U.S. patent application number 15/707818 was filed with the patent office on 2018-03-08 for method and system for selecting drug on basis of individual protein damage information for preventing side effects of drug.
The applicant listed for this patent is Cipherome, Inc.. Invention is credited to Su Yeon BAIK, Ju Han KIM, Soo Youn LEE.
Application Number | 20180068056 15/707818 |
Document ID | / |
Family ID | 52483861 |
Filed Date | 2018-03-08 |
United States Patent
Application |
20180068056 |
Kind Code |
A1 |
KIM; Ju Han ; et
al. |
March 8, 2018 |
METHOD AND SYSTEM FOR SELECTING DRUG ON BASIS OF INDIVIDUAL PROTEIN
DAMAGE INFORMATION FOR PREVENTING SIDE EFFECTS OF DRUG
Abstract
The present invention relates to a method and a system for
selecting a drug customized on the basis of individual protein
information by using individual genome sequences. The method and
the system of the present invention can predict the individual side
effects or danger of a certain drug by analyzing the sequence of
the exon region of a gene encoding various proteins involved in the
pharmacokinetics or pharmacodynamics of a predetermined drug or
drug group, and have high reliability and are widely applicable and
universal.
Inventors: |
KIM; Ju Han; (Seoul, KR)
; BAIK; Su Yeon; (Pyeongtaek-si, KR) ; LEE; Soo
Youn; (Gwacheon-si, KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cipherome, Inc. |
Cupertino |
CA |
US |
|
|
Family ID: |
52483861 |
Appl. No.: |
15/707818 |
Filed: |
September 18, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14912397 |
Feb 16, 2016 |
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PCT/KR2014/007685 |
Aug 19, 2014 |
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15707818 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 40/00 20190201;
G16B 20/00 20190201; C12Q 2600/106 20130101; G16B 20/20 20190201;
G16C 20/30 20190201; C12Q 2600/156 20130101; C12Q 1/6883
20130101 |
International
Class: |
G06F 19/18 20060101
G06F019/18; G06F 19/24 20060101 G06F019/24; C12Q 1/68 20060101
C12Q001/68 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 19, 2013 |
KR |
10-2013-0097651 |
Claims
1. A method of predicting a functional deficiency of a protein
based on individual genome sequence information of a subject,
comprising the steps of: determining, by an evaluation system, gene
sequence variation information of a gene encoding the protein by
using individual genome sequence information; calculating, by the
evaluation system, an individual protein damage score associated
with the protein using the gene sequence variation information; and
predicting, by the evaluation system, the functional deficiency of
the protein based on the individual protein damage score.
2. The method of claim 1, wherein the gene sequence variation
information comprises a gene sequence variation score calculated
using one or more algorithms selected from the group consisting of
SIFT (Sorting Intolerant From Tolerant), PolyPhen (Polymorphism
Phenotyping), PolyPhen-2, MAPP (Multivariate Analysis of Protein
Polymorphism), Logre (Log R Pfam E-value), MutationAssessor,
MutationTaster, MutationTaster2, PROVEAN (Protein Variation Effect
Analyzer), PMut, Condel, GERP (Genomic Evolutionary Rate
Profiling), GERP++, CEO (Combinatorial Entropy Optimization),
SNPeffect, fathmm, and CADD (Combined Annotation-Dependent
Depletion).
3. The method of claim 2, wherein gene sequence variation
information comprises two or more gene sequence variation scores,
and the protein damage score is calculated as a mean of the two or
more gene sequence variation scores.
4. The method of claim 3, wherein the mean is calculated by one or
more selected from the group consisting of: a geometric mean, an
arithmetic mean, a harmonic mean, an arithmetic geometric mean, an
arithmetic harmonic mean, a geometric harmonic mean, Pythagorean
means, a Heronian mean, a contraharmonic mean, a root mean square
deviation, a centroid mean, an interquartile mean, a quadratic
mean, a truncated mean, a Winsorized mean, a weighted mean, a
weighted geometric mean, a weighted arithmetic mean, a weighted
harmonic mean, a mean of a function, a generalized mean, a
generalized f-mean, a percentile, a maximum value, a minimum value,
a mode, a median, a mid-range, a central tendency, or by simple
multiplication or weighted multiplication.
5. The method of claim 1, wherein the gene sequence variation
information is determined by a comparison analysis with a genome
sequence of a reference group.
6. The method of claim 1, wherein the individual protein damage
score is calculated by the following Equation 1: S g ( v 1 v n ) =
( 1 n i = 1 n v i p ) 1 p [ Equation 1 ] ##EQU00006## wherein in
Equation 1, S.sub.g is a protein damage score of a protein encoded
by a gene g, n is the number of target sequence variations for
analysis among sequence variations of the gene g, v.sub.i is a gene
sequence variation score of an i.sup.th gene sequence variation,
and p is a real number other than 0.
7. The method of claim 1, wherein the individual protein damage
score is calculated by the following Equation 2: S g ( v 1 v n ) =
( i = 1 n v i w i ) 1 / i = 1 n w i [ Equation 2 ] ##EQU00007##
wherein in Equation 2, S.sub.g is a protein damage score of a
protein encoded by a gene g, n is the number of target sequence
variations for analysis among sequence variations of the gene g,
v.sub.i is a gene sequence variation score of an i.sup.th gene
sequence variation, and w.sub.i is a weighting assigned to the gene
sequence variation score v.sub.i of the i.sup.th gene sequence
variation.
8. The method of claim 1, wherein the individual protein damage
score is calculated by assigning a weighting determined considering
a class of the protein, pharmacodynamics or pharmacokinetics of the
protein, a population group of the subject, or a distribution of
protein damage scores in the population.
9. The method of claim 1, further comprising the step of:
predicting, by the evaluation system, an effect of a drug on the
subject based on the functional deficiency of the protein, wherein
the protein is associated with pharmacodynamics or pharmacokinetics
of the drug.
10. The method of claim 9, the protein is a target, an enzyme, a
carrier, or a transporter protein.
11. The method of claim 10, wherein the drug is an inhibitor of the
protein.
12. The method of claim 11, wherein the drug is Rivaroxaban and the
protein is Factor 10.
13. The method of claim 9, wherein the effect of the drug to the
subject comprises: a therapeutic effect, or a side effect to the
subject.
14. The method of claim 1, further comprising the step of:
providing, by the evaluation system, one or more of the information
selected from the group consisting of the gene sequence variation
information, the gene sequence variation score, the individual
protein damage score, and information used for calculation
thereof.
15. The method of claim 14, wherein the one or more of the
information is provided on a user interface of a device of the
subject or a doctor to prevent a side effect of the drug to the
subject.
16. A system for predicting a functional deficiency of a protein,
comprising: a processor; a communication unit configured to receive
individual genome sequence information; a computer readable medium
storing modules executable by the processor, the modules
comprising: a first calculation module configured to determine gene
sequence variation information of a gene encoding the protein by
using the individual genome sequence information, a second
calculation module configured to calculate an individual protein
damage score associated with the protein by using the gene sequence
variation information, the individual protein damage score
providing for prediction of the functional deficiency of the
protein based on the individual protein damage score; and a display
unit configured to display the individual protein damage score for
the prediction of the functional deficiency of the protein.
17. The system of claim 16, further comprising: a fourth
calculation module configured to predict an effect of a drug on the
subject based on the functional deficiency of the protein, wherein
the protein is associated with pharmacodynamics or pharmacokinetics
of the drug.
18. A computer-readable medium comprising an execution module for
executing a processor that performs an operation of predicting a
functional deficiency of a protein, comprising the steps of:
acquiring individual genome sequence information; calculating gene
sequence variation information of a gene encoding the protein by
using the individual genome sequence information; calculating an
individual protein damage score associated with the protein by
using the gene sequence variation information; and predicting the
functional deficiency of the protein based on the individual
protein damage score.
19. The computer-readable medium of claim 18, wherein the execution
module further performs an operation of: predicting an effect of a
drug to the subject based on the functional deficiency of the
protein, wherein the protein is associated with pharmacodynamics or
pharmacokinetics of the drug.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/912,397 filed Feb. 16, 2016, which is the National Stage of
International Application No. PCT/KR2014/007685, filed Aug. 19,
2014, which claims the benefit of KR Application No.
10-2013-0097651, filed Aug. 19, 2013, each of which is incorporated
herein by reference in its entirety.
TECHNICAL FIELD
[0002] The present invention relates to a method and a system for
personalizing drug selection on the basis of individual deleterious
protein sequence variation by using individual genome sequence
analysis.
BACKGROUND ART
[0003] With the advancement of biotechnology, at present, it is
possible to predict a disease of each individual and provide
personalized prevention and treatment of disease by analyzing whole
genome sequence of human.
[0004] Recently, as a result of comparison of individual genome
sequences, it was found that different bases may be present at the
same position in chromosomes. Accordingly, such a difference in a
sequence has been used to predict an individual difference in drug
response. For example, drug metabolism may be slow or rapid
depending on a specific individual genome sequence information,
and, thus, each individual may have different therapeutic effects
or side effects of drug.
[0005] Accordingly, there has been an increase in a demand for
personalizing drug selection which is capable of selecting a drug
and a dose suitable for a patient by using a difference of the
individual genome sequence. Also, pharmacogenetics or
pharmacogenomics, which uses genomic information, for example,
single nucleotide polymorphism (SNP), as a marker and correlation
between the marker and drug response/drug side effect, has
emerged.
[0006] Pharmacogenetics is the study of predicting differences in
metabolism of drugs or chemicals and response thereto in a general
population or between individuals by genetic analysis. Some
individuals may show unexpected drug responses. Such drug side
effects may be due to severity of a disease under treatment, drug
interaction, ages, nutritive conditions, liver and kidney functions
of patients, and environmental factors such as weather or
nourishment. However, they may be also caused by drug
metabolism-related genetic differences, for example, polymorphism
of drug metabolizing enzyme gene. Therefore, the study thereof has
been conducted.
[0007] For example, Korean Patent Laid-open Publication No.
2007-0111475 discloses a technology relating to biomarkers for
identifying efficacy of tegaserod in patients with chronic
constipation, and uses pharmacogenetics to evaluate the effect of
polymorphisms in selecting candidate genes on the response of
patients with chronic constipation to tegaserod
(Zelmac.RTM./Zelnorm.RTM.).
[0008] Meanwhile, it is not easy to find a disease predicting
marker by using statistics on investigating correlation between an
individual genome sequence variation and a disease. This is because
most of the single nucleotide polymorphisms showing statistical
significance have an insignificant effect on development of disease
(odds ratio of 1.1 to 1.5) and are positioned in introns and
intergenic regions, and, thus, it is difficult to deduce a
functional correlation thereof (Hindorff et al., Proc. Natl. Acad.
Sci. 2009; 106(23):9362 to 9367).
[0009] Accordingly, beyond a method based on a result of population
observational studys using a marker such as single nucleotide
polymorphism, a method for providing personalized drug selection
information which is more useful and reliable by directly using
individual genome sequence variation information and conducting
theoretical deduction on protein damage caused thereby and
biological effect thereof strongly needs to be introduced.
DISCLOSURE
Technical Problem
[0010] The present invention conceived in view of the foregoing is
directed to providing a method and a system for providing
information for personalizing drug selection by analyzing
individual genome sequence variation information, calculating an
individual protein damage score from gene sequence variation
information involved in the pharmacodynamics or pharmacokinetics of
a predetermined drug or drug group, and then associating the score
with a drug-protein relation to thereby calculate an individual
drug score.
Technical Solution
[0011] One aspect of the present invention provides a method for
providing information for personalizing drug selection using
individual genome sequence variations, including: determining one
or more gene sequence variation information involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group on the basis of individual genome sequence information;
calculating an individual protein damage score by using the gene
sequence variation information; and associating the individual
protein damage score with a drug-protein relation to thereby
calculate an individual drug score.
[0012] In another aspect, the present invention provides a system
for personalizing drug selection using individual genome sequence
variations, the system including: a database from which information
relevant to a gene or protein related to a drug or drug group
applicable to an individual can be searched or extracted; a
communication unit accessible to the database; a first calculation
module configured to calculate one or more gene sequence variation
information involved in the pharmacodynamics or pharmacokinetics of
the drug or drug group on the basis of the information; a second
calculation module configured to calculate an individual protein
damage score by using the gene sequence variation information; a
third calculation module configured to calculate an individual drug
score by associating the individual protein damage score with a
drug-protein relation; and a display unit configured to display the
values calculated by the calculation modules.
[0013] In another aspect, the present invention provides a
computer-readable medium including an execution module for
executing a processor that performs an operation including:
acquiring gene sequence variation information involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group from individual genome sequence information; calculating
an individual protein damage score by using the gene sequence
variation information; and associating the individual protein
damage score with a drug-protein relation to thereby calculate an
individual drug score.
Advantageous Effects
[0014] A method and a system for personalizing drug selection on
the basis of individual genome sequence variation information of
the present invention can predict the individual responsiveness to
a specific drug by analyzing the sequence of the exon region of a
gene encoding various proteins involved in the pharmacodynamics or
pharmacokinetics of a predetermined drug or drug group, and have
high reliability and are widely applicable to a whole range of
drugs and universal. That is, the method and the system of the
present invention are universal technologies applicable to a whole
range of drugs from which protein information involved in the
pharmacodynamics or pharmacokinetics can be acquired with respect
to metabolism, effects or side effects of drugs.
[0015] Further, conventionally, while a pharmacogenomics study
needs to be conducted on each drug-gene pair, it is practically
impossible to study all of the numerous drug-gene pairs because the
number of pairs increases in proportion to the multiple of the
number of drugs and the number of gene markers. Thus, sufficient
supporting data have not yet been generated, and selection of study
subjects and a difference between population groups lead to a high
statistical error. However, according to the method of the present
invention, results of study and analysis at a molecular level are
directly applied to personalized drug treatment, and, thus, grounds
of almost all of drug-gene pairs can be acquired and the method can
be applied without being significantly affected by a difference
between population groups.
[0016] If the method and the system of the present invention are
used, it is possible to effectively personalize drug selection
among one selected drug, two or more drugs in need of selection, or
various comparable drugs belonging to the same drug group which can
be used in a specific medical condition, and also possible to
predict side effects or risks of drugs. Therefore, the method and
the system of the present invention can be used to determine the
order of priorities among drugs applicable to an individual or to
determine whether or not to use the drugs.
[0017] Further, if new information about a drug-protein relation is
found or provided, it can be easily added and applied to the method
of the present invention. Thus, it is possible to provide an
improved personalized drug treatment method according to further
accumulation of information as results of studies.
DESCRIPTION OF DRAWINGS
[0018] FIG. 1 is a flowchart illustrating each step of a method for
providing information for personalizing drug selection using
individual genome sequence variations according to an exemplary
embodiment of the present invention.
[0019] FIG. 2 is a schematic configuration view of a system for
personalizing drug selection using individual genome sequence
variations according to an exemplary embodiment of the present
invention (DB: Database).
[0020] FIG. 3 is a diagram illustrating the number of gene
variations for each protein, a protein damage score, and a drug
score of a corresponding individual as calculated with respect to a
drug Terbutaline, by using a method according to the present
invention on the basis of individual genome sequence variation
information.
[0021] FIG. 4 is a diagram illustrating the number of gene
variations for each protein, a protein damage score, and a drug
score of a corresponding individual as calculated with respect to a
plurality of drugs (Aspirin (acetylsalicylic acid) and Tylenol
(acetaminophen)) as comparison targets, by using a method according
to the present invention on the basis of individual genome sequence
variation information.
[0022] FIG. 5 is a diagram illustrating a calculated individual
drug score profile of 14 persons with respect to 22 drugs belonging
to ATC (Anatomical Therapeutic Chemical Classification System) Code
C07 beta blockers, by using a method according to the present
invention on the basis of individual genome sequence variation
information.
[0023] FIG. 6 is a diagram illustrating the number of gene
variations for each protein, a protein damage score, and a drug
score of an individual as calculated with respect to propranolol as
a non-specific beta blocker and betaxolol as a specific beta
blocker, by using a method according to the present invention on
the basis of individual genome sequence variation information.
[0024] FIGS. 7A-7B illustrate the validity of drug score
calculation (AUC (Area Under Curve) calculated on the basis of a
comparison analysis with gene-drug pairs provided by PharmGKB
Knowledge Base and individual genome sequence variation information
of 1092 persons provided by the 1000 Genomes Project, by using a
method according to the present invention. FIG. 7A illustrates the
validity of individual drug score calculation (AUC) calculated by a
simple geometric mean formula to which a weighting for each protein
class is not applied, FIG. 7B illustrates the validity of
individual drug score calculation (AUC) calculated by a weighted
geometric mean formula to which a weighting for each protein class
is applied).
[0025] FIGS. 8A-8B illustrate the distribution of means and
standard deviations of individual protein damage scores and drug
scores calculated by using a method according to the present
invention on the basis of individual genome sequence variation
information in 12 pediatric leukemia patients (FIG. 8A) exhibiting
warning signs of serious side effects during a treatment with
Busulfan as an anticancer drug and bone-marrow inhibitor and 14
cases in a normal control group (FIG. 8B). A size of each shape
means the number of gene sequence variations.
[0026] FIGS. 9A-9B illustrate a relative frequency histogram
displaying a relative frequency of withdrawn drugs from the market
as obtained from DrugBank and Wikipedia (FIG. 9A) and withdrawn
drugs from the market and drugs restricted to use as obtained from
the U.N. (FIG. 9B) against a population group drug score calculated
on the basis of individual genome sequence variation information of
1092 persons provided by the 1000 Genomes Project by using a method
according to the present invention.
MODES OF THE INVENTION
[0027] The present invention is based on the finding that it is
possible to select a highly safe drug and dose/usage individually
in a drug treatment for treating a specific disease by analyzing
individual genome sequence variation information.
[0028] One aspect of the present invention provides a method for
providing information for personalizing drug selection using
individual genome sequence variations, including: determining one
or more gene sequence variation information involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group on the basis of individual genome sequence information;
calculating an individual protein damage score by using the gene
sequence variation information; and associating the individual
protein damage score with a drug-protein relation to thereby
calculate an individual drug score.
[0029] The gene sequence variation used as information in the
method of the present invention refers to an individual gene
sequence variation or polymorphism. In the present invention, the
gene sequence variation or polymorphism occurs particularly in an
exon region of a gene encoding proteins involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group, but is not limited thereto.
[0030] The term "sequence variation information" used herein means
information about substitution, addition, or deletion of a base
constituting an exon of a gene. Such substitution, addition, or
deletion of the base may result from various causes, for example,
structural differences including mutation, breakage, deletion,
duplication, inversion, and/or translocation of a chromosome.
[0031] In another aspect, a polymorphism of a sequence refers to
individual differences in a sequence present in a genome. In the
polymorphism of a sequence, single nucleotide polymorphisms (SNPs)
are in the majority. The single nucleotide polymorphism refers to
individual differences in one base of a sequence consisting of A,
T, C, and G bases. The sequence polymorphism including the SNP can
be expressed as a SNV (Single Nucleotide Variation), STRP (short
tandem repeat polymorphism), or a polyalleic variation including
VNTR (various number of tandem repeat) and CNV (Copy number
variation).
[0032] In the method of the present invention, sequence variation
or polymorphism information found in an individual genome is
collected in association with a protein involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group. That is, the sequence variation information used in the
present invention is variation information found particularly in an
exon region of one or more genes involved in the pharmacodynamics
or pharmacokinetics of a drug or drug group effective in treating a
specific disease, for example, genes encoding a target protein
relevant to a drug, an enzyme protein involved in drug metabolism,
a transporter protein, and a carrier protein, among the obtained
individual genome sequence information, but is not limited
thereto.
[0033] The term "pharmacokinetics (pk) or pharmacokinetic
parameters" used herein refers to characteristics of a drug
involved in absorption, migration, distribution, conversion, and
excretion of the drug in the body for a predetermined time period,
and includes a volume of distribution (Vd), a clearance rate (CL),
bioavailability (F) and absorption rate coefficient (ka) of a drug,
or a maximum plasma concentration (Cmax), a time point of maximum
plasma concentration (Tmax), an area under the curve (AUC)
regarding a change in plasma concentration for a certain time
period, and so on.
[0034] The term "pharmacodynamics or pharmacodynamic parameters"
used herein refers to characteristics involved in physiological and
biochemical behaviors of a drug with respect to the body and
mechanisms thereof, i.e., responses or effects in the body caused
by the drug.
[0035] A list of genes involved in the pharmacodynamics or
pharmacokinetics of a predetermined drug or drug group is provided
in the following Table 1 to Table 15. To be more specific, among
920 drugs extracted by mapping top 15 frequently prescribed drug
classes during 2005 to 2008 in the United States provided in a
report (Health, United States, 2011, Centers for Disease Control
and Prevention (CDC)) issued from the CDC with ATC codes as the
standard drug classification codes, 395 drugs, of which at least
one gene involved in the pharmacodynamics or pharmacokinetics is
known, provided from DrugBank ver 3.0 and KEGG Drug database and
pairs of the drugs and genes are listed in the following Table 1 to
Table 15. In the following Table 1 to Table 15, genes/proteins are
expressed according to the HGNC (HUGO Gene Nomenclature Committee)
nomenclature (Gray K A, Daugherty L C, Gordon S M, Seal R L, Wright
M W, Bruford E A. genenames.org: the HGNC resources in 2013.
Nucleic Acids Res. 2013 January; 41(Database issue):D545-52. doi:
10.1093/nar/gks1066. Epub 2012 Nov. 17 PMID:23161694).
[0036] Further, gene/protein information involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group can be acquired from the database such as
DrugBank(http://www.drugbank.ca/), KEGG
Drughttp://www.genome.jp/kegg/drug/), or
PharmGKB(https://www.pharmgkb.org/). The following Table 1 to Table
15 are just examples, but the present invention is not limited
thereto.
TABLE-US-00001 TABLE 1 ACE inhibitors [C09A] Enzyme Transporter
Drugs Target protein protein Carrier protein protein Benazepril ACE
MTHFR SLC15A1, SLC15A2 Captopril ACE, MMP2 CYP2D6 ABCB1, ALB
SLC15A1, SLC15A2, SLC22A6 Cilazapril ACE ABCB1, SLC15A1, SLC15A2
Enalapril ACE CYP3A4 ABCB1, SLC15A1, SLC15A2, SLC22A6, SLC22A7,
SLC22A8, SLCO1A2 Fosinopril ACE SLC15A1, SLC15A2 Lisinopril ACE,
ACE2 ABCB1, SLC15A1 Moexipril ACE, ACE2 SLC15A1, SLC15A2
Perindopril ACE ABCB1, SLC15A1, SLC15A2 Quinapril ACE SLC15A1,
SLC15A2 Ramipril ACE SLC15A1, SLC15A2 Spirapril ACE SLC15A1,
SLC15A2 Trandolapril ACE SLC15A1, SLC15A2 Bupropion CHRNA3, CYP1A2,
ORM1 SLC6A2, CYP2A6, SLC6A3 CYP2B6, CYP2C8, CYP2C9, CYP2D6, CYP2E1,
CYP3A4
TABLE-US-00002 TABLE 2 Analgesics [N02] Transporter Drugs Target
protein Enzyme protein Carrier protein protein Acetaminophen PTGS1,
PTGS2 CYP1A1, CYP1A2, ABCB1, CYP2A6, CYP2C8, SLC22A6 CYP2C9,
CYP2D6, CYP2E1, CYP3A4 Acetylsalicylic AKR1C1, CYP2C19, CYP2C8,
ABCB1, ALB acid PTGS1, PTGS2 CYP2C9 SLC16A1, SLC22A10, SLC22A11,
SLC22A6, SLC22A7, SLC22A8, SLCO2B1 Almotriptan HTR1B, HTR1D CYP1A2,
CYP2C19, CYP2C8, CYP2D6, CYP2E1, CYP3A4, FMO3, MAOA Aminophenazone
CYP17A1, CYP1A2, SLC22A6 CYP2C18, CYP2C19, CYP2C8, CYP2C9, CYP2D6,
CYP3A4, CYP3A7 Antipyrine PTGS1, PTGS2 CYP1A2, CYP2A6, SLC22A6
CYP2B6, CYP2C18, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP2E1, CYP3A4
Buprenorphine OPRD1, OPRK1, CYP1A2, CYP2A6, ABCB1, OPRM1 CYP2C18,
ABCG2 CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP3A4, CYP3A5, CYP3A7,
UGT1A9 Butorphanol OPRD1, OPRK1, OPRM1 Dezocine OPRK1, OPRM1
Diflunisal PTGS1, PTGS2 SLC22A6 ALB, TTR Dihydroergotamine ADRA1A,
CYP3A4 ABCB1 ADRA1B, ADRA1D, ADRA2A, DRD1, DRD2, GABRA1, HTR1A,
HTR1B, HTR1D, HTR2A, HTR2B Dipyrone PTGS1 CYP2B6, CYP3A4 Eletriptan
HTR1A, HTR1B, CYP2A6, CYP2C19, ABCB1 HTR1D, HTR1E, CYP2C9, CYP2D6,
HTR1F, HTR2B, CYP3A4, PTGS1 HTR7 Ergotamine ADRA1A, CYP1A2, CYP3A4
ABCB1 ADRA1B, ADRA1D, ADRA2A, ADRA2B, DRD2, HTR1A, HTR1B, HTR1D,
HTR2A, SLC6A2 Fentanyl OPRD1, OPRK1, CYP3A4, CYP3A5, ABCB1 OPRM1
CYP3A7 Frovatriptan HTR1B, HTR1D CYP1A2 Heroin OPRD1, OPRK1,
CYP2C8, CYP2D6, ABCB1 OPRM1 CYP3A4, UGT1A1, UGT1A3, UGT1A8, UGT2B4,
UGT2B7 Hydromorphone OPRD1, OPRK1, CYP2C9, CYP2D6, OPRM1 CYP3A4,
PTGS1, UGT1A9 Lisuride ADRA2A, CYP2D6, CYP3A4 ADRA2B, ADRA2C, DRD1,
DRD2, DRD3, DRD4, DRD5, HTR1A, HTR1B, HTR1D, HTR2A, HTR2B, HTR2C
Meperidine GRIN1, CYP2B6, CYP2C19, ALB, ORM1 GRIN2A, CYP2D6, CYP3A4
GRIN2B, GRIN2C, GRIN2D, OPRK1, OPRM1 Methoxyflurane ATP2C1, CYP1A2,
CYP2A6, ATP5D, CYP2B6, CYP2C9, GABRA1, CYP2D6, CYP2E1, GLRA1,
GRIA1, CYP3A4 KCNA1 Methysergide HTR1A, HTR1B, HTR1D, HTR2A, HTR2B,
HTR2C, HTR7 Nalbuphine OPRD1, OPRK1, OPRM1 Naratriptan HTR1A,
HTR1B, HTR1D, HTR1F Oxycodone OPRD1, OPRK1, CYP2D6, CYP3A4, OPRM1
CYP3A5, CYP3A7 Pentazocine OPRK1, OPRM1, SIGMAR1 Phenacetin PTGS1
CYP1A1, CYP1A2, SLC22A6 CYP2A13, CYP2A6, CYP2C19, CYP2C9, CYP2D6,
CYP2E1, CYP3A4 Propoxyphene OPRD1, OPRK1, CYP2C8, CYP2C9, OPRM1
CYP2D6, CYP3A4, CYP3A7 Rizatriptan HTR1B, HTR1D CYP1A2, MAOA HTR1F
Salicylamide PTGS1, PTGS2 Salsalate PTGS1, PTGS2 Sumatriptan HTR1A,
HTR1B, MAOA ABCB1, HTR1D, HTR1F ABCG2, SLCO1A2, SLCO1B1 Tramadol
CHRFAM7A, CYP2B6, CYP2D6, CHRM3, CYP3A4 GRIN3A, HTR2C, OPRD1,
OPRK1, OPRM1, SLC6A2, SLC6A4 Ziconotide CACNA1B Zolmitriptan HTR1A,
HTR1B, CYP1A2, MAOA HTR1D, HTR1F Clonidine ADRA2A, CYP1A1, CYP1A2,
ABCB1, ADRA2B, CYP2D6, CYP3A4, SLC22A1, ADRA2C CYP3A5 SLC22A3,
SLC22A4, SLC22A5
TABLE-US-00003 TABLE 3 Anti-diabetes [A10] Carrier Transporter
Drugs Target protein Enzyme protein protein protein Acarbose AMY2A,
AMY2B, GAA, GANC, MGAM, SI Acetohexamide ABCC8, CBR1 ABCC9, KCNJ1
Buformin PRKAA1, SLC22A1 PRKAA2 Chlorpropamide ABCC8, CYP2C19,
ABCB1, ABCC9 CYP2C9, PTGS1 SLC15A1, SLC15A2, SLC22A6 Exenatide
GLP1R Gliclazide ABCC8, CYP2C19, CYP2C9 ALB ABCC9, VEGFA
Glimepiride ABCC8, CYP2C9 ABCC9, KCNJ1, KCNJ11 Glipizide ABCC8,
CYP2C9, CYP3A4 ABCC9, PPARG Gliquidone ABCC8, ABCC9, KCNJ8
Glisoxepide ABCC8, ABCC9, KCNJ8 Glyburide ABCA1, CYP2C19, ABCB1,
ALB ABCB11, CYP2C9, CYP3A4 ABCB11, ABCC8, ABCC1, ABCC9, ABCC2,
CFTR, KCNJ1, ABCC3, KCNJ11, ABCG2, KCNJ5 SLC15A1, SLC15A2, SLC22A6,
SLC22A7, SLCO1A2, SLCO2B1 Glycodiazine ABCC8, ABCC9, KCNJ1 Insulin
Aspart INSR CYP1A2 Insulin Detemir INSR CYP1A2 ALB Insulin Glargine
IGF1R, INSR CYP1A2 Insulin INSR CYP1A2 Glulisine Insulin Lispro
IGF1R, INSR CYP1A2 Insulin aspart INSR (genetical recombination)
Insulin detemir INSR (genetical recombination) Insulin glargine
INSR (genetical recombination) Insulin lispro INSR (genetical
recombination) Liraglutide GLP1R Liraglutide GLP1R (genetical
recombination) Metformin PRKAA1, SLC22A1, PRKAA2, SLC22A2, PRKAB1
SLC47A1, SLC47A2 Miglitol GAA, GANC, AMY2A MGAM Mitiglinide ABCC8,
UGT1A3, ABCC9, UGT1A9, UGT2B7 PPARG Nateglinide ABCC8, CYP2C9,
CYP2D6, ABCC4, ALB, ORM1 ABCC9, CYP3A4, CYP3A5, SLC15A1, PPARG
CYP3A7, PTGS1, SLC15A2, UGT1A9 SLC16A1, SLC22A6 Phenformin KCNJ8,
CYP2D6 SLC22A1, PRKAA1 SLC22A2 Pioglitazone PPARG CYP2C19, SLCO1B1,
CYP2C8, CYP2C9, SLCO1B3 CYP2D6, CYP3A4, PTGS1 Pramlintide CALCR,
RAMP1, RAMP2, RAMP3 Repaglinide ABCC8, CYP2C8, CYP3A4 SLCO1B1 ALB
ABCC9, PPARG Rosiglitazone ACSL4, CYP1A2, CYP2A6, SLCO1B1 PPARG
CYP2C19, CYP2C8, CYP2C9, CYP2D6, PTGS1 Saxagliptin DPP4 CYP3A4,
CYP3A5 Sitagliptin DPP4 CYP2C8, CYP3A4 ABCB1 Tolazamide ABCC8,
ABCC9, KCNJ1 Tolbutamide ABCC8, CYP2C18, SLC15A1, KCNJ1 CYP2C19,
SLC15A2, CYP2C8, CYP2C9 SLC22A6, SLCO1A2, SLCO2B1 Troglitazone
ACSL4, CYP19A1, ABCB11, FABP4 CYP2C8, CYP1A1, CYP2B6, SLCO1B1
ESRRA, CYP2C19, ESRRG, CYP2C8, CYP2C9, PPARG, CYP3A4, CYP3A5,
SERPINE1, CYP3A7, UGT1A1, SLC29A1 UGT1A10, UGT1A3, UGT1A4, UGT1A8,
UGT1A9, UGT2B7 Vildagliptin DPP4 Voglibose GAA, GANC, MGAM
TABLE-US-00004 TABLE 4 Antidepressants [N06A] Transporter Drugs
Target protein Enzyme protein Carrier protein protein Amineptine
SLC6A2, SLC6A4 Amitriptyline ADRA1A, CYP1A2, CYP2B6, ABCB1 ALB,
ORM1 ADRA1D, CYP2C19, CYP2C8, ADRA2A, CYP2C9, CYP2D6, CHRM1,
CYP2E1, CYP3A4, CHRM2, CYP3A5 CHRM3, CHRM4, CHRM5, HRH1, HTR1A,
HTR2A, KCNA1, KCND2, KCND3, KCNQ2, NTRK1, NTRK2, OPRD1, OPRK1,
SLC6A2, SLC6A4 Amoxapine ADRA1A, CYP2D6 ORM1 ADRA2A, CHRM1, DRD1,
DRD2, GABRA1, SLC6A2, SLC6A4 Citalopram ADRA1A, CYP1A2, CYP2B6,
ABCB1 CHRM1, HRH1, CYP2C19, CYP2D6, SLC6A2, CYP2E1, CYP3A4 SLC6A3,
SLC6A4 Clomipramine GSTP1, HTR2A, CYP1A2, CYP2C19, ABCB1 HTR2B,
CYP2D6, CYP3A4 HTR2C, SLC6A2, SLC6A4 Desipramine ADRA1A, CYP1A2,
CYP2A6, ABCB1, ORM1 ADRB1, CYP2B6, CYP2C18, SLC22A1, ADRB2,
CYP2C19, CYP2D6, SLC22A2, CHRM1, CYP2E1, CYP3A4 SLC22A3, CHRM2,
SLC22A4, CHRM3, SLC22A5 CHRM4, CHRM5, HRH1, HTR2A, SLC6A2, SLC6A4,
SMPD1 Desvenlafaxine SLC6A2, CYP3A4 SLC6A4 Doxepin ADRA1A, CYP1A2,
CYP2C19, ABCB1 ORM1 ADRA1B, CYP2C9, CYP2D6 ADRA1D, ADRA2A, ADRA2B,
ADRA2C, CHRM1, CHRM2, CHRM3, CHRM4, CHRM5, DRD2, HRH1, HRH2, HTR1A,
HTR2A, HTR2B, HTR2C, SLC6A2, SLC6A4 Duloxetine SLC6A2, CYP1A2,
CYP2D6 SLC6A3, SLC6A4 Fluoxetine HTR2A, CYP1A2, CYP2B6, ABCB1
SLC6A4 CYP2C19, CYP2C9, CYP2D6, CYP2E1, CYP3A4 Fluvoxamine SLC6A4
CYP1A1, CYP1A2, ABCB1 CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP2E1,
CYP3A4, CYP3A5, CYP3A7 Imipramine ADRA1A, CYP1A2, CYP2B6 ABCB1,
ORM1 ADRA1D, CYP2C18, CYP2C19, SLC22A1, CHRM1, CYP2D6, CYP2E1,
SLC22A2, CHRM2, CYP3A4, CYP3A7 SLC22A3, CHRM3, SLC22A4 CHRM4,
CHRM5, HRH1, HTR2A, KCND2, KCND3, SLC6A2, SLC6A4 Iproniazid MAOA,
MAOB MAOB Isocarboxazid MAOA, MAOB L-Citrulline ASS1, DDAH1, DDAH2,
NOS1, NOS2, NOS3, OTC, PADI1, PADI2, PADI3, PADI4, PADI6
L-Tryptophan WARS, WARS2 DDC, IDO1, TDO2, SLC16A10, TPH1, TPH2,
WARS, SLC16A2 WARS2 Maprotiline ADRA1A, CYP1A2, CYP2D6 ABCB1 ORM1
CHRM1, CHRM2, CHRM3, CHRM4, CHRM5, HRH1, SLC6A2 Mianserin ADRA1A,
CYP1A2, CYP2B6, ADRA1B, CYP2C19, CYP2D6, ADRA1D, CYP3A4 ADRA2A,
ADRA2B, ADRA2C, HRH1, HTR1A, HTR1B, HTR1D, HTR1E, HTR1F, HTR2A,
HTR2B, HTR2C, SLC6A2, SLC6A4 Milnacipran SLC6A2, SLC6A4 Minaprine
ACHE, CHRM1, CYP2D6 DRD1, DRD2, HTR2A, HTR2B, HTR2C, MAOA, SLC6A4
Mirtazapine ADRA2A, CYP1A2, CYP2C8, ADRA2B, CYP2C9, CYP2D6, ADRA2C,
CYP3A4 HRH1, HTR2A, HTR2B, HTR2C, HTR3A, HTR3B, HTR3C, HTR3D,
HTR3E, OPRK1 Moclobemide MAOA CYP1A2, CYP2C19, CYP2C9, CYP2D6,
MAOA, MAOB Nefazodone ADRA1A, CYP2B6, CYP2D6, ABCB1 ADRA1B, CYP3A4,
CYP3A5, ADRA2A, CYP3A7 HTR1A, HTR2A, HTR2C, SLC6A2, SLC6A3, SLC6A4
Nialamide MAOA, MAOB Nomifensine SLC6A2, ORM1 SLC6A3 Nortriptyline
ADRA1A, CYP1A2, CYP2C19, ALB, ORM1 ADRA1D, CYP2C9, CYP2D6, CHRM1,
CYP2E1, CYP3A4, CHRM2, CYP3A5, PTGS1 CHRM3, CHRM4, CHRM5, HRH1
HTR1A, HTR2A, SLC6A2, SLC6A4 Paroxetine CHRM1, CYP2B6, CYP2C8,
ABCB1 CHRM2, CYP2C9, CYP2D6 CHRM3, CHRM4, CHRM5, HTR2A, SLC6A2,
SLC6A4 Phenelzine ABAT, AOC3, CYP2C19, CYP2C8, GAD2, GPT, CYP2E1,
CYP3A4, GPT2, MAOA, CYP3A43, CYP3A5, MAOB CYP3A7, MAOA, MAOB
Protriptyline SLC6A2, CYP2D6 ABCB1 SLC6A4 Reboxetine SLC6A2 CYP2D6,
CYP3A4 ABCB1 Sertraline SLC6A3, CYP1A2, CYP2B6, ABCB1 SLC6A4
CYP2C19, CYP2C9, CYP2D6, CYP3A4, MAOA, MAOB Tranylcypromine MAOA,
MAOB CYP1A2, CYP2A6, CYP2C19, CYP2C9, CYP2D6, CYP2E1, CYP3A4
Trazodone ADRA1A, CYP2D6, CYP3A4, ABCB1 ORM1 ADRA2A, CYP3A5, CYP3A7
HRH1, HTR1A, HTR2A, HTR2C, SLC6A4 Trimipramine ADRA1A, CYP2C19,
CYP2C9, ABCB1 ADRA1B, CYP2D6, CYP3A4 ADRA2B, DRD1, DRD2, HRH1,
HTR1A, HTR2A, SLC6A2, SLC6A3, SLC6A4 Venlafaxine SLC6A2, CYP2B6,
CYP2C19, ABCB1 SLC6A3, CYP2C9, CYP2D6, SLC6A4 CYP3A4 Vilazodone
HTR1A, CYP2C18, CYP2D6, SLC6A4 CYP3A4 Zimelidine SLC6A4 ABCB1
TABLE-US-00005 TABLE 5 Antihistamines for systemic use [R06]
Carrier Transporter Drugs Target protein Enzyme protein protein
protein Antazoline HRH1 Astemizole HRH1, CYP2D6, CYP2J2, ABCB1
KCNH2 CYP3A4, CYP3A5, CYP3A7 Azatadine HRH1 CYP3A4 Azelastine HRH1
CYP1A1, CYP1A2, ABCB1 CYP2A6, CYP2B6, CYP2C19, CYP2C8, CYP2C9,
CYP2D6, CYP2E1, CYP3A4, CYP3A5 Bromodiphenhydramine HRH1 SLC22A6,
SLC47A1 Brompheniramine CHRM1, CYP2B6, CHRM2, CYP2C19, CHRM3,
CYP2C8, CYP2C9, CHRM4, CYP2D6, CYP2E1, CHRM5, CYP3A4 HRH1 Buclizine
CHRM1, HRH1 Carbinoxamine HRH1 CYP2B6, CYP2C19, CYP2C8, CYP2C9,
CYP2D6, CYP2E1, CYP3A4 Cetirizine HRH1 Chloropyramine HRH1
Chlorpheniramine HRH1, CYP2D6, CYP3A4, SLC22A1, SLC6A2, CYP3A5,
CYP3A7 SLC22A2 SLC6A3, SLC6A4 Clemastine HRH1 CYP2D6, CYP3A4
Cyclizine HRH1, CYP2C9 SULT1E1 Cyproheptadine CHRM1, CHRM2, CHRM3,
HRH1, HTR2A, HTR2C Desloratadine HRH1 CYP1A2, ABCB1 CYP2C19,
CYP2C9, CYP2D6 Dimethindene CHRM2, HRH1 Diphenhydramine HRH1,
CYP1A2, CYP2B6, SLC22A1, SLC6A3 CYP2C18, SLC22A2, CYP2C19, SLC22A5
CYP2C9, CYP2D6, PTGS1 Doxylamine CHRM1, HRH1 Epinastine ADRA1A,
CYP2B6, CYP2D6, ABCB1 ADRA2A, CYP3A4 HRH1, HRH2, HTR2A, HTR7
Fexofenadine HRH1 CYP2D6 ABCB1, ABCC3, SLCO1A2, SLCO1B3, SLCO2B1
Isothipendyl HRH1 Ketotifen HRH1, PDE4A, PDE4B, PDE4C, PDE4D,
PDE7A, PDE7B, PDE8A, PDE8B, PGD Loratadine HRH1 CYP2C19, ABCB1
CYP2C8, CYP2C9, CYP2D6, CYP3A4 Meclizine HRH1 CYP1A2 Mepyramine
HRH1 CYP2D6 Mequitazine HRH1 CYP2D6, CYP3A4 Methdilazine HRH1
Phenindamine HRH1 Pheniramine HRH1 Promethazine ADRA1A, CYP2B6,
CYP2C9, ABCB1 CALM1, CYP2D6 CHRM1, CHRM2, CHRM3, CHRM4, CHRM5,
DRD2, HRH1, HTR2A Terfenadine CHRM3, CYP2C8, CYP2C9, ABCB1 HRH1,
CYP2D6, CYP2J2, KCNH2 CYP3A4, CYP3A5, CYP3A7 Thiethylperazine DRD1,
DRD2, DRD4 Trimeprazine HRH1 CYP3A4 Tripelennamine HRH1 CYP2D6
Triprolidine HRH1 CYP2D6
TABLE-US-00006 TABLE 6 Antihypertensives [C02] Transporter Drugs
Target protein Enzyme protein Carrier protein protein Ambrisentan
EDNRA Bethanidine ADRA2A, ADRA2B, ADRA2C, KCNJ1 Bosentan EDNRA,
EDNRB CYP2C19, CYP2C9, ABCB11 CYP3A4 Debrisoquin SLC6A2 CYP1A1,
CYP2D6 ABCB1 Deserpidine SLC18A2 Diazoxide ABCC8, ATP1A1, CA1, CA2,
KCNJ11, KCNMA1, SLC12A3 Doxazosin ADRA1A, CYP2C19, CYP2D6 ABCB1
ORM1 ADRA1B, ADRA1D, KCNH2, KCNH6, KCNH7 Guanethidine SLC6A2 CYP3A4
Guanfacine ADRA2A, CYP2C19, CYP2C9, ADRA2B, CYP3A4 ADRA2C
Hydralazine AOC3, P4HA1 CYP3A4 Mecamylamine CHRNA1, CHRNA10,
CHRNA2, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNA9, CHRNB1,
CHRNB2, CHRNB3, CHRNB4, CHRND, CHRNE, CHRNG Methyldopa ADRA2A, COMT
SLC15A1 ADRA2B, ADRA2C, DDC Metyrosine TH Minoxidil ABCC8, KCNJ1,
PTGS1 Nitroprusside NPR1 CYP1A1, CYP1A2 Pargyline MAOA, MAOB
Prazosin ADRA1A, CYP1A1 ABCB1, ABCG2, ORM1 ADRA1B, SLC22A1, ADRA1D,
SLC22A2, KCNH2, SLC22A3 KCNH6, KCNH7 Rescinnamine ACE Reserpine
SLC18A1, CYP3A5 ABCB1, SLC18A2 ABCB11, ABCC2, SLC22A1, SLC22A2
Sitaxentan EDNRA, EDNRB CYP2C19, CYP2C9, CYP3A4 Trimethaphan
CHRNA10 BCHE
TABLE-US-00007 TABLE 7 Anxiolytics and Hypnotics/sedatives
[N05B|N05C] Transporter Drugs Target protein Enzyme protein Carrier
protein protein Adinazolam GABRA1, CYP2C19, CYP3A4 GABRA2, GABRA3,
GABRA5, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2,
GABRG3, GABRP, GABRR1, GABRR2, GABRR3 Alprazolam GABRA1, CYP2C9,
CYP3A4, GABRA2, CYP3A5, CYP3A7 GABRA3, GABRA4, GABRA5, GABRA6,
GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3,
GABRP, GABRQ, GABRR1, GABRR2, GABRR3, TSPO Amobarbital CHRNA4,
CYP2A6 CHRNA7, GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6,
GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3,
GABRP, GABRQ, GRIA2, GRIK2 Aprobarbital CHRNA4, CHRNA7, GABRA1,
GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GRIA2, GRIK2 Bromazepam
GABRA1, CYP1A2, CYP2C19, GABRA2, CYP2E1, CYP3A4 GABRA3, GABRA4,
GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1,
GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3 Buspirone
DRD2, HTR1A CYP2D6, CYP3A4, ABCB1 CYP3A5, CYP3A7 Butethal CHRNA4,
CHRNA7, GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GRIA2,
GRIK2 Chlordiazepoxide GABRA1, CYP2D6, CYP3A4 GABRA2, GABRA3,
GABRA4, GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE,
GABRG1, GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3
Cinolazepam GABRA1, GABRA2, GABRA3, GABRA5, GABRB1, GABRB2, GABRB3,
GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRR1, GABRR2, GABRR3
Clobazam GABRA1, CYP2B6, CYP2C18, GABRA2, CYP2C19, CYP3A4 GABRA3,
GABRA4, GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE,
GABRG1, GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3
Clorazepate GABRA1, CYP3A4 GABRA2, GABRA3, GABRA4, GABRA5, GABRA6,
GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3,
GABRP, GABRQ, GABRR1, GABRR2, GABRR3, TSPO Clotiazepam GABRA1,
CYP2B6, CYP2C18, GABRA2, CYP2C19, CYP3A4 GABRA3, GABRA4, GABRA5,
GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2,
GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3 Dexmedetomidine
ADRA2A, ADRA2B, ADRA2C Diazepam GABRA1, CYP1A2, CYP2B6, ABCB1 ALB
GABRA2, CYP2C18, CYP2C19, GABRA3, CYP2C8, CYP2C9, GABRA4, CYP3A4,
CYP3A5, GABRA5, CYP3A7, PTGS1 GABRA6, GABRB1, GABRB2, GABRB3,
GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2,
GABRR3, TSPO Estazolam GABRA1, CYP3A4 GABRA2, GABRA3, GABRA4,
GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1,
GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3 Ethchlorvynol
GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GABRB1, GABRB2,
GABRB3 Fludiazepam GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6,
GABRB1, GABRB2, GABRB3,
GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ, GABRR1, GABRR2,
GABRR3 Flunitrazepam GABRA1, CYP1A2, CYP2A6, GABRA2, CYP2B6,
CYP2C19, GABRA3, CYP2C9, CYP3A4, GABRA4, UGT2B7 GABRA5, GABRA6,
GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3,
GABRP, GABRQ, TSPO Flurazepam GABRA1, CYP2A6, CYP2C19, ABCB1
GABRA2, CYP2E1, CYP3A4 GABRA3, GABRA4, GABRA5, GABRA6, GABRB1,
GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ,
GABRR1, GABRR2, GABRR3 Glutethimide GABRA1, CYP11A1, CYP2D6 GABRA2,
GABRA3, GABRA4, GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD,
GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ Halazepam GABRA1,
GABRA2, GABRA3, GABRA5, GABRB1, GABRB2, GABRB3, GABRD, GABRE,
GABRG1, GABRG2, GABRG3, GABRP, GABRR1, GABRR2, GABRR3 Heptabarbital
CHRNA4, CHRNA7, GABRA1, GABRA2, GABRA3, GABRA4, GABRA5, GABRA6,
GRIA2, GRIK2 Hexobarbital CHRNA4, CYP1A2, CYP2C19, CHRNA7, CYP2C9,
CYP2E1, GABRA1, CYP3A4, PTGS1 GABRA2, GABRA3, GABRA4, GABRA5,
GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2,
GABRG3, GABRP, GABRQ, GRIA2, GRIK2 Hydroxyzine HRH1 CYP2D6, CYP3A4,
CYP3A5 Ketazolam GABRA1, CYP3A4 ABCB1 ALB GABRB1, GABRD, GABRE,
GABRG1, TSPO Lorazepam GABRA1, GABRA2, GABRA3, GABRA4, GABRA5,
GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2,
GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3, TSPO Melatonin ASMT,
CALM1, ASMT, CYP19A1, SLC22A8 CALR, EPX, CYP1A1, CYP1A2, ESR1, MPO,
CYP1B1, CYP2C19, MTNR1A, CYP2C9, IDO1, MPO MTNR1B, NQO2, RORB
Meprobamate GABRA1, CYP2C19, CYP2E1 ABCB1 GABRA2, GABRA3, GABRA4,
GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1,
GABRG2, GABRG3, GABRP, GABRQ Methaqualone GABRA1, CYP3A4 GABRA2,
GABRA3, GABRA4, GABRA5, GABRA6, GABRB1, GABRB2, GABRB3, GABRD,
GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ Methohexital GABRA1
Methyprylon GABRA1 CYP2D6 Midazolam GABRA1, CYP2B6, CYP3A4, ABCB1,
GABRA2, CYP3A5, CYP3A7, SLC22A1 GABRA3, CYP4B1 GABRA4, GABRA5,
GABRA6, GABRB1, GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2,
GABRG3, GABRP, GABRQ, GABRR1, GABRR2, GABRR3 Nitrazepam GABRA1,
CYP3A4 ABCB1 GABRA2, GABRA3, GABRA4, GABRA5, GABRA6, GABRB1,
GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ,
GABRR1, GABRR2, GABRR3, SCN1A Oxazepam GABRA1, CYP3A4, CYP3A43,
GABRA2, CYP3A5, CYP3A7 GABRA3, GABRA4, GABRA5, GABRA6, GABRB1,
GABRB2, GABRB3, GABRD, GABRE, GABRG1, GABRG2, GABRG3, GABRP, GABRQ,
GABRR1, GABRR2, GABRR3
[0037] [Table 8] Beta blocking agents [C07]
TABLE-US-00008 TABLE 8 Beta blocking agents [C07] Transporter Drugs
Target protein Enzyme protein Carrier protein protein Acebutolol
ADRB1, ADRB2 CYP2D6 ABCB1, SLC22A1 Alprenolol ADRB1, ADRB2, CYP2D6
ADRB3, HTR1A Atenolol ADRB1, LTF, ABCB1 PLA2G2E Betaxolol ADRB1,
ADRB2 CYP1A2, CYP2D6 Bevantolol ADRA1A, ADRA1B, ADRA1D, ADRB1,
ADRB2 Bisoprolol ADRB1, ADRB2 CYP2D6, CYP3A4 Bopindolol ADRB1,
ADRB2, ADRB3, HTR1A, HTR1B Bupranolol ADRB1, ADRB2, ADRB3 Carteolol
ADRB1, ADRB2, CYP2D6 ADRB3 Carvedilol ADRA1A, CYP1A1, CYP1A2, ABCB1
ADRA1B, CYP2C9, CYP2D6, ADRA1D, CYP2E1, CYP3A4, ADRB1, ADRB2,
PTGS1, XDH ADRB3, GJA1, KCNH2, NDUFC2, NPPB, VCAM1, VEGFA Esmolol
ADRB1 Labetalol ADRA1A, CYP2D6 ADRA1B, ADRA1D, ADRB1, ADRB2, ADRB3
Metoprolol ADRB1, ADRB2 CYP2C19, CYP2D6 ABCB1, SLC22A2 Nadolol
ADRB1, ADRB2, ABCB1 ADRB3 Nebivolol ADRB1, ADRB2 CYP2D6 Oxprenolol
ADRB1, ADRB2, CYP2D6 SLC22A2 ADRB3 Penbutolol ADRB1, ADRB2, ORM1
ADRB3 Pindolol ADRB1, ADRB2, CYP2D6 SLC22A2 ADRB3, HTR1A, HTR1B
Practolol ADRB1 Propranolol ADRB1, ADRB2, CYP1A1, CYP1A2, ABCB1,
ORM1 ADRB3, HTR1A, CYP2C19, CYP2D6, SLC22A2 HTR1B CYP3A4, CYP3A5,
CYP3A7 Sotalol ADRB1, ADRB2, KCNH2 Timolol ADRB1, ADRB2, CYP2C19,
CYP2D6 ABCB1 ADRB3, LYZ
TABLE-US-00009 TABLE 9 Calcium channel blockers [C08] Transporter
Drugs Target protein Enzyme protein Carrier protein protein
Amlodipine CA1, CACNA1B, CYP1A1, CYP1A2, ABCB1 CACNA1C, CYP2A6,
CYP2B6, CACNA1D, CYP2C8, CYP2C9, CACNA1F, CYP2D6, CYP3A4, CACNA1S,
CYP3A5, CYP3A7 CACNA2D1, CACNA2D3, CACNB1, CACNB2 Bepridil ATP1A1,
CYP2C9, CYP2D6, ABCB1 CACNA1A, CYP3A4 CACNA1C, CACNA1D, CACNA1F,
CACNA1G, CACNA1H, CACNA1I, CACNA1S, CACNA2D2, CALM1, KCNA5, KCND3,
KCNH2, KCNJ12, KCNJ3, KCNJ5, KCNJ8, KCNQ1, PDE1A, PDE1B, TNNC1
Diltiazem CACNA1C, CYP2C19, CYP2C8, ABCB1 CACNA1D, CYP2C9, CYP2D6,
CACNA1F, CYP3A4, CYP3A5, CACNA1S, CYP3A7 CACNG1 Felodipine CACNA1C,
CYP2C8, CYP2C9, ABCB1 CACNA1D, CYP2D6, CYP3A4, CACNA1F, CYP3A5,
CYP3A7 CACNA1H, CACNA1S, CACNA2D1, CACNA2D2, CACNB2, CALM1, NR3C2,
PDE1A, PDE1B, TNNC1, TNNC2 Isradipine CACNA1C, CYP3A4 CACNA1D,
CACNA1F, CACNA1H, CACNA1S, CACNA2D1, CACNA2D2, CACNB2 Lercanidipine
CACNG1 CYP2D6, CYP3A4, CYP3A5, CYP3A7 Losartan AGTR1 CYP1A2,
CYP2C19, ABCB1, CYP2C8, CYP2C9, SLC22A6 CYP3A4, CYP3A5, UGT1A1,
UGTIA10, UGT1A3, UGT2B17, UGT2B7 Mibefradil CACNA1C, CYP11B1,
CYP11B2, ABCB1 CACNA1D, CYP1A1, CYP1A2, CACNA1F, CYP2D6, CYP3A4,
CACNA1G, CYP3A5, CYP3A7 CACNA1H, CACNA1I, CACNA1S, CACNB1, CACNB2,
CACNB3, CACNB4 Nicardipine ADRA1A, CYP2B6, CYP2C19, ABCB1 ADRA1B,
CYP2C8, CYP2C9, ADRA1D, CYP2D6, CYP2E1, CACNA1C, CYP3A4, CYP3A5
CACNA1D, CACNA1F, CACNA1S, CACNA2D1, CACNB2, CALM1, CHRM1, CHRM2,
CHRM3, CHRM4, CHRM5, PDE1A, PDE1B Nifedipine CACNA1C, CYP1A1,
CYP1A2, ABCB1, ABCC2, CACNA1D, CYP2A6, CYP2B6, ABCC3 CACNA1F,
CYP2C8, CYP2C9, CACNA1H, CYP2D6, CYP2E1, CACNA1S, CYP3A4, CYP3A5,
CACNA2D1, CYP3A7 CACNB2, CALM1, KCNA1 Nilvadipine CACNA1C, CYP1A2,
CYP2A6, CACNA1D, CYP2C19, CYP2C8, CACNA1F, CYP2C9, CYP2E1, CACNA1S,
CYP3A4 CACNA2D1, CACNA2D3, CACNB2 Nimodipine AHR, CACNA1C, CYP3A4,
CYP3A5 CACNA1D, CACNA1F, CACNA1S, CACNB1, CACNB2, CACNB3, CACNB4,
NR3C2 Nisoldipine CACNA1C, CYP1A2, CYP3A4, ABCB1 CACNA1D, CYP3A5,
CYP3A7 CACNA1F, CACNA1S, CACNA2D1, CACNB2 Nitrendipine CACNA1C,
CYP3A4, CYP3A5, ABCB1 CACNA1D, CYP3A7 CACNA1F, CACNA1H, CACNA1S,
CACNA2D1, CACNA2D2, CACNB2, CACNG1 Perhexiline CPT1A, CPT2 CYP2B6,
CYP2D6, CYP3A4 Verapamil CACNA1A, CYP1A2, CYP2B6, ABCB1, CACNA1B,
CYP2C18, CYP2C19, ABCB11, CACNA1C, CYP2C8, CYP2C9, ABCC1, CACNA1D,
CYP2D6, CYP3A4, ABCC10, CACNA1F, CYP3A5, CYP3A7 ABCC2, ABCC3,
CACNA1G, ABCC4, ABCG2, CACNA1I, SLC22A1, CACNA1S, SLC22A4, CACNB1,
SLC22A5, CACNB2, SLCO1A2, CACNB3, SLCO1B1 CACNB4, KCNA10, KCNA3,
KCNA7, KCNC2, KCNH2, KCNJ11, KCNJ6, SCN5A, SLC6A4
TABLE-US-00010 TABLE 10 Diuretics [C03] Transporter Drugs Target
protein Enzyme protein Carrier protein protein Amiloride ABP1,
ASIC1, ABP1 SLC22A1, ASIC2, PLAU, SLC22A2, SCNN1A, SLC22A4 SCNN1B,
SCNN1D, SCNN1G, SLC9A1 Bendroflumethiazide CA1, CA2, CA4, KCNMA1,
SLC12A3 Bumetanide CFTR, SLC10A1, SLC12A1, SLC22A11, SLC12A2,
SLC22A6, SLC12A4, SLC22A7, SLC12A5 SLC22A8, SLCO1A2 Chlorothiazide
CA1, CA2, SLC22A6 CA4, SLC12A3 Chlorthalidone CA2, SLC12A1, SLC12A3
Conivaptan AVPR1A, CYP3A4 AVPR2 Cyclothiazide CA1, CA2, SLC22A6
CA4, FXYD2 Eplerenone NR3C2 CYP11B2, CYP3A4, CYP3A5, CYP3A7
Ethacrynic acid ATP1A1, GSTA2 SLC22A6 ALB SLC12A1, SLC12A2
Furosemide CA2, GABRA1, PGD ABCC2, ALB GABRA2, ABCC4, GABRA3,
SLC22A11, GABRA4, SLC22A5, GABRA5, SLC22A6, GABRA6, SLC22A8,
SLC12A1, SLCO2A1 SLC12A2 Hydrochlorothiazide CA1, CA12, SLC22A6
CA2, CA4, CA9, KCNMA1, SLC12A3 Hydroflumethiazide ATP1A1, CA1,
CA12, CA2, CA4, CA9, KCNMA1, SLC12A1, SLC12A3 Indapamide CA2,
KCNE1, CYP3A4 KCNQ1, SLC12A3 Methyclothiazide CA1, CA2, CA4,
SLC12A1, SLC12A3 Metolazone SLC12A3 Polythiazide SLC12A3
Quinethazone CA1, CA2, SLC12A1, SLC12A2, SLC12A3 Spironolactone AR,
NR3C2 CYP11B1, CYP2C8 ABCB1, ABCC2, SLCO1A2 Tolvaptan AVPR2
Torasemide SLC12A1, CYP2C19, CYP2C8, SLC12A2 CYP2C9, PTGS1
Triamterene SCNN1A, CYP1A2 SCNN1B, SCNN1D, SCNN1G
Trichlormethiazide ATP1A1, CA1, CA2, CA4, SLC12A1, SLC12A3
Theobromine ADORA1, CYP1A2, CYP2E1 ADORA2A, PDE4B
TABLE-US-00011 TABLE 11 Drugs for obstructive airway diseases [R03]
Transporter Drugs Target protein Enzyme protein Carrier protein
protein Aminophylline ADORA1, CYP1A2, CYP2E1, ADORA2A, CYP3A4
ADORA3, PDE3A, PDE3B Amlexanox FGF1, IL3, S100A12, S100A13
Bambuterol ADRB2 BCHE Beclomethasone NR3C1 CYP3A5 SERPINA6
Betamethasone ANXA1, NOS2, CYP11A1, ABCB1, NR0B1, NR3C1 CYP17A1,
ABCB11, CYP19A1, CYP1A1, ABCC2, CYP1B1, CYP2A6, ABCG2, CYP2B6,
CYP2C19, SLCO1A2 CYP2C8, CYP2C9, CYP2D6, CYP2E1, CYP3A4, CYP3A43,
CYP3A5, CYP3A7, CYP4A11 Betamethasone NR3C1 valerate Bitolterol
ADRB2 Budesonide NR3C1 CYP3A4 Ciclesonide NR3C1 CES1, CYP2D6,
SERPINA6 CYP3A4 Clenbuterol ADRB1, CYP1A1, CYP1A2 ADRB2, ADRB3,
NGF, TNF Cromoglicate KCNMA1, S100P Dexamethasone NR3C1 propionate
Dyphylline ADORA1, ADORA2A, PDE4A, PDE4B, PDE4C, PDE4D, PDE7A,
PDE7B Ephedrine ACHE, CYP2D6, MAOA ADRA1A, ADRA1B, ADRA1D, ADRA2A,
ADRA2B, ADRA2C, ADRB1, ADRB2, ADRB3, SLC18A2, SLC6A2, SLC6A3,
SLC6A4 Epinephrine ADRA1A, CYP1A2, CYP2C9, SLC22A1, ADRA1B, CYP3A4
SLC22A2, ADRA1D, SLC22A5 ADRA2A, ADRA2B, ADRA2C, ADRB1, ADRB2,
ADRB3, PAH Fenoterol ADRB1, ADRB2, ADRB3 Flunisolide NR3C1 CYP3A4
SERPINA6 Fluticasone NR3C1, NR3C2, CYP3A4, CYP3A5, SERPINA6
Propionate PGR, PLA2G4A CYP3A7 Fluticasone NR3C1 CYP3A4 furoate
Formoterol ADRB2 CYP2A6, CYP2C19, CYP2C9, CYP2D6 Ibudilast CYSLTR1,
PDE3A, PDE3B, PDE4A, PDE4B, PDE4C, PDE4D, PTGIR Indacaterol ADRB2
Ipratropium CHRM1, CYP2D6, CYP3A4 SLC22A4, CHRM2, SLC22A5 CHRM3,
CHRM4, CHRM5 Isoetharine ADRB1, ADRB2 Isoproterenol ADRB1, CYP1A1
ADRB2, ADRB3, MAPK1, PIK3R1, PIK3R2, PIK3R3 Mometasone NR3C1
CYP2C8, CYP3A4 Mometasone NR3C1 CYP3A4 furoate Montelukast ALOX5,
CYP2A6, CYP2C8, SLCO2B1 CYSLTR1 CYP2C9, CYP3A4, PTGS1 Nedocromil
CYSLTR1, CYSLTR2, FPR1, HSP90AA1, PTGDR Omalizumab FCER1A, MS4A2
Orciprenaline ADRB2 Oxtriphylline ADORA1, CYP1A2 SLC22A7 ADORA2A,
PDE3A, PDE4A Pirbuterol ADRB1, ADRB2 Pranlukast CYSLTR1, CYP2C9,
CYP3A4 ABCC2 CYSLTR2, IL5 MUC2, NFKB1, RNASE3, TNF Procaterol ADRB2
Roflumilast PDE4A, PDE4B, PDE4C, PDE4D Salbutamol ADRB1, CYP3A4
ADRB2 Salmeterol ADRB2 CYP2C8, CYP3A4, CYP3A5, CYP3A7 Salmeterol
ADRB2 CYP3A4 xinafoate Salmeterol CYP3A4 xinafoate - fluticasone
propionate mixt Terbutaline ADRB2 BCHE Theophylline ADORA1, ADA,
CYP1A1, SLC22A7 ADORA2A, CYP1A2, CYP1B1, ADORA2B, CYP2C8, CYP2C9,
PDE3A, PDE4A, CYP2D6, CYP2E1, PDE4B, PDE5A CYP3A4 Tiotropium CHRM1,
CYP2D6, CYP3A4 SLC22A4, CHRM2, SLC22A5 CHRM3 Triamcinolone NR3C1
SERPINA6 Zafirlukast CYSLTR1 CYP1A2, CYP2C19, CYP2C8, CYP2C9,
CYP2D6, CYP2E1, CYP3A4, PTGS1
TABLE-US-00012 TABLE 12 Lipid modifying agents [C10] Transporter
Drugs Target protein Enzyme protein Carrier protein protein
Atorvastatin AHR, DPP4, CYP2B6, CYP2C19, ABCB1, HMGCR CYP2C8,
CYP2C9, ABCC1, CYP2D6, CYP3A4, ABCC4, CYP3A5, CYP3A7 ABCC5,
SLCO1A2, SLCO1B1 Bezafibrate PPARA, CYP1A1, CYP2C8, SLCO1B1 PPARD,
CYP3A4 PPARG Cerivastatin HMGCR CYP2B6, CYP2C19, ABCB1, CYP2C8,
CYP2C9, ABCC2, CYP2D6, CYP3A4, ABCG2, CYP3A5, CYP3A7 SLCO1B1
Clofibrate PPARA CYP1A1, CYP2A6, CYP2B6, CYP2E1, CYP3A4, CYP4A11,
GSTA2 Ezetimibe ANPEP, CYP3A4, UGT1A1, ABCB1, NPC1L1, UGT1A3,
UGT2B7 ABCC2, SOAT1 SLCO1B1 Fenofibrate MMP20, CYP2C8, CYP2C9,
PPARA CYP3A4 Fluvastatin HMGCR CYP1A1, CYP1A2, CYP2B6, CYP2C19,
CYP2C8, CYP2C9, CYP2D6, CYP3A4 Gemfibrozil PPARA CYP1A2, CYP2C19,
SLCO1B1 CYP2C8, CYP2C9, CYP3A4 Lovastatin HDAC2, CYP2C19, CYP2C8,
ABCB1, HMGCR, CYP2C9, CYP2D6, SLCO1A2, ITGAL CYP3A4, CYP3A5,
SLCO1B1 CYP3A7, PON3 Niacin HCAR2, CYP2D6 SLC16A1, HCAR3, SLC22A5,
NNMT, QPRT SLCO2B1 Pravastatin HMGCR CYP2C8, CYP2C9, ABCB1, CYP2D6,
CYP3A4, ABCB11, CYP3A5 ABCC2, ABCG2, SLC22A11, SLC22A6, SLC22A7,
SLC22A8, SLCO1A2, SLCO1B1, SLCO2B1 Probucol ABCA1 Rosuvastatin
HMGCR CYP2C19, CYP2C9, ABCC1, ABCC4 CYP3A4, CYP3A5 Simvastatin
HMGCR, CYP2B6, CYP2C19, ABCB1, ITGB2 CYP2C8, CYP2C9, SLCO1A2,
CYP2D6, CYP3A4, SLCO1B1 CYP3A5, CYP3A7 Levothyroxine THRA, THRB,
CYP2C8, CYP3A4 ABCB1, ALB, TPO SLC16A2, SERPINA7, SLCO1C1 TTR
TABLE-US-00013 TABLE 13 Proton Pump Inhibitors [A02BC] Transporter
Drugs Target protein Enzyme protein Carrier protein protein
Lansoprazole ATP4A, ATP4B CYP1A1, CYP1A2, ABCB1, ABCG2 CYP1B1,
CYP2C18, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP3A4, CYP4A11
Omeprazole ATP4A, ATP4B CYP11A1, CYP1A1, ABCB1, ABCC3, CYP1A2,
CYP1B1, ABCG2 CYP2C18, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP3A4
Pantoprazole ATP4A, ATP4B CYP1A2, CYP2C19, ABCB1, ABCG2, CYP2C9,
CYP3A4 SLCO1B1 Rabeprazole ATP4A, ATP4B CYP1A1, CYP1A2, ABCG2
CYP2C19, CYP2C9, CYP2D6, CYP3A4
TABLE-US-00014 TABLE 14 Sex hormones and modulators of the genital
system [G03] Transporter Drugs Target protein Enzyme protein
Carrier protein protein Allylestrenol ESR1, PGR CYP3A4
Chlorotrianisene ESR1 Choriogonadotropin FSHR, LHCGR alfa Clomifene
ESR1, ESR2 CYP11A1, ABCB1 CYP19A1, CYP1A1, CYP1A2, CYP2A6, CYP2E1,
CYP3A4 Conjugated estrogens ESR1, ESR2 Cyproterone AR CYP19A1
Danazol AR, CCL2, CYP19A1, CYP3A4 SHBG ESR1, GNRHR, GNRHR2, PGR
Desogestrel ESR1, PGR Dienestrol ESR1, ESR2 Diethylstilbestrol
AKR1C1, COMT, CYP19A1, ABCB1, TTR ESR1, ESR2, CYP2A6, CYP2C8, ABCG2
ESRRG CYP2C9, CYP2E1, CYP3A4 Dydrogesterone PGR Estradiol ESR1,
ESR2, CYP1A1, CYP1A2, ABCB1, ALB, NR1I2 CYP1B1, CYP2C19, ABCC10,
FABP2, CYP2C8, CYP2C9, ABCG2, SHBG CYP3A4, CYP3A5, SLC22A1, CYP3A7,
UGT1A1 SLC22A11, SLC22A2, SLC22A3, SLCO1A2, SLCO1B1, SLCO2B1
Estradiol - CYP3A4 levonorgestrel mixt Estriol ESR1, ESR2, ABCB1,
NCOA5 SLCO1A2 Estrone ESR1, ESR2 COMT, CYP1A1, ABCB1, ALB CYP1A2,
CYP1B1, ABCC1, CYP2B6, CYP2C9, ABCC11, CYP2E1, CYP3A4, ABCC2,
CYP3A5, CYP4A11 ABCC3, ABCC4, ABCG2, SLC10A1, SLC22A10, SLC22A11,
SLC22A6, SLC22A8, SLCO1A2, SLCO1B1, SLCO1B3, SLCO1C1, SLCO2B1,
SLCO3A1, SLCO4A1 Ethinyl Estradiol ESR1, ESR2 CYP2C8, CYP3A4 ABCB1,
NR1I2 ABCB11, ABCC2, SLC10A1 Ethynodiol ESR1, PGR Etonogestrel
ESR1, PGR CYP3A4 Fluoxymesterone AR, ESR1, SHBG NR3C1, PRLR
Follitropin alfa FSHR (genetical recombination) Follitropin beta
FSHR Follitropin beta FSHR (genetical recombination)
Hydroxyprogesterone PGR caproate Levonorgestrel AR, ESR1, CYP19A1,
CYP3A4 PGR, SRD5A1 Lutropin alfa LHCGR Medroxyprogesterone ESR1,
PGR CYP2C8, CYP2C9, CYP3A4, HSD3B2 Megestrol NR3C1, PGR ABCB1
Methyltestosterone AR CYP19A1, SLC22A8, ALB, SHBG CYP2B6, CYP3A4
SLCO1A2 Mifepristone NR3C1, PGR CYP2D6, CYP3A4, ABCB1, CYP3A5,
CYP3A7 ABCC1 Nandrolone AR CYP19A1 phenpropionate Norethindrone PGR
CYP2C19, ABCB1 CYP3A4, CYP3A5, CYP3A7 Progesterone CYP17A1,
CYP17A1, ABCB1, ESR1, NR3C2, CYP1A1, CYP1A2, ABCB11, PGR CYP1B1,
CYP2A6, ABCC1, CYP2C19, CYP2C8, SLC10A1, CYP2C9, CYP2D6, SLC22A1,
CYP3A4, CYP3A5, SLC22A2, CYP3A7 SLC22A3 Raloxifene ESR1, ESR2 AOX1,
CYP19A1, CYP2B6, CYP2C8, CYP3A4 Synthetic conjugated ESR1, ESR2
estrogens, B Testosterone AKR1C1, CYP11A1, ABCB1, ALB, SHBG AKR1C2,
AR CYP19A1, ABCG2, CYP1A1, CYP1B1, SLC10A1, CYP2A13, SLC22A1,
CYP2B6, CYP2C19, SLC22A3, CYP2C8, CYP2C9, SLC22A4, CYP3A4, SLC22A7,
CYP3A43, SLC22A8, CYP3A5, CYP3A7, MAOA SLCO1A2 Testosterone AR
Propionate Urofollitropin FSHR
TABLE-US-00015 TABLE 15 Thyroid therapy [H03] Transporter Drugs
Target protein Enzyme protein Carrier protein protein Carbimazole
TPO Liotrix THRA, THRB CYP2C8 ABCB1, ALB, SLC10A1, SERPINA7,
SLC16A10, TTR SLC16A2, SLC22A8, SLCO1A2, SLCO1B1, SLCO1B3, SLCO1C1,
SLCO4A1, SLCO4C1 Methimazole TPO CYP1A2, CYP2A6, CYP2B6, CYP2C19,
CYP2C9, CYP2D6, CYP2E1, CYP3A4 Propylthiouracil DIO1, DIO2, TPO
[0038] The individual genome sequence information used herein may
be determined by using a well-known sequencing method. Further,
services such as Complete Genomics, BGI (Beijing Genome Institute),
Knome, Macrogen, and DNALink which provide commercialized services
may be used, but the present invention is not limited thereto.
[0039] In the present invention, gene sequence variation
information present in an individual genome sequence may be
extracted by using various methods, and may be acquired through
sequence comparison analysis by using a program, for example,
ANNOVAR (Wang et al., Nucleic Acids Research, 2010; 38(16): e164),
SVA (Sequence Variant Analyzer) (Ge et al., Bioinformatics. 2011;
27(14): 1998-2000), BreakDancer (Chen et al., Nat Methods. 2009
September; 6(9):677-81), and the like, which compare a sequence to
a reference group, for example, the genome sequence of HG19.
[0040] The gene sequence variation information may be
received/acquired through a computer system. In this aspect, the
method of the present invention may further include receiving the
gene sequence variation information through a computer system. The
computer system used in the present invention may include or access
one or more databases including information about the gene involved
in the pharmacodynamics or pharmacokinetics of a specific drug or
drug group, for example, a gene encoding a target protein relevant
to a drug, an enzyme protein involved in drug metabolism, a
transporter protein, a carrier protein, or the like. These
databases may include a public or non-public database or a
knowledge base, which provides information about
gene/protein/drug-protein interaction, and the like, including such
as DrugBank (http://www.drugbank.ca/), KEGG Drug
(http://www.genome.jp/kegg/drug/), and PharmGKB
(http://www.pharmgkb.org/), but are not limited thereto.
[0041] In the present invention, the predetermined drug or drug
group may be information input by a user, information input from a
prescription, or information input from a database including
information about a drug effective in treating a specific disease.
The prescription may include an electronic prescription, but is not
limited thereto.
[0042] The term "gene sequence variation score" used herein refers
to a numerical score of a degree of the individual genome sequence
variation that causes an amino acid sequence variation
(substitution, addition, or deletion) of a protein encoded by a
gene or a transcription control variation and thus causes a
significant change or damage to a structure and/or function of the
protein when the genome sequence variation is found in an exon
region of the gene encoding the protein. The gene sequence
variation score can be calculated considering a degree of
evolutionary conservation of amino acid in a genome sequence, a
degree of an effect of a physical characteristic of modified amino
acid on a structure or function of the corresponding protein.
[0043] The gene sequence variation score used for calculating the
individual protein damage score and the individual drug score
according to the present invention can be calculated by using a
method known in the art. For example, the gene sequence variation
score can be calculated from the gene sequence variation
information by using an algorithm such as SIFT (Sorting Intolerant
From Tolerant, Pauline C et al., Genome Res. 2001 May; 11(5):
863-874; Pauline C et al., Genome Res. 2002 March; 12(3): 436-446;
Jing Hul et al., Genome Biol. 2012; 13(2): R9), PolyPhen,
PolyPhen-2 (Polymorphism Phenotyping, Ramensky V et al., Nucleic
Acids Res. 2002 Sep. 1; 30(17): 3894-3900; Adzhubei I A et al., Nat
Methods 7(4):248-249 (2010)), MAPP (Eric A. et al., Multivariate
Analysis of Protein Polymorphism, Genome Research 2005;
15:978-986), Logre (Log R Pfam E-value, Clifford R. J et al.,
Bioinformatics 2004; 20:1006-1014), Mutation Assessor (Reva B et
al., Genome Biol. 2007; 8:R232, http://mutationassessor.org/),
Condel (Gonzalez-Perez A et al., The American Journal of Human
Genetics 2011; 88:440-449, http://bg.upfedu/fannsdb/), GERP (Cooper
et al., Genomic Evolutionary Rate Profiling, Genome Res. 2005;
15:901-913, http://mendel.stanford.edu/SidowLab/downloads/gerp/),
CADD (Combined Annotation-Dependent Depletion,
http://cadd.gs.washington.edu/), MutationTaster, MutationTaster2
(Schwarz et al., MutationTaster2: mutation prediction for the
deep-sequencing age. Nature Methods 2014; 11:361-362,
http://www.mutationtaster.org/), PROVEAN (Choi et al., PLoS One.
2012; 7(10):e46688), PMut (Ferrer-Costa et al., Proteins 2004;
57(4):811-819, http://mmb.pcb.ub.es/PMut/), CEO (Combinatorial
Entropy Optimization, Reva et al., Genome Biol 2007; 8(11):R232),
SNPeffect (Reumers et al., Bioinformatics. 2006; 22(17):2183-2185,
http://snpeffect.vib.be), fathmm (Shihab et al., Functional
Analysis through Hidden Markov Models, Hum Mutat 2013; 34:57-65,
http://fathmm.biocompute.org.uk/), and the like, but the present
invention is not limited thereto.
[0044] The above-described algorithms are configured to identify
how much each gene sequence variation has an effect on a protein
function, how much the effect damage the protein, or whether or not
there are any other effects. These algorithms are basically
configured to consider an amino acid sequence of a protein encoded
by a corresponding gene and its relevant change caused by an
individual gene sequence variation and thereby to determine an
effect on a structure and/or function of the corresponding
protein.
[0045] In an exemplary embodiment of the present invention, a SIFT
(Sorting Intolerant From Tolerant) algorithm is used to calculate
an individual gene sequence variation score. In the case of the
SIFT algorithm, gene sequence variation information is input in the
form of a VCF (Variant Call Format) file, and a degree of damage
caused by each gene sequence variation to the corresponding gene is
scored. In the case of the SIFT algorithm, as a calculated score is
closer to 0, it is considered that a protein encoded by a
corresponding gene is severely damaged and thus its function is
damaged, and as the calculated score is closer to 1, it is
considered that the protein encoded by the corresponding gene
maintains its normal function.
[0046] In the case of another algorithm PolyPhen-2, the higher a
calculated score is, it is considered that the more damaged a
function of a protein encoded by a corresponding gene is.
[0047] Recently, a study (Gonzalez-Perez, A. & Lopez-Bigas, N.
Improving the assessment of the outcome of nonsynonymous SNVs with
a consensus deleteriousness score, Condel. The American Journal of
Human Genetics, 2011; 88(4):440-449.) suggesting a Condel algorithm
by comparing and combining SIFT, Polyphen2, MAPP, Logre, and
Mutation Assessor was reported. In this study, the above-described
five algorithms are compared by using HumVar and HumDiv (Adzhubei,
I A et al., A method and server for predicting damaging missense
mutations. Nature methods, 2010; 7(4):248-249) as set of known data
relating to gene sequence variations damaging a protein and gene
sequence variations with less effect. As a result, 97.9% of the
gene sequence variations damaging a protein and 97.3% of the gene
sequence variations with less effect of HumVar were identically
detected by at least three of the above-described five algorithms,
and 99.7% of the gene sequence variations damaging a protein and
the 98.8% of gene sequence variations with less effect of HumDiv
were identically detected by at least three of the above-described
five algorithms. Further, as a result of drawing an ROC (Receiver
Operating Curve) showing accuracy of calculation results of the
five algorithms and a combination of the algorithms utilizing the
HumDiv and HumVar, it was confirmed that an AUC (Area Under the
Receiver Operating Curve) consistency is considerably high (69% to
88.2%). That is, the above-described algorithms are different in
calculation method but the calculated gene sequence variation
scores are significantly correlated to each other. Therefore, it is
included in the scope of the present invention regardless of kinds
of algorithms calculating gene sequence variation scores to apply a
gene sequence variation score calculated by applying the
above-described algorithms or a method employing the algorithms to
the steps of calculating an individual protein damage score and an
individual drug score according to the present invention.
[0048] When a gene sequence variation occurs in an exon region of a
gene encoding a protein, the gene sequence variation may directly
affect a structure and/or function of the protein. Therefore, the
gene sequence variation information may be associated with a degree
of damage to a protein function. In this aspect, the method of the
present invention calculates an individual protein damage score on
the basis of the above-described gene sequence variation score in
the following step.
[0049] The "protein damage score" used herein refers to a score
calculated by summarizing gene sequence variation scores when two
or more significant sequence variations are found in a gene region
encoding a single protein so that the single protein has two or
more gene sequence variation scores. If there is a single
significant sequence variation in the gene region encoding the
protein, a gene sequence variation score is identical to a protein
damage score. Herein, if there are two or more gene sequence
variations encoding a protein, a protein damage score is calculated
as a mean of gene sequence variation scores calculated for the
respective variations. Such a mean can be calculated by, for
example, but not limited to, measuring a geometric mean, an
arithmetic mean, a harmonic mean, an arithmetic geometric mean, an
arithmetic harmonic mean, a geometric harmonic mean, Pythagorean
means, an interquartile mean, a quadratic mean, a truncated mean, a
Winsorized mean, a weighted mean, a weighted geometric mean, a
weighted arithmetic mean, a weighted harmonic mean, a mean of a
function, a generalized mean, a generalized f-mean, a percentile, a
maximum value, a minimum value, a mode, a median, a mid-range, a
central tendency, simple multiplication or weighted multiplication,
or by a functional operation of the calculated values.
[0050] In an exemplary embodiment of the present invention, the
protein damage score is calculated by the following Equation 1. The
following Equation 1 can be modified in various ways, and, thus,
the present invention is not limited thereto.
S g ( v 1 v n ) = ( 1 n i = 1 n v i p ) 1 p [ Equation 1 ]
##EQU00001##
[0051] In Equation 1, S.sub.g is a protein damage score of a
protein encoded by a gene g, n is the number of target sequence
variations for analysis among sequence variations of the gene g,
v.sub.i is a gene sequence variation score of an i.sup.th gene
sequence variation, and p is a real number other than 0. In
Equation 1, when a value of the p is 1, the protein damage score is
an arithmetic mean, if the value of the p is -1, the protein damage
score is a harmonic mean, and if the value of the p is close to the
limit 0, the protein damage score is a geometric mean.
[0052] In another exemplary embodiment of the present invention,
the protein damage score is calculated by the following Equation
2.
S g ( v 1 v n ) = ( i = 1 n v i w i ) 1 / i = 1 n w i [ Equation 2
] ##EQU00002##
[0053] In Equation 2, S.sub.g is a protein damage score of a
protein encoded by a gene g, n is the number of target sequence
variations for analysis among sequence variations of the gene g,
v.sub.i is a gene sequence variation score of an ith gene sequence
variation, and w.sub.i is a weighting assigned to the v.sub.i. If
all weightings w.sub.i have the same value, the protein damage
score S.sub.g is a geometric mean of the gene sequence variation
scores v.sub.i. The weighting may be assigned considering a class
of the corresponding protein, pharmacodynamic or pharmacokinetic
classification of the corresponding protein, pharmacokinetic
parameters of the enzyme protein of a corresponding drug, a
population group, or a race distribution.
[0054] The term "pharmacokinetic parameters of the enzyme protein
of a corresponding drug" used herein includes Vmax, Km, Kcat/Km,
and the like. Vmax is a maximum enzyme reaction rate when a
substrate concentration is very high, and Km is a substrate
concentration that causes the reaction to reach 1/2 Vmax. Km may be
regarded as affinity between the corresponding enzyme and the
corresponding substrate. As the Km is decreased, a bonding force
between the corresponding enzyme and the corresponding substrate is
increased. Kcat called the turnover number of an enzyme refers to
the number of substrate molecules metabolized for 1 second in each
enzyme active site when the enzyme is activated at a maximum rate,
and means how fast the enzyme reaction actually occurs.
[0055] According to the method of the present invention, an
individual drug score is calculated in the following step by
associating the above-described protein damage score with a
drug-protein relation.
[0056] The term "drug score" used herein refers to a value
calculated with respect to a predetermined drug by finding out a
target protein involved in the pharmacodynamics or pharmacokinetics
of the drug, an enzyme protein involved in drug metabolism, a
transporter protein, or a carrier protein when the predetermined
drug is given, calculating protein damage scores of the proteins,
and summarizing the scores.
[0057] In the present invention, if two or more proteins involved
in the pharmacodynamics or pharmacokinetics of a predetermined drug
or drug group are damaged, a drug score is calculated as a mean of
the protein damage scores. Such a mean can be calculated by, for
example, but not limited to, measuring a geometric mean, an
arithmetic mean, a harmonic mean, an arithmetic geometric mean, an
arithmetic harmonic mean, a geometric harmonic mean, Pythagorean
means, an interquartile mean, a quadratic mean, a truncated mean, a
Winsorized mean, a weighted mean, a weighted geometric mean, a
weighted arithmetic mean, a weighted harmonic mean, a mean of a
function, a generalized mean, a generalized f-mean, a percentile, a
maximum value, a minimum value, a mode, a median, a mid-range, a
central tendency, simple multiplication or weighted multiplication,
or by a functional operation of the calculated values.
[0058] The drug score may be calculated by adjusting weightings of
a target protein involved in the pharmacodynamics or
pharmacokinetics of the corresponding drug, an enzyme protein
involved in drug metabolism, a transporter protein, or a carrier
protein in consideration of pharmacological characteristics, and
the weighting may be assigned considering pharmacokinetic
parameters of the enzyme protein of a corresponding drug, a
population group, a race distribution, or the like. Further,
although not directly interacting with the corresponding drug,
proteins interacting with a precursor of the corresponding drug and
metabolic products of the corresponding drug, for example, proteins
involved in a pharmacological pathway, may be considered, and
protein damage scores thereof may be combined to calculate the drug
score. Further, protein damage scores of proteins significantly
interacting with the proteins involved in the pharmacodynamics or
pharmacokinetics of the corresponding drug may also be considered
and combined to calculate the drug score. Information about
proteins involved in a pharmacological pathway of the corresponding
drug, significantly interacting with the proteins in the pathway,
or involved in a signal transduction pathway thereof can be
searched in publicly known biological databases such as PharmGKB
(Whirl-Carrillo et al., Clinical Pharmacology & Therapeutics
2012; 92(4):414-4171), The MIPS Mammalian Protein-Protein
Interaction Database (Pagel et al., Bioinformatics 2005;
21(6):832-834), BIND (Bader et al., Biomolecular Interaction
Network Database, Nucleic Acids Res. 2003 Jan. 1; 31(1):248-50),
Reactome (Joshi-Tope et al., Nucleic Acids Res. 2005 Jan. 1;
33(Database issue):D428-32), and the like.
[0059] In an exemplary embodiment of the present invention, the
drug score is calculated by the following Equation 3. The following
Equation 3 can be modified in various ways, and, thus, the present
invention is not limited thereto.
S d ( g 1 g n ) = ( 1 n i = 1 n g i p ) 1 p [ Equation 3 ]
##EQU00003##
[0060] In Equation 3, S.sub.d is a drug score of a drug d, n is the
number of proteins directly involved in the pharmacodynamics or
pharmacokinetics of the drug d or interacting with a precursor of
the corresponding drug or metabolic products of the corresponding
drug, for example, proteins encoded by one or more genes selected
from a gene group involved in a pharmacological pathway, g.sub.i is
a protein damage score of a protein directly involved in the
pharmacodynamics or pharmacokinetics of the drug d or interacting
with a precursor of the corresponding drug or metabolic products of
the corresponding drug, for example, a protein encoded by one or
more genes selected from a gene group involved in a pharmacological
pathway, and p is a real number other than 0. In Equation 3, when a
value of the p is 1, the drug score is an arithmetic mean, if the
value of the p is -1, the drug score is a harmonic mean, and if the
value of the p is close to the limit 0, the drug score is a
geometric mean.
[0061] In yet another exemplary embodiment of the present
invention, the drug score is calculated by the following Equation
4.
S d ( g 1 g n ) = ( i = 1 n g i w i ) 1 / i = 1 n w i [ Equation 4
] ##EQU00004##
[0062] In Equation 4, S.sub.d is a drug score of a drug d, n is the
number of proteins directly involved in the pharmacodynamics or
pharmacokinetics of the drug d or interacting with a precursor of
the corresponding drug or metabolic products of the corresponding
drug, for example, proteins encoded by one or more genes selected
from a gene group involved in a pharmacological pathway, g.sub.i is
a protein damage score of a protein directly involved in the
pharmacodynamics or pharmacokinetics of the drug d or interacting
with a precursor of the corresponding drug or metabolic products of
the corresponding drug, for example, a protein encoded by one or
more genes selected from a gene group involved in a pharmacological
pathway, and w.sub.i is a weighting assigned to the g.sub.i. If all
weightings w.sub.i have the same value, the drug score S.sub.d is a
geometric mean of the protein damage scores g.sub.i. The weighting
may be assigned considering a kind of the protein, pharmacodynamic
or pharmacokinetic classification of the protein, pharmacokinetic
parameters of the enzyme protein of a corresponding drug, a
population group, or a race distribution.
[0063] In the case of a geometric mean calculation method used in
an exemplary embodiment of the present invention, weightings are
equally assigned regardless of a characteristic of a drug-protein
relation. However, it is possible to calculate a drug score by
assigning weightings considering each characteristic of a
drug-protein relation as described in yet another exemplary
embodiment. For example, different scores may be assigned to a
target protein of a drug and a transporter protein related to the
drug. Further, it is possible to calculate a drug score by
assigning pharmacokinetic parameters Km, Vmax, and Kcat/Km as
weightings to the enzyme protein of a corresponding drug.
Furthermore, for example, since a target protein is regarded more
important than a transporter protein in terms of pharmacological
action, it may be assigned a higher weighting, or a transporter
protein or a carrier protein may be assigned high weightings with
respect to a drug whose effectiveness is sensitive to a
concentration, but the present invention is not limited thereto.
The weighting may be minutely adjusted according to characteristics
of a relation between a drug and a protein related to the drug and
characteristics of an interaction between the drug and the protein.
A sophisticated algorithm configured to assign a weighting of a
characteristic of an interaction between a drug and a protein can
be use, for example, a target protein and a transporter protein may
be assigned 2 points and 1 point, respectively.
[0064] In the above description, only the protein directly
interacting with a drug has been exemplified. However, as described
in an exemplary embodiment of the present invention, the predictive
ability of the above Equation can be improved by using information
about the protein interacting with a precursor of the corresponding
drug or metabolic products of the corresponding drug, the protein
significantly interacting with proteins involved in the
pharmacodynamics or pharmacokinetics of the corresponding drug, and
the protein involved in a signal transduction pathway thereof. That
is, by using information about a protein-protein interaction
network or pharmacological pathway, it is possible to use
information about various proteins relevant thereto. That is, even
if a significant variation is not found in the protein directly
interacting with the drug so that there is no protein damage score
calculated with respect to the protein or there is no damage (for
example, 1.0 point when a SIFT algorithm is applied), a mean (for
example, a geometric mean) of protein damage scores of proteins
interacting with the protein or involved in the same signal
transduction pathway of the protein may be used as a protein damage
score of the protein so as to be used for calculating a drug
score.
[0065] The individual drug score can be calculated with respect to
all drugs from which information about one or more associated
proteins can be acquired or some drugs selected from the drugs.
Further, the individual drug score can be converted into a
rank.
[0066] The method of the present invention may further include:
determining the order of priority among drugs applicable to an
individual by using the above-described individual drug score; or
determining whether or not to use the drugs applicable to the
individual by using the above-described individual drug score.
[0067] Although the individual drug score can be applied to each of
all drugs, it can be more useful when applied to drugs classified
by disease, clinical characteristic or activity, or medically
comparable drugs. The drug classification system which can be used
in the present invention may include, for example, ATC (Anatomical
Therapeutic Chemical Classification System) codes, top 15
frequently prescribed drug classes during 2005 to 2008 in the
United States (Health, United States, 2011, Centers for Disease
Control and Prevention), a list of drugs with known
pharmacogenomical markers which can influence the drug effect
information described in the drug label, or a list of drugs
withdrawn from the market due to side effects thereof.
[0068] The method of the present invention may further include
calculating a prescription score.
[0069] The term "prescription score" used herein refers to a score
calculated by summarizing the drug scores determined with respect
to drugs, respectively, when two or more drugs are administered at
the same time or at a short distance of time sufficient to
significantly affect pharmacological actions thereof. In the
present invention, when two or more drugs are determined on the
basis of the order of priority among drugs and need to be
administered at the same time, the prescription score may be
calculated by summarizing drug scores determined with respect to
the respective drugs. For example, if there is no protein commonly
interacting with the drugs, the prescription score may be
calculated by simply averaging, or summing up or multiplying drug
scores of the drugs. If there is a protein commonly interacting
with the drugs, the prescription score may be calculated by
assigning, for example, a double weighting to a protein damage
score of the corresponding commonly interacting protein to
calculate drug scores of the respective drugs and then summing up
the corresponding drug scores.
[0070] The prescription score is provided to determine
appropriateness or risk of the drugs included in a prescription
applied to an individual over the effects of the respective drugs.
In this aspect, the method of the present invention may further
include determining appropriateness or risk of a prescription
applied to an individual.
[0071] The invention of the present invention may be performed for
the purpose of preventing side effects of a drug, but is not
limited thereto.
[0072] FIG. 1 is a flowchart illustrating each step of a method for
providing information for personalizing drug selection using
individual genome sequence variations according to an exemplary
embodiment of the present invention. In an exemplary embodiment of
the present invention, the method for providing information for
personalizing drug selection is performed by sequentially (1)
inputting or receiving genome sequence information of an individual
user (S100), (2) inputting or receiving information relevant to a
predetermined drug or drug group (S110), (3) determining genome
sequence variation information of the individual user (S120), (4)
calculating an individual protein damage score with respect to the
predetermined drug or drug group (S130), (5) calculating an
individual drug score with respect to the predetermined drug or
drug group (S140), (6) marking the drug score and sort drugs by
ranking or determining the order of priority among drugs according
to drug score rankings (S150), and (7) selecting a drug in
consideration of the drug score and the priority and calculating a
prescription score (S160).
[0073] If a drug score sorted by ranking as described above is
selected, the method of the present invention may further include
assisting a doctor in charge of prescription in making a decision
by providing a pharmacogenomic calculation process and a ground for
calculating the drug score as information in the form of a diagram,
a chart, explanation, and the like. That is, the invention
according to the present invention may further include providing
one or more information among gene sequence variation information,
a gene sequence variation score, a protein damage score, a drug
score, and information used for calculation thereof, which are
grounds for determining the order of priority among drugs of the
present invention. For example, as illustrated in FIG. 3, when a
user selects a specific drug Terbutaline, it is possible to provide
a diagram, a chart, explanation, and the like, regarding
pharmacogenomil grounds for calculating a drug score of the
corresponding drug.
[0074] In another aspect, the present invention relates to a system
for personalizing drug selection using individual genome sequence
variations, the system including: a database from which information
relevant to a gene or protein related to a drug or drug group
applicable to an individual can be searched or extracted; a
communication unit accessible to the database; a first calculation
module configured to calculate one or more gene sequence variation
information involved in the pharmacodynamics or pharmacokinetics of
the drug or drug group on the basis of the information; a second
calculation module configured to calculate an individual protein
damage score by using the gene sequence variation information; a
third calculation module configured to calculate an individual drug
score by associating the individual protein damage score with a
drug-protein relation; and a display unit configured to display the
values calculated by the calculation modules.
[0075] In the present invention, a module may represent a
functional or structural combination of hardware for implementing
the technical spirit of the present invention and software for
driving the hardware. For example, the module may be a
predetermined code and a logical unit of a hardware resource by
which the predetermined code is executed. It is obvious to those
skilled in the art that the module does not necessarily mean
physically connected codes or one kind of hardware.
[0076] The term "calculation module" used herein may represent a
predetermined code and a logical unit of a hardware resource by
which the predetermined code is executed for calculating each score
on the basis of the gene sequence variation score, protein damage
score, drug score, and information as grounds for calculation
thereof with respect to a drug and a gene of analysis target
according to the present invention, but does not necessarily mean
physically connected codes or one kind of hardware.
[0077] The system according to the present invention may further
include a fourth calculation module configured to calculate the
order of priority among drugs applicable to the individual by using
the individual drug score calculated by the third calculation
module; or determine whether or not to use the drugs applicable to
the individual by using the above-described individual drug
score.
[0078] The system according to the present invention may further
include a fifth calculation module configured to calculate a
prescription score by summarizing drug scores determined with
respect to respective drugs if two or more drugs are determined on
the basis of the order of priority among drugs and need to be
administered at the same time.
[0079] The system according to the present invention may further
include a user interface configured to input a list of drugs or
drug groups by the user, or access a database including information
about a drug or drug group effective in treating a specific disease
and extract relevant information, and thereby calculate and provide
a drug score of the drug.
[0080] The system according to the present invention may further
include a display unit configured to display the values calculated
by the respective calculation modules or a calculation process for
determining the order of priority among drugs and information as a
ground for the calculation or determination.
[0081] In the system according to the present invention, the
database or a server including access information, the calculated
information, and the user interface connected thereto may be used
as being linked to one another.
[0082] If new pharmacological/biochemical information regarding a
drug-protein relation is produced, the system according to the
present invention is immediately updated so as to be used for
further improved personalization of drug selection. In an exemplary
embodiment of the present invention, when the database or knowledge
base is updated, the gene sequence variation information, gene
sequence variation score, protein damage score, drug score, and the
information as grounds for the calculation thereof stored in the
respective calculation modules are updated.
[0083] FIG. 2 is a schematic configuration view of a system for
personalizing drug selection using individual genome sequence
variations according to an exemplary embodiment of the present
invention. A system 10 of the present invention may include a
database (DB) 100 from which information relevant to a gene or
protein related to a drug or drug group can be searched or
extracted, a communication unit 200, a user interface or terminal
300, a calculation unit 400, and a display unit 500.
[0084] In the system according to the present invention, the user
interface or terminal 300 may be configured to request a processing
for personalizing drug selection using individual genome sequence
variations to a server and receive a result from a server and/or
store it. And the user interface or terminal 300 may consists of a
terminal, such as a smart phone, a PC (Personal Computer), a tablet
PC, a personal digital assistant (PDA), and a web pad, which
includes a memory means and has a mobile communication function
with a calculation ability using a microprocessor.
[0085] In the system according to the present invention, the server
is a means for providing an access to the database 100 with respect
to a drug, a gene variation, or a drug-protein relation and is
connected to the user interface or terminal 300 through the
communication unit 200 so as to exchange various kinds of
information. Herein, the communication unit 200 may include not
only communication in the same hardware but also a local area
network (LAN), a metropolitan area network (MAN), a wide area
network (WAN), the Internet, 2G, 3G and 4G mobile communication
networks, Wi-Fi, Wibro, and the like, and may use any communication
method regardless of whether it is wired or wireless. The database
100 may be directly installed in the server and may also be
connected to various life science databases accessible via the
Internet depending on a purpose.
[0086] In the system according to the present invention, the
calculation unit 400 may include a first calculation module 410
configured to calculate one or more gene sequence variation
information involved in the pharmacodynamics or pharmacokinetics of
the drug or drug group using the collected/inputted information, a
second calculation module 420 configured to calculate an individual
protein damage score, and a third calculation module 430 configured
to calculate an individual drug score, as described above.
[0087] The method according to the present invention can be
implemented by hardware, firmware, software, or combinations
thereof. If the method is implemented by software, a storage medium
may include any storage or transmission medium readable by a device
such as a computer. For example, the computer-readable medium may
include a ROM (read only memory); a RAM (random access memory); a
magnetic disc storage medium; an optical storage medium; a flash
memory device; and other electric, optical or acoustic signal
transmission medium.
[0088] In this aspect, the present invention provides a
computer-readable medium including an execution module for
executing a processor that performs an operation including:
acquiring gene sequence variation information involved in the
pharmacodynamics or pharmacokinetics of a predetermined drug or
drug group from individual genome sequence information; calculating
an individual protein damage score by using the gene sequence
variation information; and associating the individual protein
damage score with a drug-protein relation to thereby calculate an
individual drug score.
[0089] The processor may further include: determining the order of
priority among drugs applicable to an individual by using the
above-described individual drug score; or determining whether or
not to use the drugs applicable to the individual by using the
above-described individual drug score.
[0090] Hereinafter, the present invention will be described in more
detail with reference to the following Examples. The following
Examples are provided to explain the present invention in detail
but do not limit the scope of the present invention.
[0091] The following Examples are divided as follows.
[0092] First group: Examples show actual cases of the present
invention, and illustrate a process of providing a method for
personalizing drug selection of the present invention with a
selected one drug (Example 1), two drugs in need of selection
(Example 2), or various comparable drugs belonging to the same drug
group which can be used in a specific medical condition (Example
3).
[0093] Second group: Examples are provided to demonstrate validity
of the present invention, and include demonstration of data-based
validity on the basis of disclosed large-scale individual genome
sequence variation information (Example 4), individual genome
sequence analysis on 12 pediatric leukemia patients showing warning
signs of serious side effects during a treatment with Busulfan as
an anticancer drug and bone-marrow inhibitor and demonstration of
actual clinical validity on the basis of the analysis (Example 5),
and demonstration of the validity in a view of population genetics
for suggesting that the method for personalizing drug selection of
the present invention can be used for individual personalized
prevention of drug side effects by showing a high correlation
between an individual drug score calculated according to the
present invention and the drug's withdrawal from the market and
restriction to use (Example 6).
[0094] Third group: Example shows various application cases of the
present invention, and suggests usefulness of a method for
personalizing drug selection of the present invention by
contemplating a clinical significance of individual genome sequence
variations found in a target protein of a specific drug with a
predicted risk for an individual (Example 7).
Example 1. Providing Method for Personalizing Drug Selection with
Respect to Selected One Drug (Terbutaline)
[0095] In order to provide a method for personalizing drug
selection with respect to Terbutaline as one of drugs used for
treating asthma, the following analysis was conducted using the
method and the system of the present invention.
[0096] To be more specific, a gene sequence analysis was conducted
on an individual sg01 which was healthy but determined as having a
high medical risk of getting asthma since his/her mother was
undergoing treatment for asthma. Gene sequence variation scores of
BCHE (butyrylcholinesterase) and ADRB2 (adrenoceptor beta 2,
surface) known as genes involved in the pharmacodynamics or
pharmacokinetics of Terbutaline were calculated for each variant by
using a SIFT algorithm, and protein damage scores and drug scores
were calculated. The results thereof were as listed in Table 16 and
illustrated in FIG. 3.
TABLE-US-00016 TABLE 16 Drug name Protein Variation information
(Drug damage Protein Variation Chromosomal Reference Variant score)
Gene/protein score group score location genotype genotype
Terbutaline Adrenoceptor beta 2, 0.68 Target 0.46 chr5:148206440 G
A (0.22) surface (ADRB2) (PD) 0.45 chr5:148206473 G C 1.00
chr5:148207447 G C 1.00 chr5:148207633 G A Butyrylcholinesterase
0.07 Enzyme 0.07 chr3:165491280 C T (BCHE) (PK)
[0097] As listed in Table 16, according to the result of the gene
sequence analysis on the individual sg01, a gene sequence variation
score of a single variant (chr3:165491280) found in BCHE was 0.07,
and gene sequence variation scores of four variants
(chr5:148206440, chr5:148206473, chr5:148207447, chr5:148207633)
found in ADRB2 were 0.46, 0.45, 1, and 1, respectively. Based on
the gene sequence variation scores, individual protein damage
scores with respect to BCHE and ADRB2 were calculated using
Equation 2, and the results were 0.07 and 0.68
(=(0.46.times.0.45.times.1.times.1).sup.1/4), respectively. Based
on the protein damage scores, an individual drug score with respect
to Terbutaline was calculated using Equation 4, and the result was
0.22 (=(0.07.times.0.68).sup.1/2).
[0098] By the method according to the present invention, it was
observed that the individual sg01 had moderate (protein damage
score: 0.68) to severe (protein damage score: 0.07) damage with
respect to ADRB2 and BCHE as representative target protein and
enzyme protein, respectively, of Terbutaline, and overall, the
individual drug score of the individual sg01 with respect to
Terbutaline was at a severe level (0.22) (FIG. 3). Therefore, it
was determined that it was preferable to recommend avoiding
prescription of Terbutaline with a low drug score of 0.22 to the
individual sg01 and substituting Terbutaline with another drug.
Example 2. Providing Method for Personalizing Drug Selection with
Respect to Two Drugs (Aspirin and Tylenol) in Need of Selection
[0099] In order to provide a method for personalizing drug
selection with respect to Aspirin and Tylenol as drugs used for
treating pain, the following analysis was conducted using the
method and the system of the present invention.
[0100] Both of Aspirin (Acetylsalicylic acid) and Tylenol
(Acetaminophen) have been widely used as painkillers, but show
individual differences in responsiveness and sometimes cause severe
side effects. In particular, it has been impossible to predict
which of two drugs would provide a better medicinal effect or cause
a more severe adverse drug reaction. Therefore, hereinafter, it
will be described that the method and the system of the present
invention can be used to help in making a difficult determination,
which frequently occurs in clinical practice.
[0101] A gene sequence analysis was conducted on an individual sg09
which had felt discomfort when taking an antipyretics and a pain
killer commercially available without a prescription and thus took
half the recommended dose thereof. Gene sequence variation score of
genes involved in the pharmacodynamics or pharmacokinetics of
Aspirin and Tylenol, protein damage scores, and drug scores were
calculated. The results thereof were as listed in Table 17, Table
18, and illustrated in FIG. 4.
TABLE-US-00017 TABLE 17 Protein Variation information Drug name
damage Protein Variation Chromosomal Reference Variant (Drug score)
Gene/protein score group score location genotype genotype
Acetylsalicylic Aldo-keto 1.00 Target 1.00 chr10:5010572 A G
acid(0.76) reductase (PD) family 1, member C1 (AKR1C1)
Cyclooxygenase 0.38 Target 0.38 chr9:125133479 T C 1 (PTGS1) (PD)
Cyclooxygenase 1.00 Target 2 (PTGS2) (PD) Cytochrome 1.00 Enzyme
1.00 chr10:96602622 C T P450 2C19 (PK) (CYP2C19) Cytochrome 1.00
Enzyme 1.00 chr10:96827178 T C P450 2C8 (PK) (CYP2C8) Cytochrome
1.00 Enzyme P450 2C9 (PK) (CYP2C9) ATP-binding 1.00 Transporter
1.00 chr7:87138645 A G cassette, (PK) 1.00 chr7:87179601 A G
sub-family B, member 1 (ABCB1) Solute carrier 1.00 Transporter 1.00
chr1:113456546 A T family 16, (PK) member 1 (SLC16A1) Solute
carrier 0.17 Transporter 1.00 chr11:63066500 A G family 22, (PK)
0.03 chr11:63072226 C A member 10 (SLC22A10) Solute carrier 1.00
Transporter family 22, (PK) member 11 (SLC22A11) Solute carrier
1.00 Transporter family 22, (PK) member 6 (SLC22A6) Solute carrier
0.89 Transporter 1.00 chr6:43270097 T C family 22, (PK) 0.79
chr6:43270151 C T member 7 (SLC22A7) Solute carrier 0.32
Transporter 0.32 chr11:62766431 A T family 22, (PK) member 8
(SLC22A8) Solute carrier 0.84 Transporter 1.00 chr11:74904362 T C
organic anion (PK) 0.71 chr11:74907582 C T transporter family,
member 2B1 (SLCO2B1) Albumin 1.00 Carrier 1.00 chr4:74285239 C T
(ALB) (PK)
TABLE-US-00018 TABLE 18 Protein Variation information Drug name
damage Protein Variation Chromosomal Reference Variant (Drug score)
Gene/protein score group score location genotype genotype
Acetaminophen Cyclooxygenase 0.38 Target 0.38 chr9:125133479 T C
(0.31) 1 (PTGS1) (PD) Cyclooxygenase 1.00 Target 2 (PTGS2) (PD)
Cytochrome 2.8 .times. 10.sup.-5 Enzyme 0.08 chr15:75015305 C T
P450 1A1 (PK) 1 .times. 10.sup.-8 chr15:75015215 T G (CYP1A1)
Cytochrome 1.00 Enzyme P450 1A2 (PK) (CYP1A2) Cytochrome 0.52
Enzyme 0.55 chr19:41350664 A T P450 2A6 (PK) 0.49 chr19:41356281 T
C (CYP2A6) Cytochrome 1.00 Enzyme 1.00 chr10:96827178 T C P450 2C8
(PK) (CYP2C8) Cytochrome 1.00 Enzyme P450 2C9 (PK) (CYP2C9)
Cytochrome 0.20 Enzyme 0.98 chr22:42525182 A T P450 2D6 (PK) 0.39
chr22:42525756 G A (CYP2D6) 0.02 chr22:42526694 G A Cytochrome 0.76
Enzyme 0.57 chr10:135347397 T C P450 2E1 (PK) 1.00 chr10:135351362
T C (CYP2E1) Cytochrome 1.00 Enzyme P450 3A4 (PK) (CYP3A4)
ATP-binding 1.00 Transporter 1.00 chr7:87138645 A G cassette, (PK)
1.00 chr7:87179601 A G sub-family B, member 1 (ABCB1) Solute
carrier 1.00 Transporter family 22, (PK) member 6 (SLC22A6)
[0102] As listed in Table 17, by using gene sequence variation
information of sg09 involved in the pharmacodynamics or
pharmacokinetics of a total of 15 proteins, including 3 target
proteins, 3 enzyme proteins, 8 transporter proteins, and 1 carrier
protein, of Aspirin (Acetylsalicylic acid), an individual drug
score was obtained. Firstly, gene sequence variation information of
a gene involved in the pharmacodynamics or pharmacokinetics of
Aspirin was determined, and a gene sequence variation score was
calculated by using a SIFT algorithm. Since each of PTGS1 as a
target protein of Aspirin and SLC22A8 as a transporter protein had
a single variant (chr9:125133479 and chr11:62766431, respectively),
gene sequence variation scores (0.38, 0.32, respectively) were
determined as protein damage scores. Since SLC22A10 as another
transporter protein had two variants (chr11:63066500, chr11:
63072226), gene sequence variation scores of the respective
variants were calculated as 1.0 and 0.03, respectively, and a
protein damage score was calculated using Equation 2
(0.17(=(1.0.times.0.03).sup.1/2)). After summarization of a total
of 15 protein damage scores including the above-described three
protein damage scores, a drug score was calculated using Equation
4. As a result, it was confirmed that the drug score of the
individual sg09 with respect to Aspirin was 0.76
(=(1.0.times.0.38.times.1.0.times.1.0.times.1.0.times.1.0.times.1.0.times-
.1.0.times.0.17.times.1.0.times.1.0.times.0.89.times.0.32.times.0.84.times-
.1.0).sup.1/15).
[0103] Further, as listed in Table 18, by using gene sequence
variation information of sg09 involved in the pharmacodynamics or
pharmacokinetics of a total of 12 proteins, including 2 target
proteins, 8 enzyme proteins, and 2 transporter proteins of Tylenol
(Acetaminophen), an individual drug score was obtained. Firstly,
gene sequence variation information of a gene involved in the
pharmacodynamics or pharmacokinetics of Tylenol was determined, and
a gene sequence variation score was calculated by using a SIFT
algorithm. Since PTGS1 as a target protein of Tylenol had a single
variant (chr9:125133479), a gene sequence variation score was
determined as a protein damage score. Further, with respect to
CYP1A1 (chr15:75015305, chr15:75015215) and CYP2A6 (chr19:41350664,
chr19:41356281) as the enzyme proteins having two variants and
CYP2D6 (chr22:42525182, chr22:42525756, chr22:42526694) as the
enzyme protein having three variants, protein damage scores were
calculated as 2.8.times.10.sup.-5
(=(0.08.times.(1.times.10-8)).sup.1/2), 0.52
(=(0.55.times.0.49).sup.1/2), and 0.2
(=(0.98.times.0.39.times.0.02).sup.1/3), respectively, by obtaining
a geometric mean (using Equation 2) of gene sequence variation
scores. After summarization of a total of 12 protein damage scores
including the above-described four protein damage scores, a drug
score was calculated using a geometric mean (using Equation 4). As
a result, it was confirmed that the drug score of the individual
sg09 with respect to Tylenol was 0.31
(=(0.38.times.1.0.times.(2.8.times.10.sup.-5).times.1.0.times.0.52.t-
imes.1.0.times.1.0.times.0.2.times.0.76.times.1.0.times.1.0.times.1.0).sup-
.1/12).
[0104] Further, as illustrated in FIG. 4, the combined individual
drug score of the individual sg09 with respect to Aspirin was
calculated as 0.76, which was higher than the individual drug score
of 0.31 with respect to Tylenol. Therefore, it was determined that
it was preferable to recommend selecting Aspirin to the individual
sg09 in order to reduce discomfort caused by a drug when clinically
selecting one of Aspirin and Tylenol unless there is a particular
reason to the contrary.
Example 3. Providing Method for Personalizing Drug Selection to
Assist in Selecting Highly Safe Drug Among Various Comparable Drugs
Belonging to Same Drug Group (Same ATC Code Group)
[0105] In order to provide a method for personalizing drug
selection to assist in selecting a drug with high safety among
various comparable drugs belonging to the same drug group (same ATC
code group), the following experiment was conducted using the
method and the system of the present invention.
[0106] Among 22 drugs belonging to C07 beta blockers according to
the internally certified ATC code, 11 drugs are specific beta
blockers [C07A13], 9 drugs are non-specific beta blockers [C07AA],
and two drugs are alpha and beta blockers [C07AG]. For individual
genome sequence variation analysis on 14 individuals (sg01, sg02,
sg03, sg04, sg05, sg07, sg09, sg11, sg12, sg13, sg14, sg16, sg17,
sg19), HISEQ-2000 as an NGS (Next Generation Sequencing) device
manufactured by Illumina was used to conduct a 30.times. whole
genome sequencing. In this case, alternatively, a whole exome
sequencing (WES) as a part of the whole genome sequencing or a
targeted exome sequencing with respect to main 500 to 1000 genes
relevant to 500 to 1000 drug may be conducted. The sequenced
sequence fragments underwent data cleaning and quality check and
outputted in the form of SAM (Sequence Alignment Map) and BAM
(Binary Alignment Map) files aligned with a human reference group
sequence (for example, HG19). The cleaned alignment result was
outputted in the form of VCF (Variation Calling Format) file while
detecting variations such as single nucleotide variations (SNVs)
and Indels by using software tools such as SAMTools:pileup,
SAMTools:mpileup, GATK:recalibration, GATK:realignment, and the
like.
[0107] After the VCF file including the gene sequence variation
information was inputted and the above-described gene sequence
variation score vi was calculated for each variant, an individual
protein damage score Sg was calculated using Equation 2. Then, an
individual drug score Sd was calculated using Equation 4. Then, a
profile of drug scores and a profile of the order of priority of
drugs were calculated, respectively. The results thereof were as
listed in Table 19 and illustrated in FIG. 5.
TABLE-US-00019 TABLE 19 Drug name sg01 sg02 sg03 sg04 sg05 sg07
sg09 sg11 sg12 sg13 sg14 sg16 sg17 sg19 Alprenolol 0.53 0.64 0.60
0.60 0.54 0.71 0.51 0.65 1.00 1.00 0.69 0.81 0.82 0.59 Bopindolol
0.87 0.85 0.97 0.97 0.72 0.95 0.71 0.87 1.00 1.00 1.00 0.81 0.82
0.85 Bupranolol 0.88 0.77 0.95 0.95 0.68 0.92 0.57 0.79 1.00 1.00
1.00 0.70 0.72 0.77 Carteolol 0.49 0.57 0.52 0.52 0.52 0.65 0.44
0.59 1.00 1.00 0.63 0.76 0.78 0.52 Nadolol 0.91 0.61 0.96 0.96 0.74
0.94 0.65 0.84 1.00 1.00 1.00 0.76 0.74 0.82 Oxprenolol 0.49 0.55
0.47 0.47 0.47 0.56 0.41 0.56 0.79 1.00 0.55 0.69 0.65 0.47
Penbutolol 0.80 0.82 0.96 0.96 0.74 0.94 0.65 0.84 1.00 1.00 1.00
0.76 0.78 0.82 Pindolol 0.57 0.65 0.59 0.59 0.54 0.66 0.53 0.66
0.85 1.00 0.65 0.77 0.74 0.58 Propranolol 0.49 0.49 0.64 0.05 0.53
0.53 0.33 0.72 0.77 1.00 0.57 0.66 0.51 0.54 Sotalol 0.88 0.77 0.95
0.95 0.68 0.92 0.57 0.79 1.00 1.00 1.00 0.70 0.72 0.77 Timolol 0.67
0.62 0.69 0.69 0.69 0.78 0.62 0.74 1.00 1.00 0.77 0.86 0.84 0.69
Acebutolol 0.54 0.31 0.57 0.57 0.45 0.50 0.49 0.67 0.71 0.40 0.66
0.57 0.74 0.56 Atenolol 1.00 0.72 0.95 0.90 0.77 0.94 0.43 1.00
0.79 0.95 0.87 0.40 0.80 0.95 Betaxolol 0.49 0.57 0.39 0.01 0.52
0.49 0.44 0.49 1.00 1.00 0.63 0.76 0.58 0.52 Bevantolol 0.71 0.85
0.74 0.74 0.78 0.73 0.55 0.73 0.99 1.00 0.99 0.79 0.81 0.84
Bisoprolol 0.49 0.57 0.52 0.52 0.52 0.65 0.44 0.65 1.00 1.00 0.63
0.76 0.78 0.52 Esmolol 1.00 1.00 1.00 1.00 0.40 1.00 0.40 1.00 1.00
1.00 1.00 0.40 0.63 1.00 Metoprolol 0.55 0.50 0.54 0.54 0.53 0.62
0.47 0.66 0.83 1.00 0.61 0.74 0.68 0.53 Nebivolol 0.39 0.48 0.42
0.42 0.42 0.57 0.33 0.57 1.00 1.00 0.54 0.70 0.72 0.42 Practolol
1.00 1.00 1.00 1.00 0.40 1.00 0.40 1.00 1.00 1.00 1.00 0.40 0.63
1.00 Carvedilol 0.54 0.61 0.73 0.22 0.71 0.62 0.45 0.73 0.43 0.90
0.70 0.75 0.65 0.63 Labetalol 0.55 0.72 0.57 0.57 0.68 0.65 0.51
0.61 0.99 1.00 0.76 0.85 0.86 0.68
[0108] As listed in Table 19 and illustrated in FIG. 5, it was
observed that the individual sg04 had remarkably low individual
drug scores with respect to Propranolol among the non-specific beta
blockers [C07AA] and Betaxolol among the specific beta blockers
[C07AB]. To be more specific, as for the individual sg04, the
individual drug scores with respect to Betaxolol and Propranolol
were as low as 0.005 and 0.05, respectively. Therefore, if the
above drugs were prescribed for the individual sg04 without
consideration of such low drug scores, both of the two drugs are
highly likely to cause considerable side effects. Meanwhile, the
individual sg04 had individual drug scores of higher than 0.9 with
respect to Atenolol, Sotalol, Bupranolol, Nadolol, Penbutolol,
Bopindolol, Practolol, and Esmolol among the same drug group and
thus did not have currently known protein damage relevant to the
drugs described above. Therefore, it can be seen that these drugs
can be determined as relatively safe drugs and recommended when
selecting. Further, such analysis has an advantage of displaying
weakness of a specific individual with respect to a certain drug in
a specific drug group so as to be recognized at a glance.
[0109] Meanwhile, it can be seen from FIG. 5 that except the
individual sg04 having a tendency to show extremely low scores with
respect to Betaxolol and Propranolol, most of the 14 individuals
had drugs scores of 0.3 or more with respect to the 22 beta
blockers. Considering this point, if a specific individual has an
individual drug score of lower than 0.3 with respect to a beta
blocker, the score is not in the general range. Therefore, it is
possible to recommend attention to the drug with consideration for
the likelihood of side effects of the drug. When information for
determining whether or not to use a drug is provided as described
above, a criteria of a drug score may be different for each drug or
may varies depending on a clinical situation of using a drug, i.e.,
predicted gains and losses when using a drug.
[0110] In order to additionally analyze the reason why the
individual sg04 shows a low individual drug score with respect to a
specific drug as described above, by using gene sequence variation
information relevant to a total of 15 proteins including target
proteins, enzyme proteins, transporter proteins, and carrier
proteins involved in the pharmacodynamics or pharmacokinetics of
Betaxolol and Propranolol, an individual protein damage score and
an individual drug score were obtained. The results thereof were as
listed in Table 20, Table 21, and illustrated in FIG. 6.
TABLE-US-00020 TABLE 20 Protein Variation information Drug name
damage Protein Variation Chromosomal Reference Variant (Drug score)
Gene/protein score group score location genotype genotype Betaxolol
Adrenoceptor 1.00 Target (0.005) beta 1 (PD) (ADRB1) Adrenoceptor
0.85 Target 0.45 chr5:148206473 G C beta 2, surface (PD) 1.00
chr5:148206646 G A (ADRB2) 1.00 chr5:148206917 C A 1.00
chr5:148207447 G C 1.00 chr5:148207633 G A Cytochrome 1.0 .times.
10.sup.-8 Enzyme 1.0 .times. 10.sup.-8 chr15:75047221 T C P450 1A2
(PK) (CYP1A2) Cytochrome 0.088 Enzyme 0.39 chr22:42525756 G A P450
2D6 (PK) 0.02 chr22:42526694 G A (CYP2D6)
TABLE-US-00021 TABLE 21 Protein Variation information Drug name
damage Protein Variation Chromosomal Reference Variant (Drug score)
Gene/protein score group score location genotype genotype
Propranolol Adrenoceptor 1.00 Target (0.05) beta 1 (PD) (ADRB1)
Adrenoceptor 0.85 Target 0.45 chr5:148206473 G C beta 2, surface
(PD) 1.00 chr5:148206646 G A (ADRB2) 1.00 chr5:148206917 C A 1.00
chr5:148207447 G C 1.00 chr5:148207633 G A Adrenoceptor 1.00 Target
beta 3 (PD) (ADRB3) Serotonin 1.00 Target receptor 1A (PD) (HTR1A)
Serotonin 1.00 Target receptor 1B (PD) (HTR1B) Cytochrome 0.08
Enzyme 0.08 chr15:75015305 C T P450 1A1 (PK) (CYP1A1) Cytochrome
1.0 .times. 10.sup.-8 Enzyme 1.0 .times. 10.sup.-8 chr15:75047221 T
C P450 1A2 (PK) (CYP1A2) Cytochrome 1.00 Enzyme 1.00 chr10:96602622
C T P450 2C19 (PK) (CYP2C19) Cytochrome 0.088 Enzyme 0.39
chr22:42525756 G A P450 2D6 (PK) 0.02 chr22:42526694 G A (CYP2D6)
Cytochrome 1.00 Enzyme P450 3A4 (PK) (CYP3A4) Cytochrome 1.0
.times. 10.sup.-8 Enzyme 1.0 .times. 10.sup.-8 chr7:99245974 A G
P450 3A5 (PK) (CYP3A5) Cytochrome 0.16 Enzyme 0.16 chr7:99306685 C
G P450 3A7 (PK) (CYP3A7) ATP-binding 1.00 Transporter 1.00
chr7:87138645 A G cassette, (PK) 1.00 chr7:87179601 A G sub-family
B, member 1 (ABCB1) Solute carrier 0.32 Transporter 1.00
chr6:160645832 C T family 22, (PK) 0.10 chr6:160670282 A C member 2
(SLC22A2) Alpha-1-acid 1.00 Carrier glycoprotein (PK) (ORM1)
[0111] As listed in Table 20, the individual sg04 had one variant
(chr15:75047221) and two variants (chr22:42525756, chr22:42526694)
with respect to CYP1A2 and CYP2D6, respectively, as two main enzyme
proteins that degrade Betaxolol, and had low gene sequence
variation scores corresponding thereto (1e-08. 0.39, 0.02,
respectively). With respect to the enzyme proteins CYP1A2 and
CYP2D6, individual protein damage scores calculated using Equation
2 were as low as 1.0e-8 and 0.088, respectively, and an individual
drug score calculated using Equation 4 with respect to Betaxolol
was as low as 0.005. Meanwhile, the individual sg04 had no gene
sequence variant in ADRB1 as a target protein of Betaxolol, and 5
gene sequence variants (chr5:148206917, chr5:148206473,
chr5:148206646, chr5:148207447, chr5:148207633) were found in ADRB2
but scores thereof were not low. An individual protein damage score
calculated using Equation 2 with respect to ADRB2 was 0.85.
[0112] Further, as listed in Table 21, the individual sg04 had one
or more severe gene sequence variations in each of 5 enzymes
CYP1A1, CYP1A2, CYP2D6, CYP3A5, CYP3A7, and the like among 7
enzymes that degrade Propranolol, and had low gene sequence
variation scores corresponding thereto: CYP1A1
(0.08(chr15:75015305)); CY1A2 (1e-08(chr15:75047221)); CYP2D6 (0.39
(chr22:42525756); 0.02 (chr22:42526694)); CYP3A5
(1e-08(chr7:99245974)); and CYP3A7 (0.16 (chr7:99306685)). Further,
individual protein damage scores calculated using Equation 2 with
respect to CYP1A1, CYP1A2, CYP2D6, CYP3A5, and CYP3A7 were as low
as 0.08, 1.0e-8, 0.088, 1.0e-8, and 0.16, respectively, and an
individual drug score calculated using Equation 4 with respect to
Propranolol was as seriously low as 0.05.
[0113] Further, as illustrated in FIG. 6, it was observed that the
individual sg04 had significant damages in many proteins involved
in drug metabolism of Betaxolol and Propranolol, and the individual
drug scores of the individual sg04 with respect to Betaxolol and
Propranolol were at severe levels of 0.005 and 0.05,
respectively.
[0114] Therefore, in a clinical situation where it is recommended
for the individual sg04 to use a beta blocker, preferably, a
clinician may be provided with information so as to use drugs with
a high drug score calculated according to the method of the present
invention, i.e., Bopindolol (0.97), Bupranolol (0.95), Nadolol
(0.96), Penbutolol (0.96), and Sotalol (0.95) among the
non-specific beta blockers and Atenolol (0.9), Bevantolol (0.74),
Esmolol (1.0), and Practolol (1.0) among the specific beta
blockers, Labetalol (0.57) with a relatively high score among the
alpha and beta blockers and so as not to prescribe Betaxolol and
Propranolol, and, thus, any risk of drug side effects in the
individual sg04 can be reduced.
Example 4. Demonstration of Validity of Method for Personalizing
Drug Selection Based on Individual Genome Sequence Variation
Information
[0115] Reliable study results about individual genome sequence
variation information and an individual difference in
pharmacodynamics response have been very limited so far. The
studies conducted so far have followed a paradigm of a case-control
study in which an individual difference in responsiveness is
studied by comparing a group with a specific variation with a group
without the specific variation for each drug. In this study
paradigm, a costly case-control study needs to be conducted to each
of all combinations of pairs of numerous sequence variants and
numerous drugs, which is practically impossible. Meanwhile, the
method for personalizing drug selection according to the present
invention is applicable to all of gene sequence variations but does
not require a costly case-control study. Further, the method can
calculate an individual protein damage score and an individual drug
score just by calculating a genome sequence variation and suggests
a method of application thereof. Therefore, the method has an
advantage of being able to make a deduction for personalizing drug
selection with respect to combinations between all genome sequence
variations and all drugs.
[0116] In order to evaluate validity of a result of personalized
drug selection according to the method of the present invention,
497 frequently prescribed drugs were selected on the basis of the
following criteria; (1) drugs, of which at least one gene involved
in the pharmacodynamics or pharmacokinetics is known, among drugs
included in the ATC codes of top 15 frequently prescribed drug
classes during 2005 to 2008 in the United States (Health, United
States, 2011, Centers for Disease Control and Prevention), (2)
drugs with information on the established effects of
pharmacogenomic genome sequence variation markers in US FDA drug
labels, and (3) drugs disclosed in the database of DrugBank as
having been withdrawn from the market due to drug side effects, and
the like.
[0117] As data for evaluating validity, among the established
knowledge about 987 gene sequence variation-drug interaction pairs
provided by PharmGKB, 650 pairs (65.9%) having at least one link to
the 497 drugs were extracted. Considering that a target of the
present invention is a sequence variation in an exon region, an
overlapped part between data of a verification target and data of
evaluation standard were removed for a fair evaluation. To be more
specific, a fairer evaluation was conducted by removing pairs with
all of 36 sequence variations positioned in the exon region among
the 650 pairs and selecting only a sequence variation in a
non-coding region. As a result, 614 pairs were selected as a final
gold standard for evaluation.
[0118] Then, whole genome sequences of 1092 persons provided by the
1000 Genomes Project were analyzed, and the method according to the
present invention was applied to each of the 1092 persons to
thereby calculate individual pharmacogenomic risk and
pharmacogenomic risk of each gene sequence variation registered at
PharmGKB.
[0119] For validity evaluation, sensitivity, specificity, and an
area under the Receiver Operating Curve (ROC) were used. 497 drugs
were ranked on the basis of individual drug scores and threshold
values were set for each ranking at 496 segment positions between
ranks. Then, (1) when a ranking of a drug score of a corresponding
drug was higher than a threshold and a PharmGKB variation was
present in an individual genome variation, it was determined as
true positive, (2) when a ranking of a drug score of a
corresponding drug was lower than a threshold and a PharmGKB
variation was not present in an individual genome variation, it was
determined as true negative, (3) when a ranking of a drug score of
a corresponding drug was higher than a threshold but a PharmGKB
variation was not present in an individual genome variation, it was
determined as false positive, and (4) when a ranking of a drug
score of a corresponding drug was lower than a threshold but a
PharmGKB variation was present in an individual genome variation,
it was determined as false negative. The numbers of true positive,
true negative, false positive, and false negative cases of each
individual with respect to each ranking threshold L were
calculated, and the sensitivity and the specificity were calculated
as illustrated in the following equations.
Sensitivity = D L GS GS ##EQU00005## Specificity = 1 - D L - GS D -
GS ##EQU00005.2##
[0120] The D is a set of all 497 drugs, the GS is a set of
personalized PharmGKB drugs used as an individual gold standard
since an individual gene sequence variation in each individual is
identical with a risk allele of PharmGKB, the DL is a set of drugs
with high ranking thresholds, and the vertical bar parenthesis
means the number of elements of a corresponding set.
[0121] As a result of calculation, 18 persons had no variation
identical to a variation of PharmGKB, and, thus, a set of
personalized PharmGKB drugs used as an individual gold standard
could not be defined. Therefore, the 18 persons were excluded from
the present validity test. The sensitivity and specificity were
calculated with respect to all of the thresholds, and a ROC was
drawn to thereby calculate an AUC. To be more specific, gene
sequence variation scores of 1092 persons in the total population
group were calculated using a SIFT algorithm, and then, protein
damage scores and drugs scores were calculated using Equation 2 and
Equation 4, respectively. Further, in order to determine the
usefulness of application of weightings according to a race
distribution, race-specific sensitivity and specificity and a value
of AUC based on the sensitivity and specificity were calculated in
the same manner for each of four races (African (AFR, n=246),
American (AMR, n=181), Asian (ASN, n=286), European (EUR, n=379))
clearly stated in the 1000 Genomes Project, so that race-specific
sensitivity and specificity and an AUC were obtained. The results
thereof were as listed in Table 22, Table 23, and illustrated in
FIG. 7.
TABLE-US-00022 TABLE 22 Distribution of each protein group and
Calculation of an average protein damage score Mean Number of
protein Number of Number of protein- damage Protein group proteins
relevant drugs drug pairs score Target protein 440 486 2357 0.798
Carrier protein 10 50 65 0.728 Enzyme protein 74 330 1347 0.733
Transporter protein 54 176 457 0.733 Total 545 497 4201 0.783
TABLE-US-00023 TABLE 23 Calculation of the valididty(AUC) of the
drug score calculation for each protein group and each race using
The 1000 Genomes Project data Total AFR AMR ASN EUR Validity (AUC)
of the drug score calculation Target protein 0.617 0.634 0.608
0.614 0.614 Carrier protein 0.554 0.511 0.599 0.485 0.594 Enzyme
protein 0.587 0.642 0.580 0.558 0.579 Transporter protein 0.497
0.492 0.488 0.489 0.512 Validity (AUC) of the drug score
calculation in the case where weightings are not applied to each
protein group or the case where weightings are applied to each
protein group Simple geometric mean 0.666 0.744 0.650 0.634 0.653
Weighted geometric mean 0.667 0.742 0.652 0.633 0.654
[0122] Table 22 lists a distribution of proteins relating to 497
drugs used in the present Example for each protein group, and
indicates the number of protein-drug pairs together with an average
protein damage score for each group.
[0123] Table 23 lists validity of individual drug score calculation
(AUC) respectively calculated in the case where weightings are not
applied to each protein group (simple geometric mean) and the case
where weightings are applied to each protein group (weighted
geometric mean) when calculating a drug score using Equation 4 with
respect to each protein group and each race.
[0124] To be more specific, for example, in the total population
group, AUC values calculated for protein groups such as target
proteins, carrier proteins, metabolism enzyme proteins, and
transporter enzymes were 0.617, 0.554, 0.587, and 0.497,
respectively. These values were used as weightings for the
respective protein groups (each value was substituted for the
weighting wi of Equation 4) to thereby obtain the validity of
individual drug score calculation using weighted geometric mean
(AUC=0.667) (refer to FIG. 7b). As a result, it was confirmed that
the validity of individual drug score calculation using weighted
geometric mean in the case of assigning the weightings for the
respective protein groups were increased by 0.001 point as compared
with the validity of individual drug score calculation using simple
geometric mean (AUC=0.666) calculated by applying a simple
geometric mean calculation formula without assigning a weighting
(weighting wi=1) (refer to FIG. 7a).
[0125] Further, as illustrated in FIG. 7a, as another example of
application of weightings, weightings were assigned according to
the number of people per race and a validity of individual drug
score calculation (AUC) was analyzed. As a result, in the case of
considering a race specificity (bold line), the AUC value of the
total population group (Total) was 0.666 (African: 0.744, American:
0.650, Asian: 0.631, and European: 0.653), and in the case of not
considering a race specificity (dotted line), the AUC value of the
total population group was 0.633 (African: 0.623, American: 0.629,
Asian: 0.64, and European: 0.636). Accordingly, it was confirmed
that the validity of individual drug score calculation in the case
of considering a race specificity was improved as compared with the
case of not considering a race specificity.
[0126] Further, as illustrated in FIG. 7b, in the case of assigning
weightings for the respective protein groups without considering a
race specificity (dotted line), validity of individual drug score
calculation (AUC) of the present invention was 0.634, and in the
case of assigning weightings for the respective protein groups
while considering a race specificity (bold line), validity of
individual drug score calculation (AUC) of the present invention
was 0.667. Accordingly, it can be seen that different weightings
are useful.
Example 5. Demonstration of Validity of the Present Invention
Through Individual Genome Sequence Variation Information Analysis
on Pediatric Leukemia Patients Showing Warning Signs of Serious
Side Effects During Treatment with Anticancer Drug Busulfan
[0127] Bone marrow transplantation is one of the most important
treatment method for treating blood tumor such as leukemia. For
bone marrow transplantation, bone marrow of a patient needs to be
removed first by using two methods: total body irradiation (TBI);
and a pharmacological treatment using drugs such as Busulfan.
Busulfan is a representative alkylating agent and can substitute
for total body irradiation. However, it has a relatively narrow
therapeutic range. Thus, if a drug concentration is higher than the
therapeutic range, hepatic veno-occlusive disease (VOD) and severe
toxicity, such as neurotoxicity, relevant to the drug occurs, and
if a drug concentration is lower than the therapeutic range, the
likelihood of graft failure or recurrence is increased.
Particularly, pediatrics are greatly different from each other in
the pharamacokinetics of Busulfan. Therefore, Busulfan is used
under therapeutic drug monitoring (TDM). Toxicity of Busulfan
includes interstitial lung fibrosis commonly called "Busulfan
Lung", hyperpigmentation, epilepsy, veno-occlusive disease (VOD),
nausea, thrombocytopenia, and the like. The IARC (International
Agency for Research on Cancer) classifies Busulfan as one of Group
1 carcinogens.
[0128] In order to check whether it is possible to identify a risk
group with respect to the Busulfan treatment through the method for
personalizing drug selection using individual genome sequence
variations of the present invention, the following experiment was
conducted. Firstly, an analysis was conducted on 12 pediatric
leukemia patients showing warning signs of serious side effects
with a high AUC (AUC 6-hour) after administration according to an
opinion under TDM (therapeutic drug monitoring) during a treatment
with an anticancer drug Busulfan (Myleran, GlaxoSmithKline,
Busulfex IV, Otsuka America Pharmaceutical, Inc.) to remove bone
marrow as a pre-treatment for bone marrow transplantation. For
objective comparison, gene sequence comparison analysis was
conducted on 14 cases in a normal control group and 286 Asians
provided by the 1000 Genomes Project (http://www.1000genomes.org/).
Firstly, genes involved in the pharmacodynamics or pharmacokinetics
of Busulfan and its metabolic product were searched, and then, 12
genes (CTH, GGT1, GGT5, GGT6, GGT7, GSTA1, GSTA2, GSTM1, GSTP1,
MGMT, MGST2, MSH2) were selected.
[0129] From gene sequence variation information of the 12 pediatric
leukemia patients and the 14 cases in the normal control group with
respect to the 12 genes, gene sequence variation scores were
calculated using a SIFT algorithm. Then, from the gene sequence
variation scores, individual protein damage scores and individual
drug scores were calculated according to the present invention. To
be more specific, on the basis of individual gene sequence
variation information, individual protein damage scores with
respect to the 12 genes were calculated using Equation 2, and
individual drug scores were calculated using Equation 4. The
results thereof were as listed in Table 24. The Asians were divided
into sub-population groups CHB (Han Chinese in Beijing, China)
(n=97), CHS (Southern Han Chinese) (n=100), and JPT (Japanese in
Tokyo, Japan) (n=89), and the same analysis was conducted on these
groups. Each of individual protein damage scores and drug scores
was calculated using a geometric mean, a harmonic mean or a
product.
[0130] As listed in Table 24, according to the calculation result
of the individual drug scores using the geometric mean, the
harmonic mean or the product, respectively, in the cases of using
the geometric mean (p=0.016) and the product (p=0.001), results of
an Oneway Analysis of Variance among the pediatric leukemia
patients (n=12) exhibiting warning signs of serious side effects
after administration of Busulfan, the normal control group (n=14),
and the Asians (n=286) were statistically significant, and in the
case of using the harmonic mean, results showed a significant
tendency (p=0.088).
[0131] Meanwhile, as a result of T-test analysis of individual drug
scores calculated using the geometric mean, the harmonic mean or
the product, it was confirmed that all of the normal persons vs the
Asians (n=286) (p=0.579, 0.872, 0.173), the normal persons vs CHB
(n=97) (p=0.327, 0.942, 0.20), the normal persons vs CHS (n=100)
(p=0.967, 0.837, 0.169), and the normal persons vs JPT (n=89)
(p=0.559, 0.735, 0.154) did not show statistical significance based
on a p-value. From the above-described result, it was confirmed
that it is possible to significantly differentiate a group (a risk
group with respect to the Busulfan treatment) illustrating warning
signs of serious side effects during a treatment with Busulfan from
a no-risk group by using calculation of individual drug scores
through analysis of individual genome sequence variation
information according to the present invention and also possible to
prevent an unwanted side effect.
[0132] Further, gene sequence variation information involved in the
pharmacodynamics or pharmacokinetics and pharmacological pathway of
Busulfan as an anticancer drug and bone-marrow inhibitor was
determined by conducting individual gene sequence analysis on the
12 pediatric leukemia patients and the 14 cases in the normal
control group, and distribution of means and standard deviations of
individual protein damage scores (calculated using Equation 2) and
individual drug scores (calculated using Equation 4) calculated
from the gene sequence variation information was as illustrated in
FIG. 8.
[0133] As illustrated in FIG. 8, the two groups did not show a
remarkable difference in protein damage scores of the genes GGT1,
GSTA1, GSTP1, MGST1, but showed a certain difference in protein
damage scores of the genes CTH, GGT5, GGT6, GGT7, GSTA2, MGMT,
MSH2. Meanwhile, it was observed that a protein damage score of the
gene GSTM1 was slightly higher in the pediatric leukemia patient
group (0.665) than the normal control group (0.637). As shown in
the above-described result, it is difficult to identify the two
groups with the individual gene variation or protein damage scores,
but in the case of using the method for personalizing drug
selection of the present invention, it is possible to statistically
significantly identify the two groups by calculating a summarized
drug score (refer to Table 24).
[0134] Further, a size of each figure in FIG. 8 means the frequency
of gene sequences, and it was confirmed that sizes of figures for
the pediatric leukemia patient group (FIG. 8a) are greater than
sizes of figures for the normal control group (FIG. 8b). Therefore,
it is possible to recognize at a glance that the number of gene
sequence variations used for calculating the summarized drug score
is greater in the pediatric leukemia patient group.
[0135] With the above-described result, it is possible to predict a
group with a high likelihood of side effects when Busulfan is
administered to a pediatric leukemia patient, according to the
method of the present invention, and also possible to induce a
high-risk group to adjust a drug concentration or use an
alternative treatment method or interventional method.
Example 6. Demonstration of Validity of the Present Invention
Through Analysis of Individual Genome Sequence Variation
Information Found in Gene Involved in Pharmacodynamics or
Pharmacokinetics of Drug Withdrawn from Market
[0136] Any drug approved by the FDA and sold in the market can be
ordered to be withdrawn from the market according to a result of a
post-market surveillance (PMS) while being widely used. Such
withdrawal of a drug from the market is a medically critical issue.
Even a drug approved after the whole process of a strict clinical
trial may cause unpredicted side effects in an actual application
step with enormous sacrifices of life and economic losses and thus
may be withdrawn. An individual difference which cannot be found in
a large-scale clinical trial is regarded as one of causes for
withdrawal of a drug from the market. The method for personalizing
drug selection according to the present invention provides a method
for precluding the use of drugs with high risk for each individual
in consideration of an individual difference. Accordingly, if it is
possible to predict withdrawal of a drug, which causes enormous
medical and economic losses, from the market by the method for
personalizing drug selection according to the present invention,
the validity of the present invention can be demonstrated
again.
[0137] In order to do so, an analysis was conducted on the same
population group (n=1097) as Example 4 and the drug group (n=497)
with withdrawn drugs from the market and drugs restricted to use.
In order to construct a comprehensive list of withdrawn drugs from
the market, the document "List of Withdrawn Drugs" from Wikipedia
and "Consolidated List of Products Whose Consumption and/or Sale
Have Been Banned, Withdrawn, Severely Restricted, or Not Approved
by Governments: Pharmaceuticals" Versions 8, 10, 12, and 14 as the
most comprehensive data about the withdrawn drugs from the
worldwide market issued by the U.N. were reviewed overall in
addition to the already included list of withdrawn drugs from the
market from the DrugBank database. Finally, a list of 392 withdrawn
drugs from at least one country was constructed, and it was
confirmed that 82 drugs of them were included in the
above-described 497 drugs. Further, a drug, which has not been
withdrawn from the market but severely restricted to use, was
extracted from a union of the list of drugs given "Boxed Warning"
from the US FDA and the drugs indicated as "severely restricted" in
the U.N. report and also included in the above-described 497 drugs,
and it was confirmed that the number of drugs in the drug group was
139. An analysis was conducted on the 82 withdrawn drugs from the
market, the 139 drugs restricted to use, and the other 276 drugs. A
market safety score or a population group drug score of each drug
was obtained by calculating gene sequence variation scores using a
SIFT algorithm on the basis of genome sequence variations of the
1092 persons and acquiring an arithmetic mean of 1092 individual
drug scores calculated from the gene sequence variation scores. As
a result, the population group drug scores of the withdrawn group,
the restricted group, and the other group were 0.585.+-.0.21,
0.592.+-.0.19, and 0.664.+-.0.19, respectively, and as a result of
an Oneway Analysis of Variance, a difference thereof was
significant (F=9.282, p<0.001). Further, as a result of a post
Tukey analysis, a p-value between the withdrawn drug and the other
drug was 0.004 and a p-value between the restricted drug and the
other drug was 0.001, and the both values showed a statistical
significance. A significant difference between the withdrawn drug
and the restricted drug was not found (p-value=0.971). That is, it
can be seen that in the population group, as a mean of drug scores
suggested by the method for personalizing drug selection of the
present invention is decreased, the likelihood of withdrawal and
restriction of the drug is significantly increased and the
corresponding drug has a high risk.
[0138] The usefulness of a drug score according to the present
invention is clearly visualized with relative frequency histograms
as illustrated in FIG. 9. FIG. 9a is a relative frequency histogram
according to population group drug scores of withdrawn drugs from
the market as obtained from DrugBank and Wikipedia, and FIG. 9b is
a relative frequency histogram according to population group drug
scores of withdrawn drugs from the market and drugs restricted to
use as obtained from the U.N. data.
[0139] As illustrated in FIG. 9, drugs were respectively allotted
to 10 score sections divided by 0.1 between 0.0 and 1.0 according
to a population group drug score of a corresponding drug, and then,
withdrawal rates of the drugs corresponding to the respective 0.1
sections were represented by a histogram. It was confirmed that
when an arithmetic mean of 1092 individual drug scores calculated
according to the present invention on the basis of the population
group drug scores or genome sequence variations of the 1092 persons
was low, a withdrawal rate was remarkably high. Particularly, it
was confirmed that a drug having a population group drug score of
0.3 or less had a remarkably high likelihood of being withdrawn
from the market or restricted to use. It can be seen from the
above-described result that an individual drug score according to
the present invention can suggest a mechanism capable of avoiding a
drug with a potential risk of being withdrawn from the market or
restricted to use in a personalized manner by using characteristics
of individual gene sequence variations.
Example 7. Contemplation of Clinical and Medical Significance
Through Analysis of Individual Genome Sequence Variation Relevant
to Target Protein of Specific Drug with Predicted Risk
[0140] In order to verify the usefulness of the method for
personalizing drug selection of the present invention by
contemplating a clinical and medical significance of an individual
genome sequence variation found in a target protein of a specific
drug with a predicted risk for an individual, the following
experiment was conducted.
[0141] A detailed analysis was conducted on the individual sg01
which had a normal blood coagulation ability but had a low
individual drug score with respect to an anticoagulant Rivaroxaban
calculated according to an analysis of individual gene sequence
variation and the present invention. To be more specific, in an
individual genome sequence of the individual sg01, two gene
sequence variations (13th chromosome 113801737 and 113795262)
occurred at a coagulation factor 10 (F10) as a target protein among
5 genes involved in the pharmacodynamics or pharmacokinetics of
Rivaroxaban (an individual protein damage score calculated using
Equation 2 after calculation of a gene sequence variation score
using a SIFT algorithm was 0.0001), and one gene sequence variation
(1st chromosome 60392236) occurred at an enzyme protein CYP2J2. As
a result of calculating the individual drug score of the individual
sg01 with respect to Rivaroxaban using Equation 4 according to the
method of the present invention on the basis of the gene sequence
variation information, it was confirmed that the individual drug
score was as low as 0.148.
[0142] A hypofunction of a blood coagulation factor is a very
important mechanism as a cause for hemophilia. Hemophilia mainly
occurs due to functional deficiency of coagulation factors 8, 9 and
11, but a case caused by the coagulation factor 10 (F10) is hardly
known. The F10 is a very important enzyme for converting
prothrombin to thrombin. In the case of a homozygote including a
pair of severely damaged F10 genes, the individual sg01 may show an
extreme tendency such as a high hemorrhagic tendency or cannot
survive. However, as a result of the sequence analysis on the
individual sg01, the pair of F10 genes was a heterozygote in which
only one of the pair of F10 genes had a sequence variation and
there was no damage to a function of the other gene.
[0143] As such, the individual sg01 having a normal blood
coagulation ability did not recognize but had a high likelihood of
side effects of Rivaroxaban as a result of calculation of the drug
score according to the present invention, which has a clinical and
medical significance. Therefore, for additional analysis, detailed
analysis was conducted on the blood coagulation ability of the
individual sg01. The result thereof was as listed in Table 25.
TABLE-US-00024 TABLE 25 Blood coagulation factor Activity Normal
range reference Factor 2 109% 84-139 Factor 5 113% 63-140 Factor 7
127% 72-141 Factor 10 67% 74-146 Factor 8 87% 50-184 Factor 9 132%
48-149 Factor 11 123% 72-153 Factor 12 68% 44-142 Bleeding time
analysis Result Normal range reference PT 12.3 sec 9.8-12.2 aPTT
34.9 sec 26-35.3 Fibrinogen 235 mg/dl 180-380
[0144] As listed in Table 25, activities of blood coagulation
factors 2, 5, 7, 8, 9, 11, and 12 of the individual sg01 were in
the normal range, but an activity of the blood coagulation factor
10 was as low as 67% out of the normal range of from 74 to 146%.
That is, the blood coagulation ability of the individual sg01 was
lower than normal at least in view of the blood coagulation factor
10. Therefore, the individual sg01 had a risk of an increase in a
hemorrhagic tendency. Further, as a result of PT, aPTT, and
fibrinogen tests for directly measuring a hemorrhagic tendency, it
was confirmed that the individual sg01 had a hemorrhagic tendency
which was slightly high but maintained at an approximately upper
end of the normal range. That is, it is deemed that the individual
sg01 shows a blood coagulation condition maintained in an
approximately normal range by the activity of the other non-damaged
F10 of the pair of F10 as the heterozygote and the overall adaptive
response of the other blood coagulation mechanisms. However, as can
be seen from the activity test result of the blood coagulation
factors, the individual sg01 maintains a normal state with
difficulty and is highly likely to lack a sufficient buffering
capacity. Therefore, if the anticoagulant Rivaroxaban is prescribed
for the individual sg01 in the future due to medical necessity, the
individual sg01 is highly likely to experience severe side effects
such as a high hemorrhagic tendency. Since the blood coagulation
factor 10 is a sole and direct target protein of Rivaroxaban, it is
deemed that such a deduction is very clinically and medically
reasonable. It is confirmed from the above-described result that it
is possible to suggest a method for preventing drug side effects by
analyzing a relation between novel genome sequence variations,
which have not been known, and a use of a drug and the clinical and
medical usefulness thereof actually exists.
[0145] Although the exemplary embodiments of the present invention
have been described in detail, the scope of the right of the
present invention is not limited thereto. Various modifications and
improvements made by those skilled in the art using the basic
concept of the present invention defined in the appended claims are
also included in the scope of the right of the present
invention.
[0146] Unless defined otherwise, all technical terms used herein
have the same meaning as those commonly understood to one of
ordinary skill in the art to which this invention pertains. All the
publications cited as references in the present specification are
incorporated herein by reference in their entirety.
* * * * *
References