile computing device 450 can conduct wireless communication via communication interface 466, which may include digital signal processing circuitry when necessary. Communication interface 466 can provide communication in various modes or protocols (such as GSM voice calls (Global System for Mobile Communications), SMS (Short Message Service), EMS (Enhanced Messaging 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), etc.). Such communication can be performed, for example, using a radio frequency transceiver 468. Alternatively, short-range communication can be performed using transceivers such as Bluetooth, WiFi, or others (not shown). Additionally, GPS (Global Positioning System) receiver module 470 can provide additional navigation-related and positioning-related wireless data to mobile computing device 450, which can be used, as appropriate, by applications running on mobile computing device 450.

[0178] The mobile computing device 450 can also use an audio codec 460 for audible communication, which can receive voice information from a user and convert it into usable digital information. The audio codec 460 can also generate audible sound for the user, such as through a speaker (e.g., in the handheld portion of the mobile computing device 450). Such sound can include sounds from voice telephone calls, recorded sounds (e.g., voice messages, music files, etc.), and sounds generated by applications operating on the mobile computing device 450.

[0179] The mobile computing device 450 can be implemented in several different forms, as shown in the figure. For example, it can be implemented as a mobile phone 480. It can also be implemented as a smartphone 482, a personal digital assistant, a tablet computer, or other similar mobile device.

[0180] The systems and techniques described herein can be implemented in various ways, including digital electronic circuits, integrated circuits, specially designed ASICs (Application-Specific Integrated Circuits), computer hardware, firmware, software, and / or combinations thereof. These different implementations may include implementations within one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be coupled for specific or general purposes to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transfer data and instructions to the storage system, at least one input device, and at least one output device.

[0181] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented in high-level procedural languages ​​and / or object-oriented programming languages, and / or in assembly / machine language. As used herein, the terms machine-readable medium and computer-readable medium refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive 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.

[0182] To provide interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user) and a keyboard, clicking device (e.g., mouse or trackball), and / or touchscreen or other user interface through which the user can provide input to the computer and the software-based system implemented on the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including auditory, voice, or tactile input.

[0183] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data servers), middleware components (e.g., application servers), or front-end components (e.g., client computers with graphical user interfaces or web browsers through which users can interact with the implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via any form or medium of digital data communication (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0184] A computing system may include clients and servers. Clients and servers are typically geographically separated and usually interact through a communication network. The client-server relationship arises from computer programs running on the respective computers and having a client-server relationship with each other.

[0185] Examples disclosed herein

[0186] This disclosure includes (but is not limited to) the following exemplary embodiments:

[0187] E1. A method for estimating bleeding risk, the method comprising: (a) receiving coagulation factor VIII (FVIII) activity information by a software-based system, the software-based system including a computer program programmed to implement a recurring event occurrence time (RTTE) model [B]; (b) calculating the bleeding risk by the computer program using the RTTE model [B] and the received information; and (c) transmitting the calculated bleeding risk information from (b) by the software-based system to output the bleeding risk information.

[0188] E2. A method for estimating bleeding risk, the method comprising: (a) receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices; (b) transmitting the coagulation factor VIII (FVIII) information to a software-based system by a processing device, wherein the software-based system is programmed to implement a recurring event occurrence time (RTTE) model [B] to calculate bleeding risk; (c) receiving bleeding risk information calculated using the transmitted information in (b) and the RTTE model [B] from the software-based system; and (d) transmitting the bleeding risk information in (c) by the one or more electronic devices to output the bleeding risk information.

[0189] E3. The method as described in Example 1 or 2, wherein calculating the bleeding risk is estimating the risk of a bleeding event occurring.

[0190] E4. The method as described in any one of Examples 1-3, wherein the FVIII activity information is provided by the popPK model.

[0191] E5. The method described in Example 4, wherein the popPK model is the Evans α-popPK model.

[0192] E6. The method as described in Example 5, wherein the Evans αpopPK model includes body weight as a covariate, and wherein the Evans αpopPK model does not include VWF level or hematocrit level as a covariate.

[0193] E7. The method as described in Example 5, wherein the popPK model is the Evans α-popPK model [A].

[0194] E8. The method as described in Example 5, wherein the popPK model is the Evans α-popPK model [A'].

[0195] E9. The method as described in any one of Examples 1-8, wherein the provided FVIII activity information is specific to an individual subject.

[0196] E10. The method as described in any one of Examples 1-3, wherein the FVIII activity information provided is the target FVIII activity level.

[0197] E11. The method as described in Example 10, wherein the target FVIII activity level is a variable FVIII activity level.

[0198] E12. The method as described in any one of Examples 1-8, wherein the provided FVIII activity information is an estimated FVIII activity level of a subject population.

[0199] E13. The method as described in any one of Examples 1-8, wherein the provided FVIII activity information is directed to a virtual or hypothetical subject.

[0200] E14. The method as described in any one of Examples 1-13, wherein the RTTE model is an RTTE model [C].

[0201] E15. The method as described in any one of Examples 1-13, wherein the RTTE model is an RTTE model [D].

[0202] E16. The method as described in any one of Examples 1-13, wherein the RTTE model is an RTTE model [D1].

[0203] E17. The method as described in any one of Examples 1-13, wherein the RTTE model is an RTTE model [D2].

[0204] E18. A method for estimating the bleeding risk of an individual subject, the method comprising: a) receiving, by a software-based system, individualized subject information including the subject's weight; b) receiving, by the software-based system, expected treatment outcome information including an FVIII level; c) applying a time-recurring event (RTTE) model for the subject based on the individualized information and / or the expected treatment outcome information; d) estimating the bleeding risk of the individual subject using the RTTE model; and e) transmitting, by the software-based system, the estimated bleeding risk information of (d) to output the bleeding risk information.

[0205] E19. The method as described in Example 18, wherein the RTTE model includes treatment effect as a covariate.

[0206] E20. The method as described in Example 19, wherein the therapeutic effect is preventative or on-demand treatment.

[0207] E21. The method as described in any one of Examples 18-20, wherein the RTTE model is an RTTE model [B].

[0208] E22. The method as described in any one of Examples 18-20, wherein the RTTE model is an RTTE model [C].

[0209] E23. The method as described in any one of Examples 18-20, wherein the RTTE model is an RTTE model [D].

[0210] E24. The method as described in any one of Examples 18-20, wherein the RTTE model is an RTTE model [D1].

[0211] E25. The method as described in any one of Examples 18-20, wherein the RTTE model is an RTTE model [D2].

[0212] E26. The method as described in any one of Examples 18-25, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A].

[0213] E27. The method as described in any one of Examples 18-25, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A'].

[0214] E28. The method as described in any one of Examples 18-25, wherein the expected treatment outcome information is provided by the individual subject.

[0215] E29. The method as described in any one of Examples 18-25, wherein the expected treatment outcome information is provided by a medical professional.

[0216] E30. A method of treating a subject with hemophilia A, the method comprising: a) identifying the subject with hemophilia A; b) estimating the bleeding risk of the subject when treated with 50 IU / kg levonorgestrel α approximately once weekly; 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 levonorgestrel α approximately once weekly if the subject has a lower estimated bleeding risk under levonorgestrel α therapy compared to the estimated bleeding risk when the subject has an FVIII activity level of at least 10 IU / dL. E31. The method of Example 30, wherein if the subject has an FVIII activity level of at least 10 IU / dL, a repeated event occurrence time (RTTE) model is used to estimate the bleeding risk of the levonorgestrel α therapy and / or the bleeding risk of the subject.

[0217] E32. The method as described in Example 31, wherein the RTTE model is an RTTE model [B].

[0218] E33. The method as described in Example 31, wherein the RTTE model is an RTTE model [C].

[0219] E34. The method as described in Example 31, wherein the RTTE model is an RTTE model [D].

[0220] E35. The method as described in Example 31, wherein the RTTE model is an RTTE model [D1].

[0221] E36. The method as described in Example 31, wherein the RTTE model is an RTTE model [D2].

[0222] E37. A method for estimating the bleeding risk of an individual subject, the method comprising: (a) receiving information of the individual subject by a software-based system, wherein the system is programmed to implement: (i) a one-compartment Ivanin αpopPK model to calculate coagulation factor VIII (FVIII) activity information, wherein the Ivanin αpopPK model includes body weight as a covariate, and wherein the Ivanin αpopPK model does not include the level of VWF or the level of hematocrit as covariates; and (ii) a time-repeated event occurrence (RTTE) model that uses the FVIII activity information to estimate the bleeding risk; (b) calculating the estimated bleeding risk by the software-based system using the Ivanin αpopPK model, the RTTE model, and the received information; and (c) transmitting the calculated bleeding risk information from (b) by the software-based system to output the bleeding risk information.

[0223] E38. A method for estimating bleeding risk, the method comprising: (a) receiving information of an individual subject by one or more electronic devices; (b) transmitting the individual subject's information to a software-based system by a processing device, wherein the software-based system is programmed to implement (i) a one-compartment Ivanin αpopPK model to calculate coagulation factor VIII (FVIII) activity information, wherein the Ivanin αpopPK model includes body weight as a covariate, and wherein the Ivanin αpopPK model does not include VWF level or hematocrit level as covariates; and (ii) a time-repeated event occurrence (RTTE) model that uses the FVIII activity information to estimate bleeding risk; (c) receiving from the software-based system a bleeding risk estimated using the Ivanin αpopPK model, the RTTE model, and the received information; and (d) transmitting the bleeding risk information from (c) by the one or more electronic devices to output the bleeding risk information.

[0224] E39. The method as described in Example 37 or 38, wherein the RTTE model is an RTTE model [B].

[0225] E40. The method as described in Example 37 or 38, wherein the RTTE model is an RTTE model [C].

[0226] E41. The method as described in Example 37 or 38, wherein the RTTE model is an RTTE model [D].

[0227] E42. The method as described in Example 37 or 38, wherein the RTTE model is an RTTE model [D1].

[0228] E43. The method as described in Example 37 or 38, wherein the RTTE model is an RTTE model [D2].

[0229] E44. The method as described in any one of Examples 38-43, wherein the Evans αpopPK model is the Evans αpopPK model [A].

[0230] E45. The method as described in any one of Examples 38-43, wherein the Evans αpopPK model is the Evans αpopPK model [A'].

[0231] E46. The method as described in any one of Examples 1-45, wherein the subject is at least 12 years old.

[0232] E47. The method as described in any one of Examples 1-45, wherein the subject is at least 18 years old.

[0233] E48. The method as described in any one of Examples 1-45, wherein the subject is less than 12 years old.

[0234] E49. The method as described in any one of Examples 1-45, wherein the subject is less than 6 years old.

[0235] E50. The method as described in any one of Examples 1-45, wherein the subject is between 6 and 12 years old.

[0236] E51. A data processing device, apparatus, or system including a processor configured to implement the Evans αRTTE model.

[0237] E52. The data processing device, apparatus or system as described in Example 51, wherein the RTTE model is an RTTE model [B].

[0238] E53. The data processing device, apparatus or system as described in Example 51, wherein the RTTE model is an RTTE model [C].

[0239] E54. The data processing device, apparatus or system as described in Example 51, wherein the RTTE model is an RTTE model [D].

[0240] E55. The data processing device, apparatus or system as described in Example 51, wherein the RTTE model is an RTTE model [D1].

[0241] E56. The data processing device, apparatus or system as described in Example 51, wherein the RTTE model is an RTTE model [D2].

[0242] E57. The data processing apparatus, device, or system as described in any one of Examples 51-56 is also configured to implement a one-compartment Evans αpopPK model that includes body weight as a covariate, and wherein the Evans αpopPK model does not include the level of VWF or the level of hematocrit as a covariate.

[0243] E58. The data processing apparatus, device or system as described in any one of Examples 51-57, wherein the Evans αpopPK model is the Evans αpopPK model [A].

[0244] E59. The data processing apparatus, device or system as described in any one of Examples 51-57, wherein the Evans αpopPK model is the Evans αpopPK model [A'].

[0245] E60. The data processing device, apparatus or system as described in any one of Examples 51-59, including smartphones, tablet computers, personal digital assistants, handheld computers, laptop computers or smartwatches.

[0246] E61. The data processing device, apparatus or system as described in any one of Examples 51-59, including a smartphone.

[0247] E62. A computer program comprising instructions which, when executed by a computer, cause the computer to implement the Evans α Repeated Event Occurrence Time (RTTE) model.

[0248] E63. The computer program as described in Example 62, wherein the RTTE model is an RTTE model [B].

[0249] E64. The computer program as described in Example 62, wherein the RTTE model is an RTTE model [C].

[0250] E65. The computer program as described in Example 62, wherein the RTTE model is an RTTE model [D].

[0251] E66. The computer program as described in Example 62, wherein the RTTE model is an RTTE model [D1].

[0252] E67. The computer program as described in Example 62, wherein the RTTE model is an RTTE model [D2].

[0253] E68. The computer program as described in any one of Examples 62-67, wherein the RTTE model uses the PK curve generated from the popPK model as input.

[0254] E69. The computer program as described in Example 68, wherein the popPK model is the Evans α-popPK model.

[0255] E70. The computer program as described in Example 69, wherein the Evans αpopPK model is a one-compartment Evans αpopPK model that includes body weight as a covariate, and wherein the Evans αpopPK model does not include the level of VWF or the level of hematocrit as covariates.

[0256] E71. The computer program as described in any one of Examples 68-70, wherein the popPK model is the Evans α-popPK model [A].

[0257] E72. The computer program as described in any one of Examples 68-70, wherein the popPK model is the Evans α-popPK model [A'].

[0258] E73. A computer-readable medium including instructions that, when executed by a computer, cause the computer to perform the method as described in any one of Examples 1-50.

[0259] E74. A method for estimating bleeding risk, the method comprising: (a) receiving coagulation factor VIII (FVIII) activity information by a software-based system, the software-based system including a computer program programmed to implement a recurring event occurrence time (RTTE) model [D2]; (b) calculating the bleeding risk by the computer program using the RTTE model [D2] and the received information; and (c) transmitting the calculated bleeding risk information from (b) by the software-based system to output the bleeding risk information.

[0260] E75. A method for estimating bleeding risk, the method comprising: (a) receiving coagulation factor VIII (FVIII) activity information by one or more electronic devices; (b) transmitting the coagulation factor VIII (FVIII) information to a software-based system by a processing device, wherein the software-based system is programmed to implement a recurring event occurrence time (RTTE) model [D2] to calculate bleeding risk; (c) receiving bleeding risk information calculated from the software-based system using the transmitted information in (b) and the RTTE model [D2]; and (d) transmitting the bleeding risk information in (c) by the one or more electronic devices to output the bleeding risk information. E76. The method of embodiment 74 or 75, wherein calculating bleeding risk is estimating the risk of a bleeding event occurring.

[0261] E77. The method as described in any one of Examples 74-76, wherein the FVIII activity information is provided by a popPK model.

[0262] E78. The method as described in Example 77, wherein the popPK model is the Evans α-popPK model.

[0263] E79. The method as described in Example 78, wherein the popPK model is the Evans α-popPK model [A].

[0264] E80. The method as described in Example 78, wherein the popPK model is the Evans α-popPK model [A'].

[0265] E81. The method as described in any one of Examples 74-80, wherein the FVIII activity information provided is specific to an individual subject.

[0266] E82. The method as described in any one of Examples 74-80, wherein the FVIII activity information provided is the target FVIII activity level.

[0267] E83. The method as described in Example 82, wherein the target FVIII activity level is a variable FVIII activity level.

[0268] E84. The method as described in any one of Examples 74-80, wherein the provided FVIII activity information is an estimated FVIII activity level of a subject population.

[0269] E85. The method as described in any one of Examples 74-80, wherein the provided FVIII activity information is directed to a virtual or hypothetical subject.

[0270] E86. A method for estimating the bleeding risk of an individual subject, the method comprising: a) receiving, by a software-based system, individualized subject information including the subject's weight; b) receiving, by the software-based system, expected treatment outcome information including an FVIII level; c) applying a recurring event occurrence time (RTTE) model [D2] for the subject based on the individualized information and / or the expected treatment outcome information; d) estimating the bleeding risk of the individual subject using the RTTE model [D2]; and e) transmitting, by the software-based system, the estimated bleeding risk information from (d) to output the bleeding risk information.

[0271] E87. The method as described in Example 86, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A].

[0272] E88. The method as described in Example 86, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A'].

[0273] E89. The method as described in any one of Examples 86-88, wherein the expected treatment outcome information is provided by the individual subject.

[0274] E90. The method as described in any one of Examples 86-88, wherein the expected treatment outcome information is provided by a medical professional.

[0275] E91. A method for treating a subject with hemophilia A, the method comprising: a) identifying the subject with hemophilia A; b) estimating the bleeding risk of the subject when treated with 50 IU / kg levonorgestrel α approximately once weekly; 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 levonorgestrel α approximately once weekly if the subject has a lower estimated bleeding risk under levonorgestrel α therapy compared to the estimated bleeding risk when the subject has an FVIII activity level of at least 10 IU / dL, wherein if the subject has an FVIII activity level of at least 10 IU / dL, the bleeding risk of the levonorgestrel α therapy and / or the bleeding risk of the subject is estimated using a repeated event occurrence time (RTTE) model [D2].

[0276] E92. A method for estimating the bleeding risk of an individual subject, the method comprising: (a) receiving information of the individual subject by a software-based system, wherein the system is programmed to implement: (i) a one-compartment Ivanin αpopPK model [A'] to calculate coagulation factor VIII (FVIII) activity information, and (ii) a recurring event occurrence time (RTTE) model [D2], the model using the FVIII activity information to estimate the bleeding risk; (b) calculating the estimated bleeding risk by the software-based system using the Ivanin αpopPK model [Q], the RTTE model [D2], and the received information; and (c) transmitting the calculated bleeding risk information of (b) by the software-based system to output the bleeding risk information.

[0277] E93. A method for estimating bleeding risk, the method comprising: (a) receiving information of an individual subject by one or more electronic devices; (b) transmitting the information of the individual subject to a software-based system by a processing device, wherein the software-based system is programmed to implement (i) a one-compartment Ivanin αpopPK model [A'] to calculate coagulation factor VIII (FVIII) activity information, and (ii) a recurring event occurrence time (RTTE) model [D2], the model using the FVIII activity information to estimate bleeding risk; (c) receiving from the software-based system a bleeding risk estimated using the Ivanin αpopPK model [Q], the RTTE model [D2], and the received information; and (d) transmitting the bleeding risk information of (c) by the one or more electronic devices to output the bleeding risk information.

[0278] E94. The method as described in any one of Examples 91-93, wherein the subject is less than 12 years old.

[0279] E95. The method as described in any one of Examples 91-93, wherein the subject is less than 6 years old.

[0280] E96. The method as described in any one of Examples 91-93, wherein the subject is between 6 and 12 years old.

[0281] E97. A data processing apparatus, device, or system including a processor configured to implement the Evans αRTTE model [D2].

[0282] E98. The data processing device, apparatus, or system as described in Example 97 includes a smartphone, tablet computer, personal digital assistant, handheld computer, laptop computer, or smartwatch.

[0283] E99. The data processing device, apparatus or system as described in any one of Examples 97-98, including a smartphone.

[0284] E100. A computer program comprising instructions which, when executed by a computer, cause the computer to implement the Evans α Repeated Event Occurrence Time (RTTE) model [D2].

[0285] E101. A computer-readable medium including instructions that, when executed by a computer, cause the computer to perform the method as described in any one of Examples 74-96.

[0286] E102. A pharmaceutical composition comprising Evans thrombin α for reducing the risk of non-traumatic bleeding in a human subject with severe hemophilia A over a period of 52 weeks, wherein the risk is reduced to a probability of less than 30%, wherein Evans thrombin α is administered intravenously to the subject every approximately 4 to approximately 14 days during this period at a dose of approximately 25 IU / kg to approximately 50 IU / kg, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 30%. E103. A pharmaceutical composition comprising evaporin α for reducing the risk of non-traumatic bleeding in a human subject with severe hemophilia A over a period of 52 weeks, wherein the risk is reduced to a probability of less than 30%, wherein evaporin α 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 during this period, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 30%.

[0287] E104. The pharmaceutical composition as described in any one of Examples 102-103, wherein the bleeding, which is not caused by trauma, is spontaneous bleeding.

[0288] E105. The pharmaceutical composition as described in any one of Examples 102-104, wherein the alpha thrombin is administered at a dose of about 25 IU / kg every about 4 days during this period.

[0289] E106. The pharmaceutical composition as described in any one of Examples 102-104, wherein the alpha thrombin is administered at a dose of about 30 IU / kg every about 7 days during this period.

[0290] E107. The pharmaceutical composition as described in any one of Examples 102-104, wherein the alpha thrombin is administered at a dose of about 50 IU / kg every about 7 days during this period.

[0291] E108. The pharmaceutical composition as described in any one of Examples 102-104, wherein the alpha thrombin is administered at a dose of about 50 IU / kg every about 14 days during this period.

[0292] This disclosure has now been described in detail. A clearer understanding of this disclosure will be gained by referring to the following examples, which are included here for illustrative purposes only and are not intended to limit the scope of this disclosure. All patents and publications mentioned herein are expressly incorporated herein by reference.

[0293] Example

[0294] Example 1. A weighting used to characterize the bleeding risk in patients with severe hemophilia A treated with levonorgestrel-alpha. Time-of-Occurrence (RTTE) model

[0295] The XTEND-1 (EFC16293, NCT04161495) study was a phase 3, open-label, multicenter study of levonorgestrel-alpha in previously treated adult and adolescent (≥12 years) patients with severe hemophilia A. Some patients had undergone pre-study FVIII prophylaxis and were recruited into group A, where they received 50 IU / kg once weekly for 52 weeks. Patients who had received on-demand therapy prior to the XTEND-1 study were enrolled in group B and received 50 IU / kg once weekly for 26 weeks, followed by 26 weeks of on-demand prophylaxis.

[0296] During the XTEND-1 study, treated bleeding events in patients receiving evaporin alpha during the prophylactic and on-demand treatment periods were recorded. These data were used to develop a repeated time-of-occurrence (RTTE) model for evaporin alpha as a means of quantitatively characterizing the dependence of bleeding events during evaporin alpha treatment on baseline risk and FVIII activity in patients with severe hemophilia A. Although the RTTE model was developed for evaporin alpha, it can also be used with existing FVIII alternatives (such as recombinant FVIII, fusion proteins containing recombinant FVIII and Fc, and PEGylated FVIII) and to assess bleeding risk for hypothetical or desired FVIII activity levels.

[0297] RTTE models are developed using NONMEM (v.7.4.4 or later). A schematic diagram of RTTE model development is shown in Table 1.

[0298] Table 1. Schematic diagram of RTTE model development

[0299]

[0300] During model development, different risk distribution models were tested to estimate baseline risk. After identifying baseline risk, the FVIII activity effect was evaluated as a proportional risk effect to baseline risk. FVIII activity was predicted using model-based individual pharmacokinetic parameters from a population pharmacokinetic (popPK) model (i.e., the popPK model [A]), which used Phase 1 and Phase 3 clinical data of eparginine α. Potential covariate effects on baseline risk were tested using stepwise covariate modeling in PsN (v.5.2.6).

[0301] After stepwise covariate modeling and model refinement, treatment outcomes (weekly preventative treatment and on-demand treatment) were selected as covariates for baseline risk. Treatment outcomes for baseline risk showed that the risk (λ) of bleeding events with on-demand treatment was approximately nine times higher than the risk of bleeding events with preventative treatment.

[0302] Among the various basic and FVIII effect models evaluated, the Weibull function as the basic risk and the power model of the effect on FVIII activity both provided the lowest statistically significant target function values:

[0303]

[0304] Model [B] is abbreviated as: h: hazard function of Weibull distribution; t: time; λ: constant of basic risk; γ: shape parameter of basic risk; β: constant of FVIII effect; C: FVIII activity (plasma drug concentration); α: shape parameter of FVIII effect; η: individual random effect.

[0305] The suitability of the model was assessed by comparing the percentage of patients without bleeding events observed in XTEND-1 with the percentage of patients without bleeding events predicted by the RTTE model, using the Kaplan–Meier (KM) visual prediction test, the statistical significance of the identified covariates, and other aspects such as the precision of the parameter estimates. Parameter estimates from SIR with RSE (%) and 95% confidence intervals (CI) are shown in Table 2, thus confirming that the parameter estimates are included within the 95% CI.

[0306] Table 2 Parameter estimates of the final RTTE model with 95% CI using SIR.

[0307]

[0308] For Table 2, the abbreviations are: OFV: Objective function value; SIR: Sampling importance resampling; CI: Confidence interval; RSE: Relative standard error from NONMEM; SHR: Shrinkage rate on the standard deviation scale; CV: Coefficient of variation.

[0309] The parameter estimates from the final RTTE model [B] are shown to be 0.00247h. -1 The Weibull-based risks of λ and the shape parameter γ of 1.02 increase slowly over time (since γ > 1). For the power-law model of the FVIII activity effect, β = -1.73 (IU / dL). -1The estimate of α = 0.247 indicates that the risk of FVIII activity at 1 IU / dL is 3.76 times that at 10 IU / dL (hazard ratio [HR] = 3.76). The risk of on-demand treatment is approximately 9.12 times that of prophylactic treatment (HR = e2.21). Finally, including treatment effect as a covariate in the baseline risk reduces the baseline risk of IIV from 148% %CV (run201) to 114% %CV (run213).

[0310] Model evaluation results Using the final RTTE model [B], with typical parameters and parameter uncertainties of the RTTE model, the probability of a typical 78.3 kg patient being bleeding-free within 1 year was simulated for weekly evaporin α 50 IU / kg prophylaxis and a theoretically continuous infusion regimen (e.g., targeting a stable level of 10-20 IU / dL FVIII activity). Annualized bleeding rates were simulated using a virtual cohort of 1000 adult / adolescent patients for weekly evaporin α 50 IU / kg prophylaxis, with inter-individual variability (IIV) in pharmacokinetics incorporated using a popPK model, combined with IIV from the RTTE model.

[0311] Of the observed data, 362 bleeding events occurred during the study period, 94 during the prophylactic period (86 during the 52-week prophylactic period in Group A [n=133] and 8 during the 6-month prophylactic period in Group B [n=26]), and 268 during the on-demand treatment period (during the first 6 months in Group B [n=26]). One bleeding event occurred before the initiation of Evansin Alpha treatment and was not included in the RTTE model development. The observed data showed a significant difference in the percentage of patients without bleeding events between the on-demand treatment and prophylactic groups. The percentage of patients without bleeding events was higher with prophylactic treatment than with on-demand treatment, and the time to occurrence of bleeding events was longer with prophylactic treatment compared to on-demand treatment (see [link to data]). Figure 15 ).

[0312] Kaplan-Meier (KM) visual prediction of the final RTTE model [B] in the prevention group of the XTEND-1 study. Figure 16 The solid line represents the observed KM, while the dashed line represents the 95% confidence interval around the observed KM. The gray bars show the visual prediction check with the 95% prediction interval. RTTE, the time to recurrence of events. Visual prediction check of the KM for the final model (see [link]). Figure 16 This provides a good description of the observed KM survival and demonstrates that the model is able to capture the general trends and variability of the data.

[0313] Simulation of annualized bleeding rate (ABR) and bleeding-related outcomes: Simulations were performed using the final RTTE model to predict bleeding events in a virtual cohort of 1000 adult and adolescent patients receiving prophylactic treatment (50 IU / kg QW, for 52 weeks). Individual simulated ABRs were calculated based on the simulations. The simulated mean (SD) ABR was 0.71 (1.50), and the median ABR was 0 (Q1–Q3: 0–1). This is highly consistent with the ABR observed in Group A of the XTEND-1 study, 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 is capable of predicting ABRs in a cohort receiving prophylactic treatment with 50 IU / kg QW BIVV001.

[0314] 64.1% of the virtual patients had zero bleeding episodes in the simulation, which is similar to the percentage in Group A of the XTEND-1 study, where 64.7% (86 out of 133) of patients were observed to have zero bleeding episodes during the efficacy period (see Table 3). Therefore, the final RTTE model was also well-suited to predict the percentage of patients without bleeding in the population receiving 50 IU / kg QW levonorgestrel-alpha prophylaxis.

[0315] Table 3 Comparison of simulated (RTTE) and observed (XTEND-1) bleeding-related outcomes

[0316] Average (SD) ABR Patients without bleeding events (%) Simulated ABR 0.71(1.5) 64.1 XTEND-1 Prevention Group 0.71(1.4) 64.7

[0317] Probability of first bleeding in a 1-year simulation RTTE model simulations were used to determine the probability of bleeding-free status within one year in a typical 78.3 kg patient using different evaporin alpha (EVA) dosing regimens. The EVA dosing regimens evaluated included 50 IU / kg QW EVA (i.e., prophylaxis) and continuous infusion (CIR) regimens of 10, 12, and 15 IU / kg. In this simulation, CIR was infused over one week. For example, for a 78.3 kg patient receiving a continuous infusion of 10 IU / kg, 783 IU was infused over one week for 52 weeks. The RTTE model simulation results are shown in Table 4.

[0318] Table 4. The probability of a typical patient having no bleeding within 1 year

[0319]

[0320] CI, confidence interval; CIR, continuous infusion regimen; FVIII, factor VIII; QW, once a week. A continuous infusion is a dose infused over one week. For example, for a continuous infusion of 10 IU / kg, in a 78.3kg patient, 783 IU would be infused over one week and continue for 52 weeks.

[0321] As shown in Table 4, the probability of bleeding-free status within 1 year for the once-weekly acetaminophen α 50 IU / kg prophylactic regimen was 71% (95% CI: 50%-83%). The bleeding-free status of the once-weekly acetaminophen α 50 IU / kg prophylactic regimen was 23%-35% higher than that of the 10 IU / kg regimen, which maintained stable FVIII activity in the range of 10.8-16.1 IU / dL. During the dosing interval, none of the CIRs provided any significant time at >40 IU / dL FVIII. The bleeding-free status of once-weekly acetaminophen α 50 IU / kg prophylactic regimen and continuous infusion of 10 IU / kg was... Figure 17 As shown in the image.

[0322] in conclusion: Bleeding events in adults and adolescents treated with IV levonorgestrel-alpha were well characterized using an RTTE model [B], in which the effects of Weibull baseline risk and FVIII activity on baseline risk were modeled using a power model. Treatment outcome (on-demand or prophylactic) was identified as a statistically significant covariate of baseline risk, indicating that the bleeding risk with on-demand treatment was 9.12 times that with prophylactic treatment.

[0323] Simulations of the 50 IU / kg QW prophylaxis regimen in virtual adult and adolescent populations showed that the simulated ABR and the percentage of patients with zero bleeding were comparable to those observed in group A of the XTEND-1 study. Simulations of the 50 IU / kg QW prophylaxis regimen in typical patients predicted a 29% (17%–50%) probability of first bleeding within 1 year (95% CI) for the 50 IU / kg QW prophylaxis regimen. Compared to CIR, the probability of first bleeding within 1 year was reduced by approximately 23% to 35% for the 50 IU / kg QW prophylaxis regimen, which maintains stable FVIII activity in the range of 16.1 IU / dL to 10.8 IU / dL. Therefore, RTTE model simulations of weekly 50 IU / kg levonorgestrel alpha prophylaxis estimated a 2-fold probability of bleeding-free status within 1 year compared to the stable FVIII activity of 11–16 IU / dL achieved through CIR. These results further support the benefit of achieving sustained normal to near-normal FVIII activity (>40 IU / dL) in hemophilia A subjects via a 50 IU / kg QW prevention regimen.

[0324] Example 2. Used to characterize the activity of Evans thromboplastin α factor VIII (FVIII) in water from patients with severe hemophilia A. Population pharmacokinetic (popPK) model

[0325] Once-weekly administration of levonorgestrel-alpha (EVAα) provided sustained high FVIII activity within the normal to near-normal range for most of the week and demonstrated better bleeding protection compared to previous FVIII prophylaxis. FVIII activity data were collected from five clinical studies (a phase 1 / 2a single-dose and repeated-dose study in adults [NCT03205163 and EudraCT2018-001535-51, respectively], a phase 3 study in adults and adolescents ≥12 years of age [XTEND-1, EFC16293, NCT04161495], a phase 3 study in children ≥1 to <12 years of age [XTEND-Kids, NCT04759131], and a phase 3 long-term extension study [XTEND-ed, NCT04644575]). A popPK model was developed to characterize FVIII activity following EVAα administration, identify intrinsic and extrinsic factors influencing pharmacokinetics (PK), and assess PK variability.

[0326] The FVIII activity levels used to develop the popPK model were measured by a phase I coagulation assay from 3054 blood samples from 199 adults and adolescents and 61 children who received evaporin α in the aforementioned study. Body weight and VWF levels ranged from 12.5 kg to 133 kg and 40 IU / dL to 339 IU / dL, respectively. FVIII activity was characterized using a one-compartment model with linear elimination, and the body weight effect of allometric growth in clearance (CL) and central compartment volume (V) was estimated to account for the dependence of CL and V on body size. The evaporin α popPK model is shown in Equation [A] above.

[0327] Statistical significance of baseline VWF, baseline ethnicity, ethnicity (Caucasian and Asian), hepatitis C virus and human immunodeficiency virus status, and blood type (A, B, O) was tested in covariate analyses. The final popPK model was used to simulate multiple dosing regimens in a virtual population of adult and adolescent patients generated using baseline weight distributions from the 1 / 2a phase study and XTEND-1.

[0328] result :

[0329] Table 5. Parameter estimates of the final popPK model of alpha thrombin

[0330]

[0331] Table 5 is abbreviated as follows: CL, central compartment clearance rate; CV, coefficient of variation; popPK, population pharmacokinetics; RSE (%), percentage of relative standard error (SE) (100% x SE / estimate); V, central compartment volume; θ is the population estimate of the PK parameter.

[0332] The final popPK model depicted the curve of FVIII activity over time, captured the inter-individual variability of FVIII activity, and accurately estimated the moderate inter-individual variability of CL and V (Table 5). The body weight-effect allometric growth index showed that CL and V increased with increasing body weight and generally disappeared more rapidly with decreasing body weight. Asian race was identified as a statistically significant covariate for CL (P < 0.001); Asians had CL 10.4% lower than non-Asians. Baseline VWF levels were not identified as a statistically significant covariate in the final popPK model, consistent with previous studies demonstrating that PK of Evans α is independent of VWF. Blood type was not identified as a statistically significant covariate in the final popPK model.

[0333] Figure 4 The figure presents simulated steady-state FVIII activity over time for evaporin α, as well as population-predicted and individual-predicted FVIII activity compared to observed FVIII activity in the final popPK model. The figure shows FVIII activity >40 IU / dL within 3–4 days post-dose. The final popPK model indicates that, regardless of weight and race, a weekly evaporin α (50 IU / kg) prophylactic regimen achieves a steady-state C50 >10 IU / dL in most adult and adolescent patients. 谷 Furthermore, the time to reach FVIII activity of 40 IU / dL is 3 to 4 days. Simulations of perioperative management and treatment of massive hemorrhage during major surgery show that a loading dose of 50 IU / kg followed by 30 IU / kg every 3 days postoperatively conforms to the World Federation of Hemophilia guidelines for peak FVIII activity (>50 IU / dL to 80 IU / dL) in most adults and adolescents. Similarly, for minor surgery and the treatment of moderate to mild bleeding, a single dose of 50 IU / kg of ependymin alpha achieves peak FVIII activity conforming to these guidelines.

[0334] in conclusionIn patients with severe hemophilia A, a linear one-compartment popPK model adequately characterized FVIII activity. Although CL and V depend on body weight and Asian ethnicity was identified as a covariate of CL, the limited effect of body weight and Asian ethnicity on FVIII exposure was considered clinically insignificant. PopPK simulations demonstrated that in most adults and adolescents, weekly 50 IU / kg evaporin alpha achieved sustained FVIII activity within the normal to near-normal range (>40 IU / dL) for 3–4 days, reaching >10 IU / dL on day 7. PopPK simulations also supported the selection of a three-phase dosing regimen for regular prophylaxis, treatment of bleeding, and perioperative management. In adults and adolescents, individual clearance of evaporin alpha was independent of baseline VWF.

[0335] Further details regarding the development of the popPK model

[0336] Data available from the Phase 3 Ivanin α study in adults, adolescents, and children were incorporated into the development of the population pharmacokinetic (popPK) model. Complete data from the adult and adolescent studies, as well as partial data from the pediatric and long-term safety studies, were also included.

[0337] Table 6 Research description used in popPK analysis

[0338]

[0339] a. Number of patients exposed to BIVV001 in each study

[0340] b. 161 patients from group A of the LTS16294 study (transferred from EFC16293 or EFC16295), 37 patients from group B, and 7 patients from group C were included in the PopPK model assessment.

[0341] Table 6 defines: a) the number of patients exposed to Evans thromboplastin α in each study in the development of the popPK model; total N = 260, including 199 adult and adolescent patients and 61 pediatric patients from EFC15295; b) only patients who underwent surgery in the LTS16294 study (3 patients) were included in the development of the popPK model; c) in EFC16293, there were 17 patients in the sequential group who skipped doses on day 7 of week 1 and day 7 of week 26 to allow for estimation of the terminal half-life by collecting primary coagulation (OSC) FVIII activity samples after administration on day 1 and week 26 up to day 15.

[0342] The Phase 1 popPK analysis used a 1-CMT model to describe OSC FVIII activity data, 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 to describe the OSC FVIII activity curve, and the baseline model included the WT effect. Further covariate screening was performed on the baseline popPK model.

[0343] Weight in kg (WT) or other continuous covariates were scaled to the median baseline WT (median baseline value) (78.3 kg) for adults and adolescents to assess the effects of adult covariates or allometric growth in children.

[0344] For example, for adult covariate effects and child allometric growth effects, CL = TVCL * (WT) 时变的 / 78.3) CLexp *(exp(ETA1)). For volumes, use a similar method.

[0345] Dataset One popPK dataset for the observed data is based on the adult / adolescent study EFC16293. Figure 5 shows the baseline-corrected FVIII activity time curves. All patients are shown on day 1 (baseline). Figure 5A The sequential group patients are shown on day 1 (baseline) and at week 26. Figure 5B The FVIII activity time curve follows a general one-compartment (logarithmic scale linear decrease) kinetic. The FVIII activity curve shows that the mean half-life of ependycin α is 47.8 hours.

[0346] The correlation among the four continuous covariates at baseline was... Figure 6 The median baseline weight (WTKGB) is shown in the figure. This excludes the EFC16295 study. The median baseline ethnicity (BH) is 43 (also excluding the EFC16295 study). The median baseline weight loss (BVWF) is shown as 112 (also excluding the EFC16295 study). Age showed an association with WT, so it was not tested as a covariate. Testing ethnicity and VWF as covariates yielded no significant results.

[0347] The distribution of categorical covariates (such as blood type, race, HIV status, and HCV status) is shown in Table 7. Data in Table 7 include all studies (n = 260). Black race (1.92%) and other races (3.08%) were present in approximately 5% of patients. For blood type, blood types A and O were more common, at 29.62% and 36.15%, respectively. Blood type B was present in <10% (9.23%) of patients. Blood type AB was present in <5% (3.85%) of patients. Patients who were HCV and HIV positive were older, and no pediatric patients were HCV or HIV positive. Two patients were <2 years old. Of the categorical factors shown, only HCV status, HIV status, blood type (A, B, and O), and race (Caucasian and Asian) were tested as covariates.

[0348] Table 7 Descriptive statistics of categorical covariates

[0349]

[0350] Table 8 provides additional details about the covariate model.

[0351] Table 8: Parameter estimates of the final popPK model for Ivanin α

[0352]

[0353] OSC activity is the concentration (C) in the central chamber. All parameters of the basal and final covariate models were estimated with acceptable precision. The increased body weight effect reduced the instrumental variable estimates (IIV) of CL and V, while the increased Asian racial effect on CL reduced the IIV of CL. The WT effect exponents on CL and V were acceptable when compared with the simple allometric growth index. The Asian racial effect on CL was identified, and the clearance rate in Asians was 10.4% lower than that in non-Asian individuals of the same body weight.

[0354] The final popPK model with covariates incorporated is shown below:

[0355]

[0356] For Asians, Asian = 1; for non-Asians, Asian = 0.

[0357] Figure 7A and Figure 7B The group prediction (PRED) and individual prediction (IPRED) compared to DV are shown respectively. This demonstrates that both the group model and the individual model can describe PK data across age categories.

[0358] Figure 8 Visual predictive testing (VPC) of the final popPK model is presented. The VPC for each study shows that most of the observed FVIII activity data are within the predicted range [5th–95th percentile]. For VPC purposes, a unique patient from LTS16294 was considered in EFC16293.

[0359] Figure 9A and Figure 9B Population prediction (PRED) and individual prediction (IPRED) compared to DV are presented separately for surgical procedures. Data from 19 patients from EFC16293, EFC16295, and LTS16294 are included within the surgical timeframe. The model performs quite well in describing PK data collected during surgery and after specific surgical administration.

[0360] Figure 10 The steady-state C is shown based on baseline body weight (kg). 谷 C maxss The distribution of time when FVIII activity reached 40 IU / dL across all populations. Figure 11 The steady-state C is shown for all age groups. 谷 C maxss Distribution of time to reach 40 IU / dL in non-Asian and Asian populations. Steady-state FVIII activity C. max C 谷 The time to reach 40 IU / dL for FVIII activity increased with increasing body weight, and was higher in Asians compared to non-Asian populations. However, regardless of weight or race, a 50 IU / kg QW prevention regimen showed that for most adults and adolescents (aged ≥12 years), homeostatic C... 谷 >10 IU / dL and the time to reach FVIII activity of 40 IU / dL is 3 to 4 days. The 50 IU / kg QW prevention regimen also shows that for most children (age <12 years), steady-state C 谷 >5 IU / dL, and the time to reach 40 IU / dL for FVIII activity is 2 to 3 days.

[0361] Major surgery and massive bleeding The model was analyzed for major surgeries and massive hemorrhage. Major surgeries and massive hemorrhages were classified based on the criteria listed in Table 9.

[0362] Table 9:

[0363] sky Major surgery massive bleeding Preoperative (day of bleeding) Peak value >80-100 IU / dL Peak value >80-100 IU / dL Day 1 to Day 3 Peak value >60-80 IU / dL Peak value > 50 IU / dL (Day 3) Day 4 to Day 7 Peak value >40-60 IU / dL Peak value >50 IU / dL Day 7 to Day 14 Peak value >30-50 IU / dL Peak value >50 IU / dL

[0364] For major surgery and hemorrhage, the simulation is based on a single-dose regimen of 50 IU / kg, with additional doses of 30 or 50 IU / kg administered every 2 to 3 days if necessary. Therefore, after the preoperative dose of 50 IU / kg, possible dosing combinations are 50 IU / kg Q2D, 50 IU / kg Q3D, 30 IU / kg Q2D, and 30 IU / kg Q3D (QXD is every X days). These same simulations can be applied to both major surgery and major hemorrhage, as both involve the same dosing combinations.

[0365] Figure 12 The study demonstrated OSC FVIII activity over time in subjects under six years of age, simulating major hemorrhage and major surgery. Throughout the surgical period, over 95% of patients aged 6 years and older met the criteria for major surgery, and over 80% of patients under 6 years of age met the criteria for surgery.

[0366] Figure 13 Simulated FVIII activity was demonstrated in a virtual adult and adolescent population using 50 IU / kg of Evansin Alpha followed by 30 IU / kg every 3 days until day 14. The simulation illustrates that an initial dose of 50 IU / kg followed by 30 IU / kg every 3 days until day 14 should be sufficient for perioperative management during major surgery and for the treatment of massive hemorrhage. >95% of adult and adolescent patients were predicted to meet the World Federation of Hemophilia (WFH) guidelines (Srivastava A et al. Haemophilia. [Hemophilia] 2020; 26 Supplement 6:1-158) for peak FVIII activity (>80-100 IU / dL preoperatively or on the day of massive hemorrhage). Additionally, other dosing regimens (such as an initial dose of 50 IU / kg followed by 50 or 30 IU / kg every 2 or 3 days) were simulated, and these additional dosing regimens were also predicted to meet the WFH peak FVIII guidelines. Therefore, the simulation illustrates that an initial dose of 50 IU / kg followed by 50 IU / kg or 30 IU / kg every 2 or 3 days would be in line with the WFH peak FVIII guidelines for the majority (>95%) of adult and adolescent patients to be managed during major surgery and major hemorrhage.

[0367] Minor surgery and mild to moderate bleeding : Figure 14Simulated OSCFVIII activity over time is shown for all age groups. In all age groups, over 95% of patients met the criteria for peak FVIII > 50 IU / dL following the preoperative dose for minor surgery. Similarly, over 95% of patients in all age groups met the criteria for peak FVIII > 40 IU / dL required for the management of mild / moderate bleeding. With the use of additional doses of 30 or 50 IU / kg every 2 or 3 days, over 95% of patients in all age groups met the criteria for peak FVIII > 50 IU / dL.

[0368] in conclusion Based on this data, the one-compartment (1-CMT) model describes OSCFVIII activity data quite well in adults, adolescents, and children. The basic model includes weight effects (for CL and V), while the Asian racial effect (for CL) was identified as a statistically significant covariate. Simulations for a range of weights showed that a fixed regimen of 50 IU / kg QW provided high FVIII activity in adult, adolescent, and pediatric populations, regardless of weight or race. Simulations using this model also supported and illustrated possible levonorgestrel-alpha dosing regimens for surgical and bleeding situations.

[0369] Example 3. Timing of recurring events in children with severe hemophilia A using Evans α thromboplastin. (RTTE) analysis

[0370] The XTEND-KIDS (EFC16295, NCT04759131) study is a phase 3, open-label, multicenter study of the safety, efficacy, and pharmacokinetics of epvallarin alpha in previously treated children <12 years of age with severe hemophilia A. This example contains a description of the RTTE (bleeding) analysis of epvallarin alpha in children <6 years of age and 6 to <12 years of age with severe hemophilia A from the EFC16295 study. All bleeding events in children were used to evaluate the model performance of a previously developed RTTE model in adult / adolescent patients (model [B], see Example 1). As discussed above, this previous RTTE model was characterized by Weibull baseline risk, according to which the effect of OSC FVIII activity on baseline risk was modeled using a power relationship.

[0371] This analysis involved 1) evaluating adult / adolescent RTTE models using pediatric data to assess their ability to predict the annualized bleeding rate (ABR) observed in study EFC16295, and 2) building pediatric RTTE models by re-estimating model parameters using only pediatric data. The pediatric RTTE models were used to compare observed and simulated ABR in the pediatric population. Potential covariate-parameter relationships were also evaluated to assess whether additional factors, compared to adults and adolescents, might influence bleeding risk and ultimately bleeding event rates in the pediatric population.

[0372] data:

[0373] Bleeding event data from 74 patients in the EFC16295 study were analyzed. This group of patients included the “Pediatric BIVV001 RTTE Analysis Dataset”. Key characteristics of the Pediatric BIVV001 RTTE Analysis Dataset are provided in Table 10. Overall, 63 bleeding events were recorded in all patients, including 17 from patients <6 years of age and 46 from patients aged 6 to <12 years.

[0374] Table 10: Key features of the children's BIVV001 RTTE analysis dataset

[0375]

[0376] 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 unknown type. Of the 63 bleeding events, 42 (66.7%) were joint bleeding events, and 21 (33.3%) were non-joint bleeding events.

[0377] Baseline characteristics of patients in the pediatric BIVV001 RTTE analysis dataset are presented in Table 11 by age category and population. Baseline weight (WT) ranged from 11.4 to 66.5 kg, with a median weight of 22 kg. The age of patients in the dataset ranged from 1.4 to 11 years, with a median age of 5 years.

[0378] The age at first prophylaxis ranged from 0 years (from the first year of birth) to 5 years, with a median of 1 year. This indicates that 50% of patients received some type of FVIII prophylaxis 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%) patients recorded 0 target joints (no target joints at risk of bleeding). 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 prior to the first administration of the study drug.

[0379] Table 11: Baseline characteristics of patients in the pediatric BIVV001 RTTE analysis dataset: continuous covariates.

[0380]

[0381] Number of target joints: Assessed by the investigator. Target joints were defined as major joints (e.g., hip, elbow, wrist, shoulder, knee, or ankle) that experienced ≥3 spontaneous bleeding episodes within a consecutive 6-month period. The number of bleeding episodes in the 12 months prior to BIVV001 treatment was also referred to as bleeding history in this analysis. Hemophilia Joint Health Score ranged from 0 to 124, with higher scores indicating poorer joint health.

[0382] Covariate analysis:

[0383] Significant covariate-parameter relationships were searched on a fixed adult / adolescent RTTE model to investigate whether additional factors could explain the potential differences in bleeding risk between pediatric and adult / adolescent patients. Potential covariate-parameter relationships were evaluated using a stepwise covariate modeling procedure (SCM) with adaptive range narrowing (ASR) on models with fixed adult / adolescent parameters to assess the impact of other factors (e.g., age, bleeding history) on bleeding risk in pediatric patients. Notably, covariates from the adult / adolescent RTTE model (on-demand treatment group) were not relevant to this analysis. In the pediatric study EFC16295, levonorgestrel-releasing agent (ELA) was administered only as prophylactic therapy.

[0384] Histograms of covariates at baseline in the pediatric BIVV001 RTTE analysis dataset. Figure 18 The results showed signs of skewed distribution of some medical history factors, such as age at first prophylaxis, number of target joints, history of bleeding within 12 months prior to BIVV001 treatment, and HJHS.

[0385] To assess the correlation of covariates, Figure 19 The correlation matrix of covariates at baseline for patients in the pediatric BIVV001 RTTE analysis dataset is presented. As expected, a strong correlation was observed between weight (WT) and age and age group (i.e., age continuous and age categorical). A strong correlation (>0.8) was also observed between the number of target joints and history of bleeding. For covariates with correlation coefficients >0.7, only age categorization (<6 years; 6 to <12 years) and history of bleeding were included in the covariate model analysis.

[0386] The SCM procedure was used to search for significant covariate-parameter relationships in RTTE models with fixed parameters and adult / adolescent estimates to assess whether other factors could explain the observed differences, but no significant covariate-parameter relationships were identified.

[0387] RTTE model re-estimation

[0388] To potentially improve the fit of previous RTTE models to the pediatric population, previous RTTE models were re-estimated based on the pediatric alpha thrombin RTTE dataset. Model parameters from the adult-adolescent model and the re-estimated model parameters based on the pediatric data are presented together in Table 12. Pediatric patients are characterized by significantly lower baseline scale parameter λ and shape parameter γ < 1, leading to a decrease in baseline risk over time. Pediatric patients are also characterized by milder drug effects. Notably, the baseline scale parameter estimate λ for the re-estimated RTTE model was obtained with higher uncertainty (>50%) than in the adult / adolescent dataset, which may reflect the lower number of patients and events in the pediatric dataset compared to the adult / adolescent dataset.

[0389] Table 12: Model parameters of the child BIVV001 RTTE model based on adult-adolescent and child population estimates

[0390]

[0391]

[0392] *Based on an adult / adolescent RTTE model applied only to children's data (MAXEVAL=0)

[0393] **Condition number, estimate, and RSE based on adult / adolescent data**

[0394] The RSE of the IIV parameters is reported using an approximate SD scale.

[0395] A second covariate screening was performed on the re-estimated pediatric RTTE model to investigate whether further refinement was possible. Covariates evaluated included age group and history of bleeding. Quality control (QC) checks were conducted to reinforce 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.

[0396] Final RTTE model for children

[0397] The final RTTE model for children has the following characteristics:

[0398] ■ Baseline hazard model: Weibull model with scale parameter λ and shape parameter γ.

[0399] ■ Drug action model: with powers of parameters β and α.

[0400] ■ Covariate model: (None).

[0401] ■IIV model: Exponential IIV over λ.

[0402] The parameter estimates for the final RTTE model are shown in Table 12. The parameter estimates from the final RTTE model show that the Weibull base risk with λ of 0.0000429 1 / h and a shape parameter γ of 0.694 decreases over time (from γ < 1). The parameter estimates also show that, for the power model of the FVIII activity effect, estimates of β = -0.00535 (dL / IU) and α = 1.27 indicate that the hazard ratio for 1 IU / dL of FVIII activity is 1.1 times that for 10 IU / dL of FVIII activity.

[0403] The following RTTE model [D2] shows the final RTTE model, which contains parameter estimates for patients under 12 years of age:

[0404]

[0405] The predictive performance of the adult / adolescent RTTE model was also evaluated using the Visual Prediction Test (VPC) in children with hemophilia A, i.e., by keeping the model parameters constant to the estimates in the adult / adolescent population. The final VPC results for the RTTE model indicate that the model adequately describes the data from the complete children's dataset (see [link to VPC]). Figure 20 The final VPC of the RTTE model also fully describes the data for both age groups (data not shown).

[0406] Final RTTE model test for children:

[0407] The probability of first bleeding in a 1-year simulation The final pediatric BIVV001 RTTE model was used to predict the probability of first bleeding within one year in typical pediatric patients from two age groups using a series of BIVV001 prophylactic dosing regimens. Typical patients <6 years and 6 to <12 years of age weighed 18 kg and 32.8 kg, respectively, corresponding to the median observations in study EFC16295. Uncertainties in the final RTTE model parameters were included in these simulations using 300 SIR samples with the RTTE parameter set. Results for the selected dosing regimens are shown in Tables 13 and 14. Continuous infusion was considered as the dose infused over 1 week; for example, for a continuous infusion of 10 IU / kg, 180 IU was infused over 1 week for a 18 kg patient and continued for 52 weeks; 328 IU was infused over 1 week for a 32.8 kg patient and continued for 52 weeks.

[0408] Table 13: Probability of first bleeding within 1 year in typical pediatric patients <6 years of age and weighing 18 kg with the selected BIVV001 prophylactic dosing regimen

[0409]

[0410] As shown in Table 13, the results predict that for typical pediatric patients <6 years of age and weighing 18 kg (receiving prophylactic treatment at 50 IU / kg QW for 1 year), the probability of first bleeding within 1 year (95% CI) is 28% (14%-44%), with a steady-state trough concentration C. 谷 The rates of time >10 IU / dL (% of dosing interval) and time >40 IU / dL (% of dosing interval) were 81% and 32%, respectively. The probability of first bleeding within one year indicates that in typical pediatric patients <6 years of age and weighing 18 kg, the high sustained FVIII activity achieved with the 50 IU / kg QW regimen reduced the probability of first bleeding within one year from 38% to 28% compared to a continuous infusion regimen of 10–15 IU / kg.

[0411] Table 14: Probability of first bleeding within 1 year in typical pediatric patients aged 6 to <12 years and weighing 32.8 kg with the selected BIVV001 prophylactic dosing regimen

[0412]

[0413] As shown in Table 14, the results predicted that for typical pediatric patients aged 6 to <12 years and weighing 32.8 kg (receiving prophylactic treatment at 50 IU / kg QW for 1 year), the probability of first bleeding within 1 year (95% CI) was 26% (13%-41%), with steady-state C 谷 The rates of time >10 IU / dL (per percent of dosing interval) and time >40 IU / dL (per percent of dosing interval) were 92% and 40%, respectively. The probability of first bleeding within one year indicates that in typical pediatric patients aged 6 to <12 years and weighing 32.8 kg, the high sustained FVIII activity achieved with the 50 IU / kg QW regimen reduced the probability of first bleeding within one year from 37% to 26% compared to a continuous infusion regimen of 10–15 IU / kg.

[0414] The simulation results demonstrate the advantage 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, respectively, in pediatric patients aged <6 years and 6 to <12 years, compared to 0% of the time following continuous infusions of 10–15 IU / kg in both age groups.

[0415] Annualized bleeding rate (ABR)Simulations were conducted using a final pediatric RTTE model in virtual cohorts of 10,000 children <6 years of age and 10,000 children aged 6 to <12 years of age, who were treated with a 50 IU / kg QW BIVV001 prophylactic regimen for up to 1 year (52 weeks). Individual simulated ABRs were calculated based on the simulations. The virtual cohorts of <6 years and 6 to <12 years of age had mean (SD) weights of 16.2 kg (4.73 kg) and 35.5 kg (13.6 kg), respectively. Descriptive statistics of the simulated ABRs are shown in Table 15.

[0416] Across the entire child population, the simulated mean ABR was 0.74, and the median ABR was 0 (Q1–Q3: 0–1). This is highly consistent with the ABR observed in study EFC16295, where the mean (95% CI) ABR was 0.89 (0.56, 1.42), and the median ABR was 0. In the <6-year-old and 6–<12-year-old age groups, the simulated mean ABR was 0.76 and 0.70, respectively, which, within the 95% CI around the observed mean ABR, were 0.48 (0.30; 0.77) and 1.33 (0.64; 2.76), respectively. In the simulation, 63.6% and 65.6% of virtual patients aged <6 years and 6 to <12 years, respectively, had 0 bleeding episodes, which is similar to the percentages observed in the study EFC16295, where 63.2% (24 out of 38) and 63.8% (23 out of 36) of patients in the same age groups had 0 bleeding episodes during the efficacy period.

[0417] Table 15: Descriptive statistics of simulated ABR (lasting 52 weeks) by age group in a virtual population receiving 50 IU / kg QW BIVV001 as prophylaxis.

[0418]

[0419] Sensitivity analysis was performed to exclude participants with an abnormal number of treated bleeding episodes in the 6 to <12 year age cohort. The mean ABR estimated by the negative binomial model decreased to 0.75 (95% CI: 0.41 to 1.40) in the 6 to <12 year age cohort and to 0.61 (95% CI: 0.42 to 0.90) overall.

[0420] These simulations demonstrate that the final pediatric RTTE model performs acceptablely and can be used to predict bleeding profile and ABR in a population of children (<12 years old) who are prophylactically given 50 IU / kg QW BIVV001 in the first year.

[0421] in conclusion:

[0422] Bleeding events in children with hemophilia A treated with BIVV001 were well characterized by a final pediatric RTTE model with Weibull baseline risk [Model D2], in which the effect of OSC FVIII activity on baseline risk was modeled using a power relationship. Diagnostic plots showed that the adult / adolescent RTTE model generally described well the percentage of pediatric patients experiencing bleeding events over time, but the time to first bleeding was slightly underestimated in younger patients (<6 years), i.e., bleeding events were predicted to occur slightly earlier than observed in this age group. Covariate searches using the SCM procedure on the fixed-parameter RTTE model with adult / adolescent estimates did not identify significant covariate-parameter relationships. The fit between the pediatric data and the adult / adolescent RTTE model was improved by re-estimating the model parameters based on the pediatric Evans α RTTE dataset. Further covariate searches on the re-estimated RTTE model did not lead to further model refinement, therefore model [D2] was considered the final pediatric RTTE model.

[0423] Ultimately, the pediatric RTTE model effectively captured bleeding time (1st to 5th events) in the pediatric population. Although the observed first bleeding time was still at the upper edge of the prediction interval for younger patients (<6 years), the model was better suited to describe the ABR in this age group, with a simulated mean ABR of 0.76, compared to a simulated mean ABR of 1.32 based on the adult / adolescent RTTE model, and an observed mean (95% CI) ABR of 0.48 (0.30; 0.77).

[0424] Therefore, bleeding events in children with hemophilia A treated with levonorgestrel-alpha were adequately characterized by an RTTE model with Weibull baseline risk, based on which the effect of OSC FVIII activity on baseline risk was modeled using a power-law relationship.

[0425] Example 4. Population pharmacokinetics (popPK) model applied to pediatric patients

[0426] The goal of the following analysis was to characterize FVIII activity from the EFC16295 study using empirical Bayesian estimates (EBE) of the 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 the latent covariates (baseline values) from patients in the EFC16295 study included in the MAP Bayesian analysis are summarized in Tables 16 and 17.

[0427] Table 17: Descriptive statistics of continuous covariates in the final dataset EFC16295

[0428]

[0429]

[0430] Table 17 - Descriptive statistics of categorical covariates of subjects in the EFC16295 study in the final dataset

[0431] covariates N type n(％) age group 74 Older children (6 years and older, but less than 12 years old) 36(48.7) Young children (age <6 years) 38(51.3) Hepatitis C infection status 74 have 0(0) none 74(100) HIV infection status 74 have 0(0) none 74(100) race 74 Caucasians 55(74.3) Asian 8(10.8) African Americans 3(4.05) Other races 4(5.41) Race not reported 4(5.41) blood type 74 Blood type A 21(28.4) Blood type AB 2(2.7) Blood type B 8(10.8) Blood type O 26(35.1) Blood type unknown 17(23.0)

[0432] The baseline weight of pediatric patients (1.4 years to <12 years) from the EFC16295 study ranged from 11.4 kg to 66.5 kg. There were 2 patients in the <2 years, 36 patients in the 2 years ≤ age <6 years, and 36 patients in the 6 years ≤ age <12 years.

[0433] The baseline VWF of pediatric patients (from EFC16295) was lower than that of adult and adolescent patients from all other studies.

[0434] In EFC16295, approximately 74% were Caucasian, 11% were Asian, 4% were Black, 5% were of other races, 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. No patients in EFC16295 were HCV-positive or HIV-positive.

[0435] Apply the PopPK model described in Example 2 to EFC16295

[0436] The FVIII activity data from EFC16295 were described using the popPK model (model [A]) with the MAXEVAL=0 option in NONMEM. The parameterization in the popPK model is:

[0437]

[0438] For Asians, Asian = 1; for non-Asians, Asian = 0.

[0439] Model performance is evaluated based on goodness of fit (GOF), visual prediction check (VPC), and quality standard metrics.

[0440] PopPK models [A] and [A'] can be used for subjects of all ages.

[0441] like Figure 21As shown, the population-predicted OSC FVIII activity (PRED) versus observed (DV) data demonstrates the appropriateness of the previous popPK model in describing FVIII activity over time from the EFC16295 and LTS16294 studies. The individual-predicted OSC FVIII activity (IPRED) versus observed (DV) plots show that the inter-individual variability model can explain the variability in the predictions.

[0442] Figure 22 and Figure 23 The residual variation over time and the predicted FVIII activity are shown. When plotted over the entire duration using time, or when plotted using PRED and IPRED respectively, both CWRES and IWRES are well / uniformly distributed around 0.

[0443] The performance of the final popPK model was evaluated using VPC with the EFC16295 research data. The VPC graph is presented below. Figure 24 The results of VPC showed that most of the observed FVIII activity was within the predicted range [5th–95th percentile], and only a few FVIII activity data points were outside the predicted percentile range [5th–95th percentile].

[0444] Furthermore, Table 18 presents 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 describing the FVIII activity of EFC16295.

[0445] Table 18 - Summary of Mean Prediction Error (MPE) and Root Mean Square Error (RMSE)

[0446]

[0447] Final PopPK model performance of perioperative FVIII activity

[0448] Thirty-two patients (from EFC16295 and LTS16294) underwent surgery during treatment with levonorgestrel-alpha. According to the protocol, these patients received specific doses of levonorgestrel-alpha during preoperative treatment and postoperative follow-up, and PK samples were collected to assess patients' FVIII activity during and after surgery. Figure 25 The graphs show population-predicted (PRED) versus observed FVIII activity assessed during the perioperative period, and individual-predicted (IPRED) versus observed FVIII activity. Figure 25It can be seen that both PRED and IPRED can reasonably describe the FVIII activity data, with R2 values ​​of 0.78 and 0.88, respectively. Figure 25 The adult-adolescent patients who underwent surgery were from the LTS16294 study.

[0449] EFC16295 Post-event internal or external factor assessment

[0450] For the EFC16295 study, once the final popPK model was validated using GOF, VPC, and reference quality standards, selected intrinsic and extrinsic factors were explored using ex post-exposure parameters. The steady-state AUC was then used. tau (AUC0-168h), C max and C min Used as the FVIII exposure parameter generated at week 26 for 73 patients in EFC16295. One patient did not continue until week 26, therefore, the patient's exposure at steady state could not be calculated.

[0451] When the steady-state AUC(AUC) is... tauss C max (C maxss ) and C min (C minss When plotted using baseline VWF (BVWF) and baseline hematocrit (BH), no specific trend in BVWF or BH was observed in either young or older children. Figure 26-28 As shown. As expected, since the weight effects on the CL and V parameters have already been incorporated into the popPK model, an AUC was observed. tauss C maxss and C minss The trend of increasing exposure with weight gain is consistent with the predicted trend of increasing FVIII exposure with weight gain.

[0452] Similarly, steady-state FVIII exposure parameters were evaluated using categorical factors such as blood type A and blood type O. No major trends were observed for steady-state exposure to blood type A and blood type O.

[0453] Other categorical factors were not used for assessment because patients with these factors had lower diversity. For example, in the EFC16295 study, no patients were infected with HCV or HIV. Similarly, for blood type B, 8 out of 73 patients had blood type B, while only 2 out of 73 patients had blood type AB.

[0454] Post-event comparison parameters

[0455] Exposure parameters, such as C, are derived by simulating the FVIII activity curve of each individual over time for the actual dose administered using post-exposure PK parameters.max and C min (also known as C) 谷 Simulations were performed to derive the patient's day 1 and steady-state C in EFC16295. max and C min Simulate FVIII activity up to week 26 to obtain steady-state C. max and C min Of the 74 patients in EFC16295, 73 had been treated beyond week 26 and were included in the steady-state calculations.

[0456] Table 19 shows the C values ​​after the first day's dose. max and C min The summary statistics were compared and stratified by age group. Within the two age groups, C was predicted based on PopPK analysis. max and C min C derived from or observed in NCA of EFC16295 max and C min quite.

[0457] The mean (SD) values ​​of PK parameters derived from post-hoc assessment of population PK and non-compartmental analysis (NCA) are shown in Table 20. This figure indicates that for PK parameters derived from both population PK and NCA, CL and V increase with decreasing age, while t1 / 2 decreases. Overall, the mean t1 / 2 and SD are generally comparable between population PK and NCA across age groups, while CL and V estimated from NCA are lower compared to PopPK for each age group.

[0458] These results indicate that the popPK model predicts FVIII curves over time, individual PK and exposure parameters, and patient variability quite well.

[0459] Table 19: Comparison of PopPK and non-compartmental analysis (NCA) derived PK parameters of baseline-corrected FVIII activity after day 1 dose in EFC16295 patients in the PK group

[0460]

[0461] The predicted Cmin is a post-hoc simulated FVIII activity at time = 168 hours after the Day 1 dose. The predicted Cmax is a post-hoc simulated Cmax after the Day 1 dose. The observed Cmin was FVIII activity on day 8 of the baseline visit. The observed Cmax was obtained from NCA analysis.

[0462] Table 20: Comparison of PK parameters derived from population PK analysis and non-compartmental analysis

[0463]

[0464] Abbreviations: CL = clearance rate, t1 / 2 = half-life, NCA = non-compartmental analysis; PK = pharmacokinetics; t1 / 2z = terminal half-life, V = volume of distribution, Vss = steady-state volume of distribution.

[0465] Time to achieve FVIII activity

[0466] The steady-state time to achieve FVIII activity increased as the FVIII activity threshold decreased from 150 IU / dL to 1 IU / dL. This was observed in both age groups of children, as shown in Table 21.

[0467] Table 21 - Descriptive statistics on the time to reach predefined FVIII activity (week 26) for EFC16295

[0468]

[0469]

[0470] Abbreviations: SD stands for Standard Deviation, %CV stands for Percentage Coefficient of Variation.

[0471] For pediatric patients aged 6 years to < 12 years, the median time to reach 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.

[0472] For pediatric patients under 6 years of age, the median time to reach 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.

[0473] In post-hoc simulations, eight patients aged 6 years to < 12 years and three patients aged < 6 years were predicted to have peak FVIII activity > 150 IU / dL at steady state.

[0474] Descriptive statistics of the time to achieve FVIII activity on day 1 in patients of the two pediatric age groups in the PK group are shown in Table 22.

[0475] Table 22 - Descriptive statistics of the time to reach predefined FVIII activity on day 1 in the PK group of EFC16295.

[0476]

[0477]

[0478] in conclusion Based on GOF, VPC, and quality criteria, the popPK model is considered suitable for characterizing FVIII activity data from pediatric studies. Furthermore, this model can also describe FVIII activity data collected during specific dosing periods in pediatric and long-term safety studies during perioperative management. The impact of selected intrinsic and extrinsic factors on FVIII activity in pediatric patients is evaluated by comparing post-hoc estimates of FVIII activity exposure.

[0479] Consistent with previous findings, post-hoc steady-state FVIII activity exposure in children varied significantly only with body weight, as the weight effect is included in both CL and V in the popPK model. Based on available data, all other assessed factors, such as baseline VWF, baseline hematocrit, and blood type A and O, had no significant effect on children's FVIII activity. Previous popPK models well described FVIII activity in children and in long-term safety studies, where the effect of body weight on exposure was the only source of significant FVIII variability in the pediatric population.

[0480] Using the MAP Bayesian approach, additional FVIII activity data from pediatric patients in the EFC16295 study were fully characterized using a popPK model previously developed for adult / adolescent patients, as described in Example 2.

[0481] Table A. Ivanin α Sequence Information

[0482]

[0483]

[0484]

[0485]

[0486]

[0487]

[0488]

[0489]

[0490]

[0491]

Claims

1. A method for estimating bleeding risk, the method comprising: (a) Coagulation factor VIII (FVIII) activity information is received by a software-based system comprising a computer program programmed to implement a recurring event occurrence time (RTTE) model [B]. (b) The computer program uses the RTTE model [B] and the received information to calculate the bleeding risk, and (c) The calculated bleeding risk information of (b) is transmitted by the software-based system to output the bleeding risk information.

2. A method for estimating bleeding risk, the method comprising: (a) Receive coagulation factor VIII (FVIII) activity information by one or more electronic devices. (b) The coagulation factor VIII (FVIII) information is transmitted by the processing device to a software-based system, wherein the software-based system is programmed to implement a recurring event occurrence time (RTTE) model [B] to calculate the bleeding risk; (c) Receive from the software-based system the information transmitted using (b) and the bleeding risk information calculated by the RTTE model [B]; as well as (d) The bleeding risk information (c) transmitted by the one or more electronic devices is used to output the bleeding risk information.

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

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

5. The method of claim 4, wherein the popPK model is the Evans α-popPK model.

6. The method of claim 5, wherein the Evans αpopPK model includes body weight as a covariate, and wherein the Evans αpopPK model does not include VWF level or hematocrit level as covariates.

7. The method of claim 5, wherein the popPK model is the Evans α-popPK model [A].

8. The method of claim 5, wherein the popPK model is the Evans α-popPK model [A'].

9. The method of any one of claims 1-8, wherein the provided FVIII activity information is specific to an individual subject.

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

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

12. The method of any one of claims 1-8, wherein the provided FVIII activity information is an estimated FVIII activity level of a subject population.

13. The method of any one of claims 1-8, wherein the provided FVIII activity information is directed to a virtual or hypothetical subject.

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

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

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

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

18. A method for estimating the bleeding risk of an individual subject, the method comprising: a) Individualized subject information, including the subject's weight, is received by a software-based system; b) The software-based system receives information on expected treatment outcomes, including FVIII level; c) Apply a time-of-recurrence (RTTE) model for the subject based on the individualized information and / or the expected treatment outcome information; d) Use the RTTE model to estimate the bleeding risk of this individual subject; as well as e) The estimated bleeding risk information (d) is transmitted by the software-based system to output the bleeding risk information.

19. The method of claim 18, wherein the RTTE model includes treatment effect as a covariate.

20. The method of claim 19, wherein the therapeutic effect is preventative or on-demand treatment.

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

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

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

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

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

26. The method of any one of claims 18-25, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A].

27. The method of any one of claims 18-25, wherein the expected treatment outcome information is provided by the Evans αpopPK model [A'].

28. The method of any one of claims 18-25, wherein the expected treatment outcome information is provided by the individual subject.

29. The method of any one of claims 18-25, wherein the expected treatment outcome information is provided by a medical professional.

30. A method for treating a subject with hemophilia A, the method comprising: (a) Identifying subjects with hemophilia A; (b) Estimate the subject’s bleeding risk when treated with 50 IU / kg Evansin Al once a week; (c) If the subject has an FVIII activity level of at least 10 IU / dL, estimate the subject’s bleeding risk; as well as (d) If the subject has a lower estimated bleeding risk under levonorgestrel-alpha therapy compared to the estimated bleeding risk when the subject has an FVIII activity level of at least 10 IU / dL, then the subject shall be treated with levonorgestrel-alpha at 50 IU / kg approximately once a week.

31. A method for estimating the bleeding risk of an individual subject, the method comprising: (a) Information from an individual subject is received by a software-based system, wherein the system is programmed to implement: (i) A one-compartment alphapopPK model for calculating coagulation factor VIII (FVIII) activity information, wherein the alphapopPK model includes body weight as a covariate and wherein the alphapopPK model does not include the level of VWF or the level of hematocrit as a covariate. as well as (ii) Time-of-occurrence (RTTE) model, which uses FVIII activity information to estimate bleeding risk. (b) The estimated bleeding risk is calculated by the software-based system using the Ivanin αpopPK model, the RTTE model, and the received information, and (c) The calculated bleeding risk information of (b) is transmitted by the software-based system to output the bleeding risk information.

32. A method for estimating bleeding risk, the method comprising: (a) Information about an individual subject is received by one or more electronic devices; (b) The processing device transmits the individual subject's information to a software-based system, wherein the software-based system is programmed to implement... (i) A one-compartment alphapopPK model for calculating coagulation factor VIII (FVIII) activity information, wherein the alphapopPK model includes body weight as a covariate and wherein the alphapopPK model does not include the level of VWF or the level of hematocrit as a covariate. as well as (ii) Time-of-occurrence (RTTE) model, which uses FVIII activity information to estimate bleeding risk. (c) Receiving from the software-based system the bleeding risk estimated using the Ivanin αpopPK model, the RTTE model, and the received information; and (d) The bleeding risk information (c) transmitted by the one or more electronic devices is used to output the bleeding risk information.

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

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

35. The method of any one of claims 1-32, wherein the subject is less than 12 years old.

36. The method of any one of claims 1-32, wherein the subject is less than 6 years old.

37. The method of any one of claims 1-32, wherein the subject is between 6 and 12 years old.

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

39. A computer program comprising instructions that, when executed by a computer, cause the computer to implement the Evans α Repeated Event Occurrence Time (RTTE) model.

40. A computer-readable medium including instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 1-37.

41. A method for estimating bleeding risk, the method comprising: (a) Coagulation factor VIII (FVIII) activity information is received by a software-based system comprising a computer program programmed to implement a recurring event occurrence time (RTTE) model [D2]. (b) The computer program uses the RTTE model [D2] and the received information to calculate the bleeding risk, and (c) The calculated bleeding risk information of (b) is transmitted by the software-based system to output the bleeding risk information.

42. A method for estimating bleeding risk, the method comprising: (a) Receive coagulation factor VIII (FVIII) activity information by one or more electronic devices. (b) The coagulation factor VIII (FVIII) information is transmitted by the processing device to a software-based system, wherein the software-based system is programmed to implement a recurring event occurrence time (RTTE) model [D2] to calculate the bleeding risk; (c) Receive from the software-based system the information transmitted using (b) and the bleeding risk information calculated by the RTTE model [D2]; as well as (d) The bleeding risk information (c) transmitted by the one or more electronic devices is used to output the bleeding risk information.

43. A method for estimating the bleeding risk of an individual subject, the method comprising: (a) Individualized subject information, including the subject's weight, is received by a software-based system; (b) The software-based system receives information on expected treatment outcomes, including FVIII level; (c) Apply a time-recurring event (RTTE) model for the subject based on the individualized information and / or the expected treatment outcome information [D2]; (d) Using the RTTE model [D2], estimate the bleeding risk of this individual subject; and (e) The estimated bleeding risk information (d) is transmitted by the software-based system to output the bleeding risk information.

44. A method for treating a subject with hemophilia A, the method comprising: (a) Identifying subjects with hemophilia A; (b) Estimate the subject’s bleeding risk when treated with 50 IU / kg Evansin Al once a week; (c) If the subject has an FVIII activity level of at least 10 IU / dL, estimate the subject’s bleeding risk; as well as (d) If the subject has a lower estimated bleeding risk under levonorgestrel-alpha therapy compared to the estimated bleeding risk when the subject has an FVIII activity level of at least 10 IU / dL, then treat the subject with levonorgestrel-alpha at 50 IU / kg approximately once weekly. If the subject has an FVIII activity level of at least 10 IU / dL, the repeated event occurrence time (RTTE) model [D2] is used to estimate the bleeding risk of the levonorgestrel α therapy and / or the subject's bleeding risk.

45. A method for estimating the bleeding risk of an individual subject, the method comprising: (a) Information from an individual subject is received by a software-based system, wherein the system is programmed to implement: (i) A one-compartment Ivanin αpopPK model [A'] was used to calculate coagulation factor VIII (FVIII) activity information, and (ii) Time-of-occurrence (RTTE) model [D2], which uses FVIII activity information to estimate bleeding risk. (b) The estimated bleeding risk is calculated by the software-based system using the Ivanin αpopPK model [Q], the RTTE model [D2], and the received information, and (c) The calculated bleeding risk information of (b) is transmitted by the software-based system to output the bleeding risk information.

46. ​​A method for estimating bleeding risk, the method comprising: (a) Information about an individual subject is received by one or more electronic devices; (b) The processing device transmits the individual subject's information to a software-based system, wherein the software-based system is programmed to implement... (i) A one-compartment Ivanin αpopPK model [A'] was used to calculate coagulation factor VIII (FVIII) activity information, and (ii) Time-of-occurrence (RTTE) model [D2], which uses FVIII activity information to estimate bleeding risk. (c) Receiving from the software-based system the bleeding risk estimated using the Ivanin αpopPK model [Q], the RTTE model [D2], and the received information; and (d) The bleeding risk information (c) transmitted by the one or more electronic devices is used to output the bleeding risk information.

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

48. A computer program comprising instructions which, when executed by a computer, cause the computer to implement the Evans α Repeated Event Occurrence Time (RTTE) model [D2].

49. A computer-readable medium including instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 41-46.

50. A pharmaceutical composition comprising evapotranspiration α for reducing the risk of non-traumatic bleeding in a human subject with severe hemophilia A during a 52-week period, wherein the risk is reduced to a probability of less than 30%, wherein evapotranspiration α is administered intravenously to the subject every approximately 4 to approximately 14 days during this period at a dose of approximately 25 IU / kg to approximately 50 IU / kg, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 30%.

51. A pharmaceutical composition comprising evapotranspiration α for reducing the risk of non-traumatic bleeding in a human subject with severe hemophilia A during a 52-week period, wherein the risk is reduced to a probability of less than 30%, wherein evapotranspiration α 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 during this period, wherein the human subject is 6 to <12 years old and wherein the subject weighs 30 to 35 kg, thereby reducing the probability to less than 30%.

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