Optimization and Individualization of Medication Selection and Dosing

• US 20090171697A1
• Filed: 11/28/2006
• Published: 07/02/2009
• Est. Priority Date: 11/29/2005
• Status: Active Grant

• Current Assignee: Children's Hospital & Medical Center
• Original Assignee: Children's Hospital & Medical Center
• Inventors: Glauser, Tracy A., Wenstrup, Richard J., Vinks, Alexander A., Pestian, John
• Granted Patent: US 8,589,175 B2
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/106 Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
• CPC Class Codes: G01N 33/94 involving narcotics or drugs or pharmaceuticals, neurotransmitters or associated receptors
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

The invention provides population models, methods, and algorithms for targeting a dosing regimen or compound selection to an individual patient. The methods and algorithms of the invention utilize population models that incorporate genotype information for genes encoding drug metabolizing enzymes for one or more compounds of interest. The methods allow integration of genotype information for one or more genes encoding a drug metabolizing enzyme, particularly a cytochrome P450 gene with patient data. The methods allow integration of genotype information and the effect of one or more compounds on one or more drug metabolizing enzymes. The methods allow iterative feedback of drug metabolizing data obtained from a patient into the process of generating a dosage regimen recommendation for a compound of interest for an individual patient.

Claims

1. A computerized method and/or computer-assisted method of selecting a dosing regimen for a patient the method comprising the steps of:

(a) integrating patient data with patient associated genotype information;

(b) generating a drug concentration profile for the patient;

(c) integrating the drug concentration profile and the target drug concentration profile; and

(d) providing a dosing regimen for a first compound likely to result in the target drug concentration profile in the subject.

2. The method claim 1 further comprising the steps of:

(a) providing a biological sample;

(b) monitoring a biomarker in the biological sample; and

(c) integrating the biomarker value with the drug concentration profile information.

3. The method claim 1, wherein the patient data comprises patient demographic data and clinical data.

4. The method of claim 1, wherein the clinical data includes information regarding a second compound.

5. The method of claim 4, wherein the second compound modulates metabolism of the first compound.

6. The method of claim 1, wherein the first compound is a neuropsychiatric medication.

7. The method of claim 1, comprising the step of determining the genotype of a patient at one or more loci of interest.

8. A computerized method and/or computer-assisted method for selecting a dosing regimen for a patient the method comprising the steps of:

(a) obtaining patient data;

(b) obtaining patient associated genotype information;

(c) integrating the patient data with the patient associated genotype information;

(d) generating a drug concentration profile for the patient;

(e) integrating the drug concentration profile and a target drug concentration profile;

(f) providing a dosing regimen for the compound likely to result in the target drug concentration profile in the subject;

(g) providing a biological sample from the patient;

(h) monitoring a biomarker in the biological sample;

(i) integrating the biomarker value with the drug concentration profile information;

(j) generating a second drug concentration profile for the patient;

(k) supplying a second target drug concentration profile;

(l) providing a second dosing regiment for the compound likely to result in the second target drug concentration profile.

9. The method of claim 8, further comprising performing the processes of (f) through (l) at least a second time.

10. The method of claim 8, comprising the step of selecting a population model for the patient.

11. The method of claim 8, comprising the step of generating a probability value for a designated response by the patient.

12. A computerized method and/or computer-assisted method of selecting a dosing regimen for a patient the method comprising the steps of:

(a) generating statistical population models of drug interactions for a plurality of genotypes;

(b) obtaining patient associated genotype information;

(c) establishing a dosing regimen by applying the genotype information against the population models.

13. The method of claim 12 wherein the step of generating population models include the use of Bayesian algorithms.

14. The method of claim 12 wherein the population models of drug interactions are defined for a combination of genotypes and non-genetic information.

15. A computerized method and/or computer-assisted method for selecting one or more drugs for a patient comprising the steps of:

identifying the phenotype;

providing a first plurality of possible medications based upon the identified phenotype;

calculating a ranked list or a predictive index of medications from the first plurality of medications based upon, at least in part, patient specific genetic factors, non-heritable patient factors and drug specific factors.

16. The computerized method of claim 15, wherein the calculating step further considers one or more preclinical toxicity variables, one or more pharmacokinetic variables, one or more clinical efficacy variables, one or more clinical toxicity variables, one or more clinical safety issues, and/or one or more ease of use/adherence variables.

17. The computerized method of claim 16, wherein, in the calculating step, one or more of the following variables contribute linearly:

TI (therapeutic index−

the ratio of (50% lethal dose/50% therapeutic dose)=measure of the drug'"'"'s inherent toxicity);

F (Bioavailability=fraction of the dose which reaches the systemic circulation as intact drug);

fu (the extent to which a drug is bound in plasma or blood is called the fraction unbound=[unbound drug concentration/[total drug concentration]);

f-BIND-T (fraction of drug that is a substrate for a drug-specific efflux transporter “

T”

);

MET-L (drug with linear metabolism);

f-MET-E (fraction of drug that is metabolized by drug metabolizing enzyme “

E”

);

PEX (percentage of drug metabolizing enzyme “

E”

with functional polymorphism “

X”

);

CL_cr (creatinine clearance=the volume of blood cleared of creatinine per unit time=(liters/hour));

IDR (rate of idiosyncratic reactions);

FORM (formulation);

FREQ (frequency of daily drug administration);

MAT ED (maternal education level);

SES (socio-economic class); and

TRANS (method of transportation to/from clinic).

18. The computerized method of claim 16, wherein, in the calculating step, one or more of the following variables contribute exponentially:

ATA (number of functional non-wild type transporter polymorphisms for the specific patient);

MET-NonL (drug with non-linear metabolism);

AEA (number of functional non-wild type drug metabolizing enzyme polymorphisms for the specific patient);

MED-IND (concurrent use of medications that induce metabolizing enzymes);

MED-INH (concurrent use of medications that inhibit metabolizing enzymes);

DIET-IND (concurrent use of dietary supplements that induce metabolizing enzymes);

DIET-INH (concurrent use of dietary supplements that inhibit metabolizing enzymes);

NNT-EFF (number need to treat=number of patients who need to be treated to reach 1 desired outcome);

META-EEF (results from an efficacy meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder);

NNT-TOX (number need to treat=number of patients who need to be treated to have a 1 toxicity outcome); and

META-TOX (results from toxicity meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder).

19. The computerized method of claim 15, wherein the calculating step involves linear algebra computational science to integrate disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, and/or patient specific environmental and genetic factors to produce a ranking of potential medications.

20. The computerized method of claim 15, wherein the calculating step assigns, for each potential medication, computational values corresponding to a favorability of utilizing the potential medication for a corresponding plurality of factors.

21. The computerized method of claim 20 wherein the plurality of factors include factors from a plurality of the following categories:

disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors.

22. The computerized method of claim 20, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score.

23. The computerized method of claim 20, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors and weights corresponding to the relative importance of such factors, and the calculating step involves adding the weighted computational values to determine a score.

24. The computerized method of claim 15 further comprising a step of generating an adherence score corresponding to a predicted likelihood that the patient will adhere to a scheduled therapy or prescription.

25. The computerized method of claim 15, wherein the factors are obtained utilizing an interactive computerized tool.

26. The computerized method of claim 25, wherein the interactive computerized tool collects factors utilizing a hierarchical question and answer session.

40. The computerized method of claim 17, wherein, in the calculating step, one or more of the following variables contribute exponentially:

ATA (number of functional non-wild type transporter polymorphisms for the specific patient);

MET-NonL (drug with non-linear metabolism);

AEA (number of functional non-wild type drug metabolizing enzyme polymorphisms for the specific patient);

MED-IND (concurrent use of medications that induce metabolizing enzymes);

MED-INH (concurrent use of medications that inhibit metabolizing enzymes);

DIET-IND (concurrent use of dietary supplements that induce metabolizing enzymes);

DIET-INH (concurrent use of dietary supplements that inhibit metabolizing enzymes);

NNT-EFF (number need to treat=number of patients who need to be treated to reach 1 desired outcome);

META-EEF (results from an efficacy meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder);

NNT-TOX (number need to treat=number of patients who need to be treated to have a 1 toxicity outcome); and

META-TOX (results from toxicity meta-analysis of clinical trials involving medications used to treat a neuropsychiatric disorder).

41. The computerized method of claim 19, wherein the calculating step assigns, for each potential medication, computational values corresponding to a favorability of utilizing the potential medication for a corresponding plurality of factors.

42. The computerized method of claim 21, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score.

43. The computerized method of claim 21, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors and weights corresponding to the relative importance of such factors, and the calculating step involves adding the weighted computational values to determine a score.

44. The computerized method of claim 17, wherein the factors are obtained utilizing an interactive computerized tool.

27. A computerized method and/or computer-assisted method for selecting a starting dose of a medication for a patient comprising the steps of:

for a given medication, determining if the patient is an extensive metabolizer for the medication, an intermediate metabolizer for the medication, or a poor metabolizer for the medication;

calculating the starting dose based upon, at least in part, a usual drug dose for a given population (D_pop), the frequency of extensive metabolizers in the given population (f_EM), the frequency of intermediate metabolizers in the given population (f_IM) and/or the frequency of poor metabolizers in the general population (f_PM); and

determining a minimal dose adjustment unit for the medication based, at least in part, upon the patient'"'"'s genetic information.

28. The method of claim 27, wherein the step of determining if the patient is an extensive metabolizer for the medication, an intermediate metabolizer for the medication, or a poor metabolizer for the medication is based, at least in part, upon the patient'"'"'s genetic information.

29. The method of claim 27, wherein:

(a) the percent of the usual drug dose D_pop for an extensive metabolizer D_EM is

D_EM=100/(f_EM+f_IM·

S+f_PM·

R)

where S is the Area Under the Time Concentration Curve for extensive metabolizer subpopulation divided by the Area Under the Time Concentration Curve for intermediate metabolizer subpopulation, and where R is the Area Under the Time Concentration Curve for extensive metabolizer subpopulation divided by the Area Under the Time Concentration Curve for poor metabolizer subpopulation;

(b) the percent of the usual drug dose D_pop for a poor metabolizer D_PM is

D_PM=R·

D_EM; and

(c) the percent of the usual drug dose D_pop for an intermediate metabolizer D_IM is

D_IM=S·

D_EM.

30. The method of claim 27, wherein the minimal dose adjustment unit for the medication is based, at least in part, upon a number of non-functional alleles, D_EM, D_IM, and/or D_PM.

45. The method of claim 28, wherein:

(a) the percent of the usual drug dose D_pop for an extensive metabolizer D_EM is

D_EM=100/(f_EM+f_IM·

S+f_PM·

R)

where S is the Area Under the Time Concentration Curve for extensive metabolizer subpopulation divided by the Area Under the Time Concentration Curve for intermediate metabolizer subpopulation, and where R is the Area Under the Time Concentration Curve for extensive metabolizer subpopulation divided by the Area Under the Time Concentration Curve for poor metabolizer subpopulation;

(b) the percent of the usual drug dose D_pop for a poor metabolizer D_PM is

D_PM=R·

D_EM; and

(c) the percent of the usual drug dose D_pop for an intermediate metabolizer D_IM is

D_IM=S·

D_EM.

46. The method of claim 28, wherein the minimal dose adjustment unit for the medication is based, at least in part, upon a number of non-functional alleles, D_EM, D_IM, and/or D_PM.

47. The method of claim 29, wherein the minimal dose adjustment unit for the medication is based, at least in part, upon a number of non-functional alleles, D_EM, D_IM, and/or D_PM.

31. A computerized method and/or computer-assisted method for selecting one or more drugs for a patient comprising the steps of:

identifying the phenotype;

providing a first plurality of possible medications based upon the patient'"'"'s diagnosis; and

calculating a ranked list or a predictive index of medications from the first plurality of medications based upon, at least in part, patient specific genetic factors, non-heritable patient factors and drug specific factors.

32. The computerized method of claim 31, wherein the calculating step involves linear algebra computational science to integrate disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, and/or patient specific environmental and genetic factors to produce a ranking of potential medications.

33. The computerized method of claim 31, wherein the calculating step assigns, for each potential medication, computational values corresponding to a favorability of utilizing the potential medication for a corresponding plurality of factors.

34. The computerized method of claim 33 wherein the plurality of factors include factors from a plurality of the following categories:

disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology principles, patient specific environmental and patient specific genetic factors.

35. The computerized method of claim 33, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score.

36. The computerized method of claim 33, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors and weights corresponding to the relative importance of such factors, and the calculating step involves adding the weighted computational values to determine a score.

37. The computerized method of claim 31 further comprising a step of generating an adherence score corresponding to a predicted likelihood that the patient will adhere to a scheduled therapy or prescription.

38. The computerized method of claim 31 wherein the factors are obtained utilizing an interactive computerized tool.

39. The computerized method of claim 38, wherein the interactive computerized tool collects factors utilizing a hierarchical question and answer session.

48. The computerized method of claim 32, wherein the calculating step assigns, for each potential medication, computational values corresponding to a favorability of utilizing the potential medication for a corresponding plurality of factors.

49. The computerized method of claim 34, wherein the plurality of computational values include positive values for favorable factors and negative values for unfavorable factors, and the calculating step involves adding the computational values to determine a score.

50. The computerized method of claim 32, wherein the factors are obtained utilizing an interactive computerized tool.