Systems and methods for modulating hypoxia inducible factor stabilizer therapy based on anemia modeling

Abstract

A method of determining a dosage of a next hypoxia inducible factor prolyl hydroxylase inhibitor (HIF-PHI) for a first patient using a patient HIF-PHI model is provided. The method includes obtaining population patient data indicative of a patient's HIF-PHI dose and hemoglobin measurements. A virtual patient avatar is then generated based on the population patient data. Each of the virtual patient avatars is indicative of a set of personalized model parameters for the HIF-PHI model. A plurality of HIF-PHI models are determined for the virtual patient avatar. In some embodiments, one or more HIF-PHI treatment regimens for administering the HIF-PHI dose are determined using the HIF-PHI model. Subsequently, a next HIF-PHI dose for the patient is determined using the HIF-PHI treatment regimen and the hematocrit and / or hemoglobin concentration for the patient, and the next HIF-PHI dose is administered to the patient.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. patent application No. 17 / 960,305, filed on October 5, 2022, the contents of which are incorporated herein by reference. Technical Field

[0003] (none) Background Art

[0004] Red blood cells (erythrocytes) are indispensable for the transport of oxygen in the body. Understanding the regulation of erythropoiesis (called erythropoiesis) is very important for treating patients in various clinical situations. Patients may be prescribed hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitors (HIF-PHI) or HIF stabilizers, which are members of a class of drugs that work by inhibiting HIF-PH, and HIF-PH is responsible for decomposing HIF under the condition of normal oxygen concentration. Such patients include but are not limited to patients with chronic kidney disease, patients with planned surgery, dialysis patients and / or other patients. However, the dosage and frequency of HIF stabilizer treatment are usually determined based on the previous experience of the physician and the guidance of manufacturers such as guidance and / or professional guidance, because the prediction model of HIF-PHI is not easy to obtain. Summary of the invention

[0005] This Summary is provided to introduce certain exemplary embodiments that are further described below. This Summary is not intended to identify key features or essential features of the present disclosure.

[0006] In one embodiment, a method for determining a next hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) dose for a first patient using a patient HIF-PHI model is provided. The method includes: obtaining population patient data associated with a plurality of patients, wherein the population patient data indicates previous HIF-PHI doses and hemoglobin measurements for the plurality of patients; generating a plurality of virtual patient avatars based on the population patient data, wherein each of the plurality of virtual patient avatars indicates a set of personalized model parameters; determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized model parameters; determining one or more HIF-PHI treatment regimens for administering a HIF-PHI dose based on the plurality of HIF-PHI models; and administering a next HIF-PHI dose to a first patient of the plurality of patients based on the use of the one or more determined HIF-PHI treatment regimens and a hematocrit and / or hemoglobin concentration for the first patient.

[0007] In some cases, the method further includes: obtaining individualized patient data for a second patient from the plurality of patients, wherein the individualized patient data indicates a previous HIF-PHI dose and a hemoglobin measurement for the second patient; determining a patient HIF-PHI model for the second patient based on the previous HIF-PHI dose, the hemoglobin measurement, and a mathematical model for hydroxylase inhibitor (HIF) stabilizer therapy, wherein the HIF-PHI model indicates a set of individualized model parameters for the second patient; determining a next HIF-PHI dose for the second patient using the patient HIF-PHI model based on the hemoglobin measurement for the second patient exceeding a patient threshold; and administering the next HIF-PHI dose to the second patient to adjust the hemoglobin concentration to within the patient threshold.

[0008] In some examples, administering the next HIF-PHI dose includes causing the next HIF-PHI dose to be displayed on a display device.

[0009] In some embodiments, the method further comprises: obtaining a mathematical model for hydroxylase inhibitor (HIF) stabilizer therapy; wherein determining the multiple HIF-PHI models for the multiple virtual patient avatars comprises generating the multiple HIF-PHI models by inserting the set of personalized model parameters into the mathematical model.

[0010] In some cases, each of the one or more HIF-PHI treatment regimens is indicative of a decision tree comprising a plurality of branches indicating different HIF-PHI doses based on hemoglobin concentration.

[0011] In some examples, determining the one or more HIF-PHI treatment regimens includes: generating a plurality of HIF-PHI treatment regimens for performing HIF stabilizer therapy; simulating a plurality of virtual trials using the plurality of HIF-PHI treatment regimens and the plurality of HIF-PHI models for the plurality of virtual patient avatars; and determining the one or more HIF-PHI treatment regimens based on simulating the plurality of virtual trials.

[0012] In some variations, the one or more HIF-PHI treatment regimens are determined based on a number of patients from the plurality of patients within a target hemoglobin threshold and an amount of a HIF-PHI drug administered to a plurality of simulated patients within the plurality of virtual trials.

[0013] In some cases, administering the next HIF-PHI dose to the first patient includes displaying the next HIF-PHI dose for the first patient on a display device.

[0014] In some examples, the set of personalized model parameters includes a HIF-PHI bioavailability parameter, a red blood cell (RBC) lifespan parameter, and a hemoglobin set point parameter, and wherein generating the plurality of virtual patient avatars includes determining the HIF-PHI bioavailability parameter, the red blood cell (RBC) lifespan parameter, and the hemoglobin set point parameter for each of the plurality of virtual patient avatars.

[0015] In some embodiments, the set of personalized model parameters also includes a basal erythropoietin (EPO) synthesis rate parameter, a HIF signal threshold parameter, and a hepcidin decay rate parameter, and wherein generating the multiple virtual patient avatars also includes determining the basal EPO synthesis rate parameter, the HIF signal threshold parameter, and the hepcidin decay rate parameter for each of the multiple virtual patient avatars.

[0016] In some cases, determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is further based on the following equations: in, Indicates the rate of change of HIF signaling activity, β h (θ) represents the formation rate of HIF complex, indicates that HIF signaling is upregulated in response to hypoxia, which reduces blood hemoglobin concentration, k h represents the decay rate of HIF signal, c hgb represents the blood hemoglobin concentration measured by the kidneys, represents the physiological hemoglobin set point, E s Indicates HIF-PHI responsiveness, represents upregulation of HIF signaling in response to HIF-PHI administration, and h represents HIF signaling.

[0017] In some examples, determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is further based on the following equations: in, represents the rate of change of EPO concentration, β e (θ) represents the basal erythropoietin (EPO) synthesis under steady-state conditions, represents additional EPO synthesis due to HIF signaling activation, and k e e represents the decay rate k e EPO decay.

[0018] In some variations, determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is further based on the following equations: in, The rate of change of the total amount of red blood cells. represents the differentiation flux from the precursor cell population to the erythroid population, K ery represents the cell flux due to apoptosis, L bleeding represents the cell flux due to bleeding events, and L donation represents the cell flux due to blood donation events.

[0019] In another embodiment, a method for adjusting a patient's hematocrit and / or hemoglobin concentration using a patient hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model is provided. The method includes: obtaining individualized patient data for the patient, wherein the individualized patient data indicates a previous HIF-PHI dose and hemoglobin measurement for the patient; determining a patient HIF-PHI model for the patient based on the previous HIF-PHI dose, the hemoglobin measurement, and a mathematical model for hydroxylase inhibitor (HIF) stabilizer therapy, wherein the HIF-PHI model indicates a set of individualized model parameters for the patient; determining a next HIF-PHI dose for the patient using the patient HIF-PHI model based on the patient's hematocrit and / or hemoglobin concentration exceeding a patient threshold; and administering the next HIF-PHI dose to the patient to adjust the hematocrit and / or hemoglobin concentration to within the patient threshold.

[0020] In some cases, administering the next HIF-PHI dose includes causing the next HIF-PHI dose to be displayed on a display device.

[0021] In some examples, the set of personalized model parameters includes a HIF-PHI bioavailability parameter, a red blood cell (RBC) lifespan parameter, and a hemoglobin set point parameter, and wherein the next HIF-PHI dose for the patient is determined based on using the HIF-PHI bioavailability parameter, the red blood cell (RBC) lifespan parameter, and the hemoglobin set point parameter.

[0022] In some variations, the set of individualized model parameters also includes a basal erythropoietin (EPO) synthesis rate parameter, a HIF signal threshold parameter, and a hepcidin decay rate parameter, and wherein the next HIF-PHI dose for the patient is determined also based on the basal EPO synthesis rate parameter, the HIF signal threshold parameter, and the hepcidin decay rate parameter.

[0023] In some cases, employing the patient HIF-PHI model to determine the next HIF-PHI dose for the patient includes: inputting a plurality of next HIF-PHI doses into the HIF-PHI model to determine a plurality of outputs indicative of expected hematocrit or hemoglobin concentrations; and selecting the next HIF-PHI dose from the plurality of next HIF-PHI doses based on an output in the plurality of outputs that is within the patient threshold.

[0024] In some examples, employing the patient HIF-PHI model to determine the next HIF-PHI dose for the patient includes: inputting a plurality of next HIF-PHI doses into the HIF-PHI model to determine a plurality of outputs indicating an expected hematocrit or hemoglobin concentration; determining a subset of next HIF-PHI doses in the plurality of next HIF-PHI doses based on one or more of the plurality of outputs associated with a subset of the next HIF-PHI doses within the patient threshold; and selecting the next HIF-PHI dose from the subset of next HIF-PHI doses based on the next HIF-PHI dose being a lowest amount of the HIF-PHI dose in the subset of next HIF-PHI doses.

[0025] In another embodiment, a computing device is provided. The computing device includes: a display configured to display information associated with a patient hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model; one or more processors; and a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by the one or more processors, facilitate: obtaining population patient data associated with a plurality of patients, wherein the population patient data indicates previous HIF-PHI doses and hemoglobin measurements for the plurality of patients; generating a plurality of virtual patient avatars based on the population patient data, wherein each of the plurality of virtual patient avatars indicates a set of personalized model parameters; determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized model parameters; determining one or more HIF-PHI treatment regimens for administering a HIF-PHI dose based on the plurality of HIF-PHI models; and displaying on the display the next HIF-PHI dose for the first patient among the plurality of patients based on the use of the one or more determined HIF-PHI treatment regimens and the hematocrit and / or hemoglobin concentration for the first patient.

[0026] Further features and aspects are described in more detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic diagram of an exemplary medical treatment system according to one or more examples of the present application.

[0028] Figure 2 is a simplified block diagram depicting an exemplary computing environment according to one or more examples of the present application.

[0029] Figure 3 According to one or more examples of this application Figure 2 A simplified block diagram of one or more devices or systems within an exemplary environment.

[0030] Figure 4 is a flow chart of an exemplary process for generating a HIF-PHI model according to one or more examples of the present application.

[0031] Figure 5A is a flow chart of an exemplary process for determining a next HIF-PHI dose for a patient based on using a treatment regimen according to one or more examples of the present application.

[0032] Figure 5B is an exemplary treatment regimen according to one or more examples of the present application.

[0033] Figure 6 is a flow chart of an exemplary process for adjusting a patient's hematocrit and / or hemoglobin concentration using a HIF-PHI model according to one or more examples of the present application.

[0034] Figure 7 is a block diagram of an exemplary mathematical model for HIF stabilizer therapy according to one or more examples of the present application.

[0035] Figure 8A-8D is a description of one or more examples according to the present application Figure 7 A block diagram of the blocks within an exemplary mathematical model.

[0036] Fig. 9 is a data flow diagram of an exemplary process for obtaining and using population patient data according to one or more examples of the present application.

[0037] Figures 10A-10L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0038] Figures 11A-11L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0039] Figure 12A-12L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0040] Figures 13A-13L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0041] Figure 14A-14L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0042] Figure 15A-15L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0043] Figures 16A-16L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown.

[0044] Figures 17A-17L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present application provide for generating a HIF-PHI model for a plurality of patients, and administering a HIF-PHI dose to one or more patients using the HIF-PHI model to adjust the hematocrit and / or hemoglobin concentration of the patient. Examples of modeling erythropoiesis using a selected erythropoiesis stimulating agent (ESA) administration regimen (including the use of iron homeostasis), predicting a patient's hematocrit and / or hemoglobin concentration using the initially selected ESA administration regimen, employing the model to determine one or more different ESA administration regimens such that the model predicts the patient's hematocrit and / or hemoglobin concentration to be within a desired range, and administering the ESA to the patient using the determined ESA administration regimen are described in U.S. Pat. No. 10,319,478, entitled “SYSTEM AND METHOD OF MODELING ERYTHROPOIESIS AND ITS MANAGEMENT,” and U.S. Pat. No. 9,679,111, entitled “SYSTEM AND METHOD OF MODELING ERYTHROPOIESIS INCLUDING IRON HOMEOSTATIS,” the contents of which are incorporated herein by reference in their entirety. However, although modeling erythropoiesis using an ESA administration regimen has been previously described, HIF-PHI is a new drug or agent that may be different from ESA. For example, HIF-PHI can stimulate the HIF signaling pathway, which also mediates the body's endogenous response to hypoxic conditions, thereby enhancing the production of endogenous erythropoietin (EPO: Endogenous Erythropoietin). In contrast, ESA directly mimics the effects of EPO without directly targeting the HIF pathway. In addition, the time intervals between consecutive ESA administrations are typically separated by weeks or months, while HIF-PHI is typically administered multiple times a week. Therefore, as will be explained in further detail below, the present disclosure describes systems and methods for generating a HIF-PHI model and using the HIF-PHI model to determine / adjust the HIF-PHI dosage for one or more patients.

[0046] Among other advantages, the present disclosure uses the HIF-PHI model to provide a more accurate HIF-PHI dose for patients receiving HIF-PHI stabilizer therapy. For example, since each patient is different, especially when responding to different types of drugs, the HIF-PHI model is used to predict the patient's physical response to the HIF-PHI dose, which in turn can determine a more accurate HIF-PHI dose, allowing the patient to better reach the desired hematocrit and / or hemoglobin concentration range, while also alleviating the lack of erythropoiesis and preventing excessive HIF-PHI dose levels from increasing the patient's blood pressure and increasing the patient's risk of stroke and cardiovascular disease. Additionally and / or alternatively, the HIF-PHI dose from the HIF-PHI model can accelerate the recovery of the patient's hemoglobin level from events such as when the patient encounters a bleeding event.

[0047] Figure 1 is a schematic diagram of an exemplary medical system (e.g., a dialysis system) according to one or more examples of the present application. As an example, Figure 1 The medical system shown in is a hemodialysis system; however, other medical systems are also contemplated. The hemodialysis system can be used to measure / determine the hematocrit (HCT), hemoglobin (HGB), and / or absolute blood volume (ABV) of the patient 10. For example, Figure 1 A patient 10 is depicted undergoing hemodialysis treatment using a hemodialysis machine 12. The hemodialysis system also includes an optical blood monitoring system 14.

[0048] An access needle or catheter 16 is inserted into an access site of the patient 10, such as in an arm, and is connected to an extracorporeal circuit 18 leading to a peristaltic pump 20 and a dialyzer 22 (or blood filter). The dialyzer 22 removes toxins and excess fluid from the patient's blood. The dialyzed blood is returned from the dialyzer 22 via an extracorporeal circuit 24 and a return needle or catheter 26. In some parts of the world, the extracorporeal blood flow may additionally receive a heparin drip to prevent clotting. Excess fluid and toxins are removed by clean dialysis fluid, which is supplied to the dialyzer 22 via tube 28, and waste fluid is removed via tube 30 for disposal. In the United States, a typical hemodialysis treatment session takes approximately 3 to 5 hours.

[0049] The optical blood monitoring system 14 includes a display device 35 and a sensor device 34. For example, the sensor device 34 may be a sensor clip assembly that is clipped onto a blood chamber 32, wherein the blood chamber 32 is disposed in an extracorporeal blood circuit. A controller (e.g., a processor) of the optical blood monitoring system 14 may be implemented in the display device 35 or in the sensor clip assembly 34, or both the display device 35 and the sensor clip assembly 34 may include corresponding controllers for performing corresponding operations associated with the medical system.

[0050] The blood chamber 32 may be arranged in line with the extracorporeal line 18 upstream of the dialyzer 22. Blood from the peristaltic pump 20 flows into the blood chamber 32 through the line 18. The sensor device 34 includes an emitter that emits light of a specific wavelength and a detector for receiving the emitted light after the emitted light passes through the blood chamber 32. For example, the emitter may include an LED emitter that emits light of about 810 nm absorbed by red blood cells, etc., light of about 1300 nm absorbed by water, etc., and light of about 660 nm sensitive to oxygenated hemoglobin, and the detector may include a silicon photodetector for detecting light of about 660 and 810 nm wavelengths, and an indium gallium arsenide photodetector for detecting light of about 1300 nm wavelength. The blood chamber 32 includes a lens or an observation window that allows light to pass through the blood chamber 32 and the blood flowing therein.

[0051] An example of an optical blood monitoring system having a sensor clip assembly configured to measure the hematocrit and oxygen saturation of extracorporeal blood flowing through a blood chamber is described in U.S. Patent No. 9,801,993, entitled “SENSOR CLIP ASSEMBLY FOR AN OPTICAL MONITORING SYSTEM,” the contents of which are incorporated herein by reference in their entirety.

[0052] The controller of the optical blood monitoring system 14 uses the light intensity measured by the detector to determine the HCT value of the blood flowing through the blood chamber 32. The controller uses a ratio model to calculate the HCT, HGB, oxygen saturation, and blood volume (e.g., ABV) changes associated with the blood passing through the blood chamber 32 to which the sensor device 34 is attached. The intensity of the light received at each of the various wavelengths is reduced by attenuation and scattering of the fixed intensity of visible and infrared light emitted from each of the LED emitters. For each wavelength of light, Beer's law describes the attenuation and scattering as follows:

[0053]

[0054] Among them, i n = the intensity of light received at wavelength n after attenuation and scattering; I0-n = the intensity of the transmitted light at wavelength n incident on the measured medium; e = the natural exponential term; ε = the extinction coefficient of the measured medium (p-blood chamber polycarbonate, b-blood); X = the molar concentration of the measured medium (p-blood chamber polycarbonate, b-blood); and d = the distance through the measured medium (pt-transmitting blood chamber polycarbonate, b-blood, pr-receiving blood chamber polycarbonate).

[0055] Since the properties of the polycarbonate blood chamber are constant, the first and third exponential terms in the above equation (A) are constants for each wavelength. Mathematically, these constant terms are related to the initial constant term I representing the fixed intensity of the radiation emitted from the corresponding LED emitter. 0-n For simplification purposes, equation (A) can be used with the volume extinction coefficient and the modified initial constant I' 0-n Rewrite it as follows:

[0056]

[0057] Among them, i n = the intensity of light received at wavelength "n" after attenuation and scattering, as if the detector was located at the receiving blood boundary; α = bulk extinction coefficient (α b =ε b ·X b ), and I' 0-n = equivalent transmitted light intensity at wavelength n, as applied to the transmitted blood boundary, taking into account losses through the blood compartment. Note that the term I' 0-n is the light intensity incident on the blood, including blood compartment losses.

[0058] Using the method defined in equation (B) above, the 810 nm wavelength, which is isoabsorptive to red blood cells, and the 1300 nm wavelength, which is isoabsorptive to water, can be used to determine the patient's hematocrit. The ratio of the normalized amplitudes of the intensities measured at these two wavelengths yields the ratio of the composite extinction values ​​α of the red blood cell and water components of the blood compartment, respectively. The mathematical function then defines the measured HCT value:

[0059]

[0060] Among them, i 810 is the light intensity of the optical receiver at 810nm, i 1300 is the infrared intensity of the photodetector at 1300nm, I 0-810 and I 0-1300 is a constant representing the intensity incident on the blood, which takes into account the losses through the blood chamber. Assuming that the blood flow through the blood chamber 32 is in a steady state, ie, a steady pressure and a steady flow rate, the above equation holds true.

[0061] Preferably the function f[] is a second order polynomial of the form:

[0062]

[0063] A second order polynomial is generally sufficient as long as the incident infrared radiation at the first and second wavelengths is substantially isosbestic.

[0064] After the controller at the sensor device 34 or at the display device 35 has determined the HCT value, the display device can be used to output the determined HCT value. In addition, the controller can also determine the HGB concentration value based on the determined HCT value, wherein the HGB concentration value is also output on the display device 35.

[0065] For example, the HGB of a blood sample corresponds to the mass (e.g., in grams) of the protein of the blood sample, and the HGB concentration value corresponds to the mass of protein per unit blood sample volume. The HGB concentration value can be determined based on multiplying the HCT value with the mean corpuscular hemoglobin concentration (MCHC) value. It should be understood that the HCT value corresponds to the volume of red blood cells (RBC) in the blood sample divided by the total volume of the blood sample, and the MCHC value corresponds to the average mass of the HGB of each RBC divided by the average volume of each RBC. It should also be understood that the MCHC value corresponds to the mean corpuscular hemoglobin (MCH) divided by the mean corpuscular volume (MCV), wherein MCH corresponds to the average mass (e.g., in picograms) of the HGB of each RBC of the patient, and wherein the MCV corresponds to the average volume (e.g., in femtoliters) of each RBC of the patient. Therefore, when the HCT value is multiplied by the MCHC value, the determined HGB concentration value corresponds to the mass of protein per unit blood sample volume.

[0066] Figure 1 The medical system (e.g., hemodialysis system) depicted in the figure can be one of multiple medical systems in a dialysis clinic and / or an ICU. Patients can enter the dialysis clinic for treatment on a regular basis, for example, on a Monday-Wednesday-Friday schedule or a Tuesday-Thursday-Saturday schedule. In some cases, Figure 1 The medical system depicted in can be located in the patient's home.

[0067] It should be understood that Figure 1 The medical system depicted in is merely exemplary. The principles discussed herein may be applicable to other medical systems in which blood monitoring operations are performed.

[0068] Figure 21 is a simplified block diagram depicting an exemplary computing environment according to one or more examples of the present application. Environment 100 includes HIF-PHI administration computing device 104, network 106, HIF-PHI model generation computing system 108, and medical system 110. Although the entities within environment 100 may be described below and / or described in the figures as a single entity, it should be understood that the entities and functions discussed herein may be implemented by and / or include one or more entities.

[0069] Entities within the environment 100, such as the HIF-PHI administration computing device 104, the HIF-PHI model generation computing system 108, and the medical system 110, can communicate with other systems within the environment 100 via a network 106. The network 106 can be a global area network (GAN), a wide area network (WAN), a local area network (LAN), or any other type of network or network combination such as the Internet. The network 106 can provide a combination of wired, wireless, or wired and wireless communications between entities within the environment 100. Additionally and / or alternatively, one or more entities within the environment 100 can communicate with each other without using the network 106. For example, the HIF-PHI administration computing device 104 and the medical system 110 can communicate with each other via one or more wireless protocols (e.g., WI-FI) and / or wired connections.

[0070] The medical system 110 may be Figure 1 The medical system 110 may provide and / or receive information from other entities within the environment 100 (eg, the backend computing system 108 and the user device 104).

[0071] In some cases, the medical system 110 can be and / or include another type of medical device, such as another type of dialysis system. For example, the medical system 110 can be a system for peritoneal dialysis, in which the patient's peritoneal cavity is regularly infused with a dialysate, and for the system, the membranous lining of the patient's peritoneum acts as a natural semipermeable membrane that allows diffusion and osmotic exchange to occur between the solution and the blood stream. In some examples, the medical system 110 can be optional. In such an example, the environment 100 can include only the HIF-PHI administration computing device 104 and the HIF-PHI model generation computing system 108.

[0072] The HIF-PHI model generation computing system 108 is a computing system that generates one or more HIF-PHI models. For example, the HIF-PHI model generation computing system 108 includes one or more computing devices, computing platforms, systems, servers, and / or other devices capable of performing functions and / or actions such as generating one or more HIF-PHI models for HIF stabilizer therapy.

[0073] In some cases, the HIF-PHI model generation computing system 108 can communicate, for example, with the HIF-PHI administration computing device 104 and / or the medical system 110. For example, the HIF-PHI model generation computing system 108 can provide one or more generated HIF-PHI models to the HIF-PHI administration computing device 104 and / or the medical system 110. In some examples, the computing system 108 can include a display device configured to display information associated with the HIF stabilizer therapy.

[0074] The HIF-PHI model generation computing system 108 can be implemented using one or more computing platforms, devices, servers and / or equipment. In some examples, the computing system 108 may include and / or be connected to a display device configured to display information associated with HIF stabilizer therapy. In some variations, the HIF-PHI model generation computing system 108 may be implemented as an engine, software function and / or application. In other words, the functions of the HIF-PHI model generation computing system 108 may be implemented as software instructions stored in a storage device (e.g., memory) and executed by one or more processors.

[0075] The HIF-PHI administration computing device 104 may be and / or include, but is not limited to, a desktop, a laptop, a tablet, a mobile device (e.g., a smartphone device, or other mobile device), a processor, a controller, a smart watch, an Internet of Things (IoT) device, or any other type of computing device that typically includes one or more communication components, one or more processing components, and one or more memory components.

[0076] The HIF-PHI administration computing device 104 can receive the generated HIF-PHI model from the HIF-PHI model generation computing system 108. The HIF-PHI administration computing device 104 can use the generated HIF-PHI model to administer the HIF stabilizer treatment. For example, a HIF-PHI dose (e.g., a previous HIF-PHI dose) can be initially prescribed to the patient. The HIF-PHI administration computing device 104 can obtain the patient's patient data, which includes the previous HIF-PHI dose. The HIF-PHI administration computing device 104 can input the patient data including the previous HIF-PHI dose into the HIF-PHI model from the HIF-PHI model generation computing system 108 to determine whether the patient's hematocrit and / or hemoglobin concentration exceeds the patient threshold. Based on the patient's hematocrit and / or hemoglobin concentration exceeding the patient threshold, the HIF-PHI administration computing device 104 can use the HIF-PHI model to determine the next HIF-PHI dose for the patient. Then, the HIF-PHI application computing device 104 can apply the next HIF-PHI dosage to the patient to adjust the patient's hematocrit and / or hemoglobin concentration to the patient threshold. For example, the HIF-PHI application computing device 104 can include and / or be connected to a display device, and display the next HIF-PHI dosage to an anemia manager, a doctor, a patient, and / or other operators who may prescribe a new prescription for the patient. For example, the HIF-PHI drug can be administered orally. The computing system 108 and the computing device 104 can be configured to be able to use the HIF-PHI model to determine the next HIF-PHI dosage for the patient and display the next HIF-PHI dosage. Then, the patient can take the HIF-PHI drug orally based on the next HIF-PHI dosage.

[0077] Additionally and / or alternatively, the HIF-PHI model generation computing system 108 can determine one or more treatment plans (e.g., decision trees) based on the use of one or more HIF-PHI models. The HIF-PHI model generation computing system 108 can provide one or more treatment plans to the HIF-PHI administration computing device 104. The HIF-PHI administration computing device 104 can determine and / or administer the next HIF-PHI dose to the patient based on the received one or more treatment plans. For example, the HIF-PHI administration computing device 104 can display the decision tree and / or the next HIF-PHI dose for the patient on a display device associated with the HIF-PHI administration computing device 104.

[0078] In some cases, a single entity may perform the functions of the HIF-PHI administration computing device 104 and the HIF-PHI model generation computing system 108. For example, the HIF-PHI model generation computing system 108 may generate a HIF-PHI model and use the HIF-PHI model to determine the next HIF-PHI dose, and display the next HIF-PHI dose to the patient.

[0079] It should be understood that Figure 2 The exemplary environment depicted in is merely an example, and the principles discussed herein may also be applicable to other situations—including, for example, other types of institutions, organizations, devices, systems, and network configurations.

[0080] Figure 3 According to one or more examples of this application Figure 2 A simplified block diagram of one or more devices or systems within an exemplary environment of FIG. For example, device / system 200 may be Figure 2 The HIF-PHI model generation computing system 108 and / or HIF-PHI administration computing device 104. The device / system 200 includes a processor 204, such as a central processing unit (CPU), a controller and / or logic, which executes computer executable instructions for performing the functions, processes and / or methods described herein. In some examples, the computer executable instructions are stored locally and accessed from a non-transitory computer readable medium such as a storage device 210, which can be a hard drive or a flash drive. The read-only memory (ROM: Read Only Memory) 206 includes computer executable instructions for initializing the processor 204, and the random access memory (RAM: Random-Access Memory) 208 is a main memory for loading and processing instructions executed by the processor 204. The network interface 212 can be connected to a wired network or a cellular network and a local area network or a wide area network, such as the network 106. The device / system 200 may also include a bus 202 that connects the processor 204, the ROM 206, the RAM 208, the storage device 210, and / or the network interface 212. The components within the device / system 200 may communicate with each other using the bus 202. The components within the device / system 200 are merely exemplary and may not include every component, server, device, computing platform, and / or computing device within the device / system 200. Additionally and / or alternatively, the device / system 200 may also include components that may not be included in every entity of the environment 100.

[0081] Figure 4 is a flow chart of an exemplary process 400 for generating a HIF-PHI model according to one or more examples of the present application. The process may be performed by, for example, Figure 2 However, it will be appreciated that any of the following blocks may be performed in any suitable order, and process 400 may be performed in any suitable environment and by any suitable device or system. Figure 4 The descriptions, diagrams, and processes of are merely exemplary, and process 400 may use other descriptions, diagrams, and processes to generate a HIF-PHI model.

[0082] At block 402, the HIF-PHI model generation computing system 108 obtains a mathematical model for HIF stabilizer therapy. For example, the computing system 108 can receive the mathematical model from another entity (e.g., a server or computing system associated with a business organization). Additionally and / or alternatively, the mathematical model for HIF stabilizer therapy can be stored in a memory (e.g., an external memory or a memory within the computing system 108), and the computing system 108 can retrieve the mathematical model from the memory. Figure 7 One exemplary mathematical model 700 for HIF stabilizer therapy that may be obtained by the HIF-PHI model generation computing system 108 is shown and will be described in further detail below.

[0083] At box 404, the HIF-PHI model generation computing system 108 generates multiple virtual patient avatars based on group patient data (e.g., historical data and / or simulation data for multiple patients) and the obtained mathematical model for HIF stabilizer treatment. Each of the virtual patient avatars is a set of personalized model parameters for the mathematical model. The group patient data can be historical data of multiple patients and / or simulation data of multiple patients. For example, the group patient data can include but is not limited to hemoglobin, hematocrit, HIF-PHI dosage (e.g., HIF-PHI dosage previously taken by the patient), iron dosage, serum ferritin, transferrin saturation (TSAT), gender, height, weight, clinical parameters (such as oxygen saturation, treatment time, bleeding events, blood transfusion, hospitalization), etc. of multiple patients. The group patient data can be within a specified time window (e.g., a certain time period).

[0084] The computing system 108 can generate a virtual patient avatar for the obtained mathematical model for HIF stabilizer therapy, which is a set of personalized model parameters. Figure 7The mathematical model described in further detail in includes a plurality of mathematical equations and patient parameters (e.g., variables). The computing system 108 can use the group patient data to determine the patient parameters (e.g., variables) for the mathematical model. In other words, each patient and / or patient group (e.g., a group of patients with similar patient characteristics (such as weight, height, gender, etc.)) may have slightly different responses to HIF-PHI treatment. Therefore, the first patient and / or patient group taking a specific HIF-PHI dose may have different effects (e.g., different hematocrit / hemoglobin concentration levels) from another patient and / or patient group. The mathematical model can be associated with a non-patient / group-specific first group of variables and a patient / group-specific second group of variables. The second group of variables can be a set of personalized model parameters indicated by a virtual patient avatar. Therefore, the computing system 108 generates a virtual patient avatar by inputting the group patient data into the mathematical model to determine the set of personalized model parameters.

[0085] A set of personalized model parameters associated with each virtual patient avatar may include, but are not limited to, HIF-PHI bioavailability, RBC lifespan, a parameter indicating the patient-specific magnitude of the hemoglobin response to a given HIF-PHI dose, a parameter indicating the patient's basal hemoglobin level in the absence of anemia medication, and / or iron-related parameters. Iron-related parameters may include, but are not limited to, a parameter indicating the patient's hepcidin kinetics and its effect on iron availability and / or a parameter indicating the patient's absorption of a specific iron-containing medication.

[0086] For example, HIF-PHI bioavailability (e.g., bioavailable fraction (f)) can be described in the following equation (1.5). And can be used to parameterize the bioavailability of a patient. In particular, the computing system 108 can determine the bioavailable fraction (f) based on the absorption rate (α) and clearance rate (η) in the gastrointestinal tract (GI) (before HIF-PHI can be absorbed). HIF-PHI bioavailability can indicate the portion of the administered HIF-PHI dose that is biologically active (e.g., not cleared from the body before absorption in the GI tract).

[0087] RBC lifespan It can be described in the following equation (1.17). RBC lifespan can indicate the lifespan of the patient's red blood cells. For example, the computing system 108 can determine the RBC lifespan based on the apoptosis rate for the cell population, the EPO for downregulating progenitor cell apoptosis, and / or the new cell lysis rate regulated by the hormone EPO. RBC lifespan can indicate the average lifespan of red blood cells.

[0088] Hemoglobin (HB) set point It can be described in the following equations (1.6) and (1.7). For example, the computing system 108 can determine the HB set point based on the blood hemoglobin concentration measured by the kidney, the number of red blood cells, activation and inhibition functions, the upregulation of the HIF signal in response to hypoxia caused by the decrease in blood hemoglobin concentration, the upregulation of the HIF signal in response to the administration of HIF-PHI, the decay rate of the HIF signal, and / or other factors. The HB set point can be defined as the blood HB concentration at which the activity of endogenous feedback such as upregulation or downregulation of RBC production is minimal.

[0089] Basal EPO synthesis rate (β e ) can be described in equation (1.8) below. For example, computing system 108 can determine basal EPO synthesis rate based on serum EPO concentration, EPO synthesis, and / or EPO decay rate. Basal EPO synthesis rate can indicate the rate at which EPO is synthesized in vivo.

[0090] HIF signal threshold It can be described in the following equation (1.6). For example, the computing system 108 can determine the HIF signal threshold based on the blood hemoglobin concentration measured by the kidney, the number of red blood cells, activation and inhibition functions, the upregulation of the HIF signal in response to hypoxia caused by the reduction of the blood hemoglobin concentration, the upregulation of the HIF signal in response to the administration of HIF-PHI, the decay rate of the HIF signal, and / or other factors. The HIF signal threshold can indicate the amount of biologically active HIF-PHI required to cause half of its maximum effect on the gain of HIF signal activity, and the HIF signal in the model is an abstract variable indicating the activity of the HIF signaling pathway.

[0091] Hepcidin decay rate (κ p ) can be described in the following equation (1.28) and / or equation (1.29). For example, the computing system 108 can determine the hepcidin decay rate based on the residual renal function of the patient. The hepcidin decay rate can indicate the decay rate of hepcidin in the patient.

[0092] Therefore, each virtual patient avatar can indicate a set of personalized model parameters, and the computing system 108 can generate a plurality of these virtual patient avatars. In order to generate a virtual patient avatar, the computing system 108 can use the input (e.g., previous HIF-PHI dose) to systematically fit (e.g., assign) a specified set of model parameters to the group patient data. Then, the computing system 108 determines the "goodness" of fit based on the deviation between the simulated and measured hemoglobin / hematocrit concentrations (e.g., the deviation between the hemoglobin / hematocrit concentration output by the HIF-PHI model using a set of model parameters assigned and the actual hemoglobin / hematocrit concentration from the group patient data). The computing system 108 can use a cost function to define the goodness of fit, and can use one or more parameter fitting algorithms to perform the fitting process. In some examples, other paired simulated and measured data elements (e.g., HIF-PHI serum concentration, erythropoietin serum concentration, iron plasma concentration, etc.) can also be used to determine the goodness of fit.

[0093] In other words, the computing system 108 may determine a subset of the population patient data based on certain characteristics (e.g., height, weight, gender, etc.). The computing system 108 may assign values ​​to the set of model parameters described above (e.g., HIF-PHI bioavailability, RBC lifespan, hemoglobin set point, basal EPO synthesis rate, HIF signal threshold, hepcidin decay rate, and / or additional model parameters). Subsequently, the computing system 108 may determine previous HIF-PHI doses and hemoglobin / hematocrit concentrations within the subset of the population patient data. The computing system 108 may then assign previous HIF-PHI doses (e.g., via time-dependent administration of HIF-PHI, From the following equation (1.1), where and i refers to the i-th dose administration, H i is the administered dose, δ is the Dirac delta function, t iThe computing system 108 may be used to input the simulated hemoglobin / hematocrit concentration (i.e., the administration time point) and the assigned set of model parameters into a mathematical model to determine a simulated hemoglobin / hematocrit concentration. The computing system 108 may compare the simulated hemoglobin / hematocrit concentration with the actual hemoglobin / hematocrit concentration to determine the deviation therebetween. Thereafter, the computing system 108 may assign new values ​​to the set of model parameters and repeat the process. The computing system 108 may continuously assign new values ​​to the set of model parameters and determine the deviation between the simulated hemoglobin / hematocrit concentration and the actual hemoglobin / hematocrit concentration. For example, the computing system 108 may use the previously calculated deviations to determine which new values ​​to try in the next iteration. In other words, the computing system 108 may use one or more parameter fitting algorithms (e.g., optimization and / or other types of comparison algorithms, such as a distance algorithm that compares the distance between the simulated concentration and the actual concentration) to determine the best set of model parameters for a subset of the population patient data. The computing system 108 may determine a set of model parameters for a virtual patient avatar as the best set of model parameters. For example, computing system 108 may compare the distance between simulated and actual hemoglobin / hematocrit concentrations and use a set of model parameters that yields the smallest distance between the simulated and actual concentrations.

[0094] The computing system 108 may determine a plurality of virtual patient avatars based on using a subset of the population data, and each of the plurality of virtual patient avatars may be associated with certain patient characteristics (eg, gender, weight, height) and a set of personalized model parameters.

[0095] In some examples, in addition to inputting a previous HIF-PHI dose and a set of assigned model parameters into the mathematical model, the computing system 108 may also input a ferritin concentration into the mathematical model to determine a simulated hemoglobin / hematocrit concentration. For example, the computing system 108 may obtain a ferritin concentration from a subset of the population patient data and input the obtained ferritin concentration into the mathematical model via equation (1.27) below. Additionally and / or alternatively, the mathematical model may include a ferritin equation (e.g., equation (1.37) below), and the computing system 108 may calculate the ferritin concentration using the ferritin equation.

[0096] In some cases, the computing system 108 may perform a pre-calibration function before generating a virtual patient avatar. For example, the computing system 108 may determine the blood volume based on a subset of the population patient data. For example, using the gender, height, weight, and / or other information from the subset of the population patient data and equation (1.38) below, the computing system 108 may determine the blood volume for the virtual patient avatar. Then, using the blood volume, the computing system 108 may determine a rate, such as a formation rate, a synthesis rate, and / or other rates, for the virtual patient avatar. For example, the computing system 108 may determine the HIF signal gain rate (such as β in equation (1.6) below) h ), EPO synthesis rate (as shown in the following equation (1.8) e ), the rate of progenitor cell formation (as shown in the following equation (1.9) prog ), and / or the hepcidin synthesis rate (as shown in equations (1.25) and (1.26) below: p ). The computing system 108 may use the determined rate, ferritin concentration, assigned set of model parameters, previous HIF-PHI doses, mathematical models, and / or other data to determine a simulated hemoglobin / hematocrit concentration. Then, based on the simulated hemoglobin / hematocrit concentration, the computing system 108 may determine an optimal set of model parameters for the virtual patient avatar.

[0097] In some variations, in addition to the simulated hemoglobin / hematocrit concentration, the computing system 108 may also use the mathematical model, previous HIF-PHI dosages, and / or other information (e.g., ferritin concentration) to determine simulated serum levels of HIF-PHI (shown and described as variable "s" in equations (1.1)-(1.4) below), EPO (shown as variable "e" in equation (1.8)), hepcidin (shown as variable "p" in equations (1.25) or (1.26)), iron, and / or other simulated data points. The computing system 108 may also obtain actual serum levels of HIF-PHI, EPO, hepcidin, iron, and / or other data points from a subset of the population patient data. The computing system 108 may compare the simulated data points to the actual data points and use one or more parameter fitting algorithms to determine an optimal set of model parameters for the virtual patient avatar.

[0098] At box 406, the HIF-PHI model generation computing system 108 uses multiple virtual patient avatars to determine the next HIF-PHI dose for one or more patients. For example, the computing system 108 can determine the next HIF-PHI dose of the amount of the HIF-PHI drug to be taken by the patient based on the virtual patient avatar. In some cases, the computing system 108 can determine one or more treatment plans or protocols based on the virtual patient avatar. The treatment plan can indicate the next HIF-PHI dose based on the hemoglobin concentration of the patient. Each virtual patient avatar can be associated with a specific treatment plan, or a specific treatment plan can be associated with two or more than two patient avatars. The treatment plan involves a fully mature universal HIF-PHI treatment algorithm for the patient. In some cases, the treatment plan can be a decision tree indicating how to change the current HIF-PHI prescription (e.g., "increase the dose due to a decrease in hemoglobin levels") based on the most recent measurements. Additionally and / or alternatively, the treatment regimen may dictate different drug dosages (HIF-PHI and / or iron), different treatment schedules (eg, daily, 3 times per week, once per week), and / or different routes of administration (eg, oral and intravenous). Figure 5A and Figure 5B Generating a treatment plan and using the generated treatment plan to determine the next HIF-PHI dose for a patient will be described.

[0099] Figure 5A is a flow chart of an exemplary process for determining the next HIF-PHI dose for a patient based on the use of a treatment regimen according to one or more examples of the present application. The process can be performed by, for example, Figure 2 500 is performed by a computing device of the HIF-PHI administration computing device 104 and / or the HIF-PHI model generation computing system 108 depicted in FIG. However, it will be appreciated that any of the following blocks may be performed in any suitable order, and process 500 may be performed in any suitable environment and by any suitable device or system. Figure 5A The descriptions, illustrations, and processes of are merely exemplary, and process 500 may use other descriptions, illustrations, and processes to adjust the patient's hematocrit and / or hemoglobin concentration.

[0100] At block 502, the computing system 108 applies multiple simulated treatment regimens to multiple virtual patient avatars to perform a simulated treatment trial. For example, as mentioned above, the computing system 108 can generate multiple virtual patient avatars (e.g., a set of personalized model parameters for a mathematical model of HIF stabilizer therapy) and store the multiple virtual patient avatars. The computing system 108 can use multiple virtual patient avatars to conduct a virtual clinical trial and evaluate one or more treatment regimens.

[0101] For example, the treatment plan involves a fully-fledged universal HIF-PHI treatment algorithm for a patient. In some cases, the treatment plan can be a decision tree indicating how to change the current HIF-PHI prescription (e.g., "increase dosage due to falling hemoglobin levels") based on the most recent measurements. Figure 5B An exemplary treatment scheme according to one or more examples of the present application is shown. In particular, Figure 5B A treatment regimen 550 is shown that uses the patient's hemoglobin concentration to determine the next HIF-PHI dose for the patient.

[0102] For example, at box 552, the patient's hemoglobin (HB) is determined. At box 554, it is determined whether to suspend the erythropoiesis stimulating agent (ESA) administration regimen. If so, the treatment regimen 550 moves to box 556 or 560. If HB is less than 11.5, the treatment regimen 550 moves to box 558. If HB is greater than or equal to 11.5, the treatment regimen 550 moves to box 562. At box 562, the treatment regimen 550 suspends ESA. In other words, the patient's prescription indicates that the ESA regimen is suspended. At box 558, the treatment regimen 550 continues ESA (e.g., continues the ESA regimen for the patient), and the dose of HIF-PHI is reduced by one step. For example, the HIF-PHI dose can be based on multiple steps (e.g., 11 steps). At step 1, the HIF-PHI dose is suspended (e.g., there is no HIF-PHI dose for the patient). At step 2, the HIF-PHI dose can be indicated as 10 milligrams (mg) per day. At step 3, the HIF-PHI dose may be 20 mg per day. At step 4, the HIF-PHI dose may be 30 mg per day. At step 5, the HIF-PHI dose may be 40 mg per day. At step 6, the HIF-PHI dose may be 50 mg per day. At step 7, the HIF-PHI dose may be 60 mg per day. At step 8, the HIF-PHI dose may be 80 mg per day. At step 9, the HIF-PHI dose may be 100 mg per day. At step 10, the HIF-PHI dose may be 130 mg per day. At step 11, the HIF-PHI dose may be 150 mg per day. Thus, at box 558 (e.g., based on the ESA being suspended and the HB being less than 11.5), the treatment regimen 550 may indicate a decrease in the HIF-PHI dose by 1 step (e.g., if the patient is at step 7, the treatment regimen 550 may indicate a decrease in the patient to step 6, and the next HIF-PHI dose for the patient may be 50 mg per day).

[0103] If the ESA is not paused, the treatment plan 550 checks boxes 564, 568, 572, and 576. Based on the HB being greater than 12, the treatment plan 550 instructs to pause the ESA at box 566. Based on the HB being between 11.0 and 11.9, the treatment plan 550 instructs to reduce the dose by 1 step at box 570. Based on the HB being between 10.0 and 11, the treatment plan 550 instructs to continue the ESA and reduce the HIF-PHI dose by 1 step. Based on the HB being between 10 and 10.9, the treatment plan 550 checks at box 578 whether the last dose increase was within 4 weeks. If yes, the treatment plan 550 moves to box 574, the ESA continues, and the HIF-PHI dose is reduced by 1 step. If no, the treatment plan 550 moves to box 580, and the HIF-PHI dose is increased by 1 step.

[0104] The computing system 108 can generate multiple simulated treatment plans, such as treatment plan 550. As mentioned above, treatment plan 550 is merely exemplary, and the computing system 108 can generate multiple different treatment plans for block 502. For example, the multiple treatment plans can include treatment plan 550 as well as other treatment plans, such as by removing blocks from treatment plan 550 (e.g., removing block 578 and connecting block 576 directly to block 580), adding additional blocks to treatment plan 550 (e.g., adding a new branch or decision block before determining to increase or decrease the HIF-PHI dose), and / or modifying blocks from treatment plan 550 (e.g., changing the range of HB within blocks 556, 560, 564, 568, 572, and / or 576). Additionally and / or alternatively, the treatment regimen box may indicate and / or be associated with recommendations for certain or special circumstances, such as, but not limited to, a severe drop or increase in HGB (e.g., a change in HGB of greater than 1 gram / deciliter (dl) in two weeks), a very low HGB situation (e.g., less than 8 g / dl), a ferritin or TSAT situation below or above a particular threshold, or exceptional circumstances such as the patient recently returned from the hospital, no HGB measurement is available within a certain number of weeks, incorrect dosage was administered, and / or the patient's treatment was suspended for a certain number of weeks.

[0105] In some cases, computing system 108 may generate a treatment plan automatically and / or based on received user input. For example, a user may provide input to create a treatment plan. Additionally and / or alternatively, computing system 108 may generate an entire treatment plan and / or a portion of a treatment plan.

[0106] After obtaining a plurality of simulated treatment regimens (e.g., treatment regimens 550), computing system 108 may apply the treatment regimens to virtual patient avatars to perform the simulated treatment trials. For example, each virtual patient avatar includes and / or is associated with a set of personalized model parameters (e.g., HIF-PHI bioavailability, RBC lifespan, a parameter indicating a patient-specific magnitude of hemoglobin response to a given HIF-PHI dose, a parameter indicating a patient's basal hemoglobin level in the absence of anemia medication, a parameter indicating a patient's hepcidin kinetics and its effect on iron availability, and / or a parameter indicating a patient's absorption of a specific iron-containing medication). Computing system 108 may use Figure 7 . For example, the virtual patient avatar may indicate the values ​​of HIF-PHI bioavailability, RBC lifespan, and other personalized model parameters. The computing system 108 may determine the HIF-PHI model based on these values ​​and the mathematical model. In other words, the HIF-PHI model may be Figure 7 , which has values ​​(e.g., RBC average lifespan) indicated by a virtual patient avatar (e.g., values ​​of HIF-PHI bioavailability and / or RBC lifespan). In addition, the computing system 108 can use the population patient data and the HIF-PHI model for the virtual patient avatar to evaluate the treatment plan 550. For example, based on the patient's HB concentration, the computing system 108 can determine the next HIF-PHI dose for the patient (e.g., maintain, increase or decrease the HIF-PHI dose indicated by boxes 580, 574, 570, 566, 558, 562). The computing system 108 can then use the next HIF-PHI dose and the HIF-PHI model to determine the expected hematocrit / hemoglobin concentration for the patient. For example, the computing system 108 can determine the output of the HIF-PHI model based on the next HIF-PHI dose indicated by the treatment plan (e.g., using equation (1.7) to determine the expected hematocrit / hemoglobin concentration for the patient).

[0107] The computing system 108 can continue to use the same treatment regimen, HIF-PHI model and group patient data to determine the expected hematocrit / hemoglobin concentration. In addition, the computing system 108 can evaluate different treatment regimens (e.g., determine the expected hematocrit / hemoglobin concentration) based on the use of group patient data and the HIF-PHI model. Each simulated treatment trial can be associated with a virtual patient avatar (e.g., a HIF-PHI model) and a specific treatment regimen (e.g., treatment regimen 550). The results of the simulated treatment trial are based on the results from the HIF-PHI model (e.g., the expected hematocrit / hemoglobin concentration output by the HIF-PHI model) and / or the simulated treatment (e.g., the time series of HIF-PHI administration doses determined by the simulated treatment regimen). The results of the simulated treatment trial can indicate and / or be associated with the HB time series and / or the HIF-PHI administration time series (e.g., the results can be based on the determination of the tested treatment algorithm and the HB dynamics of the virtual patient avatar).

[0108] At box 504, the computing system 108 determines the best performing treatment plan based on the results of applying multiple simulated treatment plans to multiple virtual patient avatars and patient outcomes and drug use standards. For example, after applying the treatment plan to the virtual patient avatar, the computing system 108 can determine multiple results (e.g., the expected hematocrit / hemoglobin concentration output by the HIF-PHI model). The computing system 108 can then evaluate which treatment plans perform best given the physiological heterogeneity of the virtual patient avatar. For example, the computing system 108 can evaluate the output (e.g., results) by performing statistical analysis. For example, the computing system 108 can determine the best treatment plan (e.g., the treatment plan that takes the longest average time within the set hemoglobin target) according to specific criteria. In some cases, in order to perform such a simulation trial, real-world aspects that affect the performance of the treatment algorithm (e.g., compliance, random events like missed treatment, laboratory sample delivery delays, etc.) can also be considered and can be part of the simulated treatment. This is further described in U.S. Patent Application No. 14 / 974,861, entitled "SYSTEM AND METHOD OF CONDUCTING IN SILICO CLINICAL TRIALS," the contents of which are incorporated herein by reference in their entirety.

[0109] In some variations, specific criteria for evaluating the "optimal treatment" regimen include, but are not limited to, the number of patients within the target threshold, the time patients spend within the target, the number of patients outside the target, the number of patients with very high and / or very low hemoglobin levels, the number of patients requiring transfusions, the amount of medication administered (HIF and / or iron), hemoglobin variability (inter- and intra-individual), the average HIF-PHI usage during a time interval, and / or other criteria.

[0110] In some examples, each virtual patient avatar can be associated with a specific treatment plan. In other examples, two or more virtual patient avatars (including all patient avatars) can be associated with a treatment plan. For example, one or more medical clinics and / or entire business organizations can use a single treatment plan. The computing system 108 can evaluate multiple treatment plans by applying multiple HIF-PHI models associated with multiple virtual patient avatars to determine the results of the treatment trial from the simulation. Based on the results, the computing system 108 can determine the best treatment plan to be performed, and use the best treatment plan for the medical clinic and / or the entire business organization. For example, the treatment plan 550 can be determined as the best treatment plan to be performed and used for the medical clinic and / or the entire business organization.

[0111] In some variations, the computing system 108 may evaluate each of the multiple treatment regimens over a period of time or time window. In other words, the computing system 108 may perform multiple iterations of the treatment regimen to determine the results from the treatment regimen (e.g., multiple hematocrits and / or hemoglobin concentrations for the patient and the HIF-PHI dosage given to the patient during each iteration). The computing system 108 may determine the best treatment regimen based on the evaluation of the treatment regimens over the time period or window (e.g., the number of patients within the target, the time that the patient spends within the target, the number of patients outside the target, the number of patients with very high / or very low hemoglobin levels, and / or the average HIF-PHI usage during the time interval).

[0112] At block 506, the computing system 108 determines the next HIF-PHI dose for one or more patients using the best performing treatment regimen. For example, the computing system 108 may administer the next HIF-PHI dose by determining the next HIF-PHI dose based on the patient's hematocrit and / or hemoglobin concentration. For example, the computing system 108 may determine that the best performing treatment regimen is treatment regimen 550, and determine the next HIF-PHI dose (e.g., increase or decrease the dose by 1 step or maintenance dose) using the patient's hematocrit and / or hemoglobin concentration and / or one or more previous HIF-PHI doses of the patient. The computing system 108 may be connected to and / or include a display device that displays the next HIF-PHI dose.

[0113] In some cases, computing system 108 may provide the best performing treatment regimen to HIF-PHI administration computing device 104, and computing device 104 may administer the next HIF-PHI dose by determining the next HIF-PHI dose and displaying the next HIF-PHI dose.

[0114] In some examples, computing system 108 can update and / or continuously update the best performing treatment plan. For example, based on the best performing treatment plan (e.g., treatment plan 550), computing system 108 can generate additional treatment plans. For example, computing system 108 can modify, add, or remove boxes from the best performing treatment plan to determine multiple new treatment plans, and then evaluate the new treatment plans using process 500. Then, computing system 108 can determine the best performing treatment plan from the new treatment plans.

[0115] Figure 6 is a flow chart of an exemplary process for adjusting a patient's hematocrit and / or hemoglobin concentration using a HIF-PHI model according to one or more examples of the present application. The process may be performed by, for example, Figure 2 6. However, it will be appreciated that any of the following blocks may be performed in any suitable order, and process 600 may be performed in any suitable environment and by any suitable device or system. Figure 6 The descriptions, illustrations, and processes of are merely exemplary, and process 600 may use other descriptions, illustrations, and processes to adjust a patient's hematocrit and / or hemoglobin concentration.

[0116] For example, in some variations, a computing device (e.g., computing system 108 and / or computing device 104) may obtain individualized patient data for one or more patients (e.g., a previous HIF-PHI dose, hematocrit / hemoglobin concentration, gender, weight, and / or other characteristics of a patient for a particular patient). The computing device may determine a patient-specific HIF-PHI model (e.g., a patient HIF-PHI model) for the patient based on the individualized patient data (e.g., patient individual data). In other words, initially, the computing system 108 may determine a treatment regimen and use the treatment regimen to determine a HIF-PHI dose for the patient. During HIF stabilizer treatment, the clinic may draw blood and / or perform other tests (e.g., weekly test data) to determine patient data specific to the patient. The computing system 108 may then use process 600 to determine a patient-specific HIF-PHI model for the patient based on the individualized patient data, and may use the patient HIF-PHI model to determine the next HIF-PHI dose for the patient.

[0117] Additionally and / or alternatively, process 600 may be performed separately from processes 400 and 500. In other words, computing system 108 may obtain individualized patient data and generate a patient HIF-PHI model without initially using a treatment regimen to determine a HIF-PHI dose for the patient. For example, in some variations, the patient data may be static (e.g., computing system 108 may not continuously receive updates to the patient data, or may rarely receive updates to the patient data), and computing system 108 may use processes 400 and 500 to determine the next HIF-PHI dose for the patient. In other variations, the patient data may be dynamic (e.g., computing system 108 may frequently receive updates to the patient data), and computing system 108 may use process 600 alone or in combination with processes 400 and 500.

[0118] At box 602, a computing device (e.g., HIF-PHI administration computing device 104 and / or HIF-PHI model generation computing system 108) obtains individualized patient data for a patient. The individualized patient data indicates a previous HIF-PHI dose and hematocrit and / or hemoglobin measurement for the patient. The previous HIF-PHI dose can be a dose prescribed for a patient for a HIF stabilizer treatment (e.g., using a treatment regimen such as treatment regimen 550 and / or a treatment regimen determined by a clinician). Additionally and / or alternatively, the individualized patient data may include, but is not limited to, hemoglobin, hematocrit, HIF-PHI dose (e.g., a HIF-PHI dose previously taken by the patient), iron dose, serum ferritin, low transferrin saturation (TSAT), sex, height, weight, clinical parameters (such as oxygen saturation, treatment time, bleeding events, transfusion, hospitalization), etc. for the patient.

[0119] At block 604, the computing device calculates a HIF stabilizer based on a previous HIF-PHI dose for the patient, hematocrit and / or hemoglobin measurements, and a mathematical model for HIF stabilizer therapy (e.g., as described below in Figure 7 The patient HIF-PHI model indicates a set of individualized model parameters for the patient. For example, reference Figure 4 At block 404, the computing system 108 generates a virtual patient avatar indicating a set of personalized model parameters based on the group patient data. At block 604, the computing device determines a set of personalized model parameters based on individualized patient data for a specific patient, rather than using the group patient data associated with a plurality of patients. The set of parameters for the patient may include, but are not limited to, HIF-PHI bioavailability, RBC lifespan, a parameter indicating the patient-specific magnitude of hemoglobin response to a given HIF-PHI dose, a parameter indicating the patient's basal hemoglobin level in the absence of anemia medication, and / or an iron-related parameter. Based on the set of personalized model parameters and the mathematical model, the computing device may determine a patient HIF-PHI model for the patient.

[0120] In some examples, the computing device can use previous HIF-PHI doses (e.g., historical HIF-PHI administration data points) to ) and / or other information (e.g., ferritin levels and / or patient characteristics such as gender, height, weight, blood volume, etc.) to determine the patient's HIF-PHI model.

[0121] In some variations, the computing device may select the patient HIF-PHI model from one of the plurality of virtual patient avatars determined at block 404. For example, based on comparing the individualized patient data to a subset of the population patient data, the computing device may determine a virtual patient avatar that is most similar to the patient based on the individualized patient data (e.g., a virtual patient avatar having the same or similar patient characteristics such as height, weight, gender, previous HIF-PHI doses, hemoglobin concentration measurements, etc.).

[0122] At block 606, based on the patient's hematocrit and / or hemoglobin concentration exceeding the patient threshold, the computing device uses the patient HIF-PHI model to determine the next HIF-PHI dose for the patient. For example, the computing device may obtain the patient's hematocrit and / or hemoglobin concentration from individualized patient data and compare these measurements with the patient threshold (e.g., the patient threshold set by a clinician). Based on the patient's hematocrit and / or hemoglobin concentration exceeding the patient threshold (e.g., above or below the threshold), the computing device may use the patient HIF-PHI model to determine the next HIF-PHI dose for the patient. For example, the computing device may input a plurality of different HIF-PHI doses into the patient HIF-PHI model (e.g., via equation (1.1) below), and the patient HIF-PHI model may provide an output such as an expected hematocrit / hemoglobin concentration based on the input HIF-PHI dose. The computing device may determine the next HIF-PHI dose based on the output from the patient HIF-PHI model. For example, the computing device may select a HIF-PHI dose as the next HIF-PHI dose based on an associated output (e.g., expected hematocrit and / or hemoglobin concentration) from the patient HIF-PHI model being within a patient threshold. Additionally and / or alternatively, the computing device may select a HIF-PHI dose based on the HIF-PHI dose being the minimum amount of HIF-PHI that results in an expected output from the patient HIF-PHI model being within the patient threshold (e.g., if multiple HIF-PHI doses result in an output within the patient threshold, the computing device may select the lowest amount of HIF-PHI dose from the multiple HIF-PHI doses). Additionally and / or alternatively, the computing device may use other factors to select a HIF-PHI dose for the patient.

[0123] At box 608, the computing device administers the next HIF-PHI dose to the patient to adjust the hematocrit and / or hemoglobin concentration to within the patient threshold. For example, the computing device may cause the next HIF-PHI dose to be displayed on a display device connected to or included in the computing device. In some cases, the computing device may cause a plurality of HIF-PHI doses output from the patient HIF-PHI model to be displayed, and a user (e.g., a clinician) may select the next HIF-PHI dose for the patient from a plurality of HIF-PHI doses (e.g., a plurality of HIF-PHI doses that bring the patient's expected hematocrit / hemoglobin concentration within the patient threshold). In other cases, the computing device may display a single HIF-PHI dose for the patient.

[0124] In some examples, the computing device may determine the patient threshold based on user input.For example, a doctor, nurse, clinician, and / or other member may provide user input indicating a patient threshold (eg, a specified hematocrit / hemoglobin concentration range).

[0125] In some variations, process 600 can be repeated. For example, after a certain time interval (e.g., every week), the computing device can obtain new patient data. For example, the patient can provide a blood sample at intervals (e.g., once a week, once every two weeks, or once a month). The computing device can receive new data indicating laboratory results after each cycle, and can update the HIF-PHI model and / or generate a new HIF-PHI model for the patient based on the new data. The computing device can use the new HIF-PHI model to determine the next HIF-PHI dose for the patient.

[0126] Figure 7 is a block diagram of an exemplary mathematical model 700 for HIF stabilizer therapy according to one or more examples of the present application. As previously mentioned, the mathematical model 700 can be used to determine / generate multiple virtual patient avatars, adjust the patient's HIF-PHI dose to adjust the patient's hematocrit / hemoglobin concentration, and / or evaluate multiple simulated treatment plans.

[0127] For example, at block 702, the inputs to the mathematical model 700 include HIF-PHD inhibitor dosage and / or administration time point. In some cases, the inputs to the mathematical model 700 may include additional information.

[0128] Box 704 of mathematical model 700 is the HIF-PHD inhibitor pharmacokinetics. For example, from a biological point of view, HIF-PHI can be administered orally. The pharmacokinetic trajectory of the serum concentration of HIF-PHI shows a typical single peak structure with a fast decaying and a slow decaying component. Therefore, the pharmacokinetic portion of the model 700 follows the dynamics of dose uptake and absorption into compartments with fast clearance and leakage into compartments with slow clearance. Overall, this is described by three variables that represent the quality of HIF-PHI in the relevant part of the gastrointestinal tract The HIF-PHI mass of the fast compartment (s1) and the HIF-PHI mass of the slow compartment (s2). This is described in equations (1.1), (1.1a), (1.2) and (1.3) below.

[0129]

[0130] Above describes the time-dependent administration of HIF-PHI, α represents the absorption rate, η represents the clearance rate in the gastrointestinal tract (before HIF-PHI can be absorbed), and k1 and k2 represent the clearance rates after absorption in the fast and slow compartments, respectively. 1→2 represents the conversion rate from the fast compartment to the slow compartment. i refers to the i-th dose administration, H i is the administered dose, δ is the Dirac delta function, t i is the administration time point. The total bioactive HIF-PHI mass represented by s is defined by the following equation (1.4).

[0131] s=s1+s2 Equation (1.4)

[0132] The bioavailable fraction of HIF-PHI, represented by f, is given by equation (1.5) below.

[0133] f=α / (α+η) Equation (1.5)

[0134] Fig. 8A Shows Figure 7 A more detailed representation of . For example, Fig. 8A Includes various physiological processes at work, such as clearance from serum, regulation of synthesis rate, degradation, cell fate, etc.

[0135] Box 706 of the mathematical model 700 is the physiology of the HIF pathway and erythropoietin (EPO). For example, HIF (hypoxia-inducible factor) is a constitutively expressed transcription factor that causes a response to reduced levels of available oxygen by upregulating the expression of various target genes. The HIF transcription complex is a heterodimer of its subunits HIF-α and HIF-β. There are three subtypes of HIF-α, called HIF-1α, HIF-2α, and HIF-3α. Under normoxic conditions, HIF-α is located in the cytoplasm; HIF-β is located in the nucleus. As the O2 concentration decreases, HIF-α (especially the subtype HIF-2α) stabilizes, translocates to the nucleus and binds to HIF-β to form the HIF transcription complex. The HIF complex regulates the transcription of target genes like EPO in the kidney and liver. This is achieved by binding to the hypoxia-responsive elements (HRE: Hypoxia-Responsive Elements) in the corresponding gene promoters.

[0136] The HIF pathway includes multiple biochemicals, such as HIF-α and HIF-β and their dimers. To reduce the complexity of the model, a single composite variable h representing the “HIF signal” is introduced. More precisely, let n cmp represents the amount of HIF transcription complex bound to the EPO promoter region, and represents its average value in the absence of HIF-PHI, then h can be defined as

[0137] Therefore, the sign of h depends on n cmp Is the value above or below the steady-state baseline level?

[0138] In healthy patients, HIF signaling is upregulated in the presence of hypoxia via feedback in the kidneys. Here, consider the set point of blood hemoglobin concentration HIF signaling responds to any deviation from the set point. In addition, since HIF-PHIs prevent HIF-α degradation, they effectively upregulate the amount of available HIF complexes that can bind to the EPO promoter. Therefore, the presence of HIF-PHIs upregulates h. These two contributions are combined in equations (1.6) and (1.7) below.

[0139]

[0140] in,

[0141] c hgb =λ hgb n ery Equation (1.7)

[0142] c hgbrepresents the blood hemoglobin concentration measured by the kidneys, which is determined by the number of red blood cells n ery (dynamic model variables, see equation (1.11)) and the conversion factor λ hgb Given, the conversion factor λ hgb accounts for the total blood volume and the hemoglobin content of each red blood cell (see equation (1.24)). In addition, represents the physiological hemoglobin set point, and β h (θ) represents the formation rate of HIF complex. and The sigmoid function is a sigmoid function that describes nonlinear activation and inhibition. and The definition is as follows:

[0143]

[0144] Where x is the input variable, and a, b, and q are shape parameters that determine the offset, height, and steepness of the function. Equation (A) and equation (B) can be referred to as Hill-type functions. Equation (C) can be referred to as a Richards-type equation.

[0145] Referring back to equation (1.6), the first term The authors describe the upregulation of HIF signaling in response to hypoxia, which results in a decrease in blood hemoglobin concentration. describes upregulation of HIF signaling in response to HIF-PHI administration, where E s is HIF-PHI responsive, is the HIF-PHI EC50. EC50 is the half maximal effective concentration (ie, the concentration that achieves half of the maximal effect). m h is the Hill coefficient, which describes the nonlinearity of the positive feedback of HIF-PHI on HIF signaling. h represents the decay rate of the HIF signal. In other words, referring to equation (1.6), Indicates the rate of change of HIF signaling activity, β h (θ) represents the formation rate of HIF complex, indicates that HIF signaling is upregulated in response to hypoxia, which reduces blood hemoglobin concentration, k h represents the decay rate of HIF signal, c hgb represents the blood hemoglobin concentration measured by the kidneys, represents the physiological hemoglobin set point, E s Indicates HIF-PHI responsiveness, represents upregulation of HIF signaling in response to HIF-PHI administration, and h represents HIF signaling.

[0146] Serum EPO concentration is described by a second variable, e, which is upregulated by HIF signaling and decays at rate ke,

[0147]

[0148] The first term describes the basal EPO synthesis under steady-state conditions. The second term describes the additional EPO synthesis due to activation of HIF signaling. The third term describes the EPO synthesis at a rate k e In other words, for equation (1.8), represents the rate of change of EPO concentration, β e (θ) represents the basal erythropoietin (EPO) synthesis under steady-state conditions, represents additional EPO synthesis due to activation of HIF signaling, and k e e represents the decay rate k e EPO decay.

[0149] Boxes 708 and 712 of mathematical model 700 are the physiology of red blood cell formation in the bone marrow and blood hemoglobin concentration (the clinical target variable). Figure 8B is a schematic diagram of the erythropoietic lineage in the bone marrow and will be used to describe these boxes in more detail. In particular, erythrocytes (red blood cells) are produced sequentially through the differentiation and proliferation of a hierarchy of stem cells and progenitor cells. The hematopoietic stem cell (HSC) is at the highest level of the hierarchy. Figure 8B As shown, they self-renew while a subset of their progeny successively differentiate into more lineage-specific cell types (megakaryocyte-erythroid progenitors, burst-forming units of erythroid [BFU-E], colony-forming units of erythroid [CFU-E], proerythroblasts, erythroblasts, and reticulocytes). This process is regulated by the HIF pathway, which in turn regulates the production of endogenous EPO. EPO inhibits apoptosis (cell death) of progenitor cells. The final step (formation of erythroid cells) also requires iron.

[0150] The erythropoietic lineage in the bone marrow consists of a variety of different cell types and states. These cell types can be grouped according to whether their cell fate is regulated by EPO or iron (the master regulator in the mathematical model 700). Therefore, the population size n of three composite cell populations is introduced prog 、n pre and n ery , which represent (i) progenitor populations (colony forming unit-erythroid (CFU-E) and proerythroblasts), (ii) immediate precursors of erythroid cells (erythroblasts and reticulocytes), and (iii) erythrocytes, respectively. To avoid huge numbers, all these population sizes were normalized so that they represent the average progeny of a representative unit pool of BFU-E (burst forming unit of erythroid cells).

[0151] In this scheme, apoptosis of the progenitor population is downregulated by EPO. Differentiation of precursor cells into erythroid cells is limited by iron availability; if insufficient iron is available, cells ready to differentiate die immediately, so the total loss of precursor cells due to "attempting" to differentiate is constant.

[0152] The corresponding dynamics are given by the following equation.

[0153]

[0154] Here, β prog (θ) represents the rate of progenitor cell formation, which depends on patient-specific demographic and physiological parameters θ, including weight, sex, etc. Therefore, β prog Proportional to the product of the number of available BFU-E and their differentiation rate. i→j represents the differentiation flux from population i to j (e.g., population progenitors (prog) to precursors (pre) or pre to erythrocytes (ery)), K i (For example, K prog or K pre ) represents the cell flux due to apoptosis, and the function L i represents cell flux due to events other than erythropoiesis, and L bleeding and L donation Refers to bleeding events and blood donations respectively. represents the d due to cell division in the progenitor or precursor compartment, respectively prog or pre In other words, for equation (1.11), The rate of change of the total amount of red blood cells. represents the differentiation flux from the precursor cell population to the erythroid population, K ery represents the cell flux due to apoptosis, L bleeding represents the cell flux due to bleeding events, and L donation represents the cell flux due to blood donation events.

[0155] The total differentiation flux is given by the following equation.

[0156] J prog → pre =ω prog→pre n prog Equation (1.12)

[0157]

[0158] Here, ω i→j(For example, ω pre→ery and ω prog → pre ) represents the differentiation rate from population i to j, l pl represents the total iron mass in plasma (a dynamic variable described further below), and represents the EC50 of plasma iron for upregulating differentiation into erythroid cells. mat (e) represents an epo-dependent regulatory factor that slows down or accelerates maturation as the Epo level changes, which is described in the following equation.

[0159]

[0160] in, Indicates the Epo set point level.

[0161] The cell death flux is given by the following equation.

[0162]

[0163] K ery =K ery n ery +k neo (e)n ery Equation (1.17)

[0164] Among them, k i (For example, k ery and κ prog ) represents the apoptosis rate of cell population i, and is the EC50 of Epo that downregulates apoptosis of progenitor cells, k neo represents the rate of new cell lysis regulated by the hormone Epo, which is given by the following equation:

[0165]

[0166] Among them, e neo Indicates the threshold for new cell lysis.

[0167] Note that the amount of available iron determines the relative proportion of precursor cells that are triggered to apoptosis or differentiation, respectively; however, the overall “loss” of the precursor cell compartment is independent of iron and is determined by J pre→ery +K pre =ω pre→ery *n pre Given.

[0168] The terms describing the losses and benefits of bleeding and blood donation are of the following form,

[0169]

[0170] Ldonation =∑ i δ(tt i )Δn ery,i Equation (1.20)

[0171] Here, the sum covers all bleeding and blood donations, ΔV i Indicates the amount of blood lost or donated. blood (ti) is the time t of loss / gain i In the case of blood donation, n ery，i represents the average number of red blood cells in each blood bottle. δ is the Dirac delta function, which is an idealized mathematical construct used to represent instantaneous changes in variables (e.g., quasi-instantaneous changes in the number of red blood cells due to bleeding or blood donation).

[0172] If explicit iron dynamics are not considered (assuming sufficient iron availability for all kinetic states), the iron regulatory factor is considered constant, which leads to the following equation.

[0173]

[0174] This means that, for reasons of self-consistency, ι must be chosen such that

[0175]

[0176] is satisfied, where if the iron module is turned on, then Indicates pl of stable state.

[0177] Regarding hemoglobin and observable values, due to the variable n i represents the average progeny of a representative pool of BFU-E (burst-forming units of erythrocytes), so the absolute cell population size is given by Ni=Q prog n i Given, where Q prog =10 9 is the conversion factor. Therefore, the blood hemoglobin concentration is given by the following equation:

[0178] c hgb =λ hgb n ery Equation (1.7)

[0179] in,

[0180]

[0181] In typical units this means:

[0182]

[0183] Box 710 of mathematical model 700 is the physiology of iron balance. Regarding biology, there are 4 iron pools in the body: the plasma pool, iron in precursor cells, iron in red blood cells, and a pool describing iron in macrophages and storage. Hepcidin regulates iron homeostasis (rapid regulation and rapid clearance in plasma). Increased erythropoiesis, increased HIF levels, and depleted stores reduce the amount of hepcidin released into the plasma. Large iron pools and inflammation increase hepcidin levels. Iron is lost via loss of cells (red blood cells).

[0184] Iron is part of hemoglobin, which provides the means for transporting O2 to tissues. The rate at which iron is released into the plasma from existing stores and macrophages can easily limit the rate of iron delivery for hemoglobin synthesis. Therefore, there is a close relationship between impaired erythropoiesis and systemic iron load. Iron is a substance that is very strictly controlled by the human body, as an excess of iron is toxic. Iron homeostasis can only be achieved by controlling absorption, as the human body cannot influence iron excretion in contrast to other mammals. Under normal circumstances, the average daily loss of iron is very small (male: ~1mg, female: ~2mg due to menstruation). However, in hemodialysis, the daily iron requirement is approximately 5-7mg or even higher. This is above the normal absorption capacity of the diet. Therefore, 80-90% of rHuEPO patients require iron supplementation at some stage of treatment.

[0185] For the iron submodel, five dynamic iron pools in the body are considered: the plasma pool (called "available iron"), iron in precursor cells, iron in erythrocytes, iron in macrophages, and the storage pool (ferritin and hemosiderin storage pools). Other iron in the body is neglected. Figure 8CThe organization of the iron submodel is shown. The available iron pool refers only to transferrin-bound iron in plasma, and we do not distinguish between monoferric and diferric transferrin. In addition, free iron is neglected. Therefore, it is assumed that the amount of iron in plasma is proportional to the amount of transferrin molecules carrying iron. Hepcidin is the main regulator of iron homeostasis and its effects are exerted through changes in ferroportin, a transmembrane protein that transports iron from the interior of the cell to the outside of the cell. Ferroportin is the only known iron exporter and is therefore essential for the absorption and redistribution of iron in the body. Note that the effects of changes in hepcidin levels can last longer than changes in this substance in plasma, as the main effect is through changes in ferroportin. In the case of inflammation or infection, hepcidin is rapidly upregulated. It also increases in the case of large iron stores. However, the effect of increased erythropoiesis outweighs the effect of large iron stores. In patients with chronic kidney disease (CKD), hepcidin levels are often increased, independent of inflammation (hepcidin is partially cleared by the kidneys). In addition, reduced hepcidin levels are observed in patients with hepatitis C. Elevated hepcidin levels are sufficient to cause anemia and hypoferremia. The amount of iron lost through urine and sweat is ignored. Therefore, in model 700, excretion of iron occurs only when cells are lost. This may be due to red blood cell losses, i.e. external bleeding, loss of red blood cells in the dialyzer, blood draws. In addition, there is a loss of epithelial cells every day, the amount of which is about 1 mg / day. Note that this is not directly modeled, but is effectively lost from the erythrocyte compartment. It is assumed that only precursor cells in the bone marrow take up iron, and ineffective erythropoiesis depends on the amount of iron that can be provided for erythropoiesis. All circulating erythrocytes carry an average amount of corpuscular hemoglobin throughout their life cycle. Iron loss from erythrocytes during aging is not taken into account in model 700.

[0186] Regarding the model for iron, and specifically the hepcidin feedback, the regulator of iron homeostasis is hepcidin, a peptide hormone produced by the liver. Hepcidin (hepaticidal protein) negatively regulates the availability of iron in the plasma by directly inhibiting ferroportin. This is the iron exporter required for the export of iron from iron-exporting cells such as enterocytes and macrophages. It acts as a mediator to prevent certain invading bacteria from acquiring iron, as these bacteria cannot proliferate under conditions of inadequate iron supply. Hepcidin is synthesized and secreted by the liver, catabolized by ferroportin-expressing cells, and partially excreted by the kidneys. The simplest description of this dynamic is as follows:

[0187]

[0188] Here, p(t) describes the level of hepcidin in plasma, β p (θ) is the concentration of secreted hepcidin at baseline, function Describes the value of x due to the baseline* The up-regulation and down-regulation of hepcidin secretion caused by various factors x, and κ p represents the degradation (catabolism and excretion) of hepcidin. Note that hepcidin is rapidly upregulated and degraded very rapidly. Therefore, the kinetics of hepcidin concentration in plasma can be calculated using the equilibrium assumption:

[0189]

[0190] Hepcidin production above baseline values ​​is due to the involvement of a progenitor cell population n pre , effective HIF concentration h, amount of stored iron I ms The latter two effects can be grouped together, with serum ferritin levels c fer It can be used as an alternative indicator to describe the impact of these two processes. The Richards curve function parameter xx* can be written as:

[0191]

[0192] Furthermore, in patients with hepatitis C, the secretion rate is downregulated, which can be explained by the decrease in baseline production of β p Change to In addition, in patients with impaired renal function, the degradation rate kp is reduced due to reduced renal clearance of hepcidin:

[0193] κ p =f(m rrf ) Equation (1.28)

[0194] Here, m rrf Refers to the patient's residual renal function. For patients receiving hemodialysis, any residual renal function is rapidly lost after starting this maintenance therapy mode, which can be simplified to

[0195]

[0196] in, It reflects the constant degradation rate of hepcidin metabolism in the body.

[0197] Regarding iron turnover, two types of iron turnover are considered: oral and intravenous (iv). Oral uptake of iron is described via the mid-gastrointestinal (GI) compartment. The dynamics of is given by:

[0198]

[0199] Here, represents the hepcidin-dependent absorption rate, Indicates the clearance rate in the gastrointestinal tract. If the absorption rate is parameterized as:

[0200]

[0201] where ωι is a constant factor and the bioavailability of iron is given by fι, which is further described in equation (1.5) above. When the GI iron mass is balanced infinitely fast, the instantaneous absorption rate The mass residing in the gastrointestinal tract tends to zero

[0202] Intravenous iron administration is directly into the plasma compartment as described below.

[0203] Figure 8C The organizational diagram of the iron sub-model is shown. For example, Figure 8C Depicted are the main iron compartments in the body. The erythroid lineage is shown by progenitor cells, precursor cells, and erythroid cells - called the "erythroid model". Plasma iron, iron stores, iron in macrophages, iron in precursor cells, and iron in erythrocytes represent iron-containing masses. Progenitor cells are not represented by iron because these types of cells carry only trace amounts of iron. Plasma iron masses refer to iron circulating in plasma. Hepcidin is the main regulator of iron homeostasis and largely determines the availability of iron for erythrocyte production. Transfer rates affected by hepcidin include iron stores to hepcidin, iron in precursor cells to hepcidin, iron in macrophages to plasma iron, and iron stores to plasma iron; constant rates include plasma iron to iron stores, iron in macrophages to iron stores, iron in erythrocytes to iron in macrophages, external losses, plasma iron to iron in precursor cells, iron in progenitor cells to precursor cells, and iron in erythrocytes. Upward and downward arrows depict downregulation or upregulation of transfer as hepcidin levels increase. The arrows and labels next to the Hepcidin box indicate whether a condition causes hepcidin to be up-regulated or down-regulated.

[0204] A complete model of iron exchange between physiological compartments is described below. The general form of the compartmental iron model is as follows:

[0205]

[0206]

[0207] Note that in the above equation, the dependence of specific components on t is not explicitly stated in order to simplify notation. In addition, ι represents the amount of iron in the different compartments, and J ι describes the flux of iron from the erythrocyte compartment to other compartments. In addition, iron administration is determined by (intravenous iron, oral iron, and dietary iron). The term Lι describes the iron loss independent of red blood cell loss and the iron gain due to transfusion, respectively. Since the time required for intravenous administration of iron is very short compared to other time constants in the system, the administration can be described as a Dirac delta series,

[0208]

[0209] Here, i is the amount of iron consumed, is the constant influx of iron through the diet (system parameter), refers to iron administered intravenously to a patient, and is a normalized Gaussian distribution used as a regularized delta pulse with width σ.

[0210] Fig.8D Similar to Figure 8C And describes how iron is lost from the system (eg, via bleeding, excretion) and gained in the case of blood transfusion (eg, donated blood in Figure 8).

[0211] A simplified model of iron exchange between physiological compartments is described below. Taking into account the model structure of the erythrocyte model described above, the equation described in equation (1.32) can be further simplified. Iron is synthesized into hemoglobin by the erythroid precursor cell population and recycled when mature erythroid cells are phagocytosed. In addition, iron is lost from the body when erythrocytes are lost (bleeding, blood draw, dialyzer, etc.). However, due to the structure of the erythroid cell compartment, the iron flux and dynamics in the erythroid compartment do not need to be explicitly described, but can be effectively associated with the erythroid cell differentiation flux Jpre→ery, erythrocyte apoptosis and new cell lysis flux Kery and the loss function Li. Therefore, equation (1.32) is simplified to:

[0212]

[0213] Here, p represents the hepcidin level, and is an effective loss term used to account for iron excretion losses through urine and sweat. The iron flux between compartments is given by:

[0214]

[0215]

[0216] J pre→ery , K ery , k neo and L bleeding Defined in equations (1.11) and (1.17). Here, Indicates the amount of iron per red blood cell.

[0217] For reference, red blood cell iron The entrainment dynamics are recorded as follows:

[0218]

[0219] The iron content of each red blood cell can be calculated as:

[0220]

[0221] Ferritin can be calculated self-consistently as:

[0222]

[0223] Fig. 9 908 is a data flow diagram of an exemplary process for obtaining and using group patient data according to one or more examples of the present application. For example, a data warehouse 908 can be a storage entity (e.g., a server and / or other type of computing device including a memory for storing information). The data warehouse 908 can receive patient information from multiple sources. For example, the data warehouse 908 can receive non-invasive and point of care (POC: Point Of Care) measurements 902, such as HGB and / or other measurements. The data warehouse 908 can also receive laboratory data 904 (e.g., HGB and / or the like) and electronic health records 906 (e.g., patient information from electronic health records).

[0224] Afterwards, data processing 910 can be performed on the information stored in the data warehouse 908. For example, data processing 910 can be performed so as to standardize the information in the data warehouse 908 before the HIF-PHI model generation system 912 using the group patient data. For example, the data warehouse 908 can receive data (e.g., non-invasive measurements and / or laboratory data) in many different data formats and from many different sources. In order to use the data to determine multiple virtual patient avatars and / or a set of individualized model parameters for patients, different data formats may need to be standardized. Therefore, data processing 910 standardizes (e.g., converts) the data from the data warehouse 908 into a standardized data format. Subsequently, the HIF-PHI model generation system 912 can use the standardized data format (e.g., group patient data) as described above. For example, the HIF-PHI model generation system 912 can use standardized data to determine (e.g., develop and / or update) anemia schemes, which are implemented using a large number of HIF avatars and / or by performing a large number of virtual clinical trials. Additionally and / or alternatively, the HIF-PHI model generation system 912 may include anemia software (e.g., instructions stored in a memory) that, when executed by a processor, determines personalized HIF avatars (e.g., personalized or individualized HIF-PHI models) and periodically updates these personalized HIF avatars using patient-specific data to provide optimal treatment recommendations for the patient.

[0225] In addition, the HIF-PHI model generation system 912 can provide information to the electronic health record 914. This information can in turn be provided to a dashboard 918 (e.g., a medical dashboard showing the next recommended HIF-PHI dose, e.g., Figure 2 918 ). Additionally, data privacy / governance 916 may also be used to ensure that the data displayed by dashboard 918 is compliant.

[0226] As will be described below, Figures 10A-17L A graphical representation of modeling HIF-PHI administration using the HIF-PHI model and mathematical model described above is shown. In particular, Figures 10A-17L The outputs (e.g., the personalized model parameters / variables described above) are shown over a period of time and for a specific group of patients. Figures 10A-17L The data represented in the graphs shown are from the sources provided below.

[0227] Figures 10A-10LA graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. For example, as previously described, the obtained patient data can be input into the mathematical model described above. Based on inputting the patient data into the mathematical model, an output result can be determined. Figures 10A-10L The graphical representation of shows the output results (e.g., the personalized model parameters / variables described above) over a period of time and for a specific group of patients (e.g., patients receiving a series of HIF-PHI ROXADUSTAT administrations and concomitant intravenous iron administrations). For example, Fig. 10A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 10B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 10C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig. 10D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.10E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.10F shows the ferritin (model variable Equation 1.32). Figure 10G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Besarab et al., "Roxadustat (FG-4592): Correction of Anemia in Incident Dialysis Patients", J. Am. Soc. Nephrol. 27, 1225-1233 (2016) Figure 2 Digital. In other words, Figure 10G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig. 10H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.10I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.10J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery, equations 1.9-1.11). Figure 10K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Fig.10L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0228] Figures 11A-11L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figures 11A-11L The graphical representation of shows the output results (e.g., the personalized model parameters / variables described above) over a period of time and for a specific group of patients (e.g., patients receiving a series of HIF-PHI ROXADUSTAT administrations without concomitant iron administration). For example, Fig.11A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 11B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 11C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.11D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.11E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.11F shows the ferritin (model variable Equation 1.32). Fig.11G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Besarab et al., "Roxadustat (FG-4592): Correction of Anemia in Incident Dialysis Patients", J. Am. Soc. Nephrol. 27, 1225-1233 (2016) Figure 2 Digital. In other words, Fig.11G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.11H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.11Ishows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.11J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 11K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Fig.11L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0229] Figure 12A-12L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figure 12A-12L The graphical representation of shows the output results (e.g., the personalized model parameters / variables described above) over a period of time and for a specific group of patients (e.g., patients receiving a series of HIF-PHI ROXADUSTAT administrations and concomitant oral iron administrations). For example, Fig. 12A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 12B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 12C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.12D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.12E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.12F shows the ferritin (model variable Equation 1.32). Figure 12G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Besarab et al., "Roxadustat (FG-4592): Correction of Anemia in Incident Dialysis Patients", J. Am. Soc. Nephrol. 27, 1225-1233 (2016) Figure 2 Digital. In other words, Figure 12GShown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.12H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.12I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.12J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 12K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 12L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0230] Figures 13A-13L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figures 13A-13L A graphical representation shows the relationship between the HIF-PHI ROXADUSTAT administration and the reference hepcidin production rate β over a period of time and for a particular group of patients (e.g., patients receiving a series of HIF-PHI ROXADUSTAT administrations, who have a reference hepcidin production rate β p (θ) (Equation 1.32), which represents the output of the patient population whose C-reactive protein (CRP) concentration is greater than the upper limit of the normal range (ULN) (e.g., the personalized model parameters / variables described above). For example, Fig.13A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 13B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 13C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.13D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.13E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.13F shows the ferritin (model variable Equation 1.32). Figure 13G shows the hemoglobin (model variable c) over time. hgb, Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Chen et al., "Roxadustat Treatment for Anemia in Patients Undergoing Long-Term Dialysis", New Engl. J. Med. 381, 1011-1022 (2019) Figure 2 A digital. In other words, Figure 13G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.13H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.13I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.13J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 13K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 13L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0231] Figure 14A-14L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figure 14A-14L A graphical representation of the hepcidin production rate β compared to the reference rate used in FIG. 13 over a period of time and for a particular group of patients (e.g., patients receiving a series of HIF-PHI ROXADUSTAT administrations). p (θ) (Eq. 1.32) is increased by 2%, which represents the output of the patient population whose C-reactive protein (CRP) concentration is less than the upper limit of the normal range (ULN) (e.g., the personalized model parameters / variables described above). For example, Fig.14A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 14B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 14CThe mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.14D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.14E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.14F shows the ferritin (model variable Equation 1.32). Figure 14G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7), and actual measured HGB data points (e.g., points) are shown. The data points are from Chen et al., "Roxadustat Treatment for Anemia in Patients Undergoing Long-Term Dialysis", New Engl. J. Med. 381, 1011-1022 (2019) Figure 2 A digital. In other words, Figure 14G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.14H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.14I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.14J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 14K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 14L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0232] Figure 15A-15L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figure 15A-15L The graphical representation shows the EPO production rate β over a period of time and for a particular group of patients (e.g., patients receiving a series of HIF-PHI VADADUSTAT administrations, compared to the reference rate used in FIG. 17 ). e(θ) (Eq. 1.32) increases by 7%, which represents the output results (e.g., the personalized model parameters / variables described above) for a patient population with a baseline hemoglobin between 10.4 g / dL and 11 g / dL. For example, Fig.15A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 15B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 15C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.15D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.15E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.15F shows the ferritin (model variable Equation 1.32). Figure 15G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Nangaku et al., "Efficacy and safety of vadadustat compared with darbepoetinalfa in Japanese anemic patients on hemodialysis: a Phase 3 multicenter, randomized, double-blind study", Nephrol. Dial. Transplant. 36, 1731-1741 (2021) Figure 2 A digital. In other words, Figure 15G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.15H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.15I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.15J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 15Kshows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 15L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0233] Figures 16A-16L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figures 16A-16L The graphical representation shows the EPO production rate β over a period of time and for a particular group of patients (e.g., patients receiving a series of HIF-PHI VADADUSTAT administrations, compared to the reference rate used in FIG. 17 ). e (θ) (Eq. 1.32) increased by 42%, which represents the output of the patient population with a baseline hemoglobin greater than 11 g / dL (e.g., the personalized model parameters / variables described above). For example, Fig.16A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 16B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 16C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.16D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.16E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.16F shows the ferritin (model variable Equation 1.32). Figure 16G shows the hemoglobin (model variable c) over time. hgb , Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Nangaku et al., "Efficacy and safety of vadadustat compared with darbepoetin alfa in Japanese anemic patients on hemodialysis: a Phase 3 multicenter, randomized, double-blind study", Nephrol. Dial. Transplant. 36, 1731-1741 (2021) Figure 2 A digital. In other words, Figure 16G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.16H shows the amount of iron in the gastrointestinal tract over time (model variable Equation 1.30). Fig.16I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.16J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 16K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 16L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0234] Figure 17A-17L A graphical representation for modeling HIF-PHI administration according to one or more examples of the present application is shown. Figures 17A-17L The graphical representation shows the time period and for a particular group of patients (e.g., patients receiving a series of HIF-PHI VADADUSTAT administrations, who have a reference EPO production rate β e (θ) (Eq. 1.32), which represents the output of the patient population with a baseline hemoglobin level below 10.4 g / dL (e.g., the personalized model parameters / variables described above). For example, Fig.17A HIF-PHI mass over time is shown (model variables from equations 1.1 and 1.2-1.4, respectively). and s). Fig. 17B The HIF signal (model variable h, equation 1.6) is shown over a period of time. Fig. 17C The mass of EPO (model variable e, Eq. 1.8) is shown over time. Fig.17D Shown is the HIF-PHI serum concentration over time (model variable s, Equation 1.1, divided by plasma volume). Fig.17E Hepcidin (model variable p, Eq. 1.26) is shown over time. Fig.17F shows the ferritin (model variable Equation 1.32). Figure 17G shows the hemoglobin (model variable c) over time. hgb, Equation 1.7) and actual measured HGB data points (e.g., points) are shown. The data points are from Nangaku et al., "Efficacy and safety of vadadustat compared with darbepoetin alfa in Japanese anemic patients on hemodialysis: a Phase 3 multicenter, randomized, double-blind study", Nephrol. Dial. Transplant. 36, 1731-1741 (2021) Figure 2 A digital. In other words, Figure 17G Shown are a curve representing the calculated HGB based on inputting the patient data into the mathematical model and data points representing the actual measured HGB from the patient data. Fig.17H The amount of iron in the gastrointestinal tract over time is shown (model variable ι, equation 1.30). Fig.17I shows the amount of iron in plasma over time (model variable ι pl , equation 1.32). Fig.17J shows the abundance of a cell population over time (model variable n prog 、n pre 、n ery , equations 1.9-1.11). Figure 17K shows the iron concentration in plasma over time (model variable ι pl , equation 1.32, divided by the plasma volume). Figure 17L shows the mass of iron in the storage device over a period of time (model variable ι ms , equation 1.32).

[0235] It should be understood that the various machine-implemented operations described herein may occur via execution by one or more respective processors of processor-executable instructions stored on a tangible, non-transitory computer-readable medium, such as a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), and / or another electronic memory mechanism. Thus, for example, the operations performed by any device described herein may be performed according to instructions stored on the device and / or an application installed on the device, and via software and / or hardware of the device.

[0236] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[0237] Although the present invention has been described and illustrated in detail in the accompanying drawings and the foregoing description, such description and illustration should be considered as illustrative or exemplary rather than restrictive. It should be understood that those of ordinary skill in the art can make changes and modifications within the scope of the appended claims. In particular, the present application covers other embodiments with any combination of features from the different embodiments described above and below.

[0238] The terms used in the claims should be interpreted as having the broadest reasonable interpretation consistent with the preceding description. For example, the use of the article "a" or "an" when introducing an element should not be interpreted as excluding multiple elements. Similarly, the narration of "or" should be interpreted as inclusive, so that the narration of "A or B" does not exclude "A and B", unless it is clear from the context or the preceding description that only one of A and B is referred to. In addition, the narration of "at least one of A, B and C" should be interpreted as one or more of a group of elements consisting of A, B and C, and should not be interpreted as requiring at least one of each of the listed elements A, B and C, regardless of whether A, B and C are related as categories or otherwise. In addition, the narration of "A, B and / or C" or "at least one of A, B or C" should be interpreted as including any single entity from the listed elements, such as A, any subset of the listed elements, such as A and B, or the entire list of elements A, B and C.

[0239] Unless otherwise specified herein, the description of the range of values ​​herein is intended only to be used as a shorthand method for individually referring to each individual value falling within the range, and each individual value is incorporated into this specification as if it were individually narrated herein. Unless otherwise specified herein or clearly contradictory to the context, all methods described herein can be performed in any suitable order. The use of any and all examples or exemplary language (e.g., "such as") provided herein is intended only to better illustrate the present invention, and is not intended to limit the scope of the present invention, unless otherwise stated. Any language in the specification should not be interpreted as indicating that any unclaimed element is essential to the practice of the present invention.

Claims

1. A method for determining a next hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) dose for a first patient using a patient HIF-PHI model, comprising: obtaining population patient data associated with a plurality of patients, wherein the population patient data indicates previous HIF-PHI doses and hemoglobin measurements for the plurality of patients; generating a plurality of virtual patient avatars based on the population patient data, wherein each of the plurality of virtual patient avatars indicates a set of personalized model parameters; determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized model parameters; determining one or more HIF-PHI treatment regimens for administering a HIF-PHI dose based on the plurality of HIF-PHI models; and A next HIF-PHI dose is administered to a first patient of the plurality of patients based on using the one or more HIF-PHI treatment regimens determined and the hematocrit and / or hemoglobin concentration for the first patient.

2. The method according to claim 1, wherein: The method further comprises: obtaining individualized patient data for a second patient from the plurality of patients, wherein the individualized patient data indicates a previous HIF-PHI dosage and hemoglobin measurement for the second patient; determining a patient HIF-PHI model for the second patient based on the previous HIF-PHI dose, the hemoglobin measurement, and a mathematical model for hydroxylase inhibitor (HIF) stabilizer therapy, wherein the HIF-PHI model indicates a set of individualized model parameters for the second patient; determining a next HIF-PHI dose for the second patient using the patient HIF-PHI model based on the second patient's hemoglobin measurement exceeding a patient threshold; and The next HIF-PHI dose is administered to the second patient to adjust the hemoglobin concentration to within the patient threshold.

3. The method according to claim 2, wherein: Administering the next HIF-PHI dose comprises: The next HIF-PHI dose is caused to be displayed on the display device.

4. The method according to claim 1, wherein: The method further comprises: Obtain a mathematical model for treatment with hydroxylase inhibitor (HIF) stabilizers; Wherein, determining the plurality of HIF-PHI models for the plurality of virtual patient avatars comprises generating the plurality of HIF-PHI models by inserting the set of personalized model parameters into the mathematical model.

5. The method according to claim 4, wherein: Each of the one or more HIF-PHI treatment regimens is indicative of a decision tree including a plurality of branches indicating different HIF-PHI doses based on hemoglobin concentration.

6. The method according to claim 5, wherein: Determining the one or more HIF-PHI treatment options includes: generating a plurality of HIF-PHI treatment protocols for administering HIF stabilizer therapy; simulating a plurality of virtual trials using the plurality of HIF-PHI treatment regimens and the plurality of HIF-PHI models for the plurality of virtual patient avatars; and The one or more HIF-PHI treatment regimens are determined based on simulating the plurality of virtual trials.

7. The method according to claim 6, wherein: The one or more HIF-PHI treatment regimens are determined based on a number of patients from the plurality of patients within a target hemoglobin threshold and an amount of a HIF-PHI drug administered to a plurality of simulated patients within the plurality of virtual trials.

8. The method according to claim 6, wherein: Administering the next HIF-PHI dose to the first patient includes displaying the next HIF-PHI dose for the first patient on a display device.

9. The method according to claim 1, wherein: The set of personalized model parameters includes a HIF-PHI bioavailability parameter, a red blood cell (RBC) lifespan parameter, and a hemoglobin set point parameter, and Wherein, generating the plurality of virtual patient avatars includes determining the HIF-PHI bioavailability parameter, the red blood cell (RBC) lifespan parameter, and the hemoglobin set point parameter for each of the plurality of virtual patient avatars.

10. The method according to claim 9, wherein: The set of personalized model parameters also includes a basal erythropoietin (EPO) synthesis rate parameter, a HIF signal threshold parameter, and a hepcidin decay rate parameter, and Wherein, generating the plurality of virtual patient avatars further comprises determining the basal EPO synthesis rate parameter, the HIF signal threshold parameter and the hepcidin decay rate parameter for each of the plurality of virtual patient avatars.

11. The method according to claim 1, wherein: Determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is also based on the following equations: in, Indicates the rate of change of HIF signaling activity, β h (θ) represents the formation rate of HIF complex, indicates that HIF signaling is upregulated in response to hypoxia, which reduces blood hemoglobin concentration, k h represents the decay rate of HIF signal, c hgb represents the blood hemoglobin concentration measured by the kidneys, represents the physiological hemoglobin set point, E s Indicates HIF-PHI responsiveness, represents upregulation of HIF signaling in response to HIF-PHI administration, and h represents HIF signaling.

12. The method according to claim 1, wherein: Determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is also based on the following equations: in, represents the rate of change of EPO concentration, β e (θ) represents the basal erythropoietin (EPO) synthesis under steady-state conditions, represents additional EPO synthesis due to HIF signaling activation, and k e e represents the decay rate k e EPO decay.

13. The method according to claim 1, wherein: Determining the plurality of HIF-PHI models for the plurality of virtual patient avatars is also based on the following equations: in, The rate of change of the total amount of red blood cells. represents the differentiation flux from the precursor cell population to the erythroid population, K ery represents the cell flux due to apoptosis, L bleeding represents the cell flux due to bleeding events, and L donation represents the cell flux due to blood donation events.

14. A method of adjusting the hematocrit and / or hemoglobin concentration of a patient using a patient hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model, comprising: obtaining individualized patient data for the patient, wherein the individualized patient data indicates a previous HIF-PHI dosage and a hemoglobin measurement for the patient; determining a patient HIF-PHI model for the patient based on the previous HIF-PHI dose, the hemoglobin measurement, and a mathematical model for hydroxylase inhibitor (HIF) stabilizer therapy, wherein the HIF-PHI model indicates a set of individualized model parameters for the patient; determining a next HIF-PHI dose for the patient using the patient HIF-PHI model based on the patient's hematocrit and / or hemoglobin concentration exceeding a patient threshold; and The next HIF-PHI dose is administered to the patient to adjust the hematocrit and / or the hemoglobin concentration to within the patient threshold.

15. The method according to claim 14, wherein: Administering the next HIF-PHI dose comprises: The next HIF-PHI dose is caused to be displayed on the display device.

16. The method according to claim 14, wherein: The set of individualized model parameters includes a HIF-PHI bioavailability parameter, a red blood cell (RBC) lifespan parameter, and a hemoglobin set point parameter, and Wherein, the next HIF-PHI dose for the patient is determined based on using the HIF-PHI bioavailability parameter, the red blood cell (RBC) lifespan parameter, and the hemoglobin set point parameter.

17. The method according to claim 16, wherein: The set of individualized model parameters also includes a basic erythropoietin (EPO) synthesis rate parameter, a HIF signal threshold parameter, and a hepcidin decay rate parameter, and Wherein, the next HIF-PHI dose for the patient is determined based on the basal EPO synthesis rate parameter, the HIF signal threshold parameter and the hepcidin decay rate parameter.

18. The method according to claim 14, wherein: Using the patient HIF-PHI model to determine the next HIF-PHI dose for the patient comprises: inputting a plurality of next HIF-PHI doses into the HIF-PHI model to determine a plurality of outputs indicative of expected hematocrit or hemoglobin concentrations; and The next HIF-PHI dose is selected from the plurality of next HIF-PHI doses based on an output of the plurality of outputs that is within the patient threshold.

19. The method according to claim 14, wherein: Using the patient HIF-PHI model to determine the next HIF-PHI dose for the patient comprises: inputting a plurality of next HIF-PHI doses into the HIF-PHI model to determine a plurality of outputs indicative of expected hematocrit or hemoglobin concentrations; determining a subset of next HIF-PHI doses in the plurality of next HIF-PHI doses based on one or more of the plurality of outputs associated with the subset of the next HIF-PHI doses within the patient threshold; and The next HIF-PHI dose is selected from the subset of next HIF-PHI doses based on the next HIF-PHI dose being the lowest amount of the HIF-PHI dose within the subset of next HIF-PHI doses.

20. A computing device comprising: a display configured to display information associated with a hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model of a patient; one or more processors; as well as A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by the one or more processors, facilitate: obtaining population patient data associated with a plurality of patients, wherein the population patient data indicates previous HIF-PHI doses and hemoglobin measurements for the plurality of patients; generating a plurality of virtual patient avatars based on the population patient data, wherein each of the plurality of virtual patient avatars indicates a set of personalized model parameters; determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized model parameters; determining one or more HIF-PHI treatment regimens for administering a HIF-PHI dose based on the plurality of HIF-PHI models; and The next HIF-PHI dose for the first patient of the plurality of patients is displayed on the display based on using the one or more determined HIF-PHI treatment regimens and the hematocrit and / or hemoglobin concentration for the first patient.

Images