WO2016133374A1 - Method of selecting uterine contraction inhibiting agent based on protein damage information on each individual to prevent side effects of uterine contraction inhibiting agent - Google Patents

Abstract

The present invention relates to a method and system for selecting a uterine contraction inhibiting agent on the basis of protein damage information on each individual, by using individual genome base sequence analysis. The method and system according to the present invention, as a technology that can predict the side effects or danger of certain drugs (i.e., uterine contraction inhibiting agents) for each individual through sequence analyses of gene exon regions that code for various proteins that participate in the pharmacodynamics or pharmacokinetics of uterine contraction inhibiting agents, is a general-purpose technology that is not only highly reliable but also has a wide range of applications.

Description

Method of selecting uterine contraction inhibitor based on individual protein damage information for preventing side effects of uterine contraction inhibitor

The present invention relates to a method for selecting uterine contraction inhibitors based on individual protein damage information using individual genome sequencing to prevent adverse effects.

Advances in biotechnology have led to the analysis of human whole genome sequences to predict individual diseases and provide customized disease prevention and treatment.

Recently, as a result of comparing individual genome sequences, it was found that different bases exist at the same position on the chromosome, and thus, the differences of the base sequences were used to predict the difference in the reaction between individuals for the drug. For example, because of the slow or fast drug metabolism depending on the specific genomic sequence information of an individual, the effects and side effects on the drug may vary from person to person.

Therefore, there is an increasing social demand for personalized drug selection to select a drug and dose suitable for a patient by using individual genome sequence differences, and using genomic information such as single nucleotide polymorphism (SNP) as a marker. In addition, pharmacogenetics or pharmacogenomics using research results on the correlation between the markers and drug reactivity / drug side effects is emerging.

Pharmacogenetics predicts genetic differences in metabolism and reactions of drugs or chemicals in the general population or in individuals. In some individuals, reactions other than the expected drug response to the drug may be present. These drug side effects may be related to the severity of the disease being treated, drug interactions, the patient's age, nutritional status, liver and kidney function, climate, or food. Although it may be due to factors, genetic differences related to drug metabolism, for example, polymorphism of drug enzyme genes, may be affected.

Meanwhile, ritodrine, a type of uterine contraction inhibitor, is a beta-adrenergic receptor agonist, which is used between 22 and 37 weeks of pregnancy. Ritodrin is a class of beta-adrenergic receptor agonists, as well as calcium channel blockers, oxytonin antagonists, NSAIDs (non-steroidal anti-inflammatory drugs), nitrates, and MgSO4. , Progesterone (progesterone) is the most widely used drug among uterine contraction inhibitors. Ritodrin is known to have side effects such as tachycardia, hypotension, anxiety, chest pain, abnormal ECG, pulmonary edema, hyperglycemia, hypokalemia, arrhythmia and myocardial infarction. Previous studies have shown that lithodrin was used in 62% of patients when the frequency of drugs used as a primary treatment in patients requiring uterine contraction inhibitors was used. In addition, in 586 patients who used ritodrine, 47 patients had side effects, which accounted for 8% of the total. On October 25, 2013, the European Medicines Agency (EMA) recommended the limited use due to the side effects of ritodrine, and similar recommendations were in force in the Korean Food and Drug Administration. Therefore, treatment with ritodrine should include an appropriate assessment of the patient's cardiovascular status along with cardiopulmonary function monitoring and electrocardiogram monitoring throughout the treatment period. If complications occur, medications should be discontinued immediately and, in some cases, switched to other medications, leading to failure of early analgesia.

The pharmacogenomics mentioned above may be used as a method for predicting side effects of drugs before drug administration. It has also been found that drug dynamics are associated with genotypes for ritodrine. Previous studies have shown that the time to deliver after the administration of ritodrine is related to rs1042719 of the ADRB2 (Adrenoceptor, beta 2) gene.

It is now known that genetic variation in dozens or hundreds of drug metabolism-related genes in the body's metabolism affects the function of the protein, increasing or decreasing drug metabolism, and it is also necessary to identify new genetic variations. Analysis of minority genes alone is not enough to identify all of the genes involved in drug metabolism. Therefore, beyond the method based on population observation studies using markers such as minority genes or monobasic polymorphisms, it is possible to directly utilize personal genomic sequence variation information to perform theoretical inferences about the protein mutations and their biological effects. There is a strong need for the introduction of methodologies that provide information on the selection of useful and reliable individual contractile suppressors.

The present invention was devised in view of the above, and after analyzing individual genome sequence variation information and calculating individual protein damage scores from gene sequence variation information related to pharmacodynamics or pharmacokinetics of uterine contraction inhibitors, By calculating the individual drug scores in association with the interaction between the drug and the protein, it is intended to provide a method for predicting the possibility of adverse effects of uterine contraction inhibitors and providing information for selecting the uterine contraction inhibitors.

In one aspect, the present invention comprises the steps of determining one or more gene sequence variation information involved in pharmaco-dynamics or pharmaco-kinetics of a uterine contraction inhibitor from personal genome sequence information; Calculating an individual protein damage score using the gene sequence variation information; And correlating the individual protein damage scores with the correlation between the drug and the protein to calculate the individual drug scores, thereby providing a method for providing information for selecting the uterine contraction inhibitor using the personal genome sequence variation. .

In another aspect, the present invention provides a uterine contraction inhibitor that can be applied to an individual, the database for retrieving or extracting information related to the gene or protein associated with the uterine contraction inhibitor; A communication unit accessible to the database; A first calculation module configured to calculate one or more gene sequence variation information related to pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor based on the information; A second calculation module for calculating an individual protein damage score using the gene sequence variation information; A third calculating module that calculates an individual drug score by associating the individual protein damage score with a correlation between a drug and a protein; And it provides a uterine contraction inhibitor selection system using a personal genome sequence variation comprising a display unit for displaying the calculated value calculated by the calculation module.

In another aspect, the present invention comprises the steps of obtaining genetic sequence variation information involved in the pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor from the individual genome sequence information; Calculating an individual protein damage score using the gene sequence variation information; And correlating the individual protein damage score with a correlation between the drug and the protein to calculate the individual drug score, wherein the execution module executes a processor to perform the operation. .

Individual uterine contraction inhibitor selection method and system based on the individual genome sequence variation information of the present invention through the sequence analysis of the exon region of the gene encoding various proteins involved in the pharmacodynamics or pharmacokinetics of the uterine contraction inhibitors In other words, the reliability of the uterine contraction inhibitors is high.

In addition, the development of experimental methods will facilitate the clinical use of techniques for determining genotypes (PCR, SNP chip, capillary sequencing, NGS, etc.), and the present invention based on pharmacogenomics is expected to be widely used in clinical field. Can be. In particular, the price of Next Generation Sequencing (NGS) has fallen rapidly, allowing access from a genomic point of view at prices ranging from tens of thousands to hundreds of thousands of won per patient.

The use of minor mutations or minor genes as markers in existing pharmacogenomics comparisons has a large statistical error due to the selection of the study group and the difference between the population groups, while the method of the present invention can range from tens to hundreds. Molecular-level studies and analyzes of drug-related pharmacokinetics and pharmacodynamics are applied directly to the selection of inhibitors of uterine contractions, making it possible to apply them without significantly affecting the differences between population groups. Has the advantage of being high.

In particular, when using the method and system according to the present invention, it is useful to determine whether and how to use the uterine contraction inhibitors applied to the individual by predicting the side effects or risks of uterine contraction inhibitors, for example, ritodrine in advance. This may lead to improved success rate of early analgesic suppression therapy by administering an appropriate dose of ritodrine in patients with early analgesia. In addition, it may be possible to introduce a new treatment method by increasing the safety of ritodrine administration in a patient group that could not withstand the drug or had to stop the drug due to side effects.

Further, when new knowledge is discovered or provided about the drug-protein interaction of uterine contractile inhibitors, for example, ritodrine, it can be easily added and applied to the method of the present invention, and accordingly to the accumulation of future research information There is an advantage that an improved customized treatment method can be provided.

1 is a flow chart showing each step of the method for providing information for the selection of uterine contraction inhibitors using individual genome sequence variation according to an embodiment of the present invention.

2 is a schematic configuration diagram of a system for selecting a uterine contraction inhibitor using personal genome sequence variation according to an embodiment of the present invention (DB: database).

3A and 3B are flowcharts illustrating a receiver operating curve (ROC) curve for verification of a method for selecting a uterine contraction inhibitor using a genome sequence variation according to an embodiment of the present invention.

The present invention is based on the discovery that individual drugs can be selected for high-safety drugs and doses / uses in the treatment of uterine contraction inhibitors by analyzing individual genome sequence variation information.

In one aspect, the present invention comprises the steps of determining one or more gene sequence variation information involved in pharmaco-dynamics or pharmaco-kinetics of a uterine contraction inhibitor from personal genome sequence information; Calculating an individual protein damage score using the gene sequence variation information; And calculating the individual drug score by associating the individual protein damage score with the correlation between the drug and the protein, thereby providing information for selecting the uterine contraction inhibitor using the personal genome sequence variation. .

In the present invention, uterine contraction inhibitors include, but are not limited to, all substances that exhibit the same, similar pharmacological activity, such as drugs belonging to the drug class disclosed in Table 1, derivatives thereof, and pharmaceutically acceptable salts thereof.

Drug class	drug
Beta-adrenergic receptors	Terbutaline, Ritodrine, Fenoterol, Albuterol
Calcium channel blockers	Nicardipine, Nifedipine
Oxytonin antagonists	Atosiban
Nonsteroidal Anti-inflammatory Drugs (NSAIDs)	Indomethacin, Ketorolac, Sulindac
Myosin light chain inhibitor	MgSO4
nitrate	Nitroglycerin
Progesterone	Hydroxyprogesterone caproate, Micronized progesterone
ethyl alcohol

Gene information involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitor in the present invention is DrugBank (http://www.drugbank.ca/) or KEGG Drug (http://www.genome.jp/kegg/drug/) or PharmGKB (https://www.pharmgkb.org/) and the like, preferably BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXT1, HHATL, SERPINA7, TNKS, PSMD9, ZNF273, FAT4, GALNT10, OR6B1, RBBP8NL, KNDC1, UGT1A10, ARL13B, SLC15A2, SPINK6, C10orf1 1ML2, TPB, BA, M2N LAMA4, ADCY3, GRM7, SNAP47, LRIT2, LRRC3C, EFCAB4A, CPOX, VARS2 and the like, but are not limited to these. More specifically, in the present invention, the genes involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitors are BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXG1, , HHATL, SERPINA7, TNKS, PSMD9, ZNF273, FAT4, GALNT10, OR6B1, RBBP8NL, KNDC1, UGT1A10, and ARL13B, and one selected from the group consisting of SLC15A2, SPINK6, C10orf113, and TP53, TP53. , MAD1L1, ASZ1, MAN2B2, CAPN14, BAAT, LAMA4, ADCY3, GRM7, SNAP47, LRIT2, LRRC3C, EFCAB4A, CPOX, and VARS2.

In the present invention, the genes / proteins are expressed according to the nomenclature of the HUGO Gene Nomenclature Committee (HGNC) (Gray KA, Daugherty LC, Gordon SM, Seal RL, Wright MW, Bruford EA.genenames.org: the HGNC resources in 2013. Nucleic Acids Res. 2013 Jan; 41 (Database issue): D545-52. Doi: 10.1093 / nar / gks1066. Epub 2012 Nov 17 PMID: 23161694).

Gene sequence variation used as one information in the present invention refers to a variation or polymorphism of the individual gene sequence. In the present invention, the gene sequence mutation or polymorphism occurs at a gene region, particularly an exon region, which encodes a protein associated with pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor, but is not limited thereto.

The term "base sequence variation information" used in the present invention means information about the substitution, addition or deletion of the base constituting the exon of the gene. Substitution, addition, or deletion of such bases can occur for a variety of reasons, for example, by structural differences including mutations, truncation, deletions, duplications, inversions and / or translocations of chromosomes.

In another aspect, nucleotide polymorphism refers to differences between individuals of nucleotide sequences present in the genome, and the largest number of nucleotide polymorphisms is Single Nucleotide Polymorphism (SNP), and A, T, C, There is a difference between individuals in one base of the base sequence consisting of G. Sequence polymorphism is a form of polyalleic variation including single nucleotide variation (SNV), short tandem repeat polymorphism (STRP), or variable number of tandem repeat (VNTR) and copy number variation (CNV), including SNPs. May appear.

In the method of the present invention, sequence variation or polymorphism information found in an individual's genome is collected in association with proteins associated with pharmacodynamics or pharmacokinetics of uterine contraction inhibitors. That is, the nucleotide sequence information used in the method of the present invention is one or more genes involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitors, such as a target protein associated with the drug, Mutation information found in the exon region, in particular but not limited to, genes encoding enzyme proteins, transporter proteins or carrier proteins involved in drug metabolism.

The genome sequence information of an individual used in the present invention may be determined using a known sequence decoding method, and services such as Complete Genomics, BGI (Beijing Genome Institute), Knome, Macrogen, DNALink, etc., which provide commercially available services. May be used, but is not limited thereto.

Gene sequence variation information present in the genome sequence of an individual in the present invention can be extracted using a variety of methods, a sequence comparison program with a genomic sequence of a reference group, for example HG19, for example, ANNOVAR ( Wang et al., Nucleic Acids Research, 2010; 38 (16): e164), Sequence Variant Analyzer (SVA) (Ge et al., Bioinformatics. 2011; 27 (14): 19982000), Break Dancer (Chen et al., Nat Methods.2009 Sep; 6 (9): 677-81) and the like.

The gene sequence variation information may be received / obtained through a computer system, and in this aspect, the method of the present invention may further include receiving the genetic variation information into a computer system. The computer system used in the present invention is a gene involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitors, for example, a gene encoding a target protein associated with a drug, an enzyme protein involved in drug metabolism, a transporter protein or a carrier protein. Include or have access to one or more databases containing information about them. Such databases include, for example, DrugBank (http://www.drugbank.ca/), KEGG Drug (http://www.genome.jp/kegg/drug/), PharmGKB (http://www.pharmgkb.org /) May include, but is not limited to, public or private databases or knowledge bases that provide information about genes / proteins / drug-protein interactions, and the like.

In the present invention, the uterine contraction inhibitor may be information input by a user, information input from a prescription (pre), or information input from a database including information on the uterine contraction inhibitor. The prescription includes, but is not limited to, electronic prescription.

As used herein, the term "pharmaco-kinetics (pk)" or "pharmacokinetic parameters" refers to the properties of a drug associated with absorption, migration, distribution, conversion, and excretion of the drug in the body over time. Distribution volume (Vd), clearance (CL), bioavailability (F), absorption rate coefficient (ka), maximum plasma concentration (Cmax), and time point of maximum plasma concentration, Tmax), area under the curve (AUC) measurements of changes in blood drug concentration over time.

As used herein, the term “pharmacodynamics or pharmacodynamic parameters” refers to the characteristics related to the physiological and biochemical action of the drug on the living body and its mechanism of action, ie the reaction or effect of the living body of the drug.

As used herein, the term “gene sequence variation score” refers to an amino acid sequence variation (substitution or addition) of a protein encoded by the gene when the genome sequence variation is found in the exon region of the gene encoding the protein. Or deletion) or a score that quantifies the extent to which transcriptional control mutations result, thereby causing significant changes or damage to the structure and / or function of the protein, wherein the gene sequence variation score is the evolution of amino acids on the genome sequence It can be calculated in consideration of the degree of preservation and the degree of change in the structure or function of the protein according to the physical properties of the modified amino acid.

In order to apply to the calculation of individual protein damage score or individual drug score in the present invention, the method of calculating the gene sequence variation score may be performed using a method known in the art. See, eg, SIFT (Sorting Intolerant From Tolerant, Pauline C et al., Genome Res. 2001 May; 11 (5): 863874; Pauline C et al., Genome Res. 2002 March; 12 (3): 436446; 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): 38943900; Adzhubei IA et al., Nat Methods 7 (4): 248-249 (2010)), MAPP (Eric A. et al., Multivariate Analysis of Protein Polymorphism, Genome Research 2005; 15: 978986), Logre (Log R Pfam E- value, Clifford RJ 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: 440449, 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: 361362, 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), SNP effect (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/) may be used to calculate the gene sequence variation score from the gene sequence variation information, but is not limited thereto.

The purpose of the algorithms described above is to determine how each gene sequence mutation affects protein function, how this damage damages the protein, or whether there is little effect. They have a common point in that they determine the impact of individual gene sequence variations on the structure and / or function of the protein in consideration of the changes that will result in the amino acid sequence of the protein encoded by the gene.

In one embodiment according to the present invention, a Sorting Intolerant From Tolerant (SIFT) algorithm was used to calculate an individual gene sequence variation score. In the SIFT algorithm, for example, gene sequence variation information is input to a VCF (Variant Call Format) format file, and each gene sequence variation is scored for damaging the gene. In the case of the SIFT algorithm, the closer the output score is to 0, the more likely the function of the protein encoded by the gene is impaired, and the closer the value is to 1, the more likely that the protein encoded by the gene is maintaining normal function. .

In another algorithm, PolyPhen-2, the higher the score, the greater the degree of functional damage of the protein encoded by the gene.

Recently, a study that compared and synthesized SIFT, Polyphen2, MAPP, Logre, and Mutation Assessor to present the Condel algorithm (Gonzae A. & LoN. 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.), in the study HumVar, a set of known data relating to gene sequencing and low impact gene sequencing that damage proteins. And the five algorithms were compared using HumDiv (Adzhubei, IA et al., A method and server for predicting damaging missense mutations.Nature methods, 2010; 7 (4): 248-249). As a result, gene sequence mutations that cause protein damage of 97.9% of HumVar and gene sequence mutations of less than 97.3% were detected identically in at least three of the five algorithms, and 99.7% protein damage of HumDiv. Gene sequencing mutations and gene sequencing mutations with less influence of 98.8% were detected in at least three of the five algorithms. In addition, as a result of drawing a receiver operating curve (ROC) curve representing the accuracy of the results obtained by integrating the five algorithms and the respective algorithms with respect to the HumDiv and HumVar, the AUC (69% ~ 88.2%) Area Under the Receiver Operating Curve) was confirmed to show the agreement. That is, the above-described various algorithms have different correlation methods, but the calculated gene sequence variation scores are significantly correlated with each other. Therefore, applying the gene sequence variation scores calculated by applying the above-described algorithms or algorithms to the individual protein damage scores and the individual drug score calculation step according to the present invention is to calculate each gene sequence variation score. It is within the scope of the present invention regardless of the type of other algorithm.

When genetic sequence variation occurs in the exon region of a gene encoding a protein, it may directly affect the structure and / or function of the protein. Therefore, the gene sequence variation information may be related to the degree of impairment of protein function. In this respect, the method of the present invention calculates individual protein damage scores based on the gene sequence variation scores described above in the next step.

As used herein, the term “protein damage score” means that two or more significant sequence mutations are found in a gene region encoding one protein, so that one protein has two or more gene sequence mutation scores. Refers to a score calculated by combining the gene sequence variation score. If there is a significant sequence variation in a gene region encoding a protein, the gene sequence variation score and the protein damage score are the same. In this case, when there are two or more gene sequence mutations encoding a protein, the protein damage score is calculated as an average value of the gene sequence variation scores calculated for each variation, and the average value is, for example, a geometric mean, an arithmetic mean, or a harmonic mean. , Arithmetic geometric mean, arithmetic harmonic mean, geometric harmonic mean, Pythagorean mean, quadrant mean, quadratic mean, cutting mean, windsorized mean, weighted mean, weighted geometric mean, weighted arithmetic mean, weighted harmonic mean, function mean, 멱 average Can be computed as a generalized f-means, percentiles, maximums, minimums, modes, medians, median ranges, measures of central tendency, simple products or weighted products, or as a function of these calculations. This is not restrictive.

In one embodiment according to the present invention, the protein damage score was calculated by Equation 1 below, and Equation 1 may be variously modified and is not limited thereto.

In Equation 1, Sg is the protein damage score of the protein encoded by the gene g, n is the number of the nucleotide sequence analysis of the nucleotide variation of the gene g, vi is the gene sequence variation score of the i-th gene sequence variation And p is a nonzero real number. In Equation 1, when the value of p is 1, it is an arithmetic mean, and when the value of p is -1, it is a harmonic mean, and in the extreme case where the value of p is close to 0, it is a geometric mean.

In another embodiment according to the present invention, the protein damage score was calculated by Equation 2 below, and Equation 2 may be variously modified and is not limited thereto.

In Equation 2, Sg is the protein damage score of the protein encoded by the gene g, n is the number of the nucleotide sequence analysis of the nucleotide sequence variation of the gene g, vi is the gene sequence variation score of the i-th gene sequence variation , wi is a weight given to vi. When all weights wi have the same value, the protein damage score Sg becomes the geometric mean value of the gene sequence variation score vi. The weight may be given in consideration of the type of the protein, the pharmacokinetic or pharmacodynamic classification of the protein, the pharmacokinetic parameters of the drug enzyme protein, and the population or race distribution.

The term "pharmacokinetic parameters of drug metabolizing enzyme" as used herein includes Vmax, Km, Kcat / Km and the like. Vmax is the maximum enzyme reaction rate when the substrate concentration is very high, and Km is the concentration of the substrate that causes the reaction to reach 1/2 Vmax. Km can be seen as an affinity between the enzyme and the substrate. The smaller the Km, the stronger the bond between the enzyme and the substrate. Kcat, also called the enzyme's metabolic rate, refers to the number of substrate molecules that are metabolized at one second per enzyme active site when the enzyme is active at its maximum rate, and how fast the enzyme reaction actually occurs.

The method of the present invention correlates the protein damage score described above in the next step with the correlation between the drug and the protein to yield an individual drug score.

As used herein, the term “drug score” refers to a given protein, eg, a target protein involved in the pharmacodynamics or pharmacokinetics of a given drug, an enzyme protein involved in drug metabolism, a transporter protein or Carrier proteins are found, the protein damage scores of the proteins are calculated, and then summed again to refer to the values calculated for one drug.

In the present invention, the drug score is calculated as an average value of the protein damage scores when the damage of the protein involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitors, for example, geometric mean, arithmetic mean, harmonic mean, Arithmetic geometric mean, Arithmetic harmonic mean, Geometric harmonic mean, Pythagorean mean, Quarter mean, Secondary mean, Cutting mean, Windsorized mean, Weighted mean, Weighted geometric mean, Weighted arithmetic mean, Weighted harmonic mean, Function mean, 멱 mean, Generalized f-means, percentiles, maximums, minimums, modes, medians, median ranges, measures of central tendency, or simple products or weighted products, or arithmetic operations of these calculations This is not restrictive.

The drug score may be calculated by adjusting the weights of the drug, that is, the target protein involved in the pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor, the enzyme protein involved in the drug metabolism, the transporter protein, and the carrier protein in consideration of pharmacological properties. The weight may be given in consideration of the pharmacokinetic parameters of the corresponding drug metabolizing enzyme, the population group or the race distribution. In addition, drug scores are also taken into account when considering protein damage scores of proteins that do not interact directly with the drug but interact with precursors or metabolites of the drug, such as proteins that make up the pharmacological pathway. Can be calculated. In addition, the drug score may be calculated by considering the protein damage scores of the proteins that significantly interact with the proteins involved in the pharmacodynamics or pharmacokinetics of the drug. For information on the proteins that make up the pharmacological pathway of the drug, or which significantly interact with the proteins that make up the pathway, or participate in the signaling pathway, see 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), etc. Searched biological databases.

In one embodiment according to the present invention, the drug score was calculated by the following Equation 3, and the following Equation 3 may be variously modified, but is not limited thereto.

In Equation 3, Sd is a drug score of drug d, n is a protein directly involved in the pharmacodynamics or pharmacokinetics of drug d or interacts with a precursor of the drug or metabolites of the drug, for example, a pharmacological pass. The number of proteins encoded by one or more genes selected from the group of genes participating in the way, gi is a protein that directly participates in the pharmacodynamics or pharmacokinetics of drug d or interacts with precursors or metabolites of the drug, eg For example, the protein damage score of a protein encoded by one or more genes selected from a group of genes participating in pharmacological pathways, where p is a nonzero real number. In Equation 3, when the value of p is 1, it is an arithmetic mean, and when the value of p is -1, it is a harmonic mean, and in the extreme case where the value of p is close to 0, it is a geometric mean.

In another embodiment according to the present invention, the drug score was calculated by Equation 4 below, and Equation 4 may be variously modified, and is not limited thereto.

In Equation 4, Sd is a drug score of drug d, n is a protein directly involved in the pharmacodynamics or pharmacokinetics of drug d or interacts with a precursor of the drug or metabolites of the drug, eg, a pharmacological pass. The number of proteins encoded by one or more genes selected from the group of genes constituting the way, gi, are proteins that directly participate in the pharmacodynamics or pharmacokinetics of drug d or interact with precursors or metabolites of the drug, eg For example, the protein damage score of the protein encoded by one or more genes selected from the gene group constituting the pharmacological pathway, wi is the weight given to the gi. The drug score Sd becomes the geometric mean value of the protein damage score gi when all weights wi have the same value. The weight may be given in consideration of the type of the protein, the pharmacokinetic or pharmacodynamic classification of the protein, the pharmacokinetic parameters of the drug enzyme protein, and the population or race distribution.

In the geometric mean calculation method used in the method of the embodiment according to the present invention, all weights were equally given regardless of the drug-protein association characteristics, but the weight was given by considering the respective characteristics of drug-protein association. It is possible to calculate the drug score. For example, different scores may be assigned to the drug's target protein and the drug's transporter protein. In addition, for example, the drug metabolizing enzyme may be weighted by the pharmacokinetic parameters Km, Vmax, and Kcat / Km to calculate the drug score. In addition, for example, the target protein may be given a higher weight by judging that the target protein is more important in pharmacological action than the transporter protein, and the transporter protein or the carrier protein may be given a high weight for the concentration-sensitive drug. This is not restrictive. Weights can be closely adjusted according to the correlation between drug and drug-related proteins, and the nature of drug-protein interactions. For example, a sophisticated algorithm can be used that assigns a weight to the nature of the drug-protein interaction, such as giving the target protein two points and the transporter protein one point.

In the above description, only the drug, that is, the protein that directly interacts with the uterine contraction inhibitor, is taken as an example, but the protein interacts with the precursor of the drug or its metabolites, and the proteins involved in the pharmacodynamics or pharmacokinetics of the drug. It is possible to improve the predictive power of the formula by utilizing protein information that interacts easily, and related protein information that participates in the signaling pathway. In other words, the protein-protein interaction network or pharmacological pathway information can be utilized to use the information of various proteins involved in it. That is, even if no significant mutation was found for a protein that directly interacts with the drug, and there is no corresponding protein damage score calculation or no damage (for example, 1.0 if SIFT algorithm is applied), The mean value (eg, geometric mean) of the protein damage scores of related proteins participating in the same signaling pathway may be used as a drug score calculation instead of the protein damage score of the protein.

The individual drug scores may be calculated for all drugs for which information on one or more related proteins can be obtained or for some selected drugs. In addition, such individual drug scores can be converted into rank (rank).

The method of the present invention may further include determining whether to use a drug applied to the individual, ie, a uterine contraction inhibitor, by using the aforementioned individual drug score.

The individual drug scores of the present invention can be applied individually to all drugs, but are more useful if they are classified by disease, clinical characteristics, or mode of action, or applied to drugs for medical comparison. Drug classification systems that can be used in the present invention are, for example, the ATC (Anatomical Therapeutic Chemical Classification System) code, the top 15 frequently prescribed drug classes during 2005-2008 in the United States (Health, United States, 2011, Centers for Disease Control and Prevention), a list of drugs that have been released from the market due to known pharmacogenomic markers that may affect drug action information on the drug label, or side effects. It may include.

The method of the invention may further comprise calculating a prescription score.

As used herein, the term “prescription score” refers to the sum of the drug scores determined for each drug when two or more drugs are administered at the same time or at intervals short enough to significantly affect the pharmacological action of each other. Say your score is calculated. In the present invention, the prescription score may be calculated by combining the drug scores determined for each drug when two or more drugs determined by the priority between the drugs and simultaneous administration are required. The calculation of the prescription score can be calculated by simply averaging, summing or multiplying the drug scores of the plurality of drugs, for example, when no protein interacts in common with the plurality of drugs. If there are proteins that interact in common with the plurality of drugs, the drug damage scores of the common interacting protein are weighted twice, for example, to calculate each drug score and then the corresponding drug scores are calculated. By summing up, the prescription score can be calculated.

Prescription scores are intended to determine the adequacy or risk of a multi-drug prescription that is included in the prescription applied to an individual, beyond the effects of individual drugs. In this respect the method of the present invention may further comprise determining the appropriateness or risk of the prescription (pre) applied to the individual.

The method of the present invention includes, but is not limited to, being performed for the purpose of preventing adverse effects of uterine contraction inhibitors.

1 is a flow chart showing each step of the method for providing information for the selection of uterine contraction inhibitors using individual genome sequence variation according to an embodiment of the present invention. In one embodiment according to the present invention (1) input or reception of genome sequence information of an individual user (S100), (2) input or reception of uterine contraction inhibitor-related information (S110), (3) genetic sequence variation of the individual user Information determination (S120), (4) Individual protein damage score calculation for uterine contraction inhibitors (S130), (5) Individual drug score calculation for uterine contraction inhibitors (S140), (6) Drug score notation, by drug score ranking A method of providing information for uterine contraction inhibitor selection in order of sorting or priority determination (S150), and (7) uterine contraction inhibitor selection and prescription score calculation in consideration of drug scores and priorities is performed.

When the method according to the present invention selects the drug scores sorted by the ranking, the step of providing a pharmacogenomic calculation process and the basis for calculating the drug scores by providing information such as pictures, charts and explanations to help the prescriber in making decisions It may further include. That is, the method according to the present invention may further include providing one or more information of gene sequence variation information, gene sequence variation score, protein damage score, drug score, and information used to calculate the same.

In another aspect, the present invention provides a uterine contraction inhibitor that can be applied to an individual, the database for retrieving or extracting information related to the gene or protein associated with the uterine contraction inhibitor; A communication unit accessible to the database; A first calculation module configured to calculate one or more gene sequence variation information related to pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor based on the information; A second calculation module for calculating an individual protein damage score using the gene sequence variation information; A third calculating module that calculates an individual drug score by associating the individual protein damage score with a correlation between a drug and a protein; And it relates to a uterine contraction inhibitor selection system using a personal genome sequence variation comprising a display unit for displaying the calculated value calculated by the calculation module.

In the present invention, the module may mean a functional and structural combination of hardware for performing the technical idea according to the present invention and software for driving the hardware. For example, the module may mean a logical unit of a predetermined code and a hardware resource for performing the predetermined code, and does not necessarily mean a physically connected code or a kind of hardware. Will be apparent to those skilled in the art.

The term “output module” used in the present invention refers to the gene sequence variation score, protein damage score, drug score, and information on which the calculation is made for the uterine contraction inhibitor and gene to be analyzed according to the method of the present invention. And a predetermined code for calculating each score based on the information and a logical unit of a hardware resource for performing the predetermined code, and means a physically connected code or a kind of code. It doesn't mean hardware.

The system according to the present invention may further include a fourth calculation module for determining whether to use the uterine contraction inhibitor applied to the individual by using the individual drug score calculated in the third calculation module.

The system according to the present invention further includes a fifth calculation module for calculating the prescription score by combining the drug scores determined for each drug when two or more drugs determined by the priority between drugs are required. It may include.

The system according to the present invention inputs a list of uterine contraction inhibitors or accesses a database including information on uterine contraction inhibitors, extracts relevant information, and calculates and provides a drug score of the drug accordingly. It may further include a user interface.

The system according to the present invention may further include a display unit for further displaying a calculation process in which a value calculated in each calculation module or a priority between drugs is determined, and information on which the calculation or calculation is based.

In the system according to the present invention, the server including the database or its access information, the calculated information and the user interface device connected thereto may be used in conjunction with each other.

The system according to the invention can be updated immediately when new pharmacological / biochemical information on the drug-protein correlation is produced and can be used for further improved uterine contraction inhibitor selection. In one embodiment according to the present invention, according to the update of the database or knowledge base, the gene sequence variation information, gene sequence variation score, protein damage score, drug score and information on which the basis of the calculation stored in each calculation module Is updated.

2 is a schematic configuration diagram of a system for selecting a uterine contraction inhibitor using personal genome sequence variation according to an embodiment of the present invention. System 10 of the present invention is a database (DB) 100, communication unit 200, user interface or terminal 300, the calculation unit 400 capable of searching or extracting information related to genes or proteins related to the uterine contraction inhibitors And a display unit 500.

In the system according to the present invention, the user interface or the terminal 300 may request, receive and / or store uterine contraction inhibitor selection process using a personal genome sequence variation from a server, and may be a smartphone, a personal computer (PC), or a tablet. It may be configured as a terminal having a mobile communication function having a computing capability by mounting a microprocessor such as a PC, a personal digital assistant (PDA), a web pad, or the like.

In the system according to the present invention, the server is a means for providing access to the database 100 for uterine contraction inhibitors, genetic variation or drug-protein correlations, and is connected to the user interface or the terminal 300 through the communication unit 200. It is configured to exchange various information. Here, the communication unit 200 may communicate in the same hardware, as well as a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, 2G, 3G, 4G mobile communication network, Wi-Fi (Wi-Fi), Wibro (Wibro) and the like can be included, and the communication method is wired, wireless, any communication method. The database 100 may also be connected to various life science databases accessible through the Internet as well as being installed directly on the server.

In the system according to the present invention, the calculation unit 400 may include the first calculation module 410 for calculating one or more gene mutation information related to pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor using the collected / input information as described above, A second calculation module 420 for calculating an individual protein damage score and a third calculation module 430 for calculating an individual drug score may be included.

 The method according to the invention can be implemented in hardware, firmware, or software or a combination thereof. When implemented in software, a storage medium includes any medium for storage or delivery in a form readable by a device such as a computer. For example, a computer readable medium may include read only memory (ROM); Random access memory (RAM); Magnetic disk storage media; Optical storage media; Flash memory devices and other electrical, optical or acoustic signaling media, and the like.

In this aspect, the present invention comprises the steps of obtaining genetic sequence variation information involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitor from the individual genome sequence information; Calculating an individual protein damage score using the gene sequence variation information; And an execution module for associating the individual protein damage score with the correlation between the drug and the protein to execute a processor to perform an operation comprising calculating the individual drug score.

The processor may further include determining whether to use a uterine contraction inhibitor applied to the individual by using the individual drug score.

In another aspect, the present invention provides a biomarker composition for predicting adverse effects of uterine contraction inhibitors.

Genes that may be included in the biomarker composition according to the present invention include BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXT1, HHATL, SERPINA7 TNKS, PSMD9, ZNF273, FAT4, GALNT10, OR6B1, RBBP8NL, KNDC1, UGT1A10, ARL13B, SLC15A2, SPINK6, C10orf113, TP53, TRIML2, MAD1L1, ASZ1, MAN2B2, CAPN14, BAAT3 LRRC3C, EFCAB4A, CPOX, VARS2, and the like, but are not limited to these. By analyzing the mutation of the gene or its protein, it is possible to predict the occurrence of adverse effects of the uterine contraction inhibitor, it can be used as a marker using the agent that can detect this.

Hereinafter, the present invention will be described in more detail with reference to the following examples. These examples are intended to illustrate the present invention in detail, but the scope of the present invention is not limited by these examples.

Example  1. Uterine contraction inhibitors Ritodrine Analysis and Application of Individual Genome Sequence Variation Information in Early Analgesic Patients with Significant Side Effects Warning Signs

Preterm labor refers to regular uterine contraction prior to 37 weeks of gestation and thus to gradual cervical dilatation and maturation, which is one of the leading causes of premature birth. In Korea, the birth rate is decreasing and the premature birth rate is gradually increasing due to the increase of elderly mothers (4.3% in 1995, 7% in 1998, 8.3% in 2000, 10% in 2003), which is becoming a big social problem. An increase in the rate of premature births is a problem that cannot be overlooked in terms of maternal and child health. This is why the development of treatment technology for early labor, which is the main cause of premature birth, is essential.

Most of the premature births that have been performed to date are performed after symptoms occur, and mothers of early labor are hospitalized at the time of diagnosis and preferentially use uterine contraction inhibitors. Representative uterine contraction inhibitors that have been widely used in Korea include ritodrine, magnesium (MgSO ₄ ), nifedipine (Nifedipine), and atoshiban (Atosiban).

Among them, ritodrine, magnesium and nifedipine, which are used most frequently, are adversely affected by the side effects of these drugs. In particular, in the case of the most frequently used ritodrine, complications such as tachycardia, hypotension, anxiety, chest pain, abnormal ECG, pulmonary edema, etc. of pregnant women is a problem, it is known that the frequency and severity of side effects are very different for each pregnant woman. As such, the responsiveness to a particular drug is different for each patient, so appropriate drug administration cannot be made to each patient and treatment failure or delay is indicated.

Thus, the following experiment was performed to determine whether a risk response group of ritodrine treatment, which is a type of uterine contraction inhibitor, can be distinguished using the method for providing selection of a uterine contraction inhibitor using the personal genome sequence variation of the present invention.

We analyzed 13 cases of side effects among mothers who received uterine contraction inhibitor ritodrine to prolong fetal gestation during preterm labor. Genetic sequences were compared with 30 normal controls for objective comparison. Side effects experienced by the mothers corresponding to the 13 cases are shown in Table 2 below.

Serial Number	Side Effect
One	Dyspnea
2	Pulmonary Edema, Pulmonary Congestion
3	Tachycardia (116 times / minute), difficulty breathing
4	Dyspnea
5	Shortness of breath, pulmonary edema with bilateral pleural effusion, CK-MB elevation (5.6ng / mL), BNP elevation (1128pg / mL)
6	Tachycardia (120 beats / minute), difficulty breathing
7	Tachycardia (114 times / minute), difficulty breathing
8	Dyspnea
9	Tachycardia (110 beats / minute), dyspnea, palpitations
10	Tachycardia (108 beats / minute), headache, sweating
11	Shortness of breath, palpitations
12	Pulmonary Edema
13	Pulmonary Edema

In order to obtain information on the 13 individual genome sequences, 80-fold full-length exome sequencing was performed using Ion AmpliSeq Exome Kit from Life Technology. In addition to the above method, alternative to full genome sequencing that obtains the entire genome of the individual in addition to the exome or target exome sequencing of major genes related to 500-1000 uterine contraction inhibitors Can be done as

The sequence fragments analyzed were sequence alignment map (SAM) and binary alignment map (BAM) files aligned with human reference sequence (eg, HG19) through data cleaning and quality check. The output was in format. The clean alignment result is detected using a software tool such as SAMTools: pileup, SAMTools: mpileup, GATK: recalibration, GATK: realignment, and the like to detect mutations such as single nucleotide variations (SNVs, Single Nucleotide Variants) and InDels. It was output to a file in VCF (Variant Calling Format) format.

After receiving the VCF file containing the gene sequence variation information, the above-described gene sequence variation score vi value was calculated for each variation by using the SIFT algorithm, and the individual protein damage score Sg was calculated using Equation 2. Subsequently, the individual protein damage scores were compared for each gene in 13 cases and 30 health control controls. 47 genes showing statistically significant differences in the adverse event group and the control group were selected as follows, and 28 genes with higher statistical significance were selected as the first group based on the p-value. The following genes are genes of high relevance to the pharmacokinetics or pharmacokinetics of ritodrine and its metabolites.

(Group 1)

BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXT1, HHATL, SERPINA7, TNKS, PSMD9, ZBF6AL1NT, ZBF673N10 KNDC1, UGT1A10, ARL13B

(2nd group)

SLC15A2, SPINK6, C10orf113, TP53, TRIML2, MAD1L1, ASZ1, MAN2B2, CAPN14, BAAT, LAMA4, ADCY3, GRM7, SNAP47, LRIT2, LRRC3C, EFCAB4A, CPOX, VARS2

Thus, the drug score for each individual gene group was calculated using the method of the present invention. More specifically, after calculating the gene sequence variation score using the SIFT algorithm from each individual gene sequence variation information, the individual protein damage score for the 47 genes was calculated using Equation 2, Equation 4 Using to calculate the individual drug score for ritordlin, statistical analysis for each group. The results are shown in Table 3.

	Ritodrin Side Effects Group (n = 13)	Normal control (n = 30)	p-value
Drug Score Average (Group 1 drugs)	0.236438666	0.582454806	1.55E-08
Drug Score Average (Group 1 and 2 Drugs)	0.155756474	0.542985491	7.62E-14
Group 1 protein damage score
BLK	0.1600	0.5800	4.35E-06
SPTA1	0.3201	0.4887	6.84E-06
IFT74	0.2489	0.5581	1.42E-05
RSPH3	0.2730	0.6106	1.85E-05
CYP8B1	0.4537	0.7053	2.01E-05
ICE1	0.4854	0.7026	2.56E-05
NKAIN3	0.2113	0.5614	2.57E-05
AASDH	0.1640	0.5829	4.15E-05
FUT6	0.1385	0.4567	0.00015
SLC12A7	0.3600	0.5967	0.00015
CD1A	0.3538	0.8553	0.00019
CYP1A1	0.0291	0.3635	0.00021
CARS2	0.2291	0.7280	0.00023
ZDHHC12	0.1658	0.4084	0.00033
CSPG5	0.2400	0.4933	0.00034
PXT1	0.0200	0.3467	0.00034
HHATL	0.2709	0.4972	0.00037
SERPINA7	0.3146	0.9010	0.00040
TNKS	0.3834	0.6769	0.00040
PSMD9	0.4306	0.9060	0.00051
ZNF273	0.0975	0.3771	0.00053
FAT4	0.2295	0.4320	0.00054
GALNT10	0.3523	0.6580	0.00055
OR6B1	0.2834	0.5028	0.00056
RBBP8NL	0.2078	0.5765	0.00056
KNDC1	0.3353	0.5280	0.00062
UGT1A10	0.6185	1.0000	0.00899
ARL13B	0.8646	0.8797	0.44518
Group 2 protein damage score
SLC15A2	0.0872	0.3610	0.00071
SPINK6	0.0400	0.3280	0.00071
C10orf113	0.0000	0.3000	0.00071
TP53	0.1700	0.4190	0.00071
TRIML2	0.3005	0.5133	0.00072
MAD1L1	0.3086	0.6232	0.00075
ASZ1	0.2638	0.6810	0.00076
MAN2B2	0.2495	0.4449	0.00080
CAPN14	0.1965	0.3976	0.00080
BAAT	0.3380	0.5339	0.00087
LAMA4	0.1080	0.3322	0.00087
ADCY3	0.3992	0.7362	0.00088
GRM7	0.3501	0.7104	0.00093
SNAP47	0.2264	0.4546	0.00103
LRIT2	0.3079	0.6232	0.00104
LRRC3C	0.1731	0.6253	0.00105
EFCAB4A	0.3505	0.4996	0.00106
CPOX	0.3562	0.8450	0.00121
VARS2	0.0697	0.3220	0.00122

As shown in Table 3, the protein damage score and the individual drug score were calculated from the gene sequence variation information using the 28 selected group 1 genes, and statistically significant between the normal control group and the ritodrine side effects group. It was confirmed that a difference appeared.

In addition, even when the individual drug scores were calculated from a total of 47 gene sequence variation information including the second group genes, the individual drug scores between the two groups showed statistically significant differences (p-value <0.05). ).

Through the above results, it is possible to significantly distinguish between the group that suffered serious drug side effect warning sign and the other group that did not suffer serious drug side effect warning by using the individual drug score calculation through analysis of individual genome sequence variation information according to the present invention. It was confirmed that unwanted side effects can be prevented in advance.

Therefore, the method of the present invention can be used to predict a group of patients with high risk of side effects in the future patients with early analgesia, and induce high-risk patients to adjust the concentration of the drug or to use other alternative or interventional therapy. Could be.

Example  2. Validation of Custom Drug Selection Method Based on Personal Genomic Sequence Variation Information

To date, reliable studies of individual genome sequence variation information and individual differences in pharmacologic responses have been limited. To date, research has followed a paradigm of case-control observation studies in which individual differences in responsiveness are studied by comparing drug-positive or negative groups by drug. In this research paradigm, expensive case-control studies must be performed for all combinations of numerous sequence variations and numerous drug pairs, but this is not practical. On the other hand, the personalized drug selection method according to the present invention not only targets all gene sequence mutations, but also does not require expensive case-control design observation studies, and damages individual proteins by pure calculation of genome sequence mutations. Since we propose a method of calculating the score and the individual drug score and applying it, it has the great advantage that it is possible to infer a personalized drug selection for the combination of all genome sequence variations and all drugs.

497 multi-frequency prescription drugs were selected for the feasibility of the results of personalized drug selection calculation according to the method of the present invention; (1) Among the drugs included in the ATC code of the top 15 frequently prescribed drug classes during 2005-2008 in the United State (Health, United States, 2011, Centers for Disease Control and Prevention). Drugs with known at least one pharmacodynamic or pharmacokinetic gene, (2) the action of established pharmacogenomic genome sequence markers applied to the drug labeling of the US Food and Drug Administration, and (3) drug side effects from the market. Drugs known to the DrugBank database.

Validity criteria data were extracted from the established knowledge of 987 gene sequence mutation-drug interaction pairs provided by PharmGKB with 650 (65.9%) having at least one link with the 497 drugs. Considering that the present invention targets the nucleotide sequence variation of the exon region, overlapping portions between the verification target data and the evaluation reference data are removed for fair evaluation. More specifically, all 36 nucleotide sequence mutations located in the exon region were removed from the 650 pairs, and only a nucleotide sequence variation of the non-coding region was selected to perform a more fair evaluation. In conclusion, we selected 614 pairs as the final gold standard for evaluation.

Next, 1092 full-length genome sequences provided by The 1000 Genomes Project were analyzed, and the method according to the present invention was applied to each of 1092 persons, and the pharmacogenomic risks of individuals and the pharmacogenomics of each gene sequence registered in PharmGKB were analyzed. Risks were each calculated.

For the validity evaluation, the sensitivity, specificity and area under the receiver operating curve were used. After ranking 497 drugs based on individual drug scores and setting thresholds by rank at 496 splits between each rank, (1) the drug score rank of the drug is above the threshold and the PharmGKB mutation is in the personal genome. True positive when mutant, (2) drug score ranking of the drug is below the threshold and true if the PharmGKB mutation is not in the individual genome variation, (3) drug score ranking of the drug is above the threshold but PharmGKB False positives when the mutation was not present in the individual genome variation, and (4) the drug score ranking of the drug was lower than the threshold, but when the PharmGKB mutation was in the individual genome variation, it was false negative.True positive for each rank threshold L in each individual, The number of true negatives, false positives and false negatives was calculated to calculate sensitivity and specificity as shown in the following equation.

D is a set of 497 total drugs, GS is a set of personalized PharmGKB drugs that are used as individual gold standards by matching individual genetic sequence mutations with the risk allele of PharmGKB, and D _L is a set of top-ranking drugs The vertical bar brackets indicate the number of elements in the set.

Calculations showed that 18 patients did not have any variations that matched PharmGKB's variation and therefore could not define a set of personalized PharmGKB drugs used as gold standards, so they were excluded from this feasibility analysis and were sensitive to all thresholds. The ROC curve was calculated by calculating the specificity and and the AUC was calculated. More specifically, first, gene sequence variation scores were calculated using a SIFT algorithm for a total population of 1092 people, and then protein damage scores and drug scores were calculated by applying Equations 2 and 4, respectively. In addition, in order to determine the usefulness of weighting according to racial distribution, the calculation of racial-specific sensitivity, specificity, and AUC values based thereon is calculated using the four races (African (AFR, n = 246), American (AMR, n = 181), Asian (ASN, n = 286), and European (EUR, n = 379) were performed in the same manner, respectively, and race-specific sensitivity, specificity, and AUC were determined, respectively. The results are shown in Table 4, Table 5 and FIG.

 Protein group distribution and average protein damage score calculation

Protein family	Protein count	Related drug count	Number of protein-drug pairs	Average protein damage score
Target protein	440	486	2357	0.798
Carrier Protein	10	50	65	0.728
Metabolic protein	74	330	1347	0.733
Transporter protein	54	176	457	0.733
system	545	497	4201	0.783

 Calculate validity (AUC) of drug scores by protein group and race using The 1000 Genomes Project data

	Total	AFR	AMR	ASN	EUR
Drug Score Calculation Validity (AUC)
Target protein	0.617	0.634	0.608	0.614	0.614
Carrier Protein	0.554	0.511	0.599	0.485	0.594
Metabolic protein	0.587	0.642	0.580	0.558	0.579
Transporter protein	0.497	0.492	0.488	0.489	0.512
Drug Score Calculation Validity (AUC) with or without protein weights
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 4 shows the distribution of the protein groups for the 497 drugs used in this example, and the number of protein-drug pairs and the average protein damage score were displayed together in each group.

In Table 5, when calculating the drug score using Equation 4, the individual drug score calculation validity (AUC) calculated when the protein group weight is not applied (simple geometric mean) and when applied (weighted geometric mean), respectively It is represented by each protein group, each race.

More specifically, taking the entire population as an example, each group of proteins, such as target protein, carrier protein, metabolic protein, and transporter protein, is not given weight (wi = 1) in Equation 4 for drug score calculation. The calculated AUC values were 0.617, 0.554, 0.587, and 0.497, respectively, and the weighted geometric mean drug score calculation validity (AUC = 0.667) was calculated using the protein group weight (substituting each value into the weight w _i of Equation 4). ) Was obtained (see FIG. 3B). As a result, the validity of the individual weighted geometric mean drug score calculation validity applied to the protein group weight was not given a weight (wi = 1), and the individual geometrical mean drug score calculation validity calculated by applying to the simple geometric mean calculation formula (AUC = 0.666), it was confirmed that the 0.001 point improvement (see Fig. 3a).

In addition, as shown in FIG. 3A, as another example of weighting, a result of performing an AUC analysis of individual drug scores by applying weights according to the number of individual races is considered, considering race specificity (bold line). The total AUC of the population was 0.666 (African 0.744, American 0.650, Asian 0.631, European 0.653), without considering race specificity (dotted line). The overall population AUC was 0.633 (African 0.623, American 0.629, Asian 0.64, European 0.636), indicating that the validity of drug score calculation considering race specificity was improved compared to that of the other population.

In addition, as shown in Figure 3b, when applying only the weight of each protein group without considering race specificity (dotted line), the individual drug score calculation validity AUC of the individual of the present invention is 0.634, and also applied to the weight of each protein group with race specificity ( Thick line), the individual drug score calculation validity AUC of the present invention is 0.667, indicating that the different weights are useful.

Although the exemplary embodiments of the present application have been described in detail above, the scope of the present application is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present application defined in the following claims are also provided. It belongs to.

All technical terms used in the present invention, unless defined otherwise, are used in the meaning as commonly understood by those skilled in the art in the related field of the present invention. The contents of all publications described herein by reference are incorporated into the present invention.

Claims (29)

1. Determining one or more gene sequence variation information related to pharmaco-dynamics or pharmaco-kinetics of the uterine contraction inhibitor from the individual genome sequence information;

   Calculating an individual protein damage score using the gene sequence variation information; And

   Calculating a personal drug score by associating the individual protein damage score with a correlation between the uterine contraction inhibitor and the protein, providing information for selecting the uterine contraction inhibitor using the personal genome sequence variation.
2. The method of claim 1,

   The uterine contraction inhibitor is a drug belonging to at least one family selected from the group consisting of beta-adrenergic receptor agonists, calcium channel blockers, oxytocin antagonists, nonsteroidal anti-inflammatory drugs, nitrates, progesterone, ethyl alcohol and magnesium sulfate And providing information for selecting a uterine contraction inhibitor using personal genome sequence variation.
3. The method of claim 1,

   Gene involved in the pharmacodynamics or pharmacokinetics

   BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXT1, HHATL, SERPINA7, TNKS, PSMD9, ZBF6AL1NT, ZBF673N10 A method of providing information for selection of a uterine contraction inhibitor using at least one individual genome sequence variation selected from the group consisting of KNDC1, UGT1A10 and ARL13B.
4. The method of claim 3, wherein

   One or more genes further selected from the group consisting of SLC15A2, SPINK6, C10orf113, TP53, TRIML2, MAD1L1, ASZ1, MAN2B2, CAPN14, BAAT, LAMA4, ADCY3, GRM7, SNAP47, LRIT2, LRRC3C, EFCAB4A, CPOX and VARS2 And providing information for selecting a uterine contraction inhibitor using personal genome sequence variation.
5. The method of claim 1,

   Gene involved in the pharmacodynamics or pharmacokinetics

   BLK, SPTA1, IFT74, RSPH3, CYP8B1, ICE1, NKAIN3, AASDH, FUT6, SLC12A7, CD1A, CYP1A1, CARS2, ZDHHC12, CSPG5, PXT1, HHATL, SERPINA7, TNKS, PSMD9, ZBF6AL1NT, ZBF673N10 A group consisting of KNDC1, UGT1A10, ARL13B, SLC15A2, SPINK6, C10orf113, TP53, TRIML2, MAD1L1, ASZ1, MAN2B2, CAPN14, BAAT, LAMA4, ADCY3, GRM7, SNAP47, LRIT2, LRRC3C, EF and VA4 The above, a method for providing information for the selection of uterine contraction inhibitors using personal genome sequence variation.
6. The method of claim 1,

   Wherein the gene sequence variation information is a substitution, addition or deletion of the base constituting the exon (exon) of the gene, a method for providing information for the selection of the uterine contraction inhibitor using the personal genomic sequence variation.
7. The method of claim 1,

   The gene sequence variation information is obtained by comparative analysis with the genome sequence of the reference group, a method for providing information for selection of the uterine contraction inhibitor using the individual genome sequence variation
8. The method of claim 1,

   The protein damage score or the drug score is SIFT (Sorting Intolerant From Tolerant), PolyPhen (Polymorphism Phenotyping), PolyPhen-2, MAPP (Multivariate Analysis of Protein Polymorphism), Logre (Log R Pfam E-value), Mutation Assessor, Mutation Tester, One selected from the group consisting of 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) A method for providing information for selecting a uterine contraction inhibitor using individual genome sequence variation, which is calculated from one or more gene sequence variation scores calculated using the above algorithm.
9. The method of claim 1,

   Wherein the protein damage score or drug score is calculated from a gene sequence variation score, wherein the protein damage score or drug score is calculated from the gene sequence variation score.
10. The method of claim 9,

    The gene sequence variation score is 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, One or more algorithms selected from the group consisting of Protein Variation Effect Analyzer (PROVEAN), PMut, Condel, Genomic Evolutionary Rate Profiling (GERP), GERP ++, Combinatorial Entropy Optimization (CEO), SNPeffect, fathmm, and Combined Annotation-Dependent Depletion (CAD) Method of providing information for selecting the uterine contraction inhibitor using the individual genome sequence variation, which is calculated by applying to the gene sequence variation.
11. The method of claim 1,

    The protein damage score is calculated as an average value of the gene sequence mutation scores when two or more nucleotide sequence mutations found in the gene encoding the protein are selected. How to provide information for.
12. The method of claim 11,

    The mean values are geometric mean, arithmetic mean, harmonic mean, arithmetic mean, arithmetic harmonic mean, geometric harmonic mean, Pythagorean mean, heron mean, inverse harmonic mean, mean square deviation, centroid mean, quadrant mean, quadratic mean, cutting mean , Windsing Mean, Weighted Average, Weighted Geometric Mean, Weighted Arithmetic Mean, Weighted Harmonic Mean, Function Mean, Power Average, Generalized f-Mean, Percentile, Maximum, Minimum, Mode, Median, Median Range, Central Trend A method of providing information for uterine contraction inhibitor selection using personal genome sequence variation, which is calculated by one or more selected from the group consisting of measures of central tendency, simple product and weighted product.
13. The method of claim 1,

    The protein damage score is calculated by Equation 1, method for providing information for the selection of uterine contraction inhibitors using individual genome sequence variation.

    [Equation 1]

    

    In Equation 1, Sg is the protein damage score of the protein encoded by the gene g, n is the number of the nucleotide sequence analysis of the nucleotide sequence variation of the gene g, vi is the gene sequence variation score of the ith nucleotide sequence variation , p is a nonzero real number.
14. The method of claim 1,

    The protein damage The score is calculated by Equation 2 below, the method for providing information for the selection of the uterine contraction inhibitor using the individual genome sequence variation.

    [Equation 2]

    

    In Equation 2, Sg is a protein damage score of the protein encoded by the gene g, n is the number of the nucleotide sequence analysis of the nucleotide sequence variation of the gene g, vi is the gene sequence variation score of the ith nucleotide sequence variation , wi is a weight given to the gene sequence variation score vi of the i-th sequence variation.
15. The method of claim 1,

    The drug score may be calculated as an average value of protein damage scores when two or more proteins are involved in pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor. How to give.
16. The method of claim 15,

    The mean values are geometric mean, arithmetic mean, harmonic mean, arithmetic mean, arithmetic harmonic mean, geometric harmonic mean, Pythagorean mean, heron mean, inverse harmonic mean, mean square deviation, centroid mean, quadrant mean, quadratic mean, cutting mean , Windsing Mean, Weighted Average, Weighted Geometric Mean, Weighted Arithmetic Mean, Weighted Harmonic Mean, Function Mean, Power Average, Generalized f-Mean, Percentile, Maximum, Minimum, Mode, Median, Median Range, Central Trend A method of providing information for uterine contraction inhibitor selection using personal genome sequence variation, which is calculated by one or more selected from the group consisting of measures of central tendency, simple product and weighted product.
17. The method of claim 1,

    The drug score is calculated by the following Equation 3, method for providing information for the selection of uterine contraction inhibitors using individual genome sequence variation.

    [Equation 3]

    

    In Equation 3, Sd is a drug score of drug d, n is the number of proteins encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of drug d, and gi is one or more genes involved in the pharmacodynamics or pharmacokinetics of drug d. Is the protein damage score that p encodes, p is a nonzero real number.
18. The method of claim 1,

    Wherein the drug score is calculated by the following equation, method for providing information for uterine contraction inhibitor selection using personal genomic sequence variation.

    [Equation 4]

    

    In Equation 4, Sd is the calculated drug score of drug d, n is the number of proteins encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of drug d, and gi is the one involved in the pharmacodynamics or pharmacokinetics of drug d. Protein damage score of the protein encoded by the above gene, wi is the weight given to the protein damage score gi of the protein encoded by one or more genes involved in the pharmacodynamics or pharmacokinetics of the drug d.
19. The method of claim 1,

    The protein damage score or drug score is calculated by weighting to a value determined in consideration of the type of the protein, the pharmacokinetic or pharmacokinetic classification of the protein, the pharmacokinetic parameters of the drug metabolizing enzyme, or the population or race distribution. Method for providing information for the selection of inhibitors of uterine contraction using phosphorus, individual genome sequence variation.
20. The method of claim 1,

    The method further includes determining whether or not to use the uterine contraction inhibitor applied to the individual using the individual drug score, providing information for selecting the uterine contraction inhibitor using a personal genomic sequence variation How to.
21. The method of claim 1,

    The method further includes the step of receiving the sequence information of the sequence of the gene involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitor to the computer system,

    The computer system provides information for selecting a uterine contraction inhibitor using personal genome sequence variation, wherein the computer system includes or has access to a database containing genetic information involved in the pharmacokinetics or pharmacokinetics of the uterine contraction inhibitor. Way.
22. The method according to any one of claims 1 to 21,

    Wherein the method is carried out for the purpose of preventing adverse effects of uterine contraction inhibitors, a method for providing information for the selection of uterine contraction inhibitors using personal genome sequence variation.
23. A database capable of searching or extracting information related to genes or proteins related to the uterine contraction inhibitors, which may be applied to an individual;

    A communication unit accessible to the database;

    A first calculation module configured to calculate one or more gene sequence variation information related to pharmacodynamics or pharmacokinetics of the uterine contraction inhibitor based on the information;

    A second calculation module for calculating an individual protein damage score using the gene sequence variation information;

    A third calculating module that calculates an individual drug score by associating the individual protein damage score with a correlation between a drug and a protein; And

    And a display unit for displaying the calculated value calculated by the calculation module.
24. The method of claim 23,

    The system further comprises a fourth calculation module for determining whether or not to use the uterine contraction inhibitor applied to the individual using the individual drug score calculated in the third calculation module, the uterus using a personal genome sequence variation Shrinkage Inhibitor Selection System.
25. The method of claim 23,

    The system further comprises a user interface for calculating and providing a personal drug score for the uterine contraction inhibitor according to the input of the uterine contraction inhibitor, the uterine contraction inhibitor selection system using a personal genome sequence variation.
26. The method of claim 23,

    The display unit further comprises a display unit for additionally displaying a value calculated by each calculation module, a calculation process, or the information on which the calculation is based, uterine contraction inhibitor selection system using a personal genome sequence variation.
27. The method according to any one of claims 23 to 26,

    Each calculation module stores the gene sequence variation information, protein damage score, drug score, and information on which the calculation is based.

    The information on each calculation module is updated according to the update of the database, the uterine contraction inhibitor selection system using a personal genome sequence variation.
28. A computer readable medium comprising an execution module for executing the processor:

    Obtaining one or more gene sequence variation information related to pharmacokinetics or pharmacokinetics of the uterine contraction inhibitor from the individual genome sequence information;

    Calculating an individual protein damage score using the gene sequence variation information; And

    Computing the individual drug score by associating the individual protein damage score with the correlation between the drug and the protein.
29. The method of claim 28,

    And determining, by the processor, whether or not to use a uterine contraction inhibitor applied to the individual by using the individual drug score.

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