US20210241849A1 - Method for personalized selection of a drug for a subject - Google Patents

Method for personalized selection of a drug for a subject Download PDF

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US20210241849A1
US20210241849A1 US17/200,447 US202117200447A US2021241849A1 US 20210241849 A1 US20210241849 A1 US 20210241849A1 US 202117200447 A US202117200447 A US 202117200447A US 2021241849 A1 US2021241849 A1 US 2021241849A1
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score
protein
gene
individual
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Ju Han Kim
Su Yeon BAIK
Soo Youn LEE
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SEOUL TECHNO HOLDINGS Inc
Cipherome Inc
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    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/40—Population genetics; Linkage disequilibrium
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • 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.
  • 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.
  • 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®/Zelnorm®).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the method and the system of the present invention can be used 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.
  • 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.
  • 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).
  • DB Database
  • 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.
  • 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.
  • drugs Aspirin (acetylsalicylic acid) and Tylenol (acetaminophen)
  • 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.
  • ATC Advanced Therapeutic Chemical Classification System
  • 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.
  • FIG. 7A-7B illustrates 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
  • AUC Average Under Curve
  • FIG. 7A 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 validity of individual drug score calculation (AUC) calculated by a weighted geometric mean formula to which a weighting for each protein class is applied).
  • FIG. 8A-8B illustrates 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.
  • FIG. 9A-9B illustrates 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.
  • 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.
  • 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.
  • the gene sequence variation used as information in the method of the present invention refers to an individual gene sequence variation or polymorphism.
  • 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.
  • sequence variation information 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.
  • a polymorphism of a sequence refers to individual differences in a sequence present in a genome.
  • single nucleotide polymorphisms SNPs
  • 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).
  • 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.
  • pharmacokinetics 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.
  • Vd volume of distribution
  • CL clearance rate
  • F bioavailability
  • ka absorption rate coefficient
  • Cmax maximum plasma concentration
  • Tmax time point of maximum plasma concentration
  • AUC area under the curve
  • pharmacodynamics or pharmacodynamic parameters 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.
  • 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.
  • 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.
  • 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/).
  • DrugBank http://www.drugbank.ca/
  • PharmGKB https://www.pharmgkb.org/
  • Beta blocking agents 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, CYP1A
  • Transporter Drugs Target protein Enzyme protein Carrier protein protein
  • 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
  • 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.
  • 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.
  • ANNOVAR Wang et al., Nucleic Acids Research, 2010; 38(16): e164
  • SVA Sequence Variant Analyzer
  • BreakDancer Chen et al., Nat Methods. 2009 September; 6(9):677-81
  • the gene sequence variation information may be received/acquired through a computer system.
  • 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.
  • DrugBank http://www.drugbank.ca/
  • KEGG Drug http://www.genome.jp/kegg/drug/
  • PharmGKB http://www.pharmgkb.org/
  • 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.
  • gene sequence variation score 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.
  • 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.
  • 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.
  • 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.
  • a SIFT (Sorting Intolerant From Tolerant) algorithm is used to calculate an individual gene sequence variation score.
  • 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.
  • VCF Variariant Call Format
  • a degree of damage caused by each gene sequence variation to the corresponding gene is scored.
  • 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.
  • the gene sequence variation information may be associated with a degree of damage to a protein function.
  • 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.
  • 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.
  • the protein damage score is calculated by the following Equation 1.
  • Equation 1 can be modified in various ways, and, thus, the present invention is not limited thereto.
  • S 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 i is a gene sequence variation score of an i th gene sequence variation
  • p is a real number other than 0.
  • the protein damage score is an arithmetic mean
  • the protein damage score is a harmonic mean
  • the protein damage score is a geometric mean.
  • the protein damage score is calculated by the following Equation 2.
  • S 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 i is a gene sequence variation score of an ith gene sequence variation
  • w i is a weighting assigned to the v i . If all weightings w i have the same value, the protein damage score S g is a geometric mean of the gene sequence variation scores v 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.
  • Vmax is a maximum enzyme reaction rate when a substrate concentration is very high
  • 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.
  • an individual drug score is calculated in the following step by associating the above-described protein damage score with a drug-protein relation.
  • drug score 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.
  • a drug score is calculated as a mean of the protein damage scores.
  • 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
  • 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.
  • 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.
  • 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 etl 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 drug score is calculated by the following Equation 3.
  • Equation 3 can be modified in various ways, and, thus, the present invention is not limited thereto.
  • S 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 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
  • p is a real number other than 0.
  • 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.
  • the drug score is calculated by the following Equation 4.
  • S 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 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
  • w i is a weighting assigned to the g i .
  • the drug score S d is a geometric mean of the protein damage scores g 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.
  • weightings are equally assigned regardless of a characteristic of a drug-protein relation.
  • 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.
  • a target protein 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ATC Advanced Therapeutic Chemical Classification System
  • the method of the present invention may further include calculating a prescription score.
  • prescription score 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.
  • 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.
  • 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.
  • 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.
  • the method of the present invention may further include determining appropriateness or risk of a prescription applied to an individual.
  • the invention of the present invention may be performed for the purpose of preventing side effects of a drug, but is not limited thereto.
  • 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.
  • the method for providing information for personalizing drug selection is performed by sequentially (1) inputting or receiving genome sequence information of an individual user (S 100 ), (2) inputting or receiving information relevant to a predetermined drug or drug group (S 110 ), (3) determining genome sequence variation information of the individual user (S 120 ), (4) calculating an individual protein damage score with respect to the predetermined drug or drug group (S 130 ), (5) calculating an individual drug score with respect to the predetermined drug or drug group (S 140 ), (6) marking the drug score and sort drugs by ranking or determining the order of priority among drugs according to drug score rankings (S 150 ), and (7) selecting a drug in consideration of the drug score and the priority and calculating a prescription score (S 160 ).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the system according to the present invention is immediately updated so as to be used for further improved personalization of drug selection.
  • 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.
  • 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 .
  • DB database
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a storage medium may include any storage or transmission medium readable by a device such as a computer.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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).
  • Example 7 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)
  • a gene sequence variation score of a single variant (chr3:165491280) found in BCHE was 0.07
  • 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.
  • Example 2 Providing Method for Personalizing Drug Selection with Respect to Two Drugs (Aspirin and Tylenol) in Need of Selection
  • 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
  • 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)
  • HISEQ-2000 as an NGS (Next Generation Sequencing) device manufactured by Illumina was used to conduct a 30 ⁇ whole genome sequencing.
  • a whole exome sequencing 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).
  • SAM Sequence Alignment Map
  • BAM Binary Alignment Map
  • 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.
  • VCF Very Calling Format
  • 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].
  • 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.
  • 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.
  • 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.
  • 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 (le-08. 0.39, 0.02, respectively).
  • 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.
  • 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.
  • 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: CYP 1A1 (0.08(chr15:75015305)); CY1A2 (le-08(chr15:75047221)); CYP2D6 (0.39 (chr22:42525756); 0.02 (chr22:42526694)); CYP3A5 (le-08(chr7:99245974)); and CYP3A7 (0.16 (chr7:99306685)).
  • 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.
  • 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.
  • ROC Receiver Operating Curve
  • 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 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
  • the vertical bar parenthesis means the number of elements of a corresponding set.
  • the results thereof were as listed in Table 22, Table 23, and illustrated in FIG. 7A and FIG. 7B .
  • 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.
  • 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.
  • AUC individual drug score calculation
  • weightings were assigned according to the number of people per race and a validity of individual drug score calculation (AUC) was analyzed.
  • AUC individual drug score calculation
  • Bone marrow transplantation is one of the most important treatment method for treating blood tumor such as leukemia.
  • 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.
  • TBI total body irradiation
  • Busulfan is a representative alkylating agent and can substitute for total body irradiation.
  • Busulfan has a relatively narrow therapeutic range.
  • 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.
  • VOD hepatic veno-occlusive disease
  • severe toxicity such as neurotoxicity
  • 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
  • Busulfan classifies Busulfan as one of Group 1 carcinogens.
  • 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.
  • 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. 8A-8B .
  • 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).
  • a size of each figure in FIG. 8A-8B 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • the individual sg01 may show an extreme tendency such as a high hemorrhagic tendency or cannot survive.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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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.

Description

    CROSS REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation of co-pending U.S. application Ser. No. 14/912,397, filed Feb. 16, 2016, which is a National Phase application of International Application No. PCT/KR2014/007685, filed Aug. 19, 2014, which claims the benefit of Korean Application No. 10-2013-0097651, filed Aug. 19, 2013, all of which are incorporated by reference in their entirety.
    • TECHNICAL FIELD
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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®/Zelnorm®).
  • 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).
  • 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
  • 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
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 7A-7B illustrates 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: 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: validity of individual drug score calculation (AUC) calculated by a weighted geometric mean formula to which a weighting for each protein class is applied).
  • FIG. 8A-8B illustrates 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.
  • FIG. 9A-9B illustrates 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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).
  • 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 1
    ACE inhibitors [C09A]
    Target Enzyme Carrier Transporter
    Drugs protein protein protein protein
    Benazepril ACE MTHFR SLC15A1,
    SLC15A2
    Captopril ACE, CYP2D6 ABCB1, ALB
    MMP2 SLC15A1,
    SLC15A2,
    SLC22A6
    Cilazapril ACE ABCB1,
    SLC15A1,
    SLC15A2
    Enalapril ACE CYP3A4 ABCB1,
    SLC15A1,
    SLC15A2,
    SLC22A6,
    SLC22A7,
    SLC22A8,
    SLCO1A2
    Fosinopril ACE SLC15A1,
    SLC15A2
    Lisinopril ACE, ABCB1,
    ACE2 SLC15A1
    Moexipril ACE, SLC15A1,
    ACE2 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, CYP2A6, ORM1
    SLC6A2, CYP2B6, CYP2C8,
    SLC6A3 CYP2C9, CYP2D6,
    CYP2E1, CYP3A4
  • TABLE 2
    Analgesics [N02]
    Drugs Target protein Enzyme protein Carrier protein Transporter 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
    Dihydroergotamme 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 3
    Anti-diabetes [A10]
    Drugs Target protein Enzyme protein Carrier protein Transporter 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, ALB
    ABCC9, CYP2C9
    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, ABCB11,
    ABCC8, CYP3A4 ABCC1,
    ABCC9, ABCC2,
    CFTR, ABCC3,
    KCNJ1, ABCG2,
    KCNJ11, SLC15A1,
    KCNJ5 SLC15A2,
    SLC22A6,
    SLC22A7,
    SLCO1A2,
    SLCO2B1
    Glycodiazine ABCC8,
    ABCC9,
    KCNJ1
    Insulin Aspart INSR CYP1A2
    Insulin Detemir INSR CYP1A2 ALB
    Insulin Glargine IGF1R, 1NSR CYP1A2
    Insulin INSR CYP1A2
    Glulisine
    Insulin Lispro IGF1R, 1NSR 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,
    PPARG UGT2B7
    Nateglinide ABCC8, CYP2C9, ABCC4, ALB, ORM1
    ABCC9, CYP2D6, SLC15A1,
    PPARG CYP3A4, SLC15A2,
    CYP3A5, SLC16A1,
    CYP3A7, PTGS1, SLC22A6
    UGT1A9
    Phenformin KCNJ8, CYP2D6 SLC22A1,
    PRKAA1 SLC22A2
    Pioglitazone PPARG CYP2C19, SLCO1B1,
    CYP2C8, SLCO1B3
    CYP2C9,
    CYP2D6,
    CYP3A4, PTGS1
    Pramlintide CALCR,
    RAMP1,
    RAMP2,
    RAMP3
    Repaglinide ABCC8, CYP2C8, SLCO1B1 ALB
    ABCC9, CYP3A4
    PPARG
    Rosiglitazone ACSL4, CYP1A2, SLCO1B1
    PPARG CYP2A6,
    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, SLCO1B1
    ESRRA, CYP2B6,
    ESRRG, CYP2C19,
    PPARG, CYP2C8,
    SERPINE1, CYP2C9,
    SLC29A1 CYP3A4,
    CYP3A5,
    CYP3A7,
    UGT1A1,
    UGT1A10,
    UGT1A3,
    UGT1A4,
    UGT1A8,
    UGT1A9,
    UGT2B7
    Vildagliptin DPP4
    Voglibose GAA, GANC,
    MGAM
  • TABLE 4
    Antidepressants [N06A]
    Drugs Target protein Enzyme protein Carrier protein Transporter 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, HTR2C, CYP2D6, CYP3A4
    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,1DO1, 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,
    SLC6A4 CYP2C19, CYP2C9, ABCB1
    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 5
    Antihistamines for systemic use [R06]
    Drugs Target protein Enzyme protein Carrier protein Transporter 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,
    SLC6A3 CYP2C18, SLC22A1,
    CYP2C19, SLC22A2,
    CYP2C9, CYP2D6, SLC22A5
    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 6
    Antihypertensives [C02]
    Drugs Target protein Enzyme protein Carrier protein Transporter protein
    Ambrisentan EDNRA
    ADRA2A,
    Bethanichne 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,
    ADRA1B,
    ADRA1D, CYP2C19, CYP2D6 ABCB1 ORM1
    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 7
    Anxiolytics and Hypnotics/sedatives [N05B|N05C]
    Drugs Target protein Enzyme protein Carrier protein Transporter 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,
    GABRB1,
    GABRD, CYP3A4 ABCB1 ALB
    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,
    ESR1, MPO, CYP1A1, CYP1A2,
    MTNR1A, CYP1B1, CYP2C19,
    MTNR1B, CYP2C9, 1DO1, MPO
    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
  • 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 9
    Calcium channel blockers [C08]
    Carrier Transporter
    Drugs Target protein Enzyme protein 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, UGT1A10,
    UGT1A3, UGT2B17,
    UGT2B7
    Mibefradil CACNA1C, CYP11B1, CYP11B2, ABCB1
    CACNA1D, CYP1A1, CYP1A2,
    CACNA1F, CYP2D6, CYP3A4,
    CACNA1G, CYP3A5, CYP3A7
    CACNA1H,
    CACNA1I,
    CACNA1S,
    CACNB1,
    CACNB2,
    CACNB3,
    CACNB4
    Nicardipme 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 10
    Diuretics [C03]
    Transpotrer
    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
    Hydrochlorothiande 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 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 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 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 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, SLCO1A2
    MAOA
    Testosterone AR
    Propionate
    Urofollitropin FSHR
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 September 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.upf.edu/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.
  • 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.
  • 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.
  • 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.
  • 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 utilzing 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.
  • 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.
  • 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.
  • 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 ]
  • In Equation 1, Sg 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, vi is a gene sequence variation score of an ith 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.
  • 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 ]
  • In Equation 2, Sg 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, vi is a gene sequence variation score of an ith gene sequence variation, and wi is a weighting assigned to the vi. If all weightings wi have the same value, the protein damage score Sg is a geometric mean of the gene sequence variation scores vi. 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.
  • 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 ½ 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.
  • 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.
  • 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.
  • 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.
  • 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 etl 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.
  • 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 ]
  • In Equation 3, Sd 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, gi 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.
  • 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 ]
  • In Equation 4, Sd 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, gi 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 wi is a weighting assigned to the gi. If all weightings wi have the same value, the drug score Sd is a geometric mean of the protein damage scores gi. 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • The method of the present invention may further include calculating a prescription score.
  • 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.
  • 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.
  • The invention of the present invention may be performed for the purpose of preventing side effects of a drug, but is not limited thereto.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • The following Examples are divided as follows.
  • 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).
  • 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).
  • 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)
  • 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.
  • 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 16
    Protein Variation information
    Drug name damage Protein Variation Chromosomal Reference Variant
    (Drug 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)
  • 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 (=(0.46×0.45×1×1)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 (=(0.07×0.68)1/2).
  • 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
  • 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.
  • 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.
  • 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 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 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 × 10−5 Enzyme 0.08 chr15: 75015305 C T
    P450 1A1 (PK) 1 × 10−8 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)
  • 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(=(1.0×0.03)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 (=(1.0×0.38×1.0×1.0×1.0×1.0×1.0×1.0×0.17×1.0×1.0×0.89×0.32×0.84×1.0)1/15).
  • 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 2.8×10−5 (=(0.08×(1×10−8))1/2), 0.52 (=(0.55×0.49)1/2), and 0.2 (=(0.98×0.39×0.02)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 (=(0.38×1.0×(2.8×10−5)×1.0×0.52×1.0×1.0×0.2×0.76×1.0×1.0×1.0)1/12).
  • 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)
  • 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.
  • Among 22 drugs belonging to C07 beta blockers according to the internally certified ATC code, 11 drugs are specific beta blockers [C07AB], 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× 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.
  • 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 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
  • 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.
  • 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.
  • 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 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 × 10−8 Enzyme 1.0 × 10−8 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 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 × 10−8 Enzyme 1.0 × 10−8 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 × 10−8 Enzyme 1.0 × 10−8 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, sub- (PK) 1.00 chr7: 87179601 A G
    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)
  • 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 (le-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.
  • 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: CYP 1A1 (0.08(chr15:75015305)); CY1A2 (le-08(chr15:75047221)); CYP2D6 (0.39 (chr22:42525756); 0.02 (chr22:42526694)); CYP3A5 (le-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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 Specificity = 1 - D L - GS D - GS
  • 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.
  • 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. 7A and FIG. 7B.
  • TABLE 22
    Distribution of each protein group and Calculation
    of an average protein damage score
    Number Number of Mean
    Number of protein- protein
    of relevant drug damage
    Protein group proteins drugs 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 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
  • 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.
  • 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.
  • 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).
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • TABLE 24
    Normal
    Busulfan Control Asian CHB CHS JPT
    (n = 12) (n = 14) (n = 286) (n = 97) (n = 100) (n = 89)
    Individual Drug score
    Geometric 0.507 ± 0.589 ± 0.576 ± 0.566 ± 0.588 ± 0.575 ±
    mean .065 .079§ .084§ .068* .082§ .101§
    Harmonic 0.282 ± 0.327 ± 0.331 ± 0.326 ± 0.332 ± 0.336 ±
    mean .049 .087 .076§ .065* .067§ .094§
    Product 2.83e−5 ± 2.00e−3 ± 2.92e−4 ± 3.94e−4 ± 2.69e−4 ± 2.06e−4 ±
    5.75e−5 4.43e−3 9.75 ± e−4§ 1.17e−3§ 1.05e−3* 5.77e−4§
    Individual Protein damage score
    Geometric mean
    CTH 0.3429 0.3714 0.6647 0.6645 0.6689 0.6638
    GGT1 1.0000 1.0000 0.9264 0.8914 0.9569 0.9201
    GGT5 0.3598 0.4800 0.6265 0.6071 0.6600 0.6139
    GGT6 0.1826 0.2171 0.2061 0.1953 0.2069 0.2174
    GGT7 0.8692 1.0000 0.9741 0.9718 0.9636 0.9889
    GSTA1 1.0000 1.0000 0.9968 1.0000 1.0000 0.9897
    GSTA2 0.3688 0.5132 0.4916 0.5067 0.4740 0.4955
    GSTM1 0.6654 0.6371 0.3513 0.3448 0.3347 0.3752
    GSTP1 1.0000 0.9665 1.0000 1.0000 1.0000 1.0000
    MGMT 0.8230 0.9621 0.8754 0.8736 0.9078 0.8361
    MGST1 1.0000 1.0000 0.9968 0.9939 0.9968 1.0000
    MSH2 0.2677 0.7793 0.3615 0.3605 0.3793 0.3412
    Harmonic mean
    CTH 0.3425 0.3714 0.6647 0.6645 0.6689 0.6638
    GGT1 1.0000 1.0000 0.9252 0.8899 0.9557 0.9189
    GGT5 0.3042 0.4664 0.6225 0.6015 0.6561 0.6115
    GGT6 0.0859 0.1437 0.0942 0.0903 0.0943 0.0985
    GGT7 0.8692 1.0000 0.9739 0.9718 0.9630 0.9889
    GSTA1 1.0000 1.0000 0.9968 1.0000 1.0000 0.9897
    GSTA2 0.3386 0.4934 0.4759 0.4948 0.4540 0.4808
    GSTM1 0.6554 0.6371 0.2965 0.3011 0.2702 0.3200
    GSTP1 1.0000 0.9533 1.0000 1.0000 1.0000 1.0000
    MGMT 0.7869 0.9543 0.8512 0.8516 0.8871 0.8045
    MGST1 1.0000 1.0000 0.9968 0.9939 0.9968 1.0000
    MSH2 0.2643 0.7793 0.3579 0.3587 0.3737 0.3381
    Product
    CTH 0.3320 0.3714 0.6643 0.6633 0.6689 0.6638
    GGT1 1.0000 1.0000 0.9237 0.8880 0.9537 0.9184
    GGT5 0.2190 0.4530 0.5942 0.5642 0.6324 0.5882
    GGT6 0.0423 0.1179 0.0372 0.0413 0.0370 0.0326
    GGT7 0.8692 1.0000 0.9725 0.9707 0.9600 0.9889
    GSTA1 1.0000 1.0000 0.9968 1.0000 1.0000 0.9897
    GSTA2 0.2528 0.4039 0.3943 0.4082 0.3728 0.4022
    GSTM1 0.6450 0.6371 0.2371 0.2542 0.2101 0.2486
    GSTP1 1.0000 0.9393 1.0000 1.0000 1.0000 1.0000
    MGMT 0.7400 0.9443 0.8223 0.8247 0.8660 0.7634
    MGST1 1.0000 1.0000 0.9968 0.9939 0.9968 1.0000
    MSH2 0.2141 0.7793 0.3375 0.3433 0.3473 0.3194
    *p-value < 0.05 and
    §p-value < 0.01 by Student T-test.
    Values are mean or mean ± S.D.
  • 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).
  • 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.
  • 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. 8A-8B.
  • As illustrated in FIG. 8A-8B, 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).
  • Further, a size of each figure in FIG. 8A-8B 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.
  • 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
  • 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.
  • 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.
  • The usefulness of a drug score according to the present invention is clearly visualized with relative frequency histograms as illustrated in FIG. 9A-9B. 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.
  • As illustrated in FIG. 9A-9B, 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
  • 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.
  • 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.
  • 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.
  • 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 25
    Blood coagulation Normal range
    factor Activity 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 Normal range
    analysis Result reference
    PT 12.3 sec  9.8-12.2
    aPTT 34.9 sec   26-35.3
    Fibrinogen 235 mg/dl 180-380
  • 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.
  • 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.
  • 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.

Claims (21)

1. A method for personalizing selection of a drug for a subject using individual genome sequence variations of the subject, comprising:
obtaining, by a computer system, information regarding gene sequences of the subject for a set of genes involved in pharmacodynamics or pharmacokinetics of a drug group, wherein the drug group consists of a plurality of drugs;
determining, by the computer system, gene sequence variation information indicating variations present in the gene sequences involved in the pharmacodynamics or pharmacokinetics of the group;
for each gene of the set of genes:
determining, by the computer system, a gene sequence variation score;
for each protein encoded by a gene of the set of genes:
calculating, by the computer system, an individual protein damage score for the protein by using [Equation 2], wherein [Equation 2] is:
S g ⁡ ( v 1 , … ⁢ , v n ) = ( ∏ i = 1 n ⁢ ⁢ v i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sg is the individual 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, vi is a gene sequence variation score of an ith gene sequence variation, and wi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation;
for each drug in the drug group:
calculating, by the computer system, an individual drug score by using [Equation 4], wherein [Equation 4] is:
S d ⁡ ( g 1 , … ⁢ , g n ) = ( ∏ i = 1 n ⁢ ⁢ g i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sd is the individual drug score of a drug d, n is a number of proteins encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, gi is a protein damage score of a protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, and wi is a weighting assigned to the protein damage score gi of the protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d;
ranking, by the computer system, drugs in the drug group by comparing the individual drug scores for the drugs; and
selecting a drug based on the ranking.
2. The method according to claim 1, wherein the plurality of drugs in the drug group have a same ATC code.
3. The method according to claim 1,
wherein the gene sequence variation information means information about substitution, addition, or deletion of a base constituting an exon of a gene.
4. The method according to claim 3,
wherein the substitution, addition, or deletion of the base results from structural abnormality including breakage, deletion, duplication, inversion, or translocation of a chromosome.
5. The method according to claim 1,
wherein the gene sequence variation score is calculated by applying 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) to a gene sequence variation.
6. The method according to claim 1,
wherein the weighting assigned to the gene sequence variation score or the weighting assigned to the protein damage score is determined considering a class 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.
7. The method according to claim 1, further comprising:
wherein the ranking step further comprises determining an order of priorities among drugs applicable to the subject by using the individual drug score; or wherein the selecting step further comprises determining whether or not to use the drugs applicable to the subject by using the individual drug score.
8. The method according to claim 1,
wherein the drug group is 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 predetermined disease.
9. The method according to claim 1,
wherein the gene sequence variation information is acquired by a comparison analysis with a genome sequence of a reference group.
10. The method according to claim 1, further comprising:
calculating a prescription score.
11. The method according to claim 10,
wherein if two or more drugs are determined on the basis of an order of priority among drugs and need to be administered at the same time, the prescription score is calculated by summarizing drug scores determined with respect to the respective drugs.
12. The method according to claim 1, further comprising:
providing one or more information selected from the group consisting of gene sequence variation information, a protein damage score, a drug score, and information used for calculation thereof.
13. The method according to claim 1,
wherein the method is performed to prevent drug side effects.
14. A computer-readable storage medium comprising stored instructions, wherein the instructions when executed by a processor cause the processor to perform steps comprising:
obtaining information regarding gene sequences of a subject for a set of genes involved in pharmacodynamics or pharmacokinetics of a drug group, wherein the drug group consists of a plurality of drugs;
determining gene sequence variation information indicating variations present in the gene sequences involved in the pharmacodynamics or pharmacokinetics of the drug group;
for each gene of the set of genes:
determining, by the computer system, a gene sequence variation score;
for each protein encoded by a gene of the set of genes
calculating an individual protein damage score for the protein by using [Equation 2], wherein [Equation 2] is:
S g ⁡ ( v 1 , … ⁢ , v n ) = ( ∏ i = 1 n ⁢ ⁢ v i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sg is the individual 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, vi is a gene sequence variation score of an ith gene sequence variation, and wi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation;
for each drug in the drug group:
calculating an individual drug score by using [Equation 4], wherein [Equation 4] is:
S d ⁡ ( g 1 , … ⁢ , g n ) = ( ∏ i = 1 n ⁢ ⁢ g i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sd is the individual drug score of a drug d, n is a number of proteins encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, gi is a protein damage score of a protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, and wi is a weighting assigned to the protein damage score gi of the protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d;
ranking drugs in the drug group by comparing the individual drug scores for the drugs; and
selecting a drug based on the ranking.
15. The computer-readable storage medium of claim 14, wherein the plurality of drugs in the drug group have a same ATC code.
16. A system for providing information for personalizing drug selection using individual genome sequence variations, the system comprising:
a processor;
a computer readable storage medium comprising stored instructions, wherein the instructions when executed by a processor cause the processor to perform steps comprising:
obtaining information regarding gene sequences of a subject for a set of genes involved in pharmacodynamics or pharmacokinetics of a drug group, wherein the drug group consists of a plurality of drugs;
determining gene sequence variation information indicating variations present in the gene sequences involved in the pharmacodynamics or pharmacokinetics of the drug group;
for each gene of the set of genes:
determining, by the computer system, a gene sequence variation score;
for each protein encoded by a gene of the set of genes
calculating an individual protein damage score for the protein by using [Equation 2], wherein [Equation 2] is:
S g ⁡ ( v 1 , … ⁢ , v n ) = ( ∏ i = 1 n ⁢ ⁢ v i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sg is the individual 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, vi is a gene sequence variation score of an ith gene sequence variation, and wi is a weighting assigned to the gene sequence variation score vi of the ith gene sequence variation;
for each drug in the drug group:
calculating an individual drug score by using [Equation 4], wherein [Equation 4] is:
S d ⁡ ( g 1 , … ⁢ , g n ) = ( ∏ i = 1 n ⁢ ⁢ g i w i ) 1 / ∑ i = 1 n ⁢ ⁢ w i
wherein Sd is the individual drug score of a drug d, n is a number of proteins encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, gi is a protein damage score of a protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d, and wi is a weighting assigned to the protein damage score gi of the protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug group d;
ranking drugs in the drug group by comparing the individual drug scores for the drugs; and
selecting a drug based on the ranking.
17. The system for providing information for personalizing drug selection using individual genome sequence variations of claim 16, wherein the plurality of drugs in the drug group have a same ATC code
18. The system for providing information for personalizing drug selection using individual genome sequence variations of claim 16, wherein the instructions when executed by the processor cause the processor to further perform steps comprising calculating a prescription score by summarizing drug scores determined with respect to respective drugs if two or more drugs are determined on the basis of an order of priority among drugs and need to be administered at the same time.
19. The system for providing information for personalizing drug selection using individual genome sequence variations of claim 16, further comprising:
a user interface configured to provide drug scores of drugs or drug groups when a list of the drug groups is inputted by a user.
20. The system for providing information for personalizing drug selection using individual genome sequence variations of claim 16, further comprising:
a display unit configured to display the protein damage or the drug score, or to display a calculation process, or information as a ground for the calculation.
21. The system for providing information for personalizing drug selection using individual genome sequence variations of claim 16, wherein the gene sequence variation information, protein damage score, drug score and information as a ground for the calculation are stored in the computer-readable storage medium and are updated when the database is updated.
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Families Citing this family (14)

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Publication number Priority date Publication date Assignee Title
EP3261008B1 (en) 2015-02-17 2021-09-08 Cipherome, Inc. Method for selecting anticancer agent based on protein damage information of individual to prevent anticancer agent side effects
US10395759B2 (en) 2015-05-18 2019-08-27 Regeneron Pharmaceuticals, Inc. Methods and systems for copy number variant detection
US20180312928A1 (en) * 2015-10-26 2018-11-01 Cipherome Method and system for selecting customized drug using genomic nucleotide sequence variation information and survival information of cancer patient
WO2017074036A2 (en) * 2015-10-26 2017-05-04 주식회사 싸이퍼롬 Method and system for selecting customized drug using genomic nucleotide sequence variation information and survival information of cancer patient
KR20180124840A (en) * 2015-12-12 2018-11-21 싸이퍼롬, 인코퍼레이티드 Assessment of drug safety for computer-implemented populations
KR102188376B1 (en) * 2017-04-25 2020-12-08 주식회사 싸이퍼롬 Method and system for tailored anti-cancer therapy based on the information of cancer genomic sequence variant, mRNA expression and patient survival
CN107463764A (en) * 2017-06-16 2017-12-12 康美健康云服务有限公司 A kind of patient medication guidance method and system based on hereditary information
CN107516013B (en) * 2017-08-25 2020-01-07 上海中医药大学附属岳阳中西医结合医院 Method and system for optimizing drugs through treatment effect triangle
US11227692B2 (en) * 2017-12-28 2022-01-18 International Business Machines Corporation Neuron model simulation
CN108682458A (en) * 2018-05-08 2018-10-19 北京岙特杰诺生物科技有限公司 Drug use administration method, apparatus and electronic equipment
EP3841115A4 (en) * 2018-08-20 2022-08-24 University of Georgia Research Foundation, Inc. Compositions and methods for increasing beiging of white adipose tissue
KR20210083080A (en) 2019-12-26 2021-07-06 서울대학교병원 Method for screening synergistic chemo-therapeutic interaction predicting pair of genes for anticancer agents
KR20230129760A (en) 2022-03-02 2023-09-11 (주)인트젠 A genome-based drug selection and side-effects prediction system, and genome-based drug selection and side-effects predicted information providing thereof
TWI812056B (en) * 2022-03-10 2023-08-11 宏碁股份有限公司 Method and electronic device of checking drug interaction

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070299646A1 (en) * 2001-04-13 2007-12-27 Yvan Chemama Method for constructing, representing or displaying protein interaction maps and data processing tool using this method
US20030096264A1 (en) * 2001-06-18 2003-05-22 Psychiatric Genomics, Inc. Multi-parameter high throughput screening assays (MPHTS)
KR20040103514A (en) * 2003-05-29 2004-12-09 주식회사 비즈모델라인 System and Method for Providing Service by Using Personal Characteristic Information
RU2007129672A (en) 2005-01-04 2009-02-20 Новартис АГ (CH) BIOMARKERS FOR DETERMINING TEGASEROD EFFICIENCY IN PATIENTS WITH CHRONIC CONSTIPATION
CA2653974A1 (en) * 2006-05-30 2008-02-14 Mayo Foundation For Medical Education And Research Detecting and treating dementia
EP3216874A1 (en) * 2008-09-05 2017-09-13 TOMA Biosciences, Inc. Methods for stratifying and annotating cancer drug treatment options
US10095829B2 (en) * 2009-07-08 2018-10-09 Worldwide Innovative Network Computer implemented methods of treating lung cancer
US20120016594A1 (en) * 2010-07-02 2012-01-19 Coriell Institute For Medical Research, Inc. Method for translating genetic information for use in pharmacogenomic molecular diagnostics and personalized medicine research
US20120310539A1 (en) * 2011-05-12 2012-12-06 University Of Utah Predicting gene variant pathogenicity
CN103827312B (en) 2011-06-23 2016-06-15 长庚医疗财团法人基隆长庚纪念医院 Assessment antiepileptic drug Phenytoin Sodium Salt causes the method for adverse drug reaction risk
US20130184999A1 (en) * 2012-01-05 2013-07-18 Yan Ding Systems and methods for cancer-specific drug targets and biomarkers discovery

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