OPTIMIZATION AND INDIVIDUALIZATION OF MEDICATION SELECTION AND DOSING

• US 20150006190A9
• Filed: 10/14/2013
• Published: 01/01/2015
• Est. Priority Date: 11/29/2005
• Status: Abandoned Application

• 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
• Application Number:
  US14/053,220
• Publication Number:
  US 20140046684A1
• Time in Patent Office: Days
• US Class Current: 705/2
• 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 systems and methods for producing a ranked list of drugs for administration to a patient in need thereof, the ranked list produced by calculating for each drug a score that is the product of a patient vector, a weighting vector, and the drug'"'"'s column value in a disease matrix.

Claims

1-39. -39. (canceled)

40. A method for producing a ranked list of drugs for administration to a patient in need thereof, the ranked list produced by calculating for each drug a score that is the product of a patient vector, a weighting vector, and the drug'"'"'s column value in a disease matrix, the method comprising the steps of:

(a) receiving at a processor the patient'"'"'s disease phenotype;

(b) providing a disease matrix comprising a first plurality of drugs based upon the patient'"'"'s disease phenotype;

the disease matrix comprising columns and rows, each column representing a drug in the plurality of drugs and consisting of a set of numbers, each number consisting of a computational value relating the drug to a factor represented by a row of the matrix such that each of the first plurality of drugs is related to a plurality of factors in the disease matrix,

the plurality of factors in the disease matrix comprising factors from a plurality of the following categories;

(1) disease specific evidence based medicine data, (2) drug specific basic pharmacology factors, (3) patient specific advanced pharmacology factors, (4) patient specific environmental factors, and (5) patient specific genetic factors,

wherein

(1) the plurality of factors for disease specific evidence based medicine data is presented for each combination of age (neonates, infants, children, adults, and elderly adults) and disease subgroup from a plurality of variables selected from the group consisting of

Drug efficacy score (score ranging from −

1.0 to 1.0 based on the level of evidence for that drug'"'"'s efficacy in that age and disease subgroup)

Drug toxicity score (score ranging from −

1.0 to 1.0 based on the level of evidence for that drug'"'"'s toxicity in that age and disease subgroup)

OR (odds ratio, a measure of the degree of association),

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

NNT-TOX (number need to treat, the number of patients who need to be treated to have a 1 toxicity outcome),

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

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

(2) the plurality of drug specific basic pharmacology characteristics comprises a plurality of preclinical toxicity variables, clinical pharmacokinetic variables, and drug safety variables selected from the group consisting of

TD50 (50% therapeutic dose=the dose of the medication that results in 50% of animals tested achieving the desired therapeutic outcome),

LD50 (50% lethal dose=the dose of the medication that results in 50% of the animals tested dying),

TI (therapeutic index, the ratio of LD500/TD50=a measure of the drug'"'"'s inherent toxicity),

F (Bioavailability, the 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-nonL (drug with non-linear metabolism),

MET-L (drug with linear metabolism),

T_1/2 (drug half life)

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

E”

),

AUC (total area under the plasma drug concentration time curve),

CL (clearance, the volume of blood cleared of drug per unit time),

IDR (rate of idiosyncratic reactions),

TERATO (rate of teratogenicity)

FORM (formulation), and

FREQ (frequency of daily drug administration;

(3) the plurality of patient specific advanced pharmacology factors comprises a plurality of variables that include clinically significant i) bidirectional pharmacokinetic or pharmacodynamic drug-drug interactions and ii) bidirectional pharmacodynamic drug-disease interactions selected from the group consisting of

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

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

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

DISEASES (all forms of altered health ranging from single organ dysfunction to whole body illness);

(4) the plurality of patient specific environmental factors comprises a plurality of variables that can cause clinically significant unidirectional pharmacokinetic or pharmacodynamic drug environment interactions selected from the group consisting of

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

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

Food

Herbal/vitamin supplements,

Voluntary toxic exposures, and

Involuntary toxic exposures;

and

(5) the plurality of patient specific genetic factors comprises a plurality of variables that can cause clinically significant unidirectional pharmacokinetic or pharmacodynamic drug gene interactions selected from the group consisting of

PTX (percentage of transporter “

T”

with functional polymorphism “

X”

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

PEX (percentage of drug metabolizing enzyme “

E”

with functional polymorphism “

X”

), and

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

(c) creating a patient vector for the patient with the same row labels as the disease matrix including the categories of disease specific evidence based medicine data, drug specific basic pharmacology characteristics, patient specific advanced pharmacology factors, patient specific environmental factors, and patient specific genetic factors in (3), (4), and (5);

(d) providing a weighting vector with the same row labels as the disease matrix;

(e) calculating, via a processor, a score for each drug in the disease matrix, where the score is the product of the patient vector, the weighting vector, and the drug'"'"'s column value in the disease matrix; and

(f) outputting a ranked list of drugs from highest to lowest of the scores calculated in step (e).

41. The method of claim 40, wherein the following variables contribute linearly:

TI, F, fu, f-BIND-T, MET-L, f-MET-E, PEX. CL_CR, IDR, FORM, FREQ, MAT ED, SES, and TRANS.

42. The method of claim 40, wherein the following variables contribute exponentially:

ATA, MET-NonL, AEA, MED-IND, MED-INH, DIET-IND, DIET-INH, NNT-EFF, META-EEF, NNT-TOX, and META-TOX.

43. The method of claim 40, wherein step (b)(3) comprises a step of assigning computational values for patient specific advanced pharmacology factors selected from clinically significant (i) bidirectional pharmacokinetic drug-drug interactions and (ii) bidirectional pharmacodynamic drug-disease interactions, wherein

(i) a significant pharmacokinetic drug-drug interaction is defined as one for which there is a documented interaction that shows one drug either induces or inhibits the activity of a specific enzyme associated with the metabolism of the other drug by ≧

20%, and

(ii) a significant pharmacodynamic drug-disease interaction is defined as one for which there is a documented interaction that shows a ≧

20% positive or negative effect of a drug on a disease.

44. The method of claim 40, wherein step (b)(4) comprises a step of assigning computational values for patient specific environmental factors selected from clinically significant (i) unidirectional pharmacokinetic or (ii) pharmacodynamics drug-environment interactions, wherein

(i) a significant pharmacokinetic drug-environment interaction is defined as one for which there is a documented interaction that shows the environmental agent either induces or inhibits the activity of a specific enzyme associated with the metabolism of the drug by ≧

20%, and

(ii) a significant pharmacodynamic drug-environment interaction is defined as one for which there is a documented interaction that shows the environmental factor alters (either positively or negatively) the action of the drug by ≧

20%.

45. The method of claim 40, wherein step (b)(5) comprises a step of assigning computational values for patient specific genetic factors selected from clinically significant (i) unidirectional pharmacokinetic or (ii) pharmacodynamic drug-gene interactions, wherein

(i) a significant pharmacokinetic drug-gene interaction is defined as one for which there is a documented interaction that shows the genetic factor either increases or reduces the activity of a specific enzyme associated with the metabolism of the drug by ≧

20%, and

(ii) a significant pharmacodynamic drug-gene interaction is defined as one for which there is a documented interaction that shows the genetic factor alters (either positively or negatively) the action of the drug by ≧

20%.

46. The method of claim 40, 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.

47. The method of claim 40, wherein the factors are obtained utilizing an interactive computerized tool.

48. The method of claim 47, wherein the interactive computerized tool collects factors utilizing a hierarchical question and answer session.

49. The method of claim 40, further comprising a step of selecting one or more drugs from the ranked list for administration to the patient and administering the one or more selected drugs to the patient.

50. The method of claim 40, wherein the plurality of factors in the disease matrix comprises all variables listed in (1) through (5) for which data is available.

51. The method of claim 40, wherein the patient vector is a column of the disease matrix.

52. The method of claim 40, wherein the patient vector is a 1 by N matrix.

53. The method of claim 40, wherein the weighting vector is a column of the disease matrix.

54. The method of claim 40, wherein the weighting vector is a 1 by N matrix.

55. The method of claim 46, wherein the factors included in the adherence score include one or more, or all, of the following factors:

MAT ED (maternal education level),

SES (socio-economic class), and

TRANS (method of transportation to/from clinic).