Claims for Patent: 10,083,400

Summary for Patent: 10,083,400

Title:	System and method for providing patient-specific dosing as a function of mathematical models updated to account for an observed patient response
Abstract:	A system and method for predicting, proposing and/or evaluating suitable medication dosing regimens for a specific individual as a function of individual-specific characteristics and observed responses of the specific individual. Mathematical models of observed patient responses are used in determining an initial dose. The system and method use the patient\'s observed response to the initial dose to refine the model for use to forecast expected responses to proposed dosing regimens more accurately for a specific patient. More specifically, the system and method uses Bayesian averaging, Bayesian updating and Bayesian forecasting techniques to develop patient-specific dosing regimens as a function of not only generic mathematical models and patient-specific characteristics accounted for in the models as covariate patient factors, but also observed patient-specific responses that are not accounted for within the models themselves, and that reflect variability that distinguishes the specific patient from the typical patient reflected by the model.
Inventor(s):	Mould; Diane R. (Fort Myers, FL)
Assignee:	Mould; Diane R. (Fort Myers, FL)
Application Number:	14/047,545
Patent Claims:	1. A method in a medication dosing system, the system comprising: a microprocessor; a memory; and one or more databases stored in the memory, the one or more databases being accessible by the microprocessor and storing information indicative of dosing regimens configured for use with a drug, and storing a plurality of mathematical models, each model describing response profiles for a population of patients treated with the drug, and having a set of covariate patient factors, wherein the response profiles for the population of patients are not specific to any particular patient; the method comprising operating the microprocessor to: receive data indicative of characteristics of a specific patient, wherein the characteristics of the specific patient include at least one of disease stage, disease status, prior therapy, concomitant diseases, demographic information, and laboratory test result information; select a mathematical model from the plurality of mathematical models in the memory to match the characteristics of the specific patient with the covariate patient factors of the selected mathematical model; receive a first plurality of proposed dosing regimens from the database; forecast, using the selected mathematical model, a predicted drug concentration time profile indicative of a patient response to the drug, wherein the predicted drug concentration time profile is not specific to any particular patient, for each of the first plurality of proposed dosing regimens; select, based on the predicted drug concentration time profiles, a recommended dosing regimen having the predicted drug concentration time profile that achieves a desired exposure level; provide, as output from the system, the recommended dosing regimen; receive data indicative of a measured drug concentration level that reflects a response of the specific patient to administration of the recommended dosing regimen or a modified version of the recommended dosing regimen, the measured drug concentration level being measured from a blood sample from the patient; update the selected model to create an updated patient-specific mathematical model that reflects the measured drug concentration level of the specific patient; receive a second plurality of proposed dosing regimens from the database; forecast, using the updated patient-specific mathematical model, a plurality of patient-specific predicted drug concentration time profiles indicative of the patient response, specific to the patient, to each of the second plurality of proposed dosing regimens; select, based on the patient-specific predicted drug concentration time profiles, a recommended patient-specific dosing regimen having the patient-specific predicted drug concentration time profile that achieves the desired exposure level; and provide, as output from the system, the recommended patient-specific dosing regimen; and administering the recommended patient-specific dosing regimen or a modified version of the recommended patient-specific dosing regimen to the specific patient. 2. The method of claim 1, wherein each model in the plurality of mathematical models does not take into account variability between patients having the set of covariate patient factors. 3. The method of claim 1, wherein the microprocessor is further configured to provide, as output from the system, a plurality of alternative recommended patient-specific dosing regimens. 4. The method of claim 1, wherein the microprocessor is configured to forecast the plurality of patient-specific drug concentration time profiles by: automatically selecting the second plurality of proposed dosing regimens according to predefined logic; and forecasting a drug concentration for each of the second plurality of proposed dosing regimens. 5. The method of claim 1, wherein the plurality of patient-specific drug concentration time profiles are forecasted by processing the updated patient-specific mathematical model using a Bayesian forecasting technique. 6. The method of claim 1, wherein the covariate patient factors comprise at least one of: a blood concentration level, a blood pressure reading, and a hematocrit level. 7. The method of claim 1, wherein the updated patient-specific mathematical model that reflects the measured drug concentration level of the specific patient takes into account variability between patients having the set of covariate patient factors. 8. The method of claim 1, wherein the drug is infliximab. 9. The method of claim 1, wherein the desired exposure level is a trough blood concentration level above a therapeutic threshold for the drug. 10. The method of claim 1, wherein the recommended dosing regimen includes a specific dose amount and a specific dose interval. 11. The method of claim 1, wherein the recommended patient-specific dosing regimen includes a specific dose amount and a specific dose interval. 12. The method of claim 1, wherein the selected model is updated by adjusting a set of parameters in the selected model to be conditional to the measured drug concentration level of the specific patient. 13. The method of claim 4, wherein automatically selecting the second plurality of proposed dosing regimens according to predefined logic comprises selecting a next proposed dosing regimen as a function of a forecasted patient-specific response to a proposed dosing regimen. 14. The method of claim 4, wherein the predefined logic provides for selection of the second plurality of proposed dosing regimens to provide a corresponding forecasted drug concentration best meeting a treatment objective. 15. The method of claim 4, wherein the predefined logic provides for selection of the second plurality of proposed dosing regimens to find a minimum of an objective function. 16. The method of claim 4, wherein the predefined logic provides for selection of the second plurality of proposed dosing regimens to find a global minimum of an objective function. 17. The method of claim 1, wherein the microprocessor is configured to provide, as output from the system, a plurality of alternative recommended dosing regimens. 18. The method of claim 1, wherein forecasting the predicted drug concentration time profile comprises: automatically selecting the first plurality of proposed dosing regimens according to predefined logic; and forecasting a patient response for each of the first plurality of proposed dosing regimens. 19. The method of claim 18, wherein automatically selecting the first plurality of proposed dosing regimens according to predefined logic comprises selecting a next proposed dosing regimen as a function of a forecasted patient response not specific to any particular patient to a proposed dosing regimen. 20. The method of claim 19, wherein the predefined logic provides for selection of the first plurality of proposed dosing regimens to provide a corresponding forecasted drug concentration best meeting a treatment objective. 21. The method of claim 19, wherein the predefined logic provides for selection of the first plurality of proposed dosing regimens to find a minimum of an objective function. 22. The method of claim 19, wherein the predefined logic provides for selection of the first plurality of proposed dosing regimens to find a global minimum of an objective function. 23. A medication dosing method, the method comprising: storing a plurality of mathematical models in a memory that stores at least one database accessible by a microprocessor, the at least one database storing data indicative of dosing regimens for use with the medication, each model describing response profiles for a population of patients treated with the medication and having a set of covariate patient factors, wherein the response profiles for the population of patients are not specific to any particular patient; receiving data indicative of characteristics of the specific patient, wherein the characteristics of the specific patient include at least one of disease stage, disease status, prior therapy, concomitant diseases, demographic information, and laboratory test result information; developing a composite model as a function of the plurality of mathematical models and characteristics of the specific patient corresponding to the covariate patient factors of the models, the composite model describing a response profile for a patient having as covariate patient factors the specific patient's characteristics; processing the composite model to forecast a predicted drug concentration time profile indicative of a patient response not specific to any particular patient, to each of a first plurality of proposed dosing regimens; selecting based on the predicted drug concentration time profiles, a recommended dosing regimen for a patient having the specific patient's characteristics, the recommended dosing regimen being selected to achieve a predefined target response; providing the recommended dosing regimen; receiving data indicative of a measured drug concentration level that reflects a response of the specific patient to administration of the recommended dosing regimen or a modified version of the recommended dosing regimen, the measured drug concentration level corresponding to a blood sample from the specific patient; receiving data indicative of a measured drug concentration level that reflects a response of the specific patient to administration of the recommended dosing regimen or a modified version of the recommended dosing regimen, the measured drug concentration level corresponding to a blood sample from the specific patient; updating each of the plurality of mathematical models, as a function of the measured drug concentration level, to create a corresponding plurality of updated patient-specific mathematical models; developing, from the plurality of updated patient-specific mathematical models an updated patient-specific composite model; forecasting, using the updated patient-specific composite model a patient-specific predicted drug concentration time profile indicative of the patient response, specific to the patient, for each of a second plurality of proposed dosing regimens; selecting, from the second plurality of proposed dosing regimens and based on the patient-specific drug concentration time profiles, a recommended patient-specific dosing regimen that achieves the predefined target response; providing the recommended patient-specific dosing regimen; and administering the recommended patient-specific dosing regimen or a modified version of the patient-specific dosing regimen to the specific patient. 24. The method of claim 23, wherein the medication is infliximab. 25. The method of claim 23, wherein the predefined target response includes a trough blood concentration level above a therapeutic threshold for the medication. 26. The method of claim 23, wherein the recommended dosing regimen includes a first dose amount and a first dose interval, and the recommended patient-specific dosing regimen includes a second specific dose amount and a second dose interval. 27. The method of claim 23, wherein the updating each of the plurality of mathematical models comprises adjusting a set of parameters in each of the plurality of mathematical models to be conditional to the measured drug concentration level of the specific patient. 28. A medical dosing method, comprising: storing, in at least one database accessible by at least one microprocessor, (1) data indicative of dosing regimens for use with a drug, (2) data indicative of a model describing response profiles for a population of patients treated with the drug and having a set of covariate patient factors, wherein the response profiles for the population of patients are not specific to any particular patient, and (3) data indicative of one or more characteristics of the specific patient, wherein the one or more characteristics of the specific patient include at least one of disease stage, disease status, prior therapy, concomitant diseases, demographic information, and laboratory test result information; identifying a first plurality of proposed dosing regimens from the at least one database; processing the model to obtain a predicted drug concentration time profile indicative of a patient response not specific to any particular patient for each of a first plurality of proposed dosing regimens; selecting, based on the predicted drug concentration time profiles, a recommended dosing regimen suitable for a patient having the specific patient's characteristics; select, based on the predicted drug concentration time profiles, a recommended dosing regimen that achieves a predefined target response; providing the recommended dosing regimen; receiving data indicative of a measured drug concentration level that reflects an observed response of the specific patient to administration of the recommended dosing regimen or a modified version of the recommended dosing regimen; updating the model to obtain an updated patient-specific model that reflects the measured drug concentration level of the specific patient; obtaining, using the updated patient-specific model, a plurality of predicted patient-specific drug concentration time profiles indicative of the predicted patient response, specific to the patient, to each of a second plurality of proposed dosing regimens; selecting, based on the predicted patient-specific drug concentration time profiles, a recommended patient-specific dosing regimen from the second plurality of proposed dosing regimens, the recommended patient-specific dosing regimen being selected to achieve the predefined target response; providing the recommended patient-specific dosing regimen; and administering the recommended patient-specific dosing regimen or a modified version of the recommended patient-specific dosing regimen to the specific patient. 29. The method of claim 28, wherein the drug is infliximab. 30. The method of claim 28, wherein the predefined target response includes a trough blood concentration level above a therapeutic threshold for the drug. 31. The method of claim 28, wherein the recommended dosing regimen includes a first dose amount and a first dose interval, and the recommended patient-specific dosing regimen includes a second specific dose amount and a second dose interval. 32. The method of claim 28, wherein the model is updated by adjusting a set of parameters in the model to be conditional to the measured drug concentration level of the specific patient.

Details for Patent 10,083,400

Applicant	Tradename	Biologic Ingredient	Dosage Form	BLA	Approval Date	Patent No.	Expiredate
Janssen Biotech, Inc.	REMICADE	infliximab	For Injection	103772	08/24/1998		2032-10-05