U.S. patent application number 17/094683 was filed with the patent office on 2021-06-03 for optimization and individualization of medication selection and dosing.
The applicant listed for this patent is CHILDREN'S HOSPITAL MEDICAL CENTER. Invention is credited to Tracy A. Glauser, John Pestian, Alexander A. Vinks, Richard J. Wenstrup.
Application Number | 20210166820 17/094683 |
Document ID | / |
Family ID | 1000005389668 |
Filed Date | 2021-06-03 |
United States Patent
Application |
20210166820 |
Kind Code |
A1 |
Glauser; Tracy A. ; et
al. |
June 3, 2021 |
OPTIMIZATION AND INDIVIDUALIZATION OF MEDICATION SELECTION AND
DOSING
Abstract
Described are methods for treating a patient with a therapeutic
drug using a combination of genetic and non-genetic information to
tailor the dose of the drug to the patient.
Inventors: |
Glauser; Tracy A.;
(Cincinnati, OH) ; Wenstrup; Richard J.;
(Cincinnati, OH) ; Vinks; Alexander A.;
(Cincinnati, OH) ; Pestian; John; (Cincinnati,
OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHILDREN'S HOSPITAL MEDICAL CENTER |
Cincinnati |
OH |
US |
|
|
Family ID: |
1000005389668 |
Appl. No.: |
17/094683 |
Filed: |
November 10, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15367950 |
Dec 2, 2016 |
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17094683 |
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14053220 |
Oct 14, 2013 |
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15367950 |
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12085606 |
Jan 13, 2009 |
8589175 |
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PCT/US2006/045631 |
Nov 28, 2006 |
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14053220 |
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60783118 |
Mar 16, 2006 |
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60740430 |
Nov 29, 2005 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/106 20130101;
G16B 40/00 20190201; G16H 70/40 20180101; C12Q 1/6883 20130101;
G16H 10/60 20180101; G01N 33/94 20130101; G16H 50/50 20180101; G16H
20/10 20180101 |
International
Class: |
G16H 50/50 20060101
G16H050/50; G16B 40/00 20060101 G16B040/00; C12Q 1/6883 20060101
C12Q001/6883; G16H 10/60 20060101 G16H010/60; G16H 20/10 20060101
G16H020/10; G16H 70/40 20060101 G16H070/40; G01N 33/94 20060101
G01N033/94 |
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.-11. (canceled)
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.
13-14. (canceled)
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.-26. (canceled)
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.sub.pop), the
frequency of extensive metabolizers in the given population
(f.sub.E.sup.u), the frequency of intermediate metabolizers in the
given population (fi.sub.M) and/or the frequency of poor
metabolizers in the general population (fp.sub.M); and determining
a minimal dose adjustment unit for the medication based, at least
in part, upon the patient's genetic information.
28.-39. (canceled)
40. A method of selecting a medication for a patient suffering from
a neuropsychiatric disease or disorder comprising: obtaining an
individualized medication report for the patient, the report
comprising at least one group of medications, wherein the report is
generated by: (a) providing a first set of possible medications for
the patient comprising at least one medication selected from
propranolol, diazepam, alprazolam and risperidone, selection of the
first set of medications determined based on the patient's
phenotype; (b) calculating a score based on the patient's genotype
for each of the possible medications, the patient's genotype having
been determined by a genotyping assay for CYP genes comprising 1A2,
2B6, 2C9, 2C19, 2D6 and 3A4 using a biological sample obtained from
the patient; and (c) providing at least one group of possible
medications based on the score of each medication; and selecting,
from the individualized medication report, a medication based on
the report.
41. The method of claim 40, wherein a score is calculated for each
of propranolol, diazepam, alprazolam and risperidone.
42. The method of claim 40, wherein the patient's genotype is
determined by genotyping one or more further genes selected from
DAT1, SLC6A3, DRD1, DRD2, DRD3, DRD4, DRD5, TPH, 5-HTTR, HTR1A,
HTR1B, HTR1D, HTR2A, HTR2C and COMT.
43. The method of claim 40, wherein the group of possible
medications includes medications selected from one or more of the
classes of medications consisting of antidepressants,
antipsychotics, and mood elevating or stabilizing agents.
44. The method of claim 40, wherein the report is generated by a
method comprising calculating the score based on the patient's
genotype and a non-heritable patient factor.
45. The method of claim 44, wherein the non-heritable patient
factor comprises toxic exposure.
46. The method of claim 45, wherein the toxic exposure is
smoke.
47. The method of claim 45, wherein the toxic exposure is
alcohol.
48. The method of claim 44, wherein the report is generated by a
method further comprising obtaining or having obtained patient
information from the patient regarding the non-heritable patient
factor, and calculating the score based on the patient's genotype
and the patient information regarding the non-heritable patient
factor.
49. The method of claim 40, additionally comprising administering
the selected medication to the patient.
50. The method of claim 40, additionally comprising determining a
starting dose of the selected medication for administration to the
patient.
51. The method of claim 50, additionally comprising administering
the starting dose of the selected medication to the patient.
52. The method of claim 40, wherein the genotyping assay for CYP
genes indicates catalytic activity of proteins encoded by 1A2, 2B6,
2C9, 2C19, 2D6 and 3A4.
53. The method of claim 52, wherein the calculated score for each
of the possible medications indicates the patient's ability to
metabolize each of the possible medications.
54. The method of claim 53, wherein selecting the medication based
on the report or determining a starting dose of the selected
medication for administration to the patient is based on the
calculated score for each of the possible medications.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a Continuation Application of and
claims the benefit of priority to U.S. application Ser. No.
15/367,950, filed Dec. 2, 2016, which is a Continuation Application
of and claims the benefit of priority to U.S. application Ser. No.
14/053,220, filed Oct. 14, 2013, which is a Continuation
Application of and claims the benefit of priority to U.S.
application Ser. No. 12/085,606, filed Jan. 13, 2009, which is a
national stage entry under 37 C.F.R. .sctn. 371 of International
Application Ser. No. PCT/US2006/045631, filed Nov. 28, 2006, which
claims the benefit of priority to U.S. Provisional Patent
Application, Ser. No. 60/740,430, filed Nov. 29, 2005 and of U.S.
Provisional Patent Application, Ser. No. 60/783,118, filed Mar. 16,
2006, the disclosures of which are incorporated herein by reference
in their entireties.
FIELD OF THE INVENTION
[0002] This invention relates to methods for combining a patient's
genetic information, a patient's non-heritable host factors and
candidate medication characteristics to optimize and individual
medication dosage and compound selection.
BACKGROUND OF THE INVENTION
[0003] One of the most important but unresolved problems in therapy
with potent and often toxic drugs has been the lack of our ability
to describe, understand, and quantify the important mechanistic
relationships and variability between drug doses, concentrations in
blood, concentrations of metabolites in other body compartments,
and the therapeutic and toxic drug effects. For the most part,
defining drug action and inter-patient variability has been limited
to simplistic, less informative descriptions of average maximum and
minimum drug does requirements that do not permit true
individualization of therapy for each patient.
[0004] For some drugs over 90% of the measurable variation in
selected pharmacokinetic parameters has been shown to be heritable.
Traditionally in pharmacokinetic (PK) analysis a series of
concentrations over time is measured. A structural model is defined
and fit to the data in order to obtain estimates of the desired
parameters such as clearance and volume of distribution. The model
is fitted to the individual data by using a least squares algorithm
that minimizes the difference between observed and the model
predicted concentrations. For reasons of simplicity the assumption
is made that differences between the observed and predicted
concentrations are caused by random error. With this traditional
type of analysis, a model is defined for each subject and the
individual parameters are then summarized across individuals.
However, imprecision in the sample mean and sample standard
deviation frequently are greater than expected, while estimates of
variability in these parameters are not well characterized.
[0005] The Food and Drug Administration (FDA) is recognizing the
importance of the genetic contribution to the inter-individual
variation in response to therapy. There has been a significant
increase in the number of new drug applications sent to the FDA
containing pharmacogenetic information (Wendy Chou, Ph.D./FDA Apr.
3, 2003). Two package inserts reflect this trend. Thioridazine
(Mellaril) which is used for neuropsychiatric conditions is
contraindicated in patients who are CYP2D6 poor metabolizers; this
warning is specifically stated in two places in the insert.
Similarly in multiple places in the package insert for Atomoxetine
(Strattera, a medication used for attention deficit hyperactivity
disorder (ADHD)), the association between genetic polymorphisms in
drug metabolism and adverse drug reactions is stated.
[0006] In certain ethnic groups as many as 10% of the adolescent
population have a CYP2D6 haplotype that is associated with poor
metabolism of many antidepressant medications. See Wong et al.
(2001) Ann. Acad. Med. Singapore, 29:401-406. Clinical genomic
testing of these individuals has clear implications for their
treatment and prognosis. In extreme cases, children who were poor
metabolizers and who were not identified have had tragic outcomes.
These negative case reports have included a reported death of a
nine-year-old boy who was not recognized to be a poor CYP2D6
metabolizer. The treatment of this child with fluoxetine continued
despite the development of multiple symptoms because these symptoms
were not recognized as being related to bis extremely high serum
levels of fluoxetine. Sallee et al. (2000) J. Child Adol.
Psychiatry, 10(1):27-34.
[0007] Adverse drug reactions occur in 28% of hospitalized patients
and in 17% of hospitalized children. In a report by Phillips et al.
in JAMA, 27 drugs were most frequently cited in adverse drug
reaction reports. 59% ( 16/27) of these drugs were metabolized by
at least one enzyme having a poor metabolizer genotype. 37% (
11/27) were metabolized by CYP2D6, specifically drugs acting on the
central nervous system. The annual cost of the morbidity and
mortality associated with adverse drug reaction is $177,000,000
dollars (Year 2000 dollars). Clearly drug toxicity is a major
health issue with 100,000 deaths a year and 2,000,000 persons
suffering permanent disability or prolonged hospitalizations as a
result of direct medication adverse reactions.
[0008] Although significant inter-individual variability exists in
the response to most medications, medication selection and
titration is usually empiric rather than individualized. The main
reason that physicians do not incorporate genetic and non-heritable
host factors responsible for this inter-individual variability into
treatment plans is the lack of applicable, easy to use algorithms
that translate the patient's characteristics into clinical
recommendations. Thus there is a need in the art for a
pharmacokinetic dose individualization technique that is
informative, cost saving, and effective.
SUMMARY OF THE INVENTION
[0009] The present invention is concerned generally with the field
of identifying appropriate medications and treatment regimens for a
disease based upon genotype in mammals, particularly in humans. It
is further concerned with the genetic basis of inter-patient
variation in response to therapy, including drug therapy.
Specifically, the invention describes the use of gene sequence
variances for optimizing efficacy and safety of drug therapy. The
invention relates to computerized methods and/or computer-assisted
methods for identifying patient population subsets that respond to
drug therapy similarly.
[0010] The invention provides computerized methods and/or
computer-assisted methods of targeting drug therapy, particularly
dosing regimens and compound selection to an individual subject or
patient. The methods incorporate genetic and non-heritable factors
into drug selection and titration. The invention provides
computational algorithms for recommending a dosing regimen for a
particular patient utilizing population models, genotype
information, and clinical information. The methods of the invention
allow iterative integration of patient information and clinical
data. The methods of the invention provide timely, easy to
understand, and easy to implement recommendations. Further the
invention provides proactive identification of patients potentially
requiring more in depth assessment by a clinical pharmacology
specialist.
[0011] It is therefore a first aspect of the present invention to
provide a computerized method and/or computer-assisted method of
selecting a dosing regimen for a patient the method that includes
the steps of: (a) integrating patient data with patient associated
genotype information; (b) generating a drag 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. In a more detailed
embodiment, the method further includes the steps of (x) providing
a biological sample; (y) monitoring a biomarker in the biological
sample; and (z) integrating the biomarker value with the drug
concentration profile information. Alternatively or in addition,
the patient data may comprise patient demographic data and clinical
data. Alternatively or in addition, the clinical data may include
information regarding a second compound, where the second compound
may modulate metabolism of the first compound. Alternatively or in
addition, the first compound may be a neuropsychiatric medication.
Alternatively or in addition, the method may further comprise the
step of determining the genotype of a patient at one or more loci
of interest.
[0012] It is a second object of the present invention to provide a
computerized method and/or computer-assisted method for selecting a
dosing regimen for a patient, where the method includes 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; G) generating a second drug
concentration profile for the patient; (k) supplying a second
target drug concentration profile; (l) providing a second dosing
regimen for the compound likely to result in the second target drug
concentration profile. In addition, the method may further include
the step of performing the processes of (f) through (l) at least a
second time. Alternatively or in addition, the method may further
include the step of selecting a population model for the patient.
Alternatively or in addition, the method may further include the
step of generating a probability value for a designated response by
the patient.
[0013] It is a third aspect of the present invention to provide a
computerized method and/or computer-assisted method of selecting a
dosing regimen for a patient, where the method includes the steps
of: (a) generating statistical population models of drug
interactions for a plurality of genotypes; (b) obtaining patient
associated genotype information; and (c) establishing a dosing
regimen by applying the genotype information against the population
models. In addition, the step of generating population models may
include the use of Bayesian algorithms. Alternatively or in
addition, the population models of drug interactions may be defined
for a combination of genotypes and non-genetic information.
[0014] It is a fourth aspect of the present invention to provide a
computerized method and/or computer-assisted method for selecting
one or more drugs for a patient that includes the steps of:
identifying the phenotype; providing a first plurality of possible
medications based upon the identified phenotype; 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. In addition, the calculating step may further
consider 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.
In addition, in the calculating step, one or more of the following
variables could 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 drag
metabolizing enzyme "E"); PEX (percentage of drug metabolizing
enzyme "E" with functional polymorphism "X"); CL.sub.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). Alternatively or
in addition, in the calculating step, one or more of the following
variables could 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).
[0015] In another alternative detailed embodiment of the fourth
aspect of the present invention, the calculating step may involve
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. In addition, or
alternatively, the calculating step may assign, for each potential
medication, computational values corresponding to a favorability of
utilizing the potential medication for a corresponding plurality of
factors. In addition, the plurality of factors may 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. Alternatively or in addition, the plurality of
computational values may 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. Alternatively or in addition, the plurality of
computational values may 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.
[0016] In yet another alternate detailed embodiment of the
invention, the computerized method may further comprise a step of
generating an adherence score corresponding to a predicted
likelihood that the patient will adhere to a scheduled therapy or
prescription.
[0017] It is a fifth aspect of the present invention to provide a
computerized method and/or computer-assisted method for selecting a
starting dose of a medication for a patient that includes 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 (Dpop), the
frequency of extensive metabolizers in the given population
(f.sub.EM), the frequency of intermediate metabolizers in the given
population (f.sub.IM) and/or the frequency of poor metabolizers in
the general population (f.sub.PM); and determining a minimal dose
adjustment unit for the medication based, at least in part, upon
the patient's genetic information. In addition, the step of
determining if the patient may be 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. Alternatively or in
addition, (a) the percent of the usual drug dose Dpop for an
extensive metabolizer DEM is
DEM=100/(fnM+f.sub.IMS+f.sub.PMR)
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.sub.pop for
a poor metabolizer D.sub.PM is
D.sub.PM=RD.sub.EM; and
(c) the percent of the usual drug dose D.sub.pop for an
intermediate metabolizer D.sub.IM is
D.sub.IM=SD.sub.EM
Alternatively or in addition, the minimal dose adjustment unit for
the medication may be based, at least in part, upon a number of
non-functional alleles, D.sub.EM, D.sub.IM, and/or D.sub.PM.
[0018] It is a sixth aspect of the present invention to provide a
computerized method and/or computer-assisted method for selecting
one or more drugs for a patient that includes 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. In addition, the calculating step may involve
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. Alternatively or in
addition, 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, where the plurality of factors may 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. Alternatively or in addition, 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,
where the plurality of computational values may 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.
[0019] In another detailed embodiment of the sixth aspect of the
present invention, the method may include a step of generating an
adherence score corresponding to a predicted likelihood that the
patient will adhere to a scheduled therapy or prescription.
[0020] It is a seventh aspect of the present invention to provide a
computer, a computer system or a computerized tool designed and
programmed to perform any or all of the above computer implemented
methods. In addition, the computer, computer system or computerized
tool may provide a graphical user interface to provide for the
collection of appropriate data from users, such as any of the
above-discussed factors. Alternatively, or in addition, the
computer, computer system or computerized tool may provide a
graphical user interface (or any other known computer output, such
as a printout) to provide the report, analysis, recommendation or
any other output resulting from any of the above-discussed
methods.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1 presents a schematic depiction of the processes
involved in a method selecting a dosing regimen for an individual
patient.
[0022] FIGS. 2A-C present risperidone pharmacokinetic profiles for
three different dosing regimens for a particular patient. FIG. 2A
depicts an exemplary pharmacokinetic model-based simulation of the
risperidone concentration time profile. FIG. 2B depicts an
exemplary pharmacokinetic model-based simulation of the risperidone
concentration time profile after altering the dosing regimen. FIG.
2C depicts an exemplary pharmacokinetic model-based simulation of
the risperidone concentration time profile with a third dosing
regimen. In each panel a solid line indicates the patient's
compound concentration predicted by the methods of the invention in
each dosing regimen and the broken line indicates the therapeutic
range, in this example arbitrarily chosen to be between 3 and 10
ng/mL. The observed biomarker value is indicated with solid circles
or triangles.
[0023] FIG. 3 is an example (very small) segment of a disease
matrix for use with an exemplary embodiment of the invention.
[0024] FIG. 4 is a screen shot illustrating a step of an exemplary
computer implemented method of the present invention.
[0025] FIG. 5 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0026] FIG. 6 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0027] FIG. 7 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0028] FIG. 8 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0029] FIG. 9 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0030] FIG. 10 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0031] FIG. 11 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0032] FIG. 12 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0033] FIG. 13 is a screen shot illustrating another step of an
exemplary computer implemented method of the present invention.
[0034] FIG. 14 is a screen shot illustrating an output
report/analysis generated by an exemplary computer implemented
method of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0035] Defining and describing the often complex relationships of
drug action and inter-patient variability has historically been
very difficult. Developing pharmacokinetic (PK) and pharmacodynamic
(PD) models of these variables provides a method of defining and
describing the relationships between drug action and patient
variability. Further drug or compound actions (effects) are
directly related to the drug concentration at the site(s) of
action. There is usually a better relationship between the effect
of a given drug and its concentration in the blood than between the
dose of the drug given and the effect.
[0036] The invention provides population models for various
compounds that incorporate pharmacokinetic and pharmacodynamic
models of drug action and interpatient variability. Further the
invention provides computerized methods and/or computer-assisted
methods (including software algorithms) that utilize the one or
more population models of the invention to predict a dosing regimen
for a particular compound or to predict patient response to a
compound. The computerized methods and/or computer-assisted methods
(including software algorithms) of the invention generate a
prediction regarding a subject's ability to metabolize a compound
of interest. The computerized methods and/or computer-assisted
methods (including software algorithms) of the invention provide
for iterative evaluation of a patient's response to a dosing
regimen or compound incorporating data obtained from monitoring at
least one suitable biomarker. Often subjects receive more than one
medication. These additional medications may affect the subject's
ability to metabolize a compound of interest. Thus, in an
embodiment computerized methods and/or computer-assisted methods
(including software algorithms) of the invention provide a means of
integrating information regarding such an additional compound or
compounds and the effects of such an additional compound on the
subject's ability to metabolize a compound of interest.
[0037] A "compound" comprises, but is not limited to, a drug,
medication, agent, therapeutically effective agent,
neuropsychiatric medications, neurotransmitter inhibitors,
neurotransmitter receptor modulators, G-proteins, G-protein
receptor inhibitors, ACE inhibitors, hormone receptor modulators,
alcohols, reverse transcriptase inhibitors, nucleic acid molecules,
aldosterone antagonists, polypeptides, peptides, peptidomimetics,
glycoproteins, transcription factors, small molecules, chemokine
receptors, antisense nucleotide sequences, chemokine receptor
ligands, lipids, antibodies, receptor inhibitors, ligands, sterols,
steroids, hormones, chemokine receptor agonists, chemokine receptor
antagonists, agonists, antagonists, ion-channel modulators,
diuretics, enzymes, enzyme inhibitors, carbohydrates, deaminases,
deaminase inhibitors, hormones, phosphatases, lactones, and
vasodilators. A compound may additionally comprise a
pharmaceutically acceptable carrier.
[0038] Neuropsychiatric medications include, but are not limited
to, antidepressants, mood elevating agents, norepinephrine-reuptake
inhibitors, tertiary amine tricyclics, amitriptyline, clomipramine,
doxepin, imipramine, secondary amine tricyclics amoxapine,
desipramine, maprotiline, protriptyline, nortriptyline, selective
serotonin-reuptake inhibitors (SSRIs), fluoxetine, fluvoxamine,
paroxetine, sertraline, citalopram, escitalopram, venlafaxine,
atypical antidepressants, bupropion, nefazodone, trazodone;
noradrenergic and specific serotonergic antidepressants,
mirtazapine, monoamine oxidase inhibitors, phenelzine,
tranylcypromine, selegiline; antipsychotic agents, tricyclic
phenothiazines, chlorpromazine, triflupromazine, thioridazine,
mesoridazine, fluphenazine, trifluoperazine, thioxanthenes,
chlorprothixene, clopenthixol, flupenthixol, piflutixol,
thiothixene, dibenzepines, loxapine, clozapine, clothiapine,
metiapine, zotapine, fluperlapine, olanzapine, butyrophenones,
haloperidol, diphenylbutylpiperidines, fluspirilene, penfluridol,
pimozide, haloperidol decanoate, indolones, neuroleptics,
anti-anxiety/sedative agents, benzodiazepines, chlordiazepoxide,
diazepam, oxazepam, clorazepate, lorazepam, prazepam, alprazolam,
and halazepam; mood stabilizing agents, lithium salts, valproic
acid; attention deficit hyperactivity disorder agents,
dextroamphetamine, methylphenidate, pemoline, and atomoxetine;
anticonvulsants, phenobarbital, phenytoin, carbamazepine, valproic
acid, felbamate, gabapentin, tiagabine, lamotrigine, topiramate,
zonisamide, oxcarbazepine, levetiracetam, pregabalin, ethotoin, and
peganone; headache medications, ibuprofen,
aspirin/acetaminophen/caffeine, diclofenac, ketoprofen, ketorolac,
flurbiprofen, meclofenamate, naproxen, ergotamine tartrate,
dihydroergotamine, ergotamine, acetaminophen/isometheptene
mucate/dichloralphenazone, sumatriptan succinate, zolmitriptan,
rizatriptan, naratriptan hydrochloride, almotriptan, frovatriptan,
eletriptan, diclofenac, fenoprofen, flurbiprofen, kepaprofen,
naproxen sodium, amitriptyline, desipramine, doxepin, imipramine,
nortriptyline, fluoxetine, paroxetine, sertraline, venlafaxine,
trazodone, bupropion, atenolol, metoprolol, nadolol, propranolol,
timolol, diltiazem, nicardipine, nifedipine, nimodipine, verapamil,
divalproex sodium, gabapentin, valproic acid, and topiramate; and
dementia medications, tacrine, donepezil, galantamine,
galanthamine, rivastigmine, and memantine.
[0039] By "drug" is intended a chemical entity, biological product,
or combination of chemical entities or biological products
administered to a person to treat, prevent, or control a disease or
condition. The term "drug" may include, without limitation, agents
that are approved for sale as pharmaceutical products by government
regulatory agencies such as the U.S. Food and Drug Administration,
European Medicines Evaluation Agency, agents that do not require
approval by a government regulatory agency, food additives or
supplements including agents commonly characterized as vitamins,
natural products, and completely or incompletely characterized
mixtures of chemical entities including natural agents or purified
or partially purified natural products. It is understood that the
methods of the invention are suitable for use with any of the drugs
or compounds in the 2005 Physicians Desk Reference, Thomson
Healthcare 59.sup.th ed., herein incorporated by reference in its
entirety.
[0040] The computerized methods and/or computer-assisted methods
(including software algorithms) of the invention utilize subject or
patient associated genotype information. The term "genotype" refers
to the alleles present in genomic DNA from a subject or patient
where an allele can be defined by the particular nucleotide(s)
present in a nucleic acid sequence at a particular site(s). Often a
genotype is the nucleotide(s) present at a single polymorphic site
known to vary in the human population. By "genotype information" is
intended information pertaining to variances or alterations in the
genetic structure of a gene or locus of interest. Genotype
information may indicate the presence or absence of a predetermined
allele. A "loci of interest" may be a gene, allele, or polymorphism
of interest. Genes or loci of interest include genes that encode a)
medication specific metabolizing enzymes, b) medication specific
transporters, c) medication specific receptors, d) enzymes,
transporters or receptors affecting other drugs that interact with
the medication in question or e) body functions that affect that
activities of the medication in question. In an embodiment of the
invention loci of interest include, but are not limited to, five
cytochrome P450 genes, the serotonin transporter gene, the dopamine
transporter gene, and the dopamine receptor genes. The five
cytochrome P450 genes can encode CYP2D6, CYP1A2, CYP2C19, CYP2C9
and CYP2E1. Alleles of particular interest include, but are not
limited to, the CYP1A2*1A or 1A2*3 allele, the CYP2C19*1A, 2C19*1B,
or 2C19*2A allele, and the CYP2D6*1A, 2D6*2, 2D6*2N, 2D6*3, 2D6*4,
2D6*5, 2D6*6, 2D6*7, 2D6*8, 2D6*10, 2D6*12, or 2D6*17 allele. The
serotonin receptor genes encode serotonin receptors IA, IB, ID, 2A,
or 2C and the dopamine receptor genes encode dopamine receptors D1,
D2, D3, D4, D5, and D6. The serotonin transported gene is also an
important part of the genotype. Additional genes, alleles,
polymorphisms, and loci of interest are presented in Tables 1 and
2.
TABLE-US-00001 TABLE 1 CYTOCHROME P450 GENES Cytochrome P450Gene
Allele Polymorphism 1A1 *1A None *2 A2455G *3 T3205C *4 C2453A 1A2
*1A None *1F -164C > a *3 G1042A 1B1 *1 None *2 R48G *3 L432V *4
N453S *11 V57C *14 E281X *18 G365W *19 P379L *20 E387K *25 R469W
2A6 *1A None *1B CYP2A 7 translocated to 3' - end *2 T479A *5 *1B +
G6440T 2B6 *1 *1'2 R22C *1'3 S259C *4 K262R *5 R487C *6 Q172H;
K262R *7 Q172H; I < 262R; R487C 2C8 *1A None *1B -271C > A
*1C -370T > G *2 I269F *3 R139K; K399R *4 I264M 2C9 *1 None *2
R144C *3 I359L *5 D360E 2C18 rot T204A m2 A460T 2C19 *1A None *1B
I331V *2A Splicing defect *2B Splicing defect; E92D *3 New stop
codon 636G > A *4 GTG initiation codon, 1A > G *5(A, B) 1297C
> T, amino acid change (R433W) *6 395G?A, amino acid change
(R132Q) *7 IVS5 + 2T > A, splicing defect *8 358T > C, amino
acid change (WI20R) 2D6 *1A None *2 G1661C, C2850T *2N Gene
duplication *3 A2549 deletion *4 G1846A *5 Gene deletion *6 T1707
deletion *7 A2935C *8 G1758T *10 C100T *12 G124A *17 C1023TCIO23T,
C2850T *35 G31A 2E1 *1A None *1C, *1D (6 or 8 bp repeats) *2 G1132A
*4 G476A *5 G(-1293)C *5 C-(1053)T *7 T(-333)A *7 G(-71)T *7
A(-353)G 3A4 *1A None *1B A(-392)G *2 Amino acid change (S222P) *5
Amino acid change (P218R) *6 Frameshift, 831 ins A *12 Amino acid
change (L373F) *13 Amino acid change (P416L) *15A Amino acid change
(RI62Q) *17 Amino acid change (F1892, Decreased) *18A Amino acid
change (L293P, increased) 3A5 *1A None *3 A6986G *5 T12952C *6
G14960A
TABLE-US-00002 TABLE 2 NON-CYTOCHROME P450 GENES Gene Symbol
Polymorphism Dopamine Transporter DATI, 40 bp VNTR SLC6A3 10 repeat
allele G710A, Q237R C124T, L42F Dopamine Receptor D1 DRDI DRD 1 B2
T244G C179T G127A T11G C81T T5950, S199A G150T, R50S C1100, T37R
AI09C, T37P Dopamine Receptor D2 DRD2 TaqI A A1051G, T35A C932G,
S311 C C928, P31 OS G460A, V1541 Dopamine Receptor D3 DRD3 Ball in
exon I MspI DRD31 Gly/Ser (allele 2) A250, S9G Dopamine Receptor D4
DRD4 48 repeat in exon 3 7 repeat allele. 12/13 bp
insertion/deletion T581G, V194G C841G, P281A Dopamine Receptor D5
DRD5 T978C L88F A889C, T297P G1252A, V4181 G181A, V61M G185C, C62S
T2630, R88L G1354A, W455 Tryptophan TPH A218C Hydroxylase A779C
G-5806T A-6526G (CT)m(CAMCT)p allele 194 in 3' UTR, 5657 bp distant
from exon Serotonin Transporter 5-HTTR Promoter repeat (44 bp
insertion (L)/deletion(S) (L = Long form; S = Short form) Exon 2
variable repeat A1815C G603C G167C Serotonin Receptor 1A HTR1A RsaI
G815A, G272D G656T, R219L C548T, P551L A82G, 128V G64A, G22S C47T,
P16L Serotonin Receptor 1B HTR1B G861C G816C, V287V T371G, F124C
T655C, F219L A1099G, I367V G1120A, E374K Serotonin Receptor 1D
HTR1D G506T C173T C794T, S265L Serotonin Receptor 2A HTR2A C74A
T102C T516C C1340T C1354T Serotonin Receptor 2C HTR2C G796C ClOG,
L4V G68C, C23S Catechol-o- COMT G158A (Also known methyltransferase
as Val/Met) G214T A72S G101C C34S G473A
[0041] In an embodiment of the invention, the computerized methods
and/or computer-assisted methods (including software algorithms)
are utilized to select a dosing regimen for a patient in need of a
neuropsychiatric medication. A major gene in the neuropsychiatric
panel is CYP2D6. Substrates of CYP2D6 typically are weak bases with
the cationic binding site located away from the carbon atom to be
oxidized. In particular, substrates of CYP2D6 include
amitriptyline, nortriptyline, haloperidol, and desipramine. Some
individuals have altered CYP2D6 gene sequences that result in
synthesis of enzymes devoid of catalytic activity or in enzymes
with diminished catalytic activity. These individuals metabolize
SSRIs and tricyclic antidepressants (TCAs) poorly.
Duplication/multiplication of the functional CYP2D6 gene also has
been observed and results in ultrarapid metabolism of SSRIs and
other drugs. Individuals without inactivating polymorphisms,
deletions, or duplications have the phenotype of an extensive drug
metabolizer and are designated as CYF2D6*1. The CYP2D6*3 and *4
alleles account for nearly 70% of the total deficiencies that
result in the poor metabolizer phenotype. The polymorphism
responsible for CYP2D6*3 (2549A>del) produces a frame-shift in
the mRNA. A polymorphism involved with the CYP2D6*4 allele
(1846G>A) disrupts mRNA splicing. These changes produce
truncated forms of CYP2D6 devoid of catalytic activity. Other poor
metabolizers are CYP2D6*5, *10, and *17. CYP2D6*5 is due to
complete gene deletion. The polymorphisms in CYF2D6*10 and *17
produce amino acid substitutions in the CYP2D6 enzyme which have
decreased enzyme activity. All of these polymorphisms are autosomal
co-dominant traits. Only individuals who are homozygous or who are
compound heterozygous for these polymorphisms are poor
metabolizers. Individuals who are heterozygous, with one normal
gene and one polymorphic gene, will have metabolism intermediate
between the extensive (normal) and poor metabolizers. Individuals
who are heterozygous for duplication/multiplication alleles are
ultra-rapid metabolizers.
[0042] CYP1A2 metabolizes many aromatic and heterocyclic anilines
including clozapine and imipraniline. The CYP1A2*IF allele can
result in a product with higher inducibility or increased activity.
(See Sachse et al. (1999) Br. J. Clin. Pharmacol. 47:445-449).
CYP2C19 also metabolizes many substrates including imipramine,
citalopram, and diazepam. The CYP2C19*2A, *2B, *3, *4, *SA, *5B,
*6, *7, and `:`8 alleles encode products with little or no
activity. See Theanu et al. (1999) J. Pharmacol. Exp. Ther. 290:
635-640.
[0043] CYP1A1 can be associated with toxic or allergic reactions by
extra-hepatic generation of reactive metabolites. CYP3A4
metabolizes a variety of substrates including alprazolam. CYP1B1
can be associated with toxic or allergic reactions by extra-hepatic
generation of reactive metabolites and also metabolizes steroid
hormones (e.g., 17-estradiol). Substrates for CYP2A6 and CYP2B6
include valproic acid and bupropion, respectively. Substrates for
CYP2C9 include Tylenol and antabuse (disulfuram). Substrates for
CYP2E1 include phenytoin and carbamazepine. Decreases in activity
in one or more of the cytochrome P450 enzymes can impact one or
more of the other cytochrome P450 enzymes.
[0044] Methods of determining genotype information are known in the
art. Genotype information obtained by any method of determining
genotype known in the art may be employed in the practice of the
invention. Any means of determining genotype known in the art may
be used in the methods of the invention.
[0045] Generally genomic DNA is used to determine genotype,
although mRNA analysis has been used as a screening method in some
cases. Routine, commercially available methods can be used to
extract genomic DNA from a blood or tissue sample such as the
QIAamp.RTM. Tissue Kit (Qiagen, Chatsworth, Calif.), Wizard.RTM.
Genomic DNA Purification IDT (Promega) and the A.S.A.P..TM. Genomic
DNA Isolation Kit (Boehringer Mannheim, Indianapolis, Ind.).
[0046] Typically before the genotype is determined, enzymatic
amplification of the DNA segment containing the loci of interest is
performed. A common type of enzymatic amplification is the
polymerase chain reaction (PCR). Known methods of PCR include, but
are not limited to, methods using paired primers, nested primers,
single specific primers, degenerate primers, gene-specific primers,
vector-specific primers, partially-mismatched primers, and the
like. Known methods of PCR include, but are not limited to, methods
using DNA polymerases from extremophiles, engineered DNA
polymerases, and long-range PCR. It is recognized that it is
preferable to use high fidelity PCR reaction conditions in the
methods of the invention. See also Innis et al, eds. (1990) PCR
Protocols: A Guide to Methods and Applications (Academic Press, New
York); Innis and Gelfand, eds. (1995) PCR Strategies (Academic
Press, New York); Innis and Gelfand, eds. (1999) PCR Methods Manual
(Academic Press, New York); and PCR Primer: A Laboratory Manual Ed.
by Dieffenbach, C. and Dveksler, G., Cold Spring Harbor Laboratory
Press, 1995. Long range PCR amplification methods include methods
such as those described in the TaK.aRa LA PCR guide, Takara Shuzo
Co., Ltd.
[0047] When using RNA as a source of template, reverse
transcriptase can be used to synthesize complementary DNA (cDNA)
strands. Ligase chain reaction, strand displacement amplification,
self-sustained sequence replication or nucleic acid sequence-based
amplification also can be used to obtain isolated nucleic acids.
See, for example, Lewis (1992) Genetic Engineering News 12(9):1;
Guatelli et al. (1990) Proc. Natl. Acad Sci USA 87:1874-1878; and
Weiss (1991) Science 254:12921293.
[0048] Methods of determining genotype include, but are not limited
to, direct nucleotide sequencing, dye primer sequencing, allele
specific hybridization, allele specific restriction digests,
mismatch cleavage reactions, MS-PCR, allele-specific PCR, and
commercially available kits such as those for the detection of
cytochrome P450 variants (TAG-ITTM kits are available from Tm
Biosciences Corporation (Toronto, Ontario). See, Stoneking et al,
1991, Am. J. Hmn. Genet. 48:370-382; Prince et al, 2001, Genome
Res. 11(I): 152-162; and Myakishev et al, 2001, Genome
11(1):163-169.
[0049] Additional methods of determining genotype include, but are
not limited to, methods involving contacting a nucleic acid
sequence corresponding to one of the loci of interest or a product
of such a locus with a probe. The probe is able to distinguish a
particular form of the gene or the gene product, or the presence of
a particular variance or variances for example by differential
binding or hybridization. Thus, exemplary probes include nucleic
acid hybridization probes, peptide nucleic acid probes,
nucleotide-containing probes that also contain at least one
nucleotide analog, and antibodies, such as monoclonal antibodies,
and other probes. Those skilled in the art are familiar with the
preparation of probes with particular specificities. One of skill
in the art will recognize that a variety of variables can be
adjusted to optimize the discrimination between variant forms of a
gene including changes in salt concentration, pH, temperature, and
addition of various agents that affect the differential affinity of
base pairing (see Ausubel et al, eds. (1995) Current Protocols in
Molecular Biology, (Greene Publishing and Wiley-Interscience, New
York).
[0050] The exemplary computerized methods and/or computer-assisted
methods (including software algorithms) of the invention may employ
the following rationale. The pharmacokinetic characteristics of a
compound, particularly a neuropsychiatric drug, affect the initial
dose of a compound more than the compound's pharmacodynamic
properties. A compound's pharmacokinetic profile is a dynamic
summation of its absorption, distribution, metabolism, and
excretion. Genetic differences in drug metabolizing enzymes (DME)
that affect enzyme activity and thus drug metabolism constitute a
major component of most compounds' pharmacokinetic variability.
DMEs include, but are not limited to, a) medication specific
metabolizing enzymes, b) medication specific transporters, c)
medication specific receptors, d) enzymes, transporters or
receptors affecting other drugs that interact with the medication
in question ore) body functions that affect that activities of the
medication in question. Most compounds' absorption, distribution,
and excretion characteristics are independent of the genetic
variability in DME activity. Specific DME polymorphisms affect the
metabolism of most compounds in a reproducible, predictable,
uniform manner. Typically a detectable polymorphism in a specific
DME will either have no effect or will reduce enzyme activity.
Thus, the subject will have either:
[0051] 1. two functional alleles (a wild-type, normal, or extensive
metabolizer);
[0052] 2. one functional allele (an intermediate metabolizer);
or
[0053] 3. no functional alleles (a poor metabolizer).
Additionally for certain genes, such as CYP2D6, multiple copies of
the gene may be present. In such instances, the presence of more
than two functional alleles for a particular gene correlates with
an ultrarapid metabolizer state.
[0054] Frequently more than one DMEs working either in series or in
parallel metabolize a particular compound. The effect of genetic
variability for each DME can be determined independently and
combined. The invention provides methods of combining or
integrating the genetic variability effect for each DME or DMEs
that function sequentially or concurrently. The methods of the
invention utilize Bayesian population pharmacokinetic modeling and
analysis to integrate and predict the effects of multiple DMEs on
metabolism of a particular compound.
[0055] Also, the concurrent use of more than one compound can
affect the activity of a subject's DMEs. Again, the effect of
genetic variability for each DME can be determined independently
for each compound. The computerized methods and/or
computer-assisted methods (including software algorithms) of the
invention utilize Bayesian population pharmacokinetic modeling and
analysis to integrate and predict the effects of multiple compounds
on one or more DMEs. The methods of the invention allow the
integration of information about the genetic variability of one or
more DMEs and one or more compounds to generate an area under the
time concentration curve (AUC) value. The AUC value reflects the
amount of a particular compound accessible to a patient and is the
clinically important variable.
[0056] The AUC value is determined by drug dose and patient
specific pharmacokinetics. Prior to this invention, medical
practice utilized a "one size fits all" approach that kept the drug
dose constant. In the "one size fits all" approach, variability in
pharmacokinetics among patients leads to variability in AUC that
results in interpatient clinical variability such as side effects
or variable efficacy levels. Thus the methods of the invention
provide a means of selecting compound dosing regimens that provide
patients with similar AUC values. The methods of the invention
integrate the number of genetic variations to be included, the
population frequency for each genetic variation, and AUC data for
each genetic variation. The methods of the invention transforms a
heterogenous population into multiple homogenous subpopulations.
Such homogenous subpopulations, suitable dosing regimens, and
suitable compounds can be described in a population profile of the
invention.
[0057] By "dosing regimen" is intended a combination of factors
including "dosage level" and "frequency of administration". An
optimized dosing regimen provides a therapeutically reasonable
balance between pharmacological effectiveness and deleterious
effects. A "frequency of administration" refers to how often in a
specified time period a treatment is administered, e.g., once,
twice, or three times per day, every other day, every other week,
etc. For a compound or compounds of interest, a frequency of
administration is chosen to achieve a pharmacologically effective
average or peak serum level without excessive deleterious effects.
Thus, it is desirable to maintain the serum level of the drug
within a therapeutic window of concentrations for a high percentage
of time.
[0058] The exemplary software program of the invention employs
Bayesian methods. The Bayesian methods allow fewer drug
measurements for individual PK parameter estimation, sample sizes
(e.g. one sample), and random samples. Therapeutic drug monitoring
data, when applied appropriately, can also be used to detect and
quantify clinically relevant drug-drug interactions. These methods
are more informative, cost-saving, and reliable than methods
relying on simply reporting results as below, within or above a
published range.
Determining a Predictive Index Called the "Simplicity Index"
Definitions
[0059] The following abbreviations and definitions will be used in
the construction of the simplicity index--the variables are grouped
by common themes:
[0060] Preclinical Toxicity variables [0061] 1. TD50=called "50%
therapeutic dose"=the dose of the medication that results in 50% of
the animals tested achieving the desired therapeutic outcome [0062]
2. LD50=called "50% lethal dose"=the dose of the medication that
results in 50% of the animals tested dying [0063] 3. TI=called
therapeutic index=the ratio of LD50/TD50=a measure of the drug's
inherent toxicity
Pharmacokinetic Variables
[0063] [0064] 4. F=Bioavailability=fraction of the dose which
reaches the systemic circulation as intact drug [0065] 5. fu=The
extent to which a drug is bound in plasma or blood is called the
fraction unbound=[unbound drug concentration]/[total drug
concentration] [0066] 6. f-BEMD-T=fraction of drug that is a
substrate for a drug-specific efflux transporter "T" [0067] 7.
PTX=percentage of transporter "T" with functional polymorphism "X"
[0068] 8. ATA=number of functional non-wild type transporter
polymorphisms for the specific patient [0069] 9. MET-NonL=drug with
non-linear metabolism [0070] 10. MET-L=drug with linear metabolism
[0071] 11. f-MET-E=fraction of drug that is metabolized by drug
metabolizing enzyme "E" [0072] 12. PEX=percentage of drug
metabolizing enzyme "E" with functional polymorphism "X" [0073] 13.
AEA=number of functional non-wild type drug metabolizing enzyme
polymorphisms for the specific patient [0074] 14. AUC=Total area
under the plasma drug concentration-time curve=mg*hour/L [0075] 15.
CL=clearance=the volume of blood cleared of drug per unit
time=(liters/hour), CL=dose/AUC [0076] 16. CL.sub.cr=creatinine
clearance=the volume of blood cleared of creatinine per unit
time=(liters/hour) [0077] 17. MED-MD=concurrent use of medications
that induce metabolizing enzymes [0078] 18. MED-INH=concurrent use
of medications that inhibit metabolizing enzymes [0079] 19.
DIET-IND=concurrent use of dietary supplements that induce
metabolizing enzymes [0080] 20. DIET-INH=concurrent use of dietary
supplements that inhibit metabolizing enzymes
Clinical Efficacy Variables
[0080] [0081] 21. NNT-EFF=number need to treat=the number of
patients who need to be treated to reach 1 desired outcome [0082]
22. OR=odds ratio=a measure of the degree of association; for
example, the odds of reaching the desired outcome among the treated
cases compared with the odds of not reaching the desired outcome
among the controls [0083] 23. META-EFF=results from an efficacy
meta-analysis of clinical trials involving medications used to
treat a neuropsychiatric disorder
Clinical Toxicity Variables
[0083] [0084] 24. NNT-TOX=number need to treat=the number of
patients who need to be treated to have 1 toxicity outcome [0085]
25. OR=odds ratio=a measure of the degree of association; for
example, the odds of reaching the drug toxicity among the treated
cases compared with the odds of not reaching drug toxicity among
the controls [0086] 26. META-TOX=results from a toxicity
meta-analysis of clinical trials involving medications used to
treat a neuropsychiatric disorder
Clinical Safety Issues
[0086] [0087] 27. IDR=rate of idiosyncratic reactions
Ease of Use/Adherence Variables
[0087] [0088] 28. FORM=formulation [0089] 29. FREQ=frequency of
daily drug administration [0090] 30. MAT ED=maternal education
level [0091] 31. SES=socio-economic class [0092] 32. TRANS=method
of transportation to/from clinic
[0093] An algorithm can be used to rank the most appropriate
medications for an individual patient. The design of the algorithm
requires the initial identification of the phenotype, which
provides a preliminary identification of the universe of possible
medications. At the next step of the algorithm, the results of the
target gene analyses can be sequentially entered. The algorithm
that produces the predictive index (called the "simplicity index")
combines the above factors using the following principles: [0094]
1. Each factor contributes differentially based on weighting and
scaling variables determined during the validation process. [0095]
2. The following variables contribute linearly to the final ranking
score: TI, F, fu, f-BIND-T, MET-L, f-MET-E, PEX, CL.sub.CR, IDR,
FORM, FREQ, MATED, SES, TRANS [0096] 3. The following variables
contribute exponentially to the final ranking score: ATA, MET-NonL,
AEA, MED-IND, MED-INH, DIET-IND, DIET-INH5NNT-EFF, META-EEF,
NNT-TOX, META-TOX
[0097] The algorithm produces a rank list of medications based on
the above patient specific genetic factors, non-heritable patient
factors and drug specific factors. An exemplary software tool for
determining such a predictive index, called the "simplicity index,"
is described in detail below.
Determining Initial Starting Dose
[0098] The following abbreviations and definitions will be used in
the determination of the initial starting dose:
Abbreviations
[0099] Dp.sub.op=the perceived usual drug dosage for the general
population
Extensive Metabolizers
[0100] EM=extensive metabolizer
[0101] f.sub.EM=frequency of extensive metabolizers in the general
population
[0102] D.sub.EM=Drug dosage for extensive metabolizer
subpopulation
[0103] AUC.sub.EM=Area Under the Time Concentration Curve for
extensive metabolizer subpopulation
Intermediate Metabolizers
[0104] IM=intermediate metabolizer
[0105] f.sub.IM=frequency of intermediate metabolizers in the
general population
[0106] D.sub.IM=Drug dosage for intermediate metabolizer
subpopulation
[0107] AUC.sub.PM=Area Under the Time Concentration Curve for
intermediate metabolizer subpopulation
Poor Metabolizers
[0108] PM=poor metabolizer [0109] f PM=frequency of poor
metabolizers in the general population [0110] D.sub.PM=Drug dosage
for poor metabolizers subpopulation [0111] AUC.sub.PM=Area Under
the Time Concentration Curve for poor metabolizers
subpopulation
[0112] The following section describes how the dosing for the more
homogeneous subgroups is determined; the dosing results are
expressed as a fraction of the clinician's usual heterogeneous
whole group dosages.
[0113] For any one specific polymorphic DME (assuming all other
relevant polymorphic DME have normal activity), the usual drug dose
seen in a population is the weighted summation of the drug dosages
in each genetic different subpopulation expressed in equation 1:
(See Kirchheiner Acta Psychiatr Scand 2001:104: 173-192 BUT note
authors made mistake in non-numbered equation between Equations 1
and 2, page 178):
Dpop=f.sub.EM*D.sub.EM+fM*DIM+f.sub.PM*DPM (Equation 1)
[0114] Assuming the goal is to maintain the same AUC for all three
subpopulations of patients, the following subpopulation dosing
relationships hold:
D.sub.PM=D.sub.EM*(AUC.sub.EM/AUC.sub.PM) OR D.sub.PM=D.sub.EM*R if
R=(AUC.sub.EM/AUC.sub.PM) (Equation 2)
D.sub.IM=D.sub.EM*(AUC.sub.EM/AUQM) OR O.sub.m=D.sub.EM*S if
S=(AUC.sub.EM/AUC.sub.IM) (Equation 3)
[0115] By substituting equations 2 and 3 into equation 1, and then
rearranging the equation to solve for the percent dose adjustment
needed for each subgroup relative to the population dose:
D.sub.EM(%)=100/(f.sub.EM.+-.f.sub.IM*S.+-.f.sub.PM*R) (Equation
4)
.sub.DPM(%)=R*.sub.DEM (Equation 5)
.sub.DIM(%)=S*.sub.DEM (Equation 6)
Equations 4, 5, and 6 show how the dosing for the more homogeneous
subgroups is determined and how the dosing results are expressed as
a fraction of the clinician's usual heterogeneous whole group
dosages.
Determining "Minimal Dose Adjustment Units"
[0116] The cumulative effect of various genetic or environmentally
based alterations in DME activity will result in interpatient
variability in subsequent drug dosing requirements. If the
variability is large enough, then "one size fits all" dosing
approach can cause noticeable toxicity in some patients and lack of
efficacy in others. In this situation, clinicians alter their drug
prescribing or drug dosing behavior. We define the smallest
clinically relevant dosing change used by clinicians to compensate
for this interpatient variability as the "minimal dose adjustment
unit" (MDA unit).
[0117] The MDA unit for neuropsychiatric drugs is 20%. This means
that a clinician will alter their dosing of neuropsychiatric
medications in response to specific information if the dosing
change is 20% or greater. Perturbations that either singly or in
combination suggest a <20% change in dosing of neuropsychiatric
medications are usually ignored.
[0118] MDA units are additive--so that a patient with one MDA unit
from a genetic polymorphism and one MDA unit from a drug
interaction needs a 40% reduction in dose.
[0119] Example: The approach in the previous section leads to
individualized initial drug dose recommendations for each of the 3
subgroups (extensive, poor and intermediate metabolizers). Each
subgroup represents a specific number of functional alleles for the
specific DME (extensive metabolizers have 2 functional,
intermediate metabolizers have 1 functional and poor metabolizers
have 0 functional). The resultant dosing recommendations are
expressed as percentages of the clinician's usual starting dose. It
is possible to investigate the effect of increasing numbers of
non-functional alleles using these new dosing recommendations. For
example, if DR.chi.% is the dosing recommendation for subgroup X
expressed as a percentage of the clinician's usual starting dose
then the following are true:
Effect of claim 1 non-functional
allele=(DR.sub.EM%-DR.sub.IM%)/DR.sub.EM%
Effect of 2 non-functional allele=(DR.sub.EM%-DR.sub.PM%)/DR.sub.EM
%
Below is a spreadsheet (Table 3) that examines this for CYP2D6,
CYP2C19 and CYP2C9. The summary table below demonstrates: [0120] a.
it is apparent that each additional nonfunctional allele alters
dosing recommendation by at least 20% [0121] b. there is a "genetic
dose"-"dosing reduction" relationship that appears constant across
these 3 CYP450 genes. This approach can be used to solidify the
importance of subsequent DM genes and to quantify their effect in
MDA units. [0122] c. 2D6 and 2C1 9 have 1 MDA unit per
non-functional allele [0123] d. 2C9 has 2 MDA units per
non-functional allele. This implies that drug metabolized through
2C9 have very large variability in dosage requirements. This
confirms the clinical impression about these drugs (warfarin,
phenytoin).
TABLE-US-00003 [0123] TABLE 3 PM IM EM UM 2D6 2D6 (%) (%) (%) (%) 2
al 1 al 2 al/1 al Antipsychotics A Atomoxetime 20 100 100 100 0.80
0.00 Psychostimulant B Imipramine 28 79 131 182 0.79 0.40 1.98
Antidepressants A Perphenazin 31 80 129 178 0.76 0.38 2.00
Antidepressants - TCA B doxepin 36 82 127 173 0.72 0.35 2.02
Antipsychotics B maprotiline 36 82 127 173 0.72 0.35 2.02
Antipsychotics B trimipramine 37 91 131 176 0.72 0.31 2.35
Antipsychotics A thioridazine 40 85 126 140 0.68 0.33 2.10
Antidepressants A desipramine 42 83 125 167 0.66 0.34 1.98
Antidepressants A nortriptyline 53 96 119 152 0.55 0.19 2.87
Antidepressants - TCA B clomipramine 60 89 117 146 0.49 0.24 2.04
Antipsychotics A olanzapine 61 105 122 139 0.50 0.14 3.59
Antidepressants - SSRIs A zuclopenthixol 63 90 116 142 0.46 0.22
2.04 Antipsychotics A paroxetine 66 90 114 138 0.42 0.21 2.00
Antipsychotics A venlafaxine 68 86 109 130 0.38 0.21 1.78
Antipsychotics B fluvoxamine 69 93 112 131 0.38 0.17 2.26
Antipsychotics A aripiprazole 70 92 113 134 0.38 0.19 2.05
Antipsychotics B amitryptiline 73 92 111 130 0.34 0.17 2.00
Antidepressants A flupentixol 74 86 116 146 0.36 0.26 1.40
Antidepressants B mianserin 74 90 114 134 0.35 0.21 1.67
Antipsychotics A haloperidol 76 97 107 126 0.29 0.09 3.10
Antidepressants - TCA A trazadone 76 93 110 127 0.31 0.15 2.00
Antidepressants - SSRIs B fluoxetine 78 94 107 120 0.27 0.12 2.23
Antidepressants - TCA A perazine 86 91 110 117 0.22 0.17 1.26
Antipsychotics A risperidone 87 96 106 116 0.18 0.09 1.90
Antidepressants - TCA A buproprion 90 97 104 111 0.13 0.07 2.00
Antidepressants - SSRIs A nefazodone 90 97 105 113 0.14 0.08 1.88
Count 26 26 25 Average 0.45 0.22 2.10 St. Dev. 0.20 0.10 0.48
Antidepressants - SSRIs A pimozide 95 99 102 105 0.07 0.03
Antidepressants - TCA B citalopram 98 100 101 102 0.03 0.01
Antidepressants B sertraline 99 100 100 100 0.01 0.00
Antidepressants A levomepromazine 100 100 100 100 0.00 0.00
Antidepressants A mirtazapine 102 101 99 97 0.03 0.02
Antidepressants - SSRIs B clozapine 113 104 94 84 0.02 0.11
Antidepressants - TCA B moclobemide 121 107 92 77 0.32 0.16 PM IM
UM 2C10 (%) (%) (%) 2 al 1 al 2 al/1 al Antidepressants - TCA
trimipramine 48 52 111 0.59 0.53 1.12 Antidepressants - TCA doxepin
48 81 105 0.54 0.13 4.07 Antidepressants - TCA amitryptiline 53 81
109 0.51 0.26 2.00 Antidepressants moclobemide 54 82 110 0.51 0.25
2.00 Antidepressants - TCA imipramine 58 83 108 0.46 0.23 2.00
Antidepressants - SSRIs citalopram 61 84 108 0.44 0.22 1.96
Antidepressants - TCA clomipramine 62 79 110 0.44 0.28 1.55
Antidepressants - SSRIs fluoxetine 70 86 107 0.35 0.20 1.76
Antidepressants - SSRIs sertraline 75 90 105 0.29 0.14 2.00
Antipsychotics clozapine 78 91 104 0.25 0.13 2.00 Antipsychotics
zotepine 82 93 104 0.21 0.11 2.00 Antidepressants - SSRIs
fluvoxamine 93 97 101 0.08 0.04 2.00 Count 12 12 12 Average 0.39
0.21 2.04 St. Dev. 0.16 0.12 0.69 Antidepressants maprotiline 100
100 100 0.00 0.00 Antidepressants mianserin 100 100 100 0.00 0.00
2C9 2 al 1 al 2 al/1 al Antidiabetic Agent, Sulfonylurea Amaryl 20%
70% 120% 0.83 0.42 2.00 Antidiabetic Agent, Solfonylurea Glucotrol,
20% 70% 120% 0.83 0.42 2.00 Glipizide Antidiabetic Agent,
Sulfonylurea DiaBeta, 20% 70% 120% 0.83 0.42 2.00 Glucovance
Angiotensin II Receptor Cozaar, Hyzaar 20% 50% 100% 0.80 0.50 1.60
Antagonist Antidiabetic Agent, Sulfonylurea Diabinese, 20% 50% 120%
0.83 0.58 1.43 Orinase, Tolinase Anticoagulant Coumadin 20% 50%
130% 0.85 0.62 1.38 Analgesic -NSAID Celebrex 38% 70% 100% 0.65
0.30 2.17 Antilipemic Lescol 38% 80% 100% 0.65 0.20 3.25
Anticonvulsant Dilantin 40% 70% 110% 0.64 0.36 1.75 Count 9 9 9
Average 0.77 0.42 1.95 St. Dev. 0.09 0.13 0.56 20 50 120 0.38 0.58
1.43 20 50 100 0.80 0.50 1.60
TABLE-US-00004 TABLE 4 RELATIONSHIP BETWEEN NON-FUNCTIONAL ALLELES
AND DOSE REDUCTION Effect on percentage Average percentage Average
percentage dose reduction dose reduction dose reduction of 2 non-
if 1 non- if 2 non- functional alleles Gene functional allele
functional allele compared to 1 2D6 22% .+-. 10% 45% .+-. 20% 2.10%
.+-. 0.48% (n = 26) (n = 26) (n = 25) 2C19 21% .+-. 12% 39% .+-.
16% 2.04% .+-. 0.69% (n = 12) (n = 26) (n = 12) 2C9 42% .+-. 13%
77% .+-. 9% 1.95% .+-. 0.56% (n = 9) (n = 9) (n = 9)
Determining Final Dosage Requirements
[0124] For some drugs, there is very little pharmacokinetic genetic
variability but rather clinically relevant pharmacodynamic genetic
variability most likely at the drug's receptor. For these
medications, the impact of genetic testing will be reflected in the
final dosage requirements instead of the initial dosage
requirements.
[0125] Studies that demonstrate this genetic-pharmacodynamic effect
will be captured in the software that encodes the calculations used
to derive the simplicity index described earlier. This invention
will incorporate this information and report not only the rank
simplicity index of the potential drug candidates but also those
candidates that would require a higher than expected dosing
requirement to achieve the desire effect.
Population Models
[0126] The purpose of population pharmacokinetic modeling is to
describe the statistical distribution of pharmacokinetic parameters
in the population under study and to identify potential sources of
intra- and inter-individual variability among patients. Population
modeling is a powerful tool to study if, and to what extent,
demographic parameters (e.g. age, weight, and gender),
pathophysiologic conditions (e.g. as reflected by creatinine
clearance) and pharmacogenetic variability can influence the
dose-concentration relationship. A population pharmacokinetic
analysis is robust, can handle sparse data (such as therapeutic
drug monitoring data) and is designed to generate a full
description of the drug's PK behavior in the population. A
"population model" of the invention provides a description of the
statistical distribution of at least one pharmacokinetic parameter
in a given population and identifies at least on potential source
of variability among patients with regards to a particular compound
or agent. A population model of the invention may further provide
mean parameter estimates with their dispersion, between subject
variability and residual variability, within subject variability,
model misspecification and measurement error for a particular
compound.
[0127] An embodiment of the invention provides several novel
population models for predicting a medication concentration-time
profile and for selecting a dosing regimen based on a user-entered
target range (see examples). The computerized methods and/or
computer-assisted methods (including software algorithms) of the
invention employ population models such as, but not limited to, the
novel population models of the invention and externally developed
population models. In an embodiment, such externally developed
population models are adjusted or rearranged in such a manner that
they can be programmed into the software of the invention.
[0128] In various embodiments, the computerized methods and/or
computer-assisted methods (including software algorithms) of the
invention comprise the step of monitoring a biomarker. By
"biomarker" is intended any molecule or species present in a
patient that is indicative of the concentration or specific
activity of an exogenous compound in the subject. Biomarkers
include, but are not limited to, a compound, a metabolite of the
compound, an active metabolite of the compound, a molecule induced
or altered by administration of the compound of interest, and a
molecule that exhibits an altered cytological, cellular, or
subcellular location concentration profile in after exposure to a
compound of interest. Methods of monitoring biomarkers are known in
the art and include, but are not limited to, therapeutic drug
monitoring. Any method of monitoring a biomarker suitable for the
indicated biomarker known in the art is useful in the practice of
the invention.
[0129] Exemplary computerized methods and/or computer-assisted
methods (including software algorithms) of the invention use data
generated by therapeutic drug monitoring (TDM). TDM is the process
of measuring one or more concentrations of a given drug or its
active metabolite(s) in biological sample such as, but not limited
to, blood (or in plasma or serum) with the purpose to optimize the
patient's dosing regimen. The invention encompasses any means of
measuring one or more concentrations of a given drug or its active
metabolite(s) in a biological sample known in the art. By
"biological sample" is intended a sample collected from a subject
including, but not limited to, tissues, cells, mucosa, fluid,
scrapings, hairs, cell lysates, blood, plasma, serum, and
secretions. Biological samples such as blood samples can be
obtained by any method known to one skilled in the art.
[0130] The following examples are offered by way of illustration
and not limitation.
EXPERIMENTAL
Example 1. Optimization of Compound Dosage in an Autistic
Patient
[0131] An 11-year-old boy with autism was started on risperidone
(Risperdal.RTM.) therapy, at 0.5 mg two times a day. The patient's
pressured speech and labile mood did not improve with time. The
lack of efficacy could be due to insufficient coverage or to
non-compliance. The patient's dosing regimen was analyzed by the
methods of this invention.
Step 1 Dose Appropriateness Analysis.
[0132] The patient demographic data (age, sex, weight) and the
risperidone dose and times of administration were entered into the
program. A population model was selected. The population model
selected was a Risperidone model based on data of pediatric
psychiatry patients. As risperidone is metabolized by CYP2D6, there
are 3 models: one for extensive metabolizers (EM model), one for
intermediate metabolizers (IM model) and one for poor metabolizers
(PM model).
[0133] The genotype of the patient was determined and found to be
CYP2D6*1/*1. This genotype fit the extensive metabolizer (EM
model). The patient's data and the genotype were analyzed by an
algorithm of the invention and a drug concentration profile for the
patient was generated. An exemplary pharmacokinetic model-based
simulation of the risperidone concentration time profile based on
this patient's data is shown in FIG. 2A. The average concentration
was predicted to be around 2 ng/mL. This information is integrated
with a target drug concentration profile or therapeutic value. The
therapeutic value for risperidone ranges between 3 and 10 ng/mL.
Comparison of the drug concentration profile for the patient and
the target drug concentration profile indicated that if the patient
were adherent, the dose may be too low. The algorithm generated two
recommendations: the dose can be increased and a biomarker should
be monitored.
Step 2. Integration of Biomarker Evaluation in Recommended Dosage
Regimen
[0134] The risperidone dose was increased to 1 mg given twice a day
(morning and evening). In addition, a biomarker evaluation was
performed. Drug levels were ordered and therapeutic drug monitoring
were performed. The pre-dose level and two post dose levels (1 h
after dose) and (4 h after dose) were measured. These data were
entered in the software program. The software program performed a
Bayesian recalculation based on the a priori information from the
model in combination with the new patient specific information
(i.e. the drug levels). Exemplary results of this Bayesian update
are shown in FIG. 2B. The concentrations were not within the target
range for the major part of the dosing interval. Depending on
patient's response this would allow for further increasing the
dose. The pharmacokinetic simulation also indicated that this
patient has a rather rapid elimination of the drug form the body.
The software program generated several recommendations. In order to
maintain the target concentration more frequent dosing has to be
considered. Based on the B ayes pharmacokinetic estimates for this
patient and given the chosen target range the dosing regimen that
best meets the criteria would be 1.5 mg dosed every 8 hours. An
exemplary model-based profile and subsequent Bayesian
individualization process are shown in FIG. 2C.
[0135] The above-described methods according the present invention
can be implemented on a computer system such as a personal
computer, a client/server system, a local area network, or the
like. The computer system may be portable including but not limited
to a laptop computer or hand-held computer. Further the computer
may be a general purpose system capable of executing a variety of
commercially available software products, or may be designed
specifically to run only the drug identification and selection
algorithms that are the subject of this invention. The computer
system may include a display unit, a main processing unit, and one
or more input/output devices. The one 01: more input/output device
may include a touchscreen, a keyboard, a mouse, and a printer. The
device may include a variety of external communication interfaces
such as universal serial bus (USB), wireless, including but not
limited to infrared and RF protocols, serial ports and parallel
ports. The display unit may be any typical display device, such as
a cathode-ray tube, liquid crystal display, or the like.
[0136] The main processing unit may further include essential
processing unit (CPU) in memory, and a persistent storage device
that are interconnected together. The CPU may control the operation
of the computer and may execute one or more software applications
that implement the steps of an embodiment of the present invention.
The software applications may be stored permanently in the
persistent storage device that stores the software applications
even when the power is off and then loaded into the memory when the
CPU is ready to execute the particular software application. The
persistent storage device may be a hard disk drive, an optimal
drive, a tape drive or the like. The memory may include a random
access memory (RAM), a read only memory (ROM), or the like.
Exemplary Simplicity Index Software Tool
[0137] As introduced above an algorithm used to construct the drug
predictive index ("simplicity index") utilizes an initial
identification of the disease phenotype (e.g. epilepsy, depression,
etc.), which provides a preliminary identification of the universe
of possible medications for that condition. An exemplary software
tool for producing the simplicity index uses linear algebra
computational science to integrate disease specific evidence based
medicine data, drug specific basic pharmacology characteristics,
patient specific advanced pharmacology principles, and patient
specific environmental and genetic factors to produce a ranking of
potential medications for an individual patient based on these
factors. There are separate algorithms for each disease phenotype
but the algorithms can be run simultaneously. Further, in the
exemplary embodiment, there are three components used to produce
the final ranking score: a disease matrix, a patient vector and a
weighting vector. Each of the five factors and three components
will be defined below followed by an example with a sample output.
The output contains both the drug predictive index and an adherence
score.
Definitions
Disease Specific Evidence Based Medicine Data
[0138] Disease specific evidence based medicine data consists of
disease specific efficacy and tolerability data for potentially
effective medications. This disease specific efficacy and
tolerability data may exist for age or disease subgroups; each age
or disease subgroup is considered separately. For example in
epilepsy, evidence based data exists for five age groups (neonates,
infants, children, adults, and elderly adults) along with four
disease subgroups (partial onset seizures, generalized tonic clonic
seizures, absence seizures, and myoclonic seizures). In this
example, there would be a maximum of 20 separate evidence based
data sets covering all age-seizure type combinations.
[0139] The first step in the evidence based approach is to identify
all relevant scientific information about the efficacy and
tolerability of any potential therapeutic modality (medical,
surgical or dietary). Articles are identified through multiple
methods including, but not limited to, electronic literature
searches of the medical literature, hand searches of major medical
journals, the Cochrane library of randomized controlled trials, and
the reference lists of all studies identified from the electronic
literature searches. These articles may include, but are not
limited to, randomized control trials, nonrandomized controlled
trials, case series, case reports, and expert opinions.
Supplementary data is found in package inserts of individual
drugs.
[0140] The data in each article is evaluated for drug specific
efficacy and tolerability data. The analysis is performed using the
grading system used by the national scientific organization
associated with that specialty. If there is no national scientific
organization associated with the specialty then the default grading
system is the American Academy of Neurology evaluation system.
After the evidence based analysis is complete, the efficacy and
tolerability data for each potential drug (stratified by age and
disease subgroup) is summarized according to the following Table 5
using a scale from +1 to -1.
TABLE-US-00005 TABLE 5 DRUG SCORING SYSTEM FOR EFFICACY AND
TOLERABILITY DATA Efficacy or Tolerability score Type of data
(shown for efficacy only) 1.0 FDA indication for condition 0.9
Evidence Based Guideline Level A recommendation 0.9 Meta-analysis
evidence of efficacy 0.7 Evidence Based Guideline Level B
recommendation 0.7 RCT evidence better efficacy than another drug
or placebo 0.3 Evidence Based Guideline Level C recommendation 0.3
non RCT clinical trial evidence of efficacy 0.3 Expert opinion -
drug is efficacious 0.0 No data -0.3 Expert opinion - evidence of
worsening -0.3 non RCT clinical trial evidence of worsening -0.7
RCT evidence worse efficacy than another drug or placebo -0.9
Meta-analysis evidence of lack of efficacy or worsening -0.9
Evidence Based Guideline evidence of lack of efficacy or worsen
-1.0 FDA contraindication for condition
Drug Specific Basic Pharmacology Characteristics
[0141] Drug specific basic pharmacology characteristics are
evaluated in three categories: Preclinical toxicity, fundamental
clinical pharmacokinetic variables and drug safety. An example in
the preclinical toxicity category is a drug's therapeutic index.
This is defined as the ratio of LD50/TD50 where TD50 is the dose of
the medication that results in 50% of the animals tested achieving
the desired therapeutic outcome while LD50 is the dose of the
medication that results in 50% of the animals tested dying.
Fundamental clinical pharmacokinetic variables include, but are not
limited to, [0142] i) a drug's bioavailability (fraction of the
dose which reaches the systemic circulation as intact drug), [0143]
ii) the fraction of the drug circulating unbound (defined by the
extent to which a drug is bound in plasma or blood=[unbound drug
concentration]/[total drug concentration]), [0144] iii) the type of
metabolism the drug undergoes (whether linear or non linear),
[0145] iv) the type of elimination the drug undergoes (e.g.
percentage of drug renally excreted or hepatically metabolized) and
[0146] v) the drug's half-life. Drug safety includes, but is not
limited to, the risk of life threatening side effects
(idiosyncratic reactions) and the risk of teratogenicity. For each
drug under consideration, each variable in the three categories is
scored on a scale from +1 (most favorable) to -1 (most
unfavorable).
Patient Specific Advanced Pharmacology Factors
[0147] Patient specific advanced pharmacology factors include i)
bidirectional pharmacokinetic or pharmacodynamic drug-drug
interactions and ii) bidirectional pharmacodynamic drug-disease
interactions. A pharmacokinetic drug-drug interaction is considered
potentially clinically significant if 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 .gtoreq.20%. Only concomitant medications actually
being taken at the time of the analysis are considered in the
analysis. For drug-disease interactions, the word "diseases" refers
to all forms of altered health ranging from single organ
dysfunction (e.g. renal failure) to whole body illness (e.g.
systemic lupus erythematosus). The potential for drug-drug or
drug-disease interactions is evaluated on a scale from +1 (most
favorable) to -1 (most unfavorable).
[0148] To clarify using an example: In a specific patient, assume
drug A is being evaluated for use in disease D. The patient is
currently taking oral contraceptives, a statin for
hypercholesterolemia and is overweight. To evaluate the "Patient
specific advanced pharmacology factors" for drug A for this patient
there are 8 potential drug-drug interactions and 4 potential
drug-disease interactions to evaluate: i) pharmacokinetic effect of
drug A on oral contraceptives, ii) pharmacokinetic effect of oral
contraceptives on drug A, iii) pharmacokinetic effect of drug A on
statin medications, iv) pharmacokinetic effect of statin medication
on drug A, v)-viii) the same four combinations mentioned previously
but examining the pharmacodynamic interactions between drugs, ix)
pharmacodynamic effect of drug A on hypercholesterolemia, x)
pharmacodynamic effect of hypercholesterolemia on drug A, xi)
pharmacodynamic effect of drug A on weight, xii) pharmacodynamic
effect of weight on drug A. If Drug A has i) a clinically
significant negative effect on statin pharmacokinetics and ii)
causes weight gain then Drug A would receive a score of -1 for
these two assessments and a score of 0 for the remaining 10
evaluations. This approach is repeated for each drug under
consideration (e.g. drugs B, C, . . . etc.).
Patient Specific Environmental Factors
[0149] Patient specific environmental factors involve
unidirectional, pharmacokinetic or pharmacodynamic,
drug-environment interactions. Unidirectional refers to the effect
of the environmental agent on the drug. A pharmacokinetic
drug-environment interaction is considered potentially clinically
significant if 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
.gtoreq.20%. A pharmacodynamic drug-environment interaction is
considered potentially clinically significant if there is a
documented interaction that shows the environmental factor alters
(either positively or negatively) the action of the drug by
.gtoreq.20%. Only environmental factors occurring at the time of
the analysis are considered in the analysis. For drug-environment
interactions, the word "environment" refers to all forms of
exposure ranging from food (grapefruit juice) to herbal/vitamin
supplements (e.g. St. Johns wort) to voluntary toxic exposures
(e.g. smoking or alcohol) to involuntary toxic exposures (second
hand smoke, pesticides). The potential for drug environment
interactions is evaluated on a scale from +1 (most favorable) to -1
(most unfavorable).
Patient Specific Genetic Factors
[0150] Patient specific genetic factors involve unidirectional,
pharmacokinetic or pharmacodynamic, drug-gene interactions.
Unidirectional refers to the effect of the genetic variation on the
pharmacokinetic or pharmacodynamic action of the drug. A
pharmacokinetic drug-gene interaction is considered potentially
clinically significant if 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
.gtoreq.20%. A pharmacodynamic drug-gene interaction is considered
potentially clinically significant if there is a documented
interaction that shows the genetic factor alters (either positively
or negatively) the action of the drug by .gtoreq.20%. For drug-gene
interactions, the word "gene" refers to all forms of genetic
variability including DNA variability, mRNA variability, protein
alterations or metabolite alterations. The potential for drug-gene
interactions is evaluated on a scale from +1 (most favorable) to -1
(most unfavorable).
Disease Matrix
[0151] An example (very small) segment of a disease matrix is
provided in FIG. 3. The disease matrix includes column headings for
distinct treatment modalities (e.g. medication, therapy, surgery,
dietary plan, etc.) while the rows are distinct factors from the
five categories listed above: disease specific evidence based
medicine data, drug specific basic pharmacology characteristics,
patient specific advanced pharmacology principles, patient specific
environmental and patient specific genetic factors. The value in
each cell in the matrix ranges from +1 (favorable quality/result)
to -1 (unfavorable quality/result).
[0152] Referring to the example disease matrix segment in FIG. 3,
the first column 10 lists the specific factor to be evaluated for a
list of specific treatments and/or drugs; column 12 provides the
category for the specific factor; and columns 14-20 provide the
specific disease matrix values that the specific factor associates
with a specific drug or treatment. For example, the factor of Row
8, "Pharmacokinetics (metabolism)," is listed in the "Basic
pharmacology" category and has a wide variance of matrix values or
scores depending upon the proposed drug or treatment: carbamazepine
has a -0.5 matrix value; phenobarbital has a 1.0 matrix value;
phenytoin has a -1.0 matrix value; and topiramate has a 1.0 matrix
value. As another example, the factor of Row 23, "Patient is a
CYP2C9 poor metabolizer," is listed in the "Genetic factors"
category and also has a variance of matrix scores depending upon
the proposed drug or treatment: carbamazepine has a -0.3 matrix
value; phenobarbital has a -1.0 matrix value; phenytoin has a -1.0
matrix value; and topiramate has a 0.0 matrix value.
Patient Vector Column (Matrix)
[0153] A patient vector is constructed for each individual patient.
In the exemplary embodiment, the patient vector is a column (not
shown in FIG. 3) of the disease matrix. Optionally, the patient
vector may be a 1 by N matrix, where N is the number of distinct
factors for that particular disease algorithm taken from the five
categories--listed above: disease specific evidence based medicine
data, drug specific basic pharmacology characteristics, patient
specific advanced pharmacology principles, patient specific
environmental and patient specific genetic factors. The items in
the patient vector are determined by the response to a series of
YES/NO/UNKNOWN questions for each of the variables considered. The
questions are yes/no questions and the matrix enters a 0 (for no),
0.5 (for unknown) or a 1 (for yes).
Weighting Vector
[0154] A weighting vector is constructed for each disease matrix.
In the exemplary embodiment, the weighting vector is a column (not
shown in FIG. 3) of the disease matrix. Optionally, the weighting
vector is a 1 by N matrix, where N is the number of distinct
factors for that particular disease algorithm taken from the five
categories listed above: disease specific evidence based medicine
data, drug specific basic pharmacology characteristics, patient
specific advanced pharmacology principles, patient specific
environmental and patient specific genetic factors. The values in
the weighting vector are determined by either a supervised system
(e.g. expert system) or an unsupervised system (e.g. neural network
or an artificial intelligence system). The weighting is usually
different for the different factors in the disease algorithm. For
example, referring back to FIG. 3, Row 2, "Child with partial
seizures starting therapy" has a weight of claim 1000, Row 13, "The
patient has migraines/headaches" has a weight of claim 150, and Row
23, "Patient is a CYP2C9 poor metabolizer" has a weight of 250.
Algorithm Output
[0155] The main output of the algorithm is a ranking of all
potential therapies (medications, surgeries or diet) for that
specific disease ranging from most likely to be successful (highest
score) to least likely to be successful (lowest score). Each drug's
score is the product of the patient vector, the weighting vector
and the particular drug's column value in the disease matrix. The
dosing for the drug is determined by the algorithm described above.
In the exemplary embodiment, the output display includes the top 5
factors contributing and the lowest 3 factor detracting from the
score are included for evaluation. Above the ranking is an
adherence score reflecting the likelihood the patient will adhere
to the proposed treatment regimen. The determination and
interpretation of this number is described in the Adherence score
section.
Adherence Score
[0156] The adherence score is determined in a similar fashion to
the simplicity index: the score is the product of an "adherence
matrix", a patient vector and a weighting vector. For each disease,
potential adherence problems are assessed using a series of
approximately 10 yes/no/unknown questions. If all questions are
answered unknown then the adherence score will be 50% implying a
50% chance the patient will adhere to the treatment regimens. The
more questions that are answered "no", the higher the adherence
score and the greater the chance the patient will adhere to the
prescribed treatment regimen. The more questions answered "yes",
the lower the adherence score and the greater the chance the
patient will not adhere to the prescribed treatment regimen.
Patient Example
[0157] History: The patient is a 7 year old male presenting with
frequent staring episodes lasting 30-60 seconds associated with
unresponsiveness, facial twitching and extreme tiredness
afterwards. He develops a funny taste in his mouth in the few
minutes before the events occur. He has had about 10 of these in
the past year with 3 in the last month. The patient does not have
depression, ADHD or anxiety but does have frequent migraines. The
patient is currently taking erythromycin for an infection but takes
no chronic medications. There is no family history of epilepsy. The
patient loves to drink grapefruit juice. The family has insurance,
no transportation problems and no identifiable stressors. [0158]
Physical examination: Normal in detail except the patient is very
overweight [0159] Lab tests: Electroencephalogram (EEG) shows
normal background and focal discharges in the temporal lobe.
Magnetic Resonance Imaging (MRI) of the brain is normal.
Pharmacogenetic testing shows a CYP2C9 polymorphism that makes him
a poor metabolism for drugs metabolized by CYP2C9. [0160]
Diagnosis: Newly diagnosed idiopathic partial epilepsy
characterized by partial onset seizures. [0161] Need: Determine the
best antiepileptic medications for this specific patient.
[0162] Step 1: As can be seen if FIG. 4, after logging onto
algorithm program--select disease--a screen will be provided in
which the physician will select in field 22 that the patient's
diagnosis is Epilepsy, but in field 24 that the patient's diagnosis
is not depression.
[0163] Step 2: As can be seen if FIG. 5, a next step--enter age,
gender and puberty status--another screen will be provided in which
the physician selects in field 26 that the patient is between 2 and
18 years old, in field 28 that the patient is male and in field 30
that the patient is pre-pubertal.
[0164] Step 3: As can be seen in FIG. 6, a next step--select type
of epilepsy and whether starting or on medications--another screen
will be provided in which the physician selects in field 32 that
the patient is a child with partial seizures and no previous
treatment. Fields 34-50 are not selected.
[0165] Step 4: As can be seen in FIG. 7, a next step--enter
comorbid conditions--another screen will be provided in which the
physician selects in field 52 that the patient is overweight and in
field 54 that the patient has migraines or headaches. Fields 56-62
are not selected.
[0166] Step 5: As can be seen in FIG. 8, a next step--enter EEG and
MRI test results--another screen will be provided in which the
physician selects in field 64 that the patient's EEG is abnormal
with epileptiform discharges and in field 66 that the patient's
MRI/computed tomography (CT) shows normal cortical structure.
[0167] Step 6: As can be seen in FIG. 9, a next step--enter
concomitant medications--another screen will be provided in which
the physician selects in field 68 that the patient is trucing an
antibiotic, antiviral, antifungal, antiparasitic or
anti-tuberculosis (TB) medications. Fields 70-88 are not
selected.
[0168] Step 7: As can be seen in FIG. 10, a next step--the enter
concomitant medications step is continued and another screen will
be provided for the physician to identify specific antibiotic,
antiviral, antifungal, antiparasitic or anti-TB medications that
the patient is taking. In this example, the physician selects in
field 104 that the patient is taking erythromycin. Fields 90-102
and 106-1 14 are not selected.
[0169] Step 8: As can be seen in FIG. 11, a next step--enter
environmental factors--another screen will be provided in which the
physician selects in field 118 that the patient drinks grapefruit
juice. Fields 116 and 120-120 are not selected since the patient
does not smoke or drink alcohol or green tea.
[0170] Step 9: As can be seen in FIG. 12, a next step--enter
genetic factors--another screen will be provided in which the
physician selects in field 126 that the patient CYP2C9 poor
metabolism. As will be appreciated by those of ordinary skill, such
genetic data may also be entered automatically with the assistance
of the system that analyzes the patient's genetic data.
[0171] Step 10: As can be seen in FIG. 13, a next step--enter
adherence variables--another screen will be provided in which the
physician selects whether the listed variables are present or not,
or are unknown. In this example, all listed variables are selected
as not being present in fields 132, 136-144 and 148-150, except for
fields 134 and 146, which are selected as unknown.
[0172] Step 11: As can be seen in FIG. 14, a next step provides the
output of the disease matrix algorithm to the physician based upon
the previous inputs. As can be seen in this exemplary output,
column 152 lists the recommended drugs for treating the patient,
column 154 provides the score for each drug listed, column 156
provides a filed in which the physician can select to prescribe the
drug, column 158 provides the recommended dosage for the patient,
column 160 provides a bar-graph display for each drug listed that
provides the five most relevant features in generating the score
(the features are defined/explained in the box 161 to the right),
and field 162 indicates the adherence percentage estimate for the
patient. In this example, topiramate is recommended by the
algorithm for the patient, having a score of 2850 and a recommended
dosage of claim 100% of the listed dosage. The patient is
calculated to have a 90% chance of adhering to the drug
treatment.
CONCLUSION
[0173] Having described the invention with reference to the
exemplary embodiments, it is to be understood that it is not
intended that any limitations or elements describing the exemplary
embodiment set forth herein are to be incorporated into the
meanings of the patent claims unless such limitations or elements
are explicitly listed in the claims. Likewise, it is to be
understood that it is not necessary to meet any or all of the
identified advantages or objects of the invention disclose herein
in order to fall within the scope of any claims, since the
invention is defined by the claims and since inherent and/or
unforeseen advantages of the present invention may exist even
though they may not be explicitly discussed herein.
[0174] Finally, it is to be understood that it is also within the
scope of the invention to provide any computer, computer-system
and/or computerized tool as is known by one of ordinary skill in
the art that is designed, programmed or otherwise configured to
perform any of the above-discussed methods, algorithms or
processes.
[0175] All publications, patents, and patent applications mentioned
in the specification are indicative of the level of those skilled
in the art to which this invention pertains. All publications,
patents, and patent applications are herein incorporated by
reference to the same extent as if each individual publication or
patent application was specifically and individually incorporated
by reference.
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