Patent Application
20220223299
Hereditary angioedema (HAE) is an autosomal dominant disease caused by problems in the C1 inhibitor protein. HAE type I is characterized by a deficiency in the C1 inhibitor protein while HAE type II is characterized by dysfunction in the C1 inhibitor protein. HAE affects an estimated 1 in 67,000 people worldwide. HAE manifests clinically as unpredictable, intermittent attacks of subcutaneous or submucosal oedema (swelling) of the face, larynx, gastrointestinal tract, limbs and/or genitalia. The underlying mechanism is due to the excess activation of the ‘contact system’ where plasma kallikrein acts on high molecular weight kininogen (HMWK), leading to bradykinin release, causing vasodilation due to binding of bradykinin to B2 receptors on endothelial cells.
Some embodiments provide for a computer-implemented method for modeling and simulating hereditary angioedema (HAE), comprising: obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; determining disease predictive descriptors; assigning the disease predictive descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises an amount of one or more contact system proteins.
Some embodiments provide for a system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer-implemented method for modeling and simulating hereditary angioedema (HAE), comprising: obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; determining disease predictive descriptors; assigning the disease predictive descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises an amount of one or more contact system proteins.
Some embodiments provide for at least one non-transitory computer-readable medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer implemented method for modeling and simulating hereditary angioedema (HAE), the comprising: obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; determining disease predictive descriptors; assigning the disease predictive descriptors to a virtual patient population; and processing the virtual patient population using the QSP model to provide processed data, wherein the processed data comprises an amount of one or more contact system proteins.
Some embodiments provide for a computer-implemented method for determining a trigger strength by estimating one or more characteristics of a contact system in a patient in response to a trigger, the method comprising: obtaining a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that the trigger has been input into the QSP model; calibrating the QSP model with known data; inputting the trigger into the QSP model, the trigger being configured to generate FXIIa by causing Factor XII of the contact system to autoactivate; obtaining, from the QSP model, an amount of a protein of the contact system generated in response to the trigger.
Some embodiments provide for a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer-implemented method for estimating one or more characteristics of a contact system in a patient in response to a trigger, the method comprising: obtaining a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that the trigger has been input into the QSP model; calibrating the QSP model with known data; inputting the trigger into the QSP model, the trigger being configured to generate FXIIa by causing Factor XII of the contact system to autoactivate; obtaining, from the QSP model, an amount of a protein of the contact system generated in response to the trigger.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer-implemented method for estimating one or more characteristics of a contact system in a patient in response to a trigger, the method comprising: obtaining a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that the trigger has been input into the QSP model; calibrating the QSP model with known data; inputting the trigger into the QSP model, the trigger being configured to generate FXIIa by causing Factor XII of the contact system to autoactivate; obtaining, from the QSP model, an amount of a protein of the contact system generated in response to the trigger.
Some embodiments provide for a computer-implemented method for determining a relationship between hereditary angioedema (HAE) attack frequency and Factor XII trigger rate, the method comprising: obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; assigning a Factor XII trigger rate for one or more patients in a virtual patient population, wherein the Factor XII trigger rate comprises a rate at which autoactivation of Factor XII is triggered in the QSP model; applying the QSP model to the one or more patients in the virtual patient population to obtain processed data, wherein the processed data comprises an amount of one or more contact system proteins; determining an HAE attack frequency for the one or more patients in the virtual patient population based on the processed data; and determining a relationship between HAE attack frequency and Factor XII trigger rate.
Some embodiments provide for a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer-implemented method for determining a relationship between hereditary angioedema (HAE) attack frequency and Factor XII trigger rate, the method comprising obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; assigning a Factor XII trigger rate for one or more patients in a virtual patient population, wherein the Factor XII trigger rate comprises a rate at which autoactivation of Factor XII is triggered in the QSP model; applying the QSP model to the one or more patients in the virtual patient population to obtain processed data, wherein the processed data comprises an amount of one or more contact system proteins; determining an HAE attack frequency for the one or more patients in the virtual patient population based on the processed data; and determining a relationship between HAE attack frequency and Factor XII trigger rate.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a computer-implemented method for determining a relationship between hereditary angioedema (HAE) attack frequency and Factor XII trigger rate, the method comprising: obtaining a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; assigning a Factor XII trigger rate for one or more patients in a virtual patient population, wherein the Factor XII trigger rate comprises a rate at which autoactivation of Factor XII is triggered in the QSP model; applying the QSP model to the one or more patients in the virtual patient population to obtain processed data, wherein the processed data comprises an amount of one or more contact system proteins; determining an HAE attack frequency for the one or more patients in the virtual patient population based on the processed data; and determining a relationship between HAE attack frequency and Factor XII trigger rate.
Some embodiments provide for a computer-implemented method for determining an effectiveness of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the administered drug on treating HAE.
Some embodiments provide for a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effectiveness of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the administered drug on treating HAE.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effectiveness of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the administered drug on treating HAE.
Some embodiments provide for a computer-implemented method for determining an effectiveness of a dosage of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the dosage of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the dosage of the administered drug on treating HAE.
Some embodiments provide for a system, comprising: at least one computer-hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effectiveness of a dosage of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the dosage of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the dosage of the administered drug on treating HAE.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effectiveness of a dosage of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the dosage of the administered drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to obtain an indicator of the effectiveness of the dosage of the administered drug on treating HAE.
Some embodiments provide for a computer-implemented method for determining an effect of non-adherence to a dosing regimen of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include a frequency of non-adherence to the dosing regimen; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine an effect of the frequency of non-adherence on treating HAE.
Some embodiments provide for a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable medium storing processor-executable instructions that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effect of non-adherence to a dosing regimen of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include a frequency of non-adherence to the dosing regimen; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine an effect of the frequency of non-adherence on treating HAE.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an effect of non-adherence to a dosing regimen of an administered drug in treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the administered drug for a virtual patient population, wherein the pharmacokinetic parameters include a frequency of non-adherence to the dosing regimen; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine an effect of the frequency of non-adherence on treating HAE.
Some embodiments provide for a computer-implemented method for determining an amount of a protein of a contact system in a patient in response to administration of a drug for treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; and determining the amount of the protein based on the processed data.
Some embodiments provide for a system, comprising: at least one computer-hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an amount of a protein of a contact system in a patient in response to administration of a drug for treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; and determining the amount of the protein based on the processed data.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining an amount of a protein of a contact system in a patient in response to administration of a drug for treating hereditary angioedema (HAE), the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model; and determining the amount of the protein based on the processed data.
Some embodiments provide for a computer-implemented method for determining a temporal profile illustrating an effect of a drug on a contact system in a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE) to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine a measure of an amount of one or more proteins of the contact system over time in response to the drug.
Some embodiments provide for a system, comprising: at least one computer-hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instruction that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining a temporal profile illustrating an effect of a drug on a contact system in a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE) to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine a measure of an amount of one or more proteins of the contact system over time in response to the drug.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instruction that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining a temporal profile illustrating an effect of a drug on a contact system in a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE) to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine a measure of an amount of one or more proteins of the contact system over time in response to the drug.
Some embodiments provide for a computer-implemented method for determining a characteristic of a hereditary angioedema (HAE) flare-up in response to administering a drug to a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine the characteristic of the HAE flare-up in response to administering the drug to the patient.
Some embodiments provide for a system, comprising: at least one computer-hardware processor; at least one non-transitory computer-readable hardware medium storing processor-executable instructions that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining a characteristic of a hereditary angioedema (HAE) flare-up in response to administering a drug to a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine the characteristic of the HAE flare-up in response to administering the drug to the patient.
Some embodiments provide for at least one non-transitory computer-readable hardware medium storing processor-executable instructions that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for determining a characteristic of a hereditary angioedema (HAE) flare-up in response to administering a drug to a patient, the method comprising: determining pharmacokinetic parameters of the drug for a virtual patient population; determining disease predictive descriptors for the virtual patient population; assigning the pharmacokinetic parameters and disease predictive descriptors to the virtual patient population; processing the virtual patient population using a quantitative systems pharmacology (QSP) model of HAE to obtain processed data, wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and the processed data comprises an amount of one or more contact system proteins; and using the processed data to determine the characteristic of the HAE flare-up in response to administering the drug to the patient.
Some embodiments provide for a method for developing a virtual patient population comprising a plurality of virtual patients for input into a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model and to output an amount of one or more contact system proteins, the method comprising: assigning pharmacokinetic parameters to the virtual patient population; determining a baseline attack frequency and baseline attack severity for each patient in the virtual patient population; and assigning the baseline attack frequency and baseline attack severity to each patient in the virtual patient population.
Some embodiments provide for a system, comprising: at least one computer-hardware processor; at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for developing a virtual population for input into a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model to output an amount of one or more contact system proteins, the method comprising: assigning pharmacokinetic parameters to the virtual patient population; determining a baseline attack frequency and a baseline attack severity for each patient in the virtual patient population; and assigning the baseline attack frequency and baseline attack severity to each patient in the virtual patient population.
Some embodiments provide for at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer-hardware processor, cause the at least one computer-hardware processor to perform a method for developing a virtual population for input into a quantitative systems pharmacology (QSP) model of hereditary angioedema (HAE), wherein the QSP model is configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model to output an amount of one or more contact system proteins, the method comprising: assigning pharmacokinetic parameters to the virtual patient population; determining a baseline attack frequency and a baseline attack severity for each patient in the virtual patient population; and assigning the baseline attack frequency and baseline attack severity to each patient in the virtual patient population.
Various aspects and embodiments of the application will be described with reference to the following figures. It should be appreciated that the figures are not necessarily drawn to scale. Items appearing in multiple figures are indicated by the same reference number in all the figures in which they appear. For purposes of clarity, not every component may be labeled in every drawing.
Aspects of the present application provide for methods and apparatuses for modeling, simulation, and treating hereditary angioedema. In particular, aspects of the present application provide for a quantitative systems pharmacology (QSP) model for modeling, simulating, and treating hereditary angioedema (HAE). In some embodiments, the QSP model may be configured to model HAE using FXII autoactivation as a trigger. For example, the QSP model may be configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model. In some embodiments, the QSP model may be applied to evaluate new and existing treatment modalities for treating HAE.
In some embodiments, use of the QSP model described in the present application may provide various types of information about the contact system in a patient which would be impractical or impossible to clinically obtain. For example, the QSP model may provide, as output, levels of proteins of the contact system (e.g., bradykinin, cHMWK, plasma kallikrein, FXIIa, etc.), HAE acute attack frequency, severity and duration, among other types of information. In some embodiments, the QSP model may be implemented with a virtual population to execute a virtual clinical trial to evaluate the effects of a therapeutic intervention on HAE. In such embodiments, an attribute of the therapeutic intervention (e.g., half-life, binding affinity, dose, dose frequency, dose regimen, nonadherence percentage) may be correlated with an output of the QSP model to determine the effectiveness of the therapeutic intervention. The inventors have recognized that such techniques may facilitate development of new and more effective treatment modalities within the HAE field.
Overview of Hereditary Angioedema
According to some aspects of the present application, the apparatuses and methods described herein may be used to model, simulate and treat hereditary angioedema (HAE), also known as “Quincke edema,” C1 esterase inhibitor deficiency, C1 inhibitor deficiency, and formerly known as hereditary angioneurotic edema (HANE). HAE is characterized by unpredictable, recurrent attacks of severe subcutaneous or submucosal swelling (angioedema), which can affect, one or more parts of the body (e.g., the limbs, face, genitals, gastrointestinal tract, and airway). (Zuraw, 2008). Symptoms of HAE may include, for example, swelling in the arms, legs, lips, eyes, tongue, and/or throat, airway blockage that can involve throat swelling, sudden hoarseness and/or cause death from asphyxiation. (Bork et al., 2012; Bork et al., 2000). Approximately 50% of all HAE patients will experience a laryngeal attack in their lifetime, and there is no way to predict which patients are at risk of a laryngeal attack. (Bork et al., 2003; Bork et al., 2006). HAE symptoms may also include repeat episodes of abdominal cramping without obvious cause, and/or swelling of the intestines, which can be severe and can lead to abdominal cramping, vomiting, dehydration, diarrhea, pain, shock, and/or intestinal symptoms resembling abdominal emergencies, which may lead to unnecessary surgery. (Zuraw, 2008). Swelling may last up to five or more days. Most patients suffer multiple attacks per year. Swelling of the airway may be life threatening and cause death in some patients. Mortality rates for HAE are estimated at 15-33%, and HAE leads to about 15,000-30,000 emergency department visits per year.
HAE is an orphan disorder, the exact prevalence of which is unknown, but current estimates range from 1 per 10,000 to 1 per 150,000 persons, with many authors agreeing that 1 per 50,000 is likely the closest estimate. (Bygum, 2009; Goring et al., 1998; Lei et al., 2011; Nordenfelt et al., 2014; Roche et al., 2005). HAE is inherited in an autosomal dominant pattern, such that an affected person can inherit the mutation from one affected parent. New mutations in the gene can also occur, and thus HAE may occur in people with no history of the disorder in their family. It is estimated that 20-25% of cases result from a new spontaneous mutation.
Like adults, children with HAE can suffer from recurrent and debilitating attacks. Symptoms may present first appear in childhood, including very early in childhood with upper airway angioedema has been reported in HAE patients as young as the age of 3, and worsen during puberty. (Bork et al., 2003). In one case study of 49 pediatric HAE patients, 23 had suffered at least one episode of airway angioedema by the age of 18 (Farkas, 2010). An important unmet medical need exists among children with HAE, especially adolescents, since the disease commonly worsens after puberty (Bennett and Craig, 2015; Zuraw, 2008).
There are three types of HAE, known as types I, II, and III, with types I and II being able to be modeled, simulated, and treated by the techniques described herein, in some embodiments. It is estimated that HAE affects 1 in 50,000 people, that type I accounts for about 85 percent of cases, and that type II accounts for about 15 percent of cases, with type III being very rare.
Mutations in the SERPING1 gene cause hereditary angioedema type I and type II. The SERPING1 gene provides instructions for making the C1 inhibitor protein (also referred to as the C1-INH protein), which is important for controlling inflammation. C1 inhibitor blocks the activity of certain proteins, including generation of plasma kallikrein, that promote inflammation. Mutations that cause hereditary angioedema type I lead to reduced levels of C1 inhibitor in the blood. In contrast, mutations that cause type II result in the production of a C1 inhibitor that functions abnormally. Approximately 85% of patients have Type I HAE, characterized by very low production of functionally normal C1-INH protein, while the remaining approximately 15% of patients have Type II HAE and produce normal or elevated levels of a functionally impaired C1-INH (Zuraw, 2008).
Without the proper levels of functional C1 inhibitor to control the activation of the kinin-kallikrein cascade of the contact activation system, excessive amounts of bradykinin are generated from high molecular weight kininogen (HMWK), and there is increased vascular leakage mediated by bradykinin binding to the B2 receptor (B2-R) on the surface of endothelial cells (Zuraw, 2008). Bradykinin promotes inflammation by increasing the leakage of fluid through the walls of blood vessels into body tissues. Excessive accumulation of fluids in body tissues causes the episodes of swelling seen in individuals with HAE type I and type II.
In particular,
Trauma or stress, for example, dental procedures, sickness (e.g., viral illnesses such as colds and the flu), menstruation, and surgery can trigger an attack of angioedema. To prevent acute attacks of HAE, patients can attempt to avoid specific stimuli that have previously caused attacks. Doing so may constitute a significant interruption to a patient's daily life, and, in many cases, regardless of a patient's actions, an attack may occur without a known trigger. On average, untreated individuals have an attack every 1 to 2 weeks, and most episodes last for about 3 to 4 days. (ghr.nlm.nih.gov/condition/hereditary-angioedema). The frequency and duration of attacks may vary greatly among people with hereditary angioedema, even among people in the same family.
There currently exist a number of treatment modalities for HAE. Some treatment modalities for HAE can stimulate the synthesis of C1 inhibitor, or reduce C1 inhibitor consumption. Androgen medications, such as danazol, can reduce the frequency and severity of attacks by stimulating production of C1 inhibitor. Newer treatments attack the contact cascade. Ecallantide (KALBITOR®, DX-88, Dyax) inhibits plasma kallikrein and has been approved in the U.S. Icatibant (FIRAZYR®, Shire) inhibits the bradykinin B2 receptor, and has been approved in Europe and the U.S. Some treatment modalities, including Lanadelumab (Takhzyro or SHP643), a fully human IgG1 recombinant monoclonal antibody inhibitor of activated plasma kallikrein, treat and/or aim to prevent HAE or a symptom thereof by administering an antibody to a subject having or suspected of having HAE, for example, as described in PCT App. No. PCT/US2016/065980 titled “PLASMA KALLIKREIN INHIBITORS AND USES THEREOF FOR TREATING HEREDITARY ANGIOEDEMA ATTACK” filed Dec. 6, 2019 under Attorney Docket No. D0617.70110WO00, which is hereby incorporated by reference in its entirety herein. In such treatments, antibodies are used to inhibit an activity (e.g., inhibit at least one activity of plasma kallikrein, e.g., reduce Factor XIIa and/or bradykinin production) of plasma kallikrein, e.g., in vivo. The binding proteins can be used by themselves or conjugated to an agent, e.g., a cytotoxic drug, cytotoxin enzyme, or radioisotope. A summary of existing treatment modalities for HAE is given in Table 1 below.
According to some aspects of the technology described herein, a QSP model is provided and used in computer-implemented methods for determining the effectiveness of therapeutic intervention in treating HAE, for example, determining an effect of an administered drug on the kinin-kallkrein cascade of the contact activation system. As shown in
Quantitative Systems Pharmacology Model Development
In order to better understand HAE and potential treatment modalities for HAE, the inventors have developed a quantitative systems pharmacology (QSP) model for modeling, simulating, and treating HAE. According to some embodiments, the QSP model is parameterized and verified with biological data in literature as well clinical data from one or more clinical trials.
The QSP model shown in
In some embodiments, the QSP model may be configured to model HAE using FXII autoactivation as a trigger. For example, as described herein, an HAE flare-up may occur at any time according to a number of triggers. In some cases, the cause of the HAE flare-up may be unknown and not directly related to a particular trigger. Thus, in some embodiments, the QSP model may be configured to represent autoactivation of Factor XII by elevating levels of FXIIa in response to an indication that a trigger has been input into the QSP model, without analyzing what the particular trigger is. In this manner, the heterogeneity of different flare-up triggers may be bypassed by the QSP model.
In some embodiments, the QSP model is utilized in computer-implemented methods for modeling, simulating, and treating HAE. For example, the various PK and PD models described herein may be used to evaluate the effectiveness of a new or existing treatment modality for HAE. In some embodiments, only some of the individual models may be utilized when implementing the QSP model in a computer-implemented method. For example, in some embodiments, the QSP model may be implemented without using the PK model(s) to better understand a response of the contact system in response to a trigger and in the absence of any therapeutic intervention. Therefore, as used herein, the quantitative systems pharmacology (QSP) model should be understood to encompass any combination of the PK and PD models described herein.
PK Model
According to some aspects, the QSP model includes a PK model for providing PK parameters to the PD model.
As shown in
The PK model may be used to model the PK behavior of a drug in a patient. For example, in some embodiments, the PK model is used to model the PK behavior of existing treatment modalities, such as lanadelumab. In some embodiments, the PK model may be used to model the PK behavior of a new and/or previously untested drug. For example, absorption rate (ka) and bioavailability (F) for a drug to be modeled may be input into the PK model and the predicted concentration of the drug in the patient may be output for inputting into the PD model.
PD Model(s)
According to some aspects, the QSP model comprises one or more PD models for modeling HAE. In the illustrated embodiment, the QSP model includes three individual PD models: (1) contact activation system PD model; (2) fluorogenic assay PD model; and (3) acute attack clinical outcome model. In the illustrated embodiment, the fluorogenic assay PD model is configured as a subset of contact activation system PD model and is used to estimate parameters (e.g., parameters relating to kallikrein inhibition) for parameterizing the QSP model. Thus, in some embodiments, the flourogenic assay PD model is used in the development of the QSP model, and the contact activation system PD model and acute attack clinical outcome model is used in applying the QSP model, as described herein. Table 2 gives a list of variables used in the PD models.
One of the proteins implicated in the contact activation system PD model is Factor XII (FXII). FXII is a 80 kDa glycosylated protein consisting of a single polypeptide chain and circulates in plasma as a zymogen at a median concentration of 30 μg/ml (375 nM) in healthy individuals. Upon contact with anionic surfaces, in the presence of Zn2+ ions, FXII undergoes a conformational rearrangement leading to autoactivation or cleavage by kallikrein to generate FXIIa (the activated form of FXII).
Another protein implicated in the contact activation system PD model is prekallikrein (preKAL), a glycoprotein of molecular weight 85 kDa consisting of a single polypetide chain that circulates in plasma as a zymogen at a median concentration of 31 μg/ml (365 nM) in health individuals, with an estimated 75% bound to HMWK. preKAL binds to endothelial cells, platelets, and granulocytes in a Zn2+—dependent interaction via the preKAL-HMWK complex. The preKAL is cleaved by FXIIa resulting in KAL, the two-chain enzyme kallikrein. Prolylcarboxypeptidase (PRCP) has been identified as an endothelial cell activator of prekallikrein to kallikrein.
A third protein implicated in the contact activation system PD model is high molecular weight kinogen (HMWK), a 120 kDa non-enzymatic glycoprotein with a plasma concentration of 80 μg/ml (670 nM) in healthy individuals. The HMWK circulates in plasma both in free or complexed form (with preKAL or KAL). The binding affinities of HMWK to preKAL and KAL are similar, having a Kd of 12 nM and 15 nM respectively.
The contact activation system PD model shown in
The assembly of the kinin-kallikrein contact factor proteins on cell surfaces is mediated via uPAR (urokinase plasminogen activating receptor), and cofactors gC1q-R (complement protein C1q) and CK1 (cytokeratin 1). On the surface of endothelial cells, gC1q-R (with elevated levels of Zn+2 ions, released from endothelial cells and activated platelets) is primarily responsible for assembly and activation of FXII/HMWK/preKAL. The model incorporates a number of assumptions based on known numbers of receptors, cofactors, and their complexes on endothelial cells. For example, gC1q-R is the most abundant with over 1 million per cell while uPAR (250,000/cell) and CK1 (72,000/cell) are less expressed. As gC1q-R/CK1 complex preferentially binds HMWK, and FXII binds primarily to uPAR within the CK1-uPAR complex, the model assumes that the least expressed CK1 is the limiting number to form the receptor complex in the activation of surface contact system. The model represents the cell surface with binding sites that may be characterized by the apparent site number and affinity to the different contact factors. The Zn+2 dependency on binding affinity was not explicitly modeled and assumed that the effect is implicitly reflected in the reactions parameters where these factors play a role.
As described herein, excessive BK (bradykinin) causes an increase in blood vessel permeability, which allows fluid to pass through the blood vessel walls, causing subcutaneous or submucosal swelling. The cleavage of HMWK by kallikrein produces a two-chain cleaved HMWK (cHMWK) and the BK peptide. BK has a short half-life (less than 30 seconds in blood of most species) and strong affinity for the cell surface (0.5 nM). These properties of BK make it challenging to obtain reliable measurements of BK level. The contact activation system PD model may model and output levels of BK as well as cHMWK to provide a better understanding of HAE and the frequency, severity, and duration of acute attacks.
The contact activation system PD model may further incorporate known plasma concentrations of BK and cHMWK for healthy individuals and untreated HAE patients in both remission and while experiencing acute attack. For example, the contact activation system PD model may incorporate measured cHMWK for HAE patients with and without lanadelumab treatment. Based on the incorporated data, the contact activation system PD model may, in some embodiments, represent the formation and degradation of BK and cHMWK as molecular reactions. In some embodiments, the contact activation system PD model may represent the BK binding to BDKR-B2, and the degradation of the bound complex as molecular reactions.
As described herein, Kallikrein (KAL) is a serine protease that plays a central role in activation of inflammation as well as in regulation of blood pressure and coagulation. In plasma, the activation of kallikrein is regulated by the physiological inhibitor, C1-INH. As described herein, HAE patients are deficient in functional C1-INH leading to irregularities in the kinin-kallikrein cascade which may, in turn, lead to an acute attack. Some treatment methods, including lanadelumab, for example, aim to inhibit excess formation of kallikrein by preventing cleavage of prekallikrein. The inventors have recognized that measuring the formation and inhibition of kallikrein ex-vivo using a fluorogenic assay, as described herein, provides a valuable way to isolate a subset of the kinin-kallikrein cascade, and to parameterize and verify the parameters within this subset.
As shown in
The arrows illustrated in
Virtual Population Development
As described herein, the kinin-kallikrein cascade leading to an acute attack in individuals with HAE may begin with autoactivation of FXII into its activated form FXIIa. Such autoactivation may happen at any time without warning. Autoactivation triggers may include stress, physical trauma, a surgical or a dental procedure, infection, hormonal changes, and mechanical pressure, for example. In some embodiments, the QSP model is configured on the assumption that each of these triggers may lead to a systematic perturbation in the contact system that autoactivates the kinin-kallikrein cascade leading to an HAE attack.
The severity and frequency of HAE attacks may vary widely from patient to patient and may also change over time, as shown in
The virtual population may comprise a virtual data set comprising a plurality of data sets. Each data set (e.g., Patient1) may represent an individual virtual patient of the virtual population and may have one or more variables defining one or more characteristics of the virtual patient.
As shown in
In some embodiments, each of the virtual patients in the virtual population may be assigned disease predictive descriptors. Example disease predictive descriptors may include a virtual patient's propensity to experience an acute attack in the absence of therapeutic intervention, for example, baseline attack frequency, baseline attack severity, and/or baseline attack duration, as shown in
In some embodiments, a constant disease predictive descriptor may be applied to each patient in a virtual patient population. For example, in some embodiments, baseline attack duration may be equal for all patients of the virtual population (e.g., being set to 24 hours, in some embodiments)
For clinical studies, attack severity may be based on a score indicating the level of pain the patient is experiencing. The QSP model may be configured on the assumption that pain score is related to the level of BK caused by FXII autoactivation. Thus, attack severity may be represented as an increase in the FXII autoactivation in the QSP model, according to some embodiments.
At act 804, one or more disease predictive descriptors may be determined for each patient in the virtual data set. For example, in some embodiments, an attack frequency and attack severity may be assigned for each patient in the virtual data set. At act 806, the disease predictive descriptors (e.g., the attack frequency and attack severity, in some embodiments) may be assigned to each patient in the virtual data set. In some embodiments, the virtual data set representing the virtual population may thereafter be input into the QSP model for modeling HAE among the patients of the virtual population.
The duration of the attack may be represented by the period of time in which FXII autoactivation remains elevated and BK levels remain above the set threshold.
As described herein, the virtual population may be input into the PD model. The contact activation system PD model may predict the level of BK to determine whether an acute attack has occurred in response to a trigger. For example, when a trigger even occurs, the state of the acute attack may be predicted by determining whether the BK level output by the contact activation system PD model exceeds a known threshold. In this way, the QSP model may provide for analysis of the contact system including during an HAE attack and evaluation of the effectiveness of new and existing treatment modalities for HAE.
Quantitative Systems Pharmacology Model Parameterization
The QSP model may be parameterized with existing clinical and literature data to provide for more accurate modeling of HAE. For example, the fluorogenic assay PD model may be parameterized with enzyme reaction rates known from literature. The contact activation system PD model may be parameterized with clinical data of protein levels of healthy subjects and subjects with HAE. The acute attack clinical outcome model may be parameterized with clinical data of protein levels of HAE patients under acute attack and time intervals of acute attacks in untreated patients with HAE. The PK model may be parameterized with clinical data. Table 4 gives a list of model parameters for the QSP model. Table 5 gives a list of model assumptions implemented in the model.
As described herein, the PK model may provide a dose level profile to the PD models. The parameters of the PK model illustrated in
The fluorogenic assay PD model may be parameterized with clinical data of measured levels of kallikrein activity inhibited by therapeutic intervention (e.g., administration of lanadelumab) measured by the in vitro assay procedure described with respect to
In some embodiments, the QSP model, and more particularly, the fluorogenic assay model may be used to estimate an effectiveness of a therapeutic intervention by determining whether the therapeutic intervention inhibits plasma kallikrein and to what extent.
The reactions and governing equations which may be implemented in the contact activation system PD model are shown in Table 3. Components of the contact activation system PD model may be parameterized with literature data and/or calibrated by data from one or more other models of the QSP model. For example, such components may include, in some embodiments, FXII, FXIIa, prekallikrein, free prekallikrein percentage, C1-INH, HMWK, BK, cHMWK, and/or percentage of cHMWK.
The acute attack model may be parameterized to calibrate the severity of an attack trigger so that the levels of proteins in the kinin-kallikrein cascade (e.g., cHMWK, BK, etc.) from simulated HAE patients under acute attack are in agreement with pre-does clinical data. As described herein, attack severity may be represented in the acute attack model by an increase in the autoactivation of FXII.
The QSP model and further aspects of the technology described herein may be implemented using a computer.
The computer 1000 may have one or more input devices and/or output devices, such as devices 1006 and 1007 illustrated in
As shown in
In some embodiments, the QSP model may be used in a computer-implemented method, as described herein. In some embodiments, at least one non-transitory computer-readable storage medium is provided having processor-executable instructions that, when executed by at least one computer-hardware processor, cause the computer-hardware to perform a computer-implemented method which utilizes the QSP model described herein.
Quantitative Systems Pharmacology Model Verification
The parameterized models may be verified in a simulation to determine that model results for treated patients with HAE match clinical data to ensure that the QSP model may accurately model HAE and provide evaluation of new existing treatment modalities. For example, the contact activation system PD model may be applied to verify the inhibitory effect of a therapeutic intervention (e.g., administration of lanadelumab) on HAE patients by comparing simulation results to biomarker data (e.g., cHMWK levels). The acute attack model may be applied to verify the inhibitory effect of a therapeutic intervention (e.g., administration of lanadelumab) on HAE patients by comparing simulation results to biomarker data (e.g., cHMWK levels). The acute attack model may further be applied to investigate the sensitivity of monthly attack rates to attack severity, attack frequency, and binding affinity of an administered drug (e.g., lanadelumab) as well as the sensitivity of the BK level to system parameters of the model.
The simulation output reflected in
In some embodiments, the threshold for determining the occurrence of an acute attack may be based on the receptor occupancy (RO) of the BDKR-B2 receptor to which BK binds.
Having verified the accuracy of the QSP model as described herein, the QSP model may be implemented in a number of different methods for evaluating the effects of HAE on the contact system and for evaluating new and existing treatment modalities for HAE, as will be described further herein.
Sensitivity Analyses
The QSP model, and in particular, the acute attack model, may be used to investigate the sensitivity of monthly attack rates to different parameters, including, for example, attack severity, frequency, and drug binding affinity under a treatment regimen. In the illustrated embodiments, the treatment regimen is 300 mg Q2W lanadelumab, which was modeled over a virtual population of 1000 virtual patients.
In some embodiments, the QSP model may be used to evaluate the sensitivity of attack frequency to attack severity, as shown in
In some embodiments, the QSP model may be used to evaluate the sensitivity of attack frequency to monthly attack rates of untreated patients, as shown in
In some embodiments, the QSP model may be used to evaluate the sensitivity of HAE attack frequency to different binding affinities, as shown in
In some embodiments, the QSP model may be used to evaluate the sensitivity of BK level to model parameters of the system, as shown in
Evaluating the sensitivity of peak BK level to model parameters may facilitate development of new treatment modalities which may target different aspects of the contact activation system. For example, the results of the sensitivity analyses described herein may provide insight into the most effective points of the contact activation system for therapeutic intervention.
Without further elaboration, it is believed that one skilled in the art can, based on the above description, utilize the present invention to its fullest extent. The following specific embodiments are, therefore, to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way whatsoever. All publications cited herein are incorporated by reference for the purposes or subject matter referenced herein.
In some embodiments, the QSP model and/or virtual population described herein may be implemented to conduct a virtual clinical trial.
At act 100, a QSP model for modeling a contact system may be established. For example, the QSP model may comprise one or more PK models and/or one or more PD models, as shown in
At act 104, parameter estimates for parameterizing the QSP model may be acquired from literature and/or clinical data. The parameter estimates may be applied to the QSP model to parameterize the model.
At act 106, the QSP model may be verified by comparing simulation output from the model to literature and/or clinical data. For example, the QSP model may be applied to obtain output for one or more biomarkers (e.g., cHMWK, KAL, BK, etc.), and the output may be compared to biomarker values from clinical data to verify the accuracy of the QSP model.
At act 108, virtual population development may begin by establishing a total number of virtual patients and duration of a virtual clinical trial. For example, in some embodiments, the total number of virtual patients is 1000. The duration of the virtual clinical trial may refer to the length of time the contact system of a patient population is observed, including a time period during which a therapeutic intervention is applied to the patient population.
At acts 110-112, PK parameters and disease predictive descriptors and their associated variabilities may be obtained from real patient data. For example, in some embodiments, clinical data may be used to inform the PK parameters and disease predictive descriptors that are to be applied to the virtual population. At act 114, virtual PK parameters and virtual disease predictive descriptors may be obtained, for example, based on the PK parameters and disease predictive descriptors obtained from clinical data. At acts 116-118, the virtual PK parameters and disease predictive descriptors may be randomly assigned to virtual patients in the virtual patient population.
At act 120, the QSP model may be used to simulate disease occurrence in virtual patients. For example, in some embodiments, the QSP model may be used to simulate occurrence of an acute attack in virtual patients and to reflect the resulting protein levels of the contact activation system. At act 122, the virtual patient disease data may be compared to disease profiles of real subjects with HAE.
At act 124, the QSP model may be used to evaluate the effectiveness of a therapeutic intervention in treating HAE. For example, parameters indicating the virtual patient population is being administered a dosage of a drug (e.g., lanadelumab) according to a dosage regimen may be input into the QSP model.
At act 126, the virtual clinical trial may be executed. For example, the resulting effect of administration of the drug applied in act 124 on the contact system may be observed. In some embodiments, protein levels of the contact system may be evaluated, to determine a relative change in protein levels resulting from administration of the therapeutic intervention. In some embodiments, a characteristic of an acute attack (e.g., attack frequency, attack severity, attack duration, etc.) may be observed. In some embodiments, the virtual clinical trial data may be compared with data from real subjects.
In some embodiments, the QSP model may be used to evaluate the effects of HAE on the contact activation system, as shown in
Method 3000 begins at act 3002 where a QSP model of HAE is obtained, for example, using any of the techniques for developing, parameterizing, and/or verifying a QSP model described herein. The QSP model may comprise one or more PK models and/or one or more PD models, as shown in
At act 3004, disease predictive descriptors may be obtained. For example, disease predictive descriptors may include a virtual patient's propensity to experience an acute attack, for example, attack frequency, attack severity, and/or attack duration. In some embodiments, the disease predictive descriptors, for example, attack frequency, are determined at least in part by a Poisson process informed by known data regarding the disease predictive descriptors.
At act 3006, the disease predictive descriptors may be assigned to a data set. For example, the data set may represent a virtual patient population for which the QSP model is applied. The virtual population may comprise a plurality of data sets. Each data set (e.g., Patients) may represent an individual virtual patient of the virtual population and may have one or more variables (e.g., for assigning PK parameters and/or disease predictive descriptors) defining one or more characteristics of the virtual patient.
At act 3008, the data set may be processed using the QSP model (e.g., by inputting the data set to the QSP model) to obtain processed data. The processed data may include, for example, protein levels of the contact system for a virtual patient. In some embodiments, the method further comprises displaying the processed data.
In some embodiments, the method further comprises determining and assigning PK parameters for the data set, and determining the effectiveness of a therapeutic intervention by processing therapeutic intervention data and the data set with the QSP model. For example, in some embodiments, the therapeutic intervention comprises administering lanadelumab. In some embodiments, the therapeutic intervention comprises administering a small molecule PKa inhibitor (e.g., orally). In some embodiments, determining the effectiveness of the therapeutic intervention comprises evaluating protein levels of the contact activation system, provided by the QSP model, as a result of administering the therapeutic intervention.
In some embodiments, the QSP model may be used to estimate one or more characteristics of a contact system in response to a trigger, as shown in
At act 3106, a trigger may be input into the QSP model. For example, the trigger may be a signal input into the QSP model causing Factor XII to autoactivate to generate Factor XIIa.
At act 3108, an amount of a protein (e.g., BK, KAL, cHMWK, etc.) of the contact system generated in response to the trigger may be obtained. In some embodiments, the amount of the protein may be compared to a known amount of the protein (e.g., obtained from clinical data), to, for example, determine whether an acute attack has occurred in response to the trigger. In some embodiments, the amount of the protein may be used to determine the severity and/or duration of an acute attack occurring in response to the trigger.
In some embodiments, the QSP model may be used to determine a relationship between HAE attack frequency and Factor XII trigger rate. For example,
At act 3204, a trigger rate for FXII autoactivation is assigned to a virtual population. For example, each patient in the virtual population may be assigned a trigger rate. In some embodiments, one or more different trigger rates may be assigned to the virtual population such that not all patients are assigned the same trigger rate. In some embodiments, the trigger rate(s) assigned to the virtual population are based on clinical data (e.g., trigger rates of HAE patients obtained from one or more clinical trials). In some embodiments, the trigger rate(s) may be assigned to the virtual population using a Poisson distribution.
At act 3206, the QSP model is applied to the virtual population. For example, the virtual population data with assigned trigger rates may be input into the QSP model to obtain information about contact system protein levels for each patient in the virtual population.
At act 3208, an HAE attack frequency for the virtual population may be obtained from the QSP model. For example, protein levels obtained from the QSP model may be used to determine the occurrence and frequency of an acute attack. At act 3210, a relationship between HAE attack frequency and trigger rate is determined. For example, the FXII autoactivation trigger rate may be compared to the HAE attack frequency. In some embodiments, the relationship between HAE attack frequency and trigger rate may reflect the frequency in which FXII autoactivation results in an HAE attack.
As described herein, the QSP model may be used to evaluate the effectiveness of new or existing therapeutic interventions for treating HAE. The inventors have recognized that use of the QSP model to evaluate new or existing therapeutic interventions may be advantageous, as it provides for more rapid evaluation when compared to a clinical trial, and allows for evaluation of new treatment modalities before testing such treatment modalities on a human patient. In addition, the QSP model may provide more accurate evaluation of new or existing treatment modalities as use of the QSP model described in the present application may provide various types of information about the contact system in a patient which would be impractical or impossible to clinically obtain.
Evaluating Effectiveness of New or Existing Drugs for Treating HAE
In some embodiments, the QSP model may be used to evaluate the effectiveness of new or existing drugs for treating HAE.
Method 3300 begins at act 3302 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters.
At act 3304, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 3306, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 3308, the virtual data set may be processed by a QSP model to obtain processed data. At act 3310, an indicator of the effectiveness of the administered drug may be obtained. In some embodiments, the processed data output by the QSP model may include one or more levels of contact system proteins (e.g., BK, cHMWK, KAL, etc.). The protein levels may be used to determine the effectiveness of the administered drug. For example, reduced levels of BK, cHMWK, and KAL may indicate the drug is effectively inhibiting HAE attacks. In some embodiments, the protein levels obtained from the QSP model may be used to determine a characteristic of an HAE acute attack (e.g., attack frequency, severity, and/or duration). In some embodiments, the acute attack characteristics may be used to determine an effectiveness of the administered drug (for example, by observing a reduction in acute attack frequency).
More particularly, in some embodiments, the QSP model may be used to determine a characteristic of an HAE flare-up (e.g., attack frequency, severity, duration, etc.) in a patient in response to receiving treatment.
Method 3400 beings at act 3402 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters.
At act 3404, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 3406, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 3408, the virtual data set may be processed by a QSP model to obtain processed data. At act 3410, one or more characteristics of an HAE flare-up in response to administration of a drug may be determined. For example, in some embodiments, characteristics of the HAE flare-up may include attack frequency, attack severity, and/or attack duration. In some embodiments, the one or more characteristics of the HAE flare-up may be used to determine the effectiveness of the administered drug, for example, by comparing the one or more characteristics of the HAE flare-up to known data. For example, HAE attack frequency obtained from the QSP model for the virtual population of patients receiving treatment may be compared to HAE attack frequency in untreated patients to determine if the administered drug reduces HAE attack frequency.
In some embodiments, the QSP model may be used to determine a protein level of the contact system of a patient in response to receiving treatment.
Method 3500 beings at act 3502 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters.
At act 3504, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 3506, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 3508, the virtual data set may be processed by a QSP model to obtain processed data. At act 3510, an amount of a protein of the contact system may be determined based on the processed data. In particular, the QSP model may produce, as output, a protein level of one or more proteins of the contact system (e.g., cHMWK, BK, KAL, etc.). In some embodiments, an effectiveness of an administered drug may be determined based on relative changes in protein levels. For example, reductions in amounts of certain proteins of the contact system (e.g., cHMWK, BK, KAL, etc.) in treated patients as compared to untreated HAE patients may indicate the administered drug is effectively inhibiting acute HAE attacks. Therefore, in some embodiments, the levels of the one or more proteins of virtual patients receiving treatment for HAE may be compared with known data of protein levels of untreated HAE patients.
In some embodiments, the QSP model may be used for determining a temporal profile of a drug's effect on HAE. For example,
Method 3800 beings at act 3802 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters.
At act 3804, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 3806, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 3808, the virtual data set may be processed by a QSP model to obtain processed data. At act 3810, amounts of proteins of the contact system may be obtained over a period of time. For example, in some embodiments, an amount of a protein (e.g., cHMWK, BK, KAL, etc.) may be obtained at different points in time to map a change in the amount of the protein over time. The change in protein amount over time may be used to determine an effectiveness of an administered drug. For example, levels of certain proteins (e.g., cHMWK, BK, KAL, etc.) showing little to no change over time may indicate that the administered drug is effectively inhibiting HAE flare-ups.
Evaluating Efficacy of Combination Therapies for Treating HAE
In some embodiments, the QSP model may be used to evaluate the effectiveness of combination therapies for treating HAE. For example, in some embodiments, a patient may be administered two or more drugs for treating HAE. The methods described herein for using the QSP model to evaluate the effectiveness of a drug may likewise be applied to evaluate the effectiveness of a combination therapy.
Evaluating Efficacy of Dosages
In some embodiments, the QSP model may be used to evaluate the effectiveness of a particular dosage of an administered drug. For example,
Method 3900 beings at act 3902 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters.
At act 3904, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 3906, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 3908, the virtual data set may be processed by a QSP model to obtain processed data. At act 3910, an indicator of the effectiveness of a dosage of an administered drug may be obtained. For example, the simulation output may provide levels of one or more proteins, including changes in protein level over time, and/or one or more characteristics of an HAE flare-up (e.g., attack frequency, severity, duration, etc.). The simulation output may be used as described herein for determining the effectiveness of the dosage of the administered drug input into the QSP model.
Evaluating Efficacy of Dosage Frequencies and/or Dosage Regimens
In some embodiments, the QSP model may be used to evaluate the effectiveness of a particular dosage frequency and/or dosage regimen (for example, evaluating the manner in which a dose is applied, e.g., orally, etc.). The methods described herein for using the QSP model to evaluate the effectiveness of a drug may likewise be applied to evaluate the effectiveness of a dosage frequency and/or dosage regimen.
Evaluating the Effect of Non-Adherence to a Dosage Schedule
In some embodiments, the QSP model may be used to evaluate the effect of non-adherence to a dosage schedule (e.g., missing one or more scheduled dosages). For example,
Method 4200 beings at act 4202 where PK parameters for a virtual data set may be obtained. As described herein, the PK parameters may be used to describe the disposition of a drug in a patient. The virtual data set may reflect a virtual patient population on which the virtual clinical trial executed by the QSP model is run. The dosage and characteristics of the drug administered to each virtual patient may be reflected by the PK parameters. In particular, the PK parameters may reflect one or more missed dosages according to the method 4200.
At act 4204, disease predictive descriptors (e.g., attack frequency, severity, duration, etc.) may be determined for the virtual data set. In some embodiments, the disease predictive descriptors may be informed by clinical data.
At act 4206, the PK parameters and disease predictive descriptors are assigned to the virtual data set. In some embodiments, the disease predictive descriptors may be assigned using a Poisson process.
At act 4208, the virtual data set may be processed by a QSP model to obtain processed data. At act 4210, an effect of non-adherence (including non-adherence frequency) may be determined. For example, the simulation output may provide levels of one or more proteins, including changes in protein level over time, and/or one or more characteristics of an HAE flare-up (e.g., attack frequency, severity, duration, etc.). The simulation output may be used as described herein for determining the effect of missing one or more scheduled dosages, as shown in
Having thus described several aspects of at least one embodiment, it is to be appreciated that various alterations, modifications, and improvements will readily occur to those skilled in the art. Such alterations, modifications, and improvements are intended to be within the spirit and scope of the present disclosure. Accordingly, the foregoing description and drawings are by way of example only.
For example, in some embodiments, the contact system may be modified and/or used to model one or more other diseases other than HAE, for example other diseases which implicate the contact system or similar biological systems (e.g., other diseases resulting in edemas).
In addition, although the QSP model has been described herein for evaluating HAE treatments which inhibit the kinin-kallikrein cascade, in some embodiments, the QSP model may be used to evaluate other HAE treatments which impact other parts of the contact system, for example, FXIIa inhibitors and/or enzymes which function to degrade BK.
The above-described embodiments of the present disclosure can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
In this respect, the concepts disclosed herein may be embodied as a non-transitory computer-readable medium (or multiple computer-readable media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory, tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the present disclosure discussed above. The computer-readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present disclosure as discussed above.
The terms “program” or “software” are used herein to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present disclosure as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present disclosure.
Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.
Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
Various features and aspects of the present disclosure may be used alone, in any combination of two or more, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.
Also, the concepts disclosed herein may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
The terms “substantially”, “approximately”, and “about” may be used to mean within ±20% of a target value in some embodiments, within ±10% of a target value in some embodiments, within ±5% of a target value in some embodiments, within ±2% of a target value in some embodiments. The terms “approximately” and “about” may include the target value.
Use of ordinal terms such as “first,” “second,” “third,” etc. in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.
Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
This application claims the benefit under 35 U.S.C. under § 119(e) of U.S. Provisional Application Ser. No. 62/852,189 titled “METHODS AND APPARATUSES FOR MODELING, SIMULATING, AND TREATING HEREDITARY ANGIOEDEMA” and filed on May 23, 2019 under Attorney Docket No. D0617.70130US00 and U.S. Provisional Application Ser. No. 62/988,285 titled “METHODS AND APPARATUS FOR MODELING, SIMULATING, AND TREATING HEREDITARY ANGIOEDEMA USING PKA INHIBITORS” and filed on Mar. 11, 2020 under Attorney Docket No. D0617.70135US00, each of which is incorporated by reference in its entirety herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2020/034196 | 5/22/2020 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62988285 | Mar 2020 | US | |
| 62852189 | May 2019 | US |
