Biomarkers for predicting drug efficacy, reagents for predicting drug efficacy, kits for predicting drug efficacy, methods for predicting drug efficacy, biomarkers for estimating tissue fluid recovery capacity, reagents for estimating tissue fluid recovery capacity, kits for estimating tissue fluid recovery capacity, methods for estimating tissue fluid recovery capacity

Abstract

To provide biomarkers for predicting drug efficacy, which can predict with excellent accuracy in advance the drug efficacy of a drug that eliminates retained body fluid attributed to organ failure.SOLUTION: There is provided a biomarker for predicting the efficacy of a drug that eliminates the body fluid retained in an organ, the biomarker for predicting a drug efficacy being at least one or more single nucleotide polymorphisms selected from the group consisting of a single nucleotide polymorphism identified by a particular rs number registered in the SNP database of the US Biotechnology Information Center and a single nucleotide polymorphism detected in a particular genomic region.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a biomarker for predicting drug efficacy, a reagent for predicting drug efficacy, a kit for predicting drug efficacy, a method for predicting drug efficacy, a biomarker for predicting tissue fluid recovery ability, a reagent for predicting tissue fluid recovery ability, a kit for predicting tissue fluid recovery ability, and a method for predicting tissue fluid recovery ability. [Background technology]

[0002] Tolvabtan (product name: Samsca, Otsuka Pharmaceutical Co., Ltd.) is known as a diuretic used for patients with cardiac edema, hepatic edema, autosomal dominant polycystic kidney disease, and the like. Tolvaptan is a V2 receptor antagonist that inhibits the reabsorption of water in the renal collecting duct by acting antagonistically against antidiuretic hormone (vasopressin) at the V2 receptor in the renal collecting duct. In other words, tolvaptan is a drug that promotes urinary excretion by suppressing the discharge of water from inside the renal collecting duct to the outside of the renal collecting duct, forcibly discharging accumulated body fluids from the body as urine, and relieves the symptoms of edema.

[0003] However, it is known that administration of tolvaptan is ineffective in some patients, and therefore, in clinical practice, a method for predicting the effectiveness of treatment in advance for patients with fluid retention is required (e.g., Non-Patent Document 1). Non-Patent Document 1 discloses that in patients with decompensated cirrhosis, when the hepatic venous pressure gradient (HVPG) value is relatively low, weight loss due to excretion of body fluids becomes significant, and the pharmacological efficacy of tolvaptan tends to be fully observed. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Ai Nakagawa, Masanori Atsukawa, Akihito Tsubota, Katsuhiko Iwakiri, et al:Usefulness of portal vein pressure for predicting the effects of tolvaptan in cirrhotic patients:World J Gastroenterol 2016 June 7;22(21):5104-5113 Summary of the Invention [Problem to be solved by the invention]

[0005] Drugs such as tolvabtan that drain fluid accumulation due to organ failure are expensive. In addition, the administration of tolvabtan requires hospitalization, which places a heavy burden on patients. Therefore, in clinical practice, there is a need to predict the efficacy of tolvabtan with high accuracy before starting treatment with tolvabtan. One aspect of the present invention provides a biomarker for predicting efficacy of a drug that can predict in advance with high accuracy the efficacy of a drug that expels accumulated body fluid caused by organ failure. [Means for solving the problem]

[0006] The inventors of the present invention conducted a genome-wide association study (GWAS) on a group of patients administered torvabtan and revealed that the efficacy of torvabtan is relatively reduced in patients with a specific group of single nucleotide polymorphisms (SNPs). In addition, the inventors of the present invention have revealed that among these SNPs are SNPs that affect the increase or decrease in the expression level of genes that are thought to contribute to the formation of vascular vessels such as lymphatic vessels and blood vessels during the developmental stage of organisms such as humans.

[0007] When vascular formation is relatively insufficient compared to healthy subjects, it becomes difficult for the vascular system to collect accumulated tissue fluid, and it is believed that the vascular system's ability to collect tissue fluid is relatively reduced. Therefore, it was suggested that in subjects in whom specific SNPs were detected, the vascular system's ability to transport tissue fluid to the kidney is reduced because the vascular system's ability to collect accumulated tissue fluid is insufficient. Therefore, it was predicted that patients in whom certain SNPs were detected would have a congenital low ability to recover tissue fluid through the vasculature, and would therefore have a tendency to have a constitution in which tissue fluid that should be recovered is not easily transported to the kidneys because tissue fluid that has accumulated due to organ failure is not easily recovered through the vasculature. Thus, the results of the GWAS suggested that certain SNPs are related to vascular formation and the ability of the vasculature to recover tissue fluid. The inventors of the present invention have identified a group of candidate single nucleotide polymorphisms that are genetic factors common to the prediction of drug efficacy against fluid retention and the prediction of fluid recovery ability by the vascular system, and have completed the present invention.

[0008] That is, the present invention has the following aspects. [1] A biomarker for predicting the efficacy of a drug to drain fluids accumulated in organs, which is at least one single nucleotide polymorphism selected from the group consisting of single nucleotide polymorphisms identified by the following rs numbers registered in the SNP database of the National Center for Biotechnology Information and single nucleotide polymorphisms detected in the following genomic regions: The rs numbers are rs77769596, rs116951693, rs199623596, rs141544115, rs78650683, rs61773260, rs16827413, rs3736931, rs77980226, rs1885744, rs68016527, rs10800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191498017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs548655480, rs9818982, rs73093151, rs28722753, rs190430047, rs146677134, rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs28724305, rs139082715, rs202039738, rs558310023, rs373193142, rs3108399, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs117565657, rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117952795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs28371759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, rs148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs6956740, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, rs150413109, rs149631544, rs188579453,rs138430375、rs146943578、rs117377672、rs4739103、rs76335247、rs139552329、rs143804156、rs2991364、rs9299186、rs4978937、rs143816412、rs16907787、rs150949929、rs141204966、rs150684513、rs143150122、rs190968040、rs79875703、rs78332808、rs151314241、rs74236102、rs141277618、rs12227766、rs17012289、rs73166235、rs73166243、rs56127528、rs17487644、rs11290772、rs145849286、rs139361259、rs141696046、rs147493082、rs149200875、rs7150903、rs79594269、rs140034212、rs186766499、rs11852611、rs118115242、rs6496797、rs4541027、rs285753、rs79740453、rs3760436、rs11361451、rs115523756、rs60619936、rs142571079、rs146099575、rs62103373、rs183728329、rs764818965、rs369720664、rs76726076、rs2071643、rs137967792、rs56689668、rs5201、rs5202、rs2070097、rs5904376、rs781940941、rs781941435、rs4898372、rs73627255、rs3761527、rs3761529、rs138461346、rs12559136、rs4898458、rs151275155、rs142251323、rs186986494、rs2872601、rs5945370、rs183746106、rs5945368、rs5987180、rs55702376、rs5987179、rs111399454、rs7886319、rs7052686、rs5945169、rs150224573、rs145658816、rs141108459、rs113549204、rs148460019、rs186585558, rs2071126, rs5945367 and rs782472424, respectively. The genomic regions are chr2:57009383, chr4:56445490, chr4:77343844, chr4:77985714, chr4:184681293, chr5:106033177, chr7:119762819, chr7:126519252, chr11:81185465, chr13:96979189, chr14:95984290 and chr22:29908953, which are the positions of the respective bases. [2] A biomarker for predicting drug efficacy described in [1], which is a single nucleotide polymorphism identified by at least one rs number selected from the group consisting of rs2991364, rs9299186, and rs4978937. [3] The biomarker for predicting drug efficacy described in [1] or [2], wherein the drug is a V2 receptor antagonist. [4] A reagent for predicting the efficacy of a drug that expels body fluids accumulated in an organ, the reagent comprising a nucleic acid specific to a genomic region in which the single nucleotide polymorphism, which is a biomarker for predicting efficacy according to any one of [1] to [3], is detected. [5] A kit for predicting the efficacy of a drug that expels body fluids accumulated in an organ, comprising the efficacy prediction reagent described in [4]. [6] A method for predicting the efficacy of a drug for expelling bodily fluids accumulated in an organ, comprising detecting a biomarker for predicting efficacy described in any one of [1] to [3] for a base sequence of a nucleic acid collected from a subject. [7] The method for predicting efficacy of a drug according to [6], further comprising, in addition to detecting the single nucleotide polymorphism, predicting efficacy of the drug based on the following clinical factors: Clinical factors: at least one selected from age, sex, height, weight, Child-Pugh classification, platelet count, albumin, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum urea nitrogen, creatinine, sodium, ammonia, and α-fetoprotein. [8] A biomarker for predicting the ability of vascular tissue fluid recovery, which is at least one single nucleotide polymorphism selected from the group consisting of single nucleotide polymorphisms identified by the following rs numbers registered in the SNP database of the National Center for Biotechnology Information and single nucleotide polymorphisms detected in the following genomic regions: The rs numbers are rs77769596, rs116951693, rs199623596, rs141544115, rs78650683, rs61773260, rs16827413, rs3736931, rs77980226, rs1885744, rs68016527, rs10800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191498017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs548655480, rs9818982, rs73093151, rs28722753, rs190430047, rs146677134, rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs28724305, rs139082715, rs202039738, rs558310023, rs373193142, rs3108399, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs117565657, rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117952795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs28371759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, rs148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs6956740, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, rs150413109, rs149631544, rs188579453,rs138430375、rs146943578、rs117377672、rs4739103、rs76335247、rs139552329、rs143804156、rs2991364、rs9299186、rs4978937、rs143816412、rs16907787、rs150949929、rs141204966、rs150684513、rs143150122、rs190968040、rs79875703、rs78332808、rs151314241、rs74236102、rs141277618、rs12227766、rs17012289、rs73166235、rs73166243、rs56127528、rs17487644、rs11290772、rs145849286、rs139361259、rs141696046、rs147493082、rs149200875、rs7150903、rs79594269、rs140034212、rs186766499、rs11852611、rs118115242、rs6496797、rs4541027、rs285753、rs79740453、rs3760436、rs11361451、rs115523756、rs60619936、rs142571079、rs146099575、rs62103373、rs183728329、rs764818965、rs369720664、rs76726076、rs2071643、rs137967792、rs56689668、rs5201、rs5202、rs2070097、rs5904376、rs781940941、rs781941435、rs4898372、rs73627255、rs3761527、rs3761529、rs138461346、rs12559136、rs4898458、rs151275155、rs142251323、rs186986494、rs2872601、rs5945370、rs183746106、rs5945368、rs5987180、rs55702376、rs5987179、rs111399454、rs7886319、rs7052686、rs5945169、rs150224573、rs145658816、rs141108459、rs113549204、rs148460019、rs186585558, rs2071126, rs5945367 and rs782472424, respectively. The genomic regions are biomarkers for predicting tissue fluid recovery ability, which are the positions of each base in chr2:57009383, chr4:56445490, chr4:77343844, chr4:77985714, chr4:184681293, chr5:106033177, chr7:119762819, chr7:126519252, chr11:81185465, chr13:96979189, chr14:95984290 and chr22:29908953. [9] A biomarker for predicting tissue fluid recovery ability described in [8], which is a single nucleotide polymorphism identified by at least one rs number selected from the group consisting of rs2991364, rs9299186, and rs4978937.

[10] The biomarker for predicting tissue fluid recovery ability described in [8] or [9], wherein the blood vessel is at least one selected from the group consisting of blood vessels connecting the liver and kidneys and lymphatic vessels connecting the liver and kidneys.

[11] A reagent for predicting the ability of blood vessels to recover tissue fluid, the reagent comprising a nucleic acid specific to a genomic region in which the single nucleotide polymorphism, which is a biomarker for predicting the ability to recover tissue fluid described in any one of [8] to

[10] , is detected.

[12] A kit for predicting the ability of blood vessels to recover tissue fluid, comprising the drug efficacy prediction reagent described in

[11] .

[13] A method for predicting the ability of blood vessels to recover tissue fluid, comprising detecting a biomarker for predicting the ability of blood vessels to recover tissue fluid described in any one of [8] to

[10] for a base sequence of nucleic acid collected from a subject.

[14] The method for predicting tissue fluid recovery ability described in

[13] , which predicts tissue fluid recovery ability by vascularization based on the following clinical factors in addition to detecting the single nucleotide polymorphism. Clinical factors: at least one selected from age, sex, height, weight, Child-Pugh classification, platelet count, albumin, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum urea nitrogen, creatinine, sodium, ammonia, and α-fetoprotein. Effect of the Invention

[0009] According to one aspect of the present invention, there is provided a biomarker for predicting efficacy of a drug that can predict in advance with high accuracy the efficacy of a drug that expels accumulated body fluid caused by organ failure. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram showing the signal transduction pathway of downstream genes of the SVEP1 gene. [Diagram 2] FIG. 1 is a schematic diagram showing the signal transduction pathway when gene expression of downstream genes of the SVEP1 gene is reduced. [Diagram 3] FIG. 1 shows an outline of the study design in the examples. [Figure 4] FIG. 1 is a Manhattan plot showing the results of the GWAS. [Diagram 5] FIG. 1 shows the results of searching for eQTL for rs2991364 using GTEx. [Figure 6] FIG. 1 shows the results of a search for gene expression levels of SVEP1 in the risk allele group of rs2991364 using the ICGC database. [Figure 7] FIG. 1 shows the results of a search for ITGA9 gene expression levels in the risk allele group of rs2991364 using the ICGC database. [Figure 8] FIG. 1 shows the results of a search for gene expression levels of Foxc2 in the risk allele group of rs2991364 using the ICGC database. [Figure 9] FIG. 1 shows the results of a search for Tie1 gene expression levels in the risk allele group of rs2991364 using the ICGC database. [Figure 10] FIG. 1 shows the results of a search for gene expression levels of Ang-2 in risk allele groups of rs2991364 using the ICGC database. [Figure 11] FIG. 1 is a schematic diagram for explaining a hypothesis regarding the relationship between the efficacy of a drug and the vascular network and vascular remodeling mechanism, which is assumed from the research results of the Examples. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] As used herein, the following terms have the meanings set forth in this paragraph. The term "specific nucleic acid" refers to a nucleic acid that, under normal formation conditions (preferably stringent conditions), forms a complementary pair with a genomic region in which a target single nucleotide polymorphism can be detected, and does not significantly cross-hybridize with other genomic regions. "Allele" refers to two sets of genomic sequences, one set inherited from each of the gametes (sperm or egg). Humans have a total of two sets of sequences (i.e., alleles) inherited from the father or mother on the same gene and the same genomic region. When both the father and mother have the same sequences, they are sometimes referred to as "homozygous," and when they have different sequences, they are sometimes referred to as "heterozygous." In this specification, an allele consisting of one or more base sequences (α) detected in a genomic region is described as "allele α". For example, when A (adenine) is detected as an allele in a specific genomic region, it is described as "allele A". Similarly, when T (thymine) is detected as an allele, it is described as "allele T", when G (guanine) is detected as an allele, it is described as "allele G", when C (cytosine) is detected as an allele, it is described as "allele C", and other bases are described in the same manner. When a base is missing in a specific genomic region, it is described as "allele-". A "risk allele" refers to an allele that increases the probability of contracting a disease or contracting a disease or showing symptoms among multiple types of single nucleotide polymorphisms detected in a genomic region. For example, among single nucleotide polymorphisms of allele A and allele G, if the risk of disease in a person carrying allele G is relatively higher (e.g., about twice as high) than that of a person carrying allele A, allele G is the risk allele, that is, the allele that increases the risk of disease. In addition, a single nucleotide polymorphism in which the nucleotide sequence 5'-..."AA"...-3' is frequently detected in a specific genomic region is referred to as an "allele AA." Other nucleotide sequences consisting of two or more nucleotides are also described in the same manner. "Vessels" are the metabolic transport pathways of organs and tissues. Blood vessels through which blood flows are called blood vessels, and lymphatic vessels through which lymph flows. "Vascular network" refers to a network of vascular lumens formed in a living body. "Vascular remodeling" refers to structural and functional changes in blood vessels caused by changes in the cells and extracellular matrix that compose the blood vessels, which occur in response to various stimuli to the blood vessels. In the present invention, structural and functional changes in blood vessels when constructing a vascular network in vascular neogenesis may be simply referred to as "vascular remodeling." Here, "vascular neogenesis" includes, for example, the formation of blood vessels during the vascular tree formation stage during ontogenesis; the formation of blood vessels for modifying circulatory hemodynamics in mature individuals; and the formation of blood vessels during the repair process after the occurrence of pathological inflammation and tissue damage. The terms "primer" and "probe" refer to nucleotides that form complementary pairs with either or both of DNA and RNA. "GWAS" stands for Genome Wide Association Study, also known as genome-wide association analysis. "SNP" is an abbreviation for Single Nucleotide Polymorphism, and is referred to as "single nucleotide polymorphism" in this specification. A collective term for multiple SNPs may be referred to as "SNPs." "Subject" means a human subject of study. The symbol "~" indicating a range of numerical values ​​means that the numerical values ​​before and after it are included as the lower limit and upper limit.

[0012] <Biomarkers for predicting drug efficacy> The biomarker for predicting a drug efficacy of the present invention is a biomarker for predicting the drug efficacy of a drug that expels body fluids accumulated in an organ. As a drug (hereinafter referred to as "drug") that discharges body fluids accumulated in organs, for example, a V2 receptor antagonist is preferable. As an example of a V2 receptor antagonist, tolvaptan (product name "Samsca", Otsuka Pharmaceutical Co., Ltd.) can be mentioned. Tolvaptan is a drug that forcibly discharges ascites, which is body fluids accumulated in the liver. However, in the present invention, the drug is not limited to tolvabtan. It is considered that the efficacy of a drug can be predicted by the biomarker for predicting drug efficacy of the present invention as long as the drug is administered to a patient with abnormal retention of body fluids and tissue fluids caused by various diseases such as heart disease, or various edema as hypothesized in the Examples described below.

[0013] The biomarker for predicting drug efficacy of the present invention is at least one single nucleotide polymorphism selected from the group consisting of single nucleotide polymorphisms identified by the following rs numbers registered in the SNP database of the National Center for Biotechnology Information and single nucleotide polymorphisms detected in the following genomic regions:

[0014] "rs number" rs77769596、rs116951693、rs199623596、rs141544115、rs78650683、rs61773260、rs16827413、rs3736931、rs77980226、rs1885744、rs68016527、rs10800602、rs4915165、rs149057569、rs78506769、rs144622901、rs191498017、rs117878160、rs2861960、rs6738731、rs139777869、rs7606583、rs548655480、rs9818982、rs73093151、rs28722753、rs190430047、rs146677134、rs147398967、rs201727204、rs146161796、rs146974727、rs76934337、rs28724305、rs139082715、rs202039738、rs558310023、rs373193142、rs3108399、rs146184416、rs149351859、rs117186452、rs192877030、rs368145706、rs142073533、rs144330279、rs185364332、rs78306822、rs73746613、rs117565657、rs142078510、rs536782924、rs13358143、rs117217176、rs145513135、rs138786151、rs75479599、rs138025072、rs73416655、rs9358200、rs117952795、rs187188904、rs17279589、rs116255027、rs41279866、rs2242480、rs28371759、rs34784390、rs12721627、rs4646474、rs368125720、rs200112828、rs148391916、rs57649937、rs77535773、rs149607532、rs76148287、rs6956740、rs140143507、rs144155862、rs190856909、rs142154029、rs138882192、rs150413109、rs149631544、rs188579453、rs138430375、rs146943578、rs117377672、rs4739103、rs76335247、rs139552329、rs143804156、rs2991364、rs9299186、rs4978937、rs143816412、rs16907787、rs150949929、rs141204966、rs150684513、rs143150122、rs190968040、rs79875703、rs78332808、rs151314241、rs74236102、rs141277618、rs12227766、rs17012289、rs73166235、rs73166243、rs56127528、rs17487644、rs11290772、rs145849286、rs139361259、rs141696046、rs147493082、rs149200875、rs7150903、rs79594269、rs140034212、rs186766499、rs11852611、rs118115242、rs6496797、rs4541027、rs285753、rs79740453、rs3760436、rs11361451、rs115523756、rs60619936、rs142571079、rs146099575、rs62103373、rs183728329、rs764818965、rs369720664、rs76726076、rs2071643、rs137967792、rs56689668、rs5201、rs5202、rs2070097、rs5904376、rs781940941、rs781941435、rs4898372、rs73627255、rs3761527、rs3761529、rs138461346、rs12559136、rs4898458、rs151275155、rs142251323、rs186986494、rs2872601、rs5945370、rs183746106、rs5945368、rs5987180、rs55702376、rs5987179、rs111399454、rs7886319、rs7052686、rs5945169、rs150224573、rs145658816、rs141108459、rs113549204、rs148460019、rs186585558、rs2071126, rs5945367, rs782472424. ,

[0015] "Genomic region" chr2:57009383, chr4:56445490, chr4:77343844, chr4:77985714, chr4:184681293, chr5:106033177, chr7:119762819, chr7:126519252, chr11:81185465, chr13:96979189, chr14:95984290, chr22:29908953. Here, for example, "chr2:57009383" means the position of the 57009383rd base on the second chromosome.

[0016] The single nucleotide polymorphisms detected in these genomic regions are as follows: chr2:57009383:C>CT, chr4:56445490:T>TTTTG, chr4:77343844:GCCCCT>G, chr4:77985714:T>TA, chr4:184681293:T>TA, chr5:106033177:T>TA, chr 7:119762819:CACACACACAT>C, chr7:126519252:GA>G, chr11:81185465:T>C, chr13:96979189:A>G, chr14:95984290:GTGTA>G, chr22:29908953:A>AG. Here, for example, "chr2:57009383:C>CT" means a C>CT polymorphism at the base located at position 57009383 of chromosome 2.

[0017] Single nucleotide polymorphisms constituting the biomarkers for predicting drug efficacy of the present invention are shown in Tables 1 to 6. That is, the biomarkers for predicting drug efficacy of the present invention are at least one or more single nucleotide polymorphisms selected from the group consisting of the following single nucleotide polymorphisms (1) to (192).

[0018] [Table 1]

[0019] [Table 2]

[0020] [Table 3]

[0021] [Table 4]

[0022] [Table 5]

[0023] [Table 6]

[0024] (1): A single nucleotide polymorphism identified by rs77769596, in which allele A is detected more frequently than allele C and allele G. (2): A single nucleotide polymorphism identified by rs116951693, in which allele T is detected more frequently than allele C. (3): A single nucleotide polymorphism identified by rs199623596, which is a deletion-type single nucleotide polymorphism in which allele A is detected more frequently than allele AGA. (4): A single nucleotide polymorphism identified by rs141544115, in which allele A is detected more frequently than allele G. (5): A single nucleotide polymorphism identified by rs78650683, in which allele C is detected more frequently than allele A and allele G. (6): A single nucleotide polymorphism identified by rs61773260, in which allele T is detected more frequently than allele C. (7): A single nucleotide polymorphism identified by rs16827413, in which allele C is detected more frequently than allele T. (8): A single nucleotide polymorphism identified by rs3736931, in which allele A is detected more frequently than allele G. (9): A single nucleotide polymorphism identified by rs77980226, in which allele C is detected more frequently than allele T. (10): A single nucleotide polymorphism identified by rs1885744, in which allele C is detected more frequently than allele G. (11): A single nucleotide polymorphism identified by rs68016527, which is an insertion type single nucleotide polymorphism in which the CCC allele is detected more frequently than the CC allele. (12): A single nucleotide polymorphism identified by rs10800602, in which allele A is detected more frequently than allele G. (13): A single nucleotide polymorphism identified by rs4915165, in which allele T is detected more frequently than allele C. (14): A single nucleotide polymorphism identified by rs149057569, in which allele C is detected more frequently than allele T. (15): A single nucleotide polymorphism identified by rs78506769, in which allele G is detected more frequently than allele C. (16): A single nucleotide polymorphism detected in the genomic region “chr2:57009383”, in which allele C is detected more frequently than allele CT, resulting in a deletion type single nucleotide polymorphism. (17): A single nucleotide polymorphism identified by rs144622901, in which allele C is detected more frequently than allele T. (18): A single nucleotide polymorphism identified by rs191498017, in which the G allele is detected more frequently than the T allele. (19): A single nucleotide polymorphism identified by rs117878160, in which allele G is detected more frequently than allele A. (20): A single nucleotide polymorphism identified by rs2861960, in which allele C is detected more frequently than allele T.

[0025] (21): A single nucleotide polymorphism identified by rs6738731, in which allele C is detected more frequently than allele T. (22): A single nucleotide polymorphism identified by rs139777869, in which allele T is detected more frequently than allele C. (23): A single nucleotide polymorphism identified by rs7606583, in which allele C is detected more frequently than allele T. (24): A single nucleotide polymorphism identified by rs548655480, in which allele C is detected more frequently than allele G. (25): A single nucleotide polymorphism identified by rs9818982, in which allele G is detected more frequently than allele A. (26): A single nucleotide polymorphism identified by rs73093151, in which allele C is detected more frequently than allele T. (27): A single nucleotide polymorphism identified by rs28722753, in which allele C is detected more frequently than allele A and allele T. (28): A single nucleotide polymorphism identified by rs190430047, in which allele T is detected more frequently than allele C. (29): A single nucleotide polymorphism identified by rs146677134, in which allele A is detected more frequently than allele G and allele T. (30): A single nucleotide polymorphism identified by rs147398967, in which allele A is detected more frequently than allele T. (31): A single nucleotide polymorphism identified by rs201727204, which is an insertion type single nucleotide polymorphism in which the allele ATAATAAAT is detected more frequently than the allele ATAAAT. (32): A single nucleotide polymorphism identified by rs146161796, in which allele A is detected more frequently than allele G. (33): A single nucleotide polymorphism identified by rs146974727, in which allele T is detected more frequently than allele A. (34): A single nucleotide polymorphism identified by rs76934337, in which allele A is detected more frequently than allele C. (35): A single nucleotide polymorphism identified by rs28724305, in which allele G is detected more frequently than allele A. (36): A single nucleotide polymorphism identified by rs139082715, in which allele C is detected more frequently than allele G. (37): A single nucleotide polymorphism identified by rs202039738, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TT allele. (38): A single nucleotide polymorphism detected in the genomic region “chr4:56445490”, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TTTTG allele. (39): A single nucleotide polymorphism detected in the genomic region “chr4:77343844”, in which the allele GCCCTCT is detected more frequently than the allele G. (40): A single nucleotide polymorphism detected in the genomic region “chr4:77985714”, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TA allele.

[0026] (41): A single nucleotide polymorphism identified by rs558310023, in which allele A is detected more frequently than allele G. (42): A single nucleotide polymorphism identified by rs373193142, which is an insertion type single nucleotide polymorphism in which the ATAGATA allele is detected more frequently than the ATA allele. (43): A single nucleotide polymorphism identified by rs3108399, in which allele A is detected more frequently than allele G. (44): A single nucleotide polymorphism identified by rs146184416, which is an insertion type single nucleotide polymorphism in which the allele CTTACCTTACCT is detected more frequently than the allele CTTACCT. (45): A single nucleotide polymorphism identified by rs149351859, in which allele T is detected more frequently than allele C. (46): A single nucleotide polymorphism identified by rs117186452, in which the T allele is detected more frequently than the G allele. (47): A single nucleotide polymorphism identified by rs192877030, in which allele G is detected more frequently than allele A. (48): A single nucleotide polymorphism identified by rs368145706, which is an insertion type single nucleotide polymorphism in which the allele GG is detected more frequently than the allele G. (49): A single nucleotide polymorphism identified by rs142073533, in which allele A is detected more frequently than allele G. (50): A single nucleotide polymorphism identified by rs144330279, in which allele G is detected more frequently than allele A. (51): A single nucleotide polymorphism detected in the genomic region “chr4:184681293”, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TA allele. (52): A single nucleotide polymorphism identified by rs185364332, in which allele G is detected more frequently than allele A. (53): A single nucleotide polymorphism identified by rs78306822, in which allele C is detected more frequently than allele T. (54): A single nucleotide polymorphism identified by rs73746613, in which allele T is detected more frequently than allele C. (55): A single nucleotide polymorphism identified by rs117565657, in which allele T is detected more frequently than allele C. (56): A single nucleotide polymorphism identified by rs142078510, in which allele G is detected more frequently than allele A. (57): A single nucleotide polymorphism identified by rs536782924, in which allele A is detected more frequently than allele G. (58): A single nucleotide polymorphism identified by rs13358143, in which the G allele is detected more frequently than the T allele. (59): A single nucleotide polymorphism identified by rs117217176, in which allele C is detected more frequently than allele T. (60): A single nucleotide polymorphism detected in the genomic region “chr5:106033177”. This is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TA allele.

[0027] (61): A single nucleotide polymorphism identified by rs145513135, in which allele C is detected more frequently than allele T. (62): A single nucleotide polymorphism identified by rs138786151, in which allele T is detected more frequently than allele C. (63): A single nucleotide polymorphism identified by rs75479599, in which allele A is detected more frequently than allele G. (64): A single nucleotide polymorphism identified by rs138025072, in which allele G is detected more frequently than allele A. (65): A single nucleotide polymorphism identified by rs73416655, in which allele A is detected more frequently than allele G and allele T. (66): A single nucleotide polymorphism identified by rs9358200, in which allele A is detected more frequently than allele G. (67): A single nucleotide polymorphism identified by rs117952795, in which allele C is detected more frequently than allele T. (68): A single nucleotide polymorphism identified by rs187188904, in which allele A is detected more frequently than allele T. (69): A single nucleotide polymorphism identified by rs17279589, in which allele A is detected more frequently than allele G. (70): A single nucleotide polymorphism identified by rs116255027, in which allele T is detected more frequently than allele C. (71): A single nucleotide polymorphism identified by rs41279866, in which allele C is detected more frequently than allele T. (72): A single nucleotide polymorphism identified by rs2242480, in which allele C is detected more frequently than allele T. (73): A single nucleotide polymorphism identified by rs28371759, in which allele A is detected more frequently than allele G. (74): A single nucleotide polymorphism identified by rs34784390, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TT allele. (75): A single nucleotide polymorphism identified by rs12721627, in which allele G is detected more frequently than allele C. (76): A single nucleotide polymorphism identified by rs4646474, in which allele C is detected more frequently than allele T. (77): A single nucleotide polymorphism identified by rs368125720, which is a deletion-type single nucleotide polymorphism in which the - allele is detected more frequently than the TCT allele. (78): A single nucleotide polymorphism identified by rs200112828, which is a deletion-type single nucleotide polymorphism in which the - allele is detected more frequently than the T allele. (79): A single nucleotide polymorphism identified by rs148391916, in which allele T is detected more frequently than allele A and allele C. (80): A single nucleotide polymorphism identified by rs57649937, which is an insertion type single nucleotide polymorphism in which allele A is detected more frequently than allele -.

[0028] (81): A single nucleotide polymorphism identified by rs77535773, in which allele G is detected more frequently than allele C. (82): A single nucleotide polymorphism identified by rs149607532, in which allele T is detected more frequently than allele C. (83): A single nucleotide polymorphism identified by rs76148287, in which allele G is detected more frequently than allele A. (84): A single nucleotide polymorphism identified by rs6956740, in which allele C is detected more frequently than allele A, allele G, and allele T. (85): A single nucleotide polymorphism detected in the genomic region “chr7:119762819”, which is an insertion type single nucleotide polymorphism in which the allele CACACACACAT is detected more frequently than the allele C. (86): A single nucleotide polymorphism identified by rs140143507, in which allele T is detected more frequently than allele A. (87): A single nucleotide polymorphism identified by rs144155862, in which allele A is detected more frequently than allele G. (88): A single nucleotide polymorphism identified by rs190856909, in which allele C is detected more frequently than allele A and allele G. (89): A single nucleotide polymorphism identified by rs142154029, in which allele T is detected more frequently than allele C. (90): A single nucleotide polymorphism identified by rs138882192, in which allele C is detected more frequently than allele T. (91): A single nucleotide polymorphism detected in the genomic region “chr7:126519252”. This is an insertion type single nucleotide polymorphism in which the GA allele is detected more frequently than the G allele. (92): A single nucleotide polymorphism identified by rs150413109, in which allele C is detected more frequently than allele T. (93): A single nucleotide polymorphism identified by rs149631544, in which allele T is detected more frequently than allele C. (94): A single nucleotide polymorphism identified by rs188579453, in which allele G is detected more frequently than allele C and allele T. (95): A single nucleotide polymorphism identified by rs138430375, in which allele A is detected more frequently than allele C. (96): A single nucleotide polymorphism identified by rs146943578, in which allele A is detected more frequently than allele C, allele G, and allele T. (97): A single nucleotide polymorphism identified by rs117377672, in which allele A is detected more frequently than allele G. (98): A single nucleotide polymorphism identified by rs4739103, in which allele C is detected more frequently than allele T. (99): A single nucleotide polymorphism identified by rs76335247, in which allele G is detected more frequently than allele A and allele C. (100): A single nucleotide polymorphism identified by rs139552329, in which allele G is detected more frequently than allele A.

[0029] (101): A single nucleotide polymorphism identified by rs143804156, in which allele C is detected more frequently than allele T. (102): A single nucleotide polymorphism identified by rs2991364, in which allele T is detected more frequently than allele C. (103): A single nucleotide polymorphism identified by rs9299186, in which allele C is detected more frequently than allele T. (104): A single nucleotide polymorphism identified by rs4978937, in which allele C is detected more frequently than allele T. (105): A single nucleotide polymorphism identified by rs143816412, in which the T allele is detected more frequently than the G allele. (106): A single nucleotide polymorphism identified by rs16907787, in which allele C is detected more frequently than allele T. (107): A single nucleotide polymorphism identified by rs150949929, in which allele T is detected more frequently than allele C. (108): A single nucleotide polymorphism identified by rs141204966, in which allele G is detected more frequently than allele A and allele T. (109): A single nucleotide polymorphism identified by rs150684513, in which allele C is detected more frequently than allele A. (110): A single nucleotide polymorphism identified by rs143150122, in which allele C is detected more frequently than allele A and allele T. (111): A single nucleotide polymorphism identified by rs190968040, in which allele T is detected more frequently than allele C. (112): A single nucleotide polymorphism identified by rs79875703, in which the T allele is detected more frequently than the G allele. (113): A single nucleotide polymorphism detected in the genomic region “chr11:81185465”, in which the T allele is detected more frequently than the C allele. (114): A single nucleotide polymorphism identified by rs78332808, in which the T allele is detected more frequently than the G allele. (115): A single nucleotide polymorphism identified by rs151314241, in which allele A is detected more frequently than allele G. (116): A single nucleotide polymorphism identified by rs74236102, in which allele C is detected more frequently than allele T. (117): A single nucleotide polymorphism identified by rs141277618, which is a deletion-type single nucleotide polymorphism in which the - allele is detected more frequently than the C allele. (118): A single nucleotide polymorphism identified by rs12227766, in which allele T is detected more frequently than allele C. (119): A single nucleotide polymorphism identified by rs17012289, in which allele A is detected more frequently than allele T. (120): A single nucleotide polymorphism identified by rs73166235, in which allele G is detected more frequently than allele A.

[0030] (121): A single nucleotide polymorphism identified by rs73166243, in which allele C is detected more frequently than allele T. (122): A single nucleotide polymorphism identified by rs56127528, in which allele T is detected more frequently than allele C. (123): A single nucleotide polymorphism identified by rs17487644, in which allele T is detected more frequently than allele C. (124): A single nucleotide polymorphism identified by rs11290772, which is an insertion type single nucleotide polymorphism in which the CCC allele is detected more frequently than the CC allele. (125): A single nucleotide polymorphism identified by rs145849286, in which allele T is detected more frequently than allele A. (126): A single nucleotide polymorphism identified by rs139361259, in which the T allele is detected more frequently than the G allele. (127): A single nucleotide polymorphism identified by rs141696046, in which allele A is detected more frequently than allele G. (128): A single nucleotide polymorphism detected in the genomic region “chr13:96979189”, in which allele A is detected more frequently than allele G. (129): A single nucleotide polymorphism identified by rs147493082, in which allele G is detected more frequently than allele A. (130): A single nucleotide polymorphism identified by rs149200875, in which allele A is detected more frequently than allele G. (131): A single nucleotide polymorphism identified by rs7150903, in which allele A is detected more frequently than allele G. (132): A single nucleotide polymorphism identified by rs79594269, in which allele C is detected more frequently than allele T. (133): A single nucleotide polymorphism identified by rs140034212, in which allele G is detected more frequently than allele A and allele C. (134): A single nucleotide polymorphism identified by rs186766499, in which allele G is detected more frequently than allele A. (135): A single nucleotide polymorphism detected in the genomic region “chr14:95984290”. This is an insertion type single nucleotide polymorphism in which the allele GTGTA is detected more frequently than the allele G. (136): A single nucleotide polymorphism identified by rs11852611, in which allele A is detected more frequently than allele G. (137): A single nucleotide polymorphism identified by rs118115242, in which allele G is detected more frequently than allele A. (138): A single nucleotide polymorphism identified by rs6496797, in which allele T is detected more frequently than allele C and allele G. (139): A single nucleotide polymorphism identified by rs4541027, in which allele C is detected more frequently than allele G. (140): A single nucleotide polymorphism identified by rs285753, in which allele G is detected more frequently than allele A.

[0031] (141): A single nucleotide polymorphism identified by rs79740453, in which allele C is detected more frequently than allele T. (142): A single nucleotide polymorphism identified by rs3760436, in which allele C is detected more frequently than allele T. (143): A single nucleotide polymorphism identified by rs11361451, which is an insertion type single nucleotide polymorphism in which allele C is detected more frequently than allele -. (144): A single nucleotide polymorphism identified by rs115523756, in which allele T is detected more frequently than allele C. (145): A single nucleotide polymorphism identified by rs60619936, which is an insertion type single nucleotide polymorphism in which allele C is detected more frequently than allele -. (146): A single nucleotide polymorphism identified by rs142571079, in which allele T is detected more frequently than allele C. (147): A single nucleotide polymorphism identified by rs146099575, in which allele A is detected more frequently than allele G. (148): A single nucleotide polymorphism identified by rs62103373, in which allele T is detected more frequently than allele C. (149): A single nucleotide polymorphism identified by rs183728329, in which allele G is detected more frequently than allele A. (150): A single nucleotide polymorphism detected in the genomic region “chr22:29908953”, which is a deletion-type single nucleotide polymorphism in which allele A is detected more frequently than allele AG. (151):A single nucleotide polymorphism identified by rs764818965. (152):A single nucleotide polymorphism identified by rs369720664. (153): A single nucleotide polymorphism identified by rs76726076, in which allele G is detected more frequently than allele A, allele C, and allele T. (154): A single nucleotide polymorphism identified by rs2071643, in which allele C is detected more frequently than allele T. (155): A single nucleotide polymorphism identified by rs137967792, in which allele C is detected more frequently than allele T. (156): A single nucleotide polymorphism identified by rs56689668, in which the T allele is detected more frequently than the G allele. (157): A single nucleotide polymorphism identified by rs5201, in which allele A is detected more frequently than allele G. (158): A single nucleotide polymorphism identified by rs5202, in which allele C is detected more frequently than allele T. (159): A single nucleotide polymorphism identified by rs2070097, in which allele A is detected more frequently than allele G. (160): A single nucleotide polymorphism identified by rs5904376, which is a deletion-type single nucleotide polymorphism in which the CCC allele is detected more frequently than the CCCC allele.

[0032] (161): A single nucleotide polymorphism identified by rs781940941, in which allele C is detected more frequently than allele A, allele G, and allele T. (162): A single nucleotide polymorphism identified by rs781941435, in which the T allele is detected more frequently than the G allele. (163): A single nucleotide polymorphism identified by rs4898372, in which allele A is detected more frequently than allele C and allele G. (164): A single nucleotide polymorphism identified by rs73627255, in which allele G is detected more frequently than allele A. (165): A single nucleotide polymorphism identified by rs3761527, in which allele T is detected more frequently than allele C. (166): A single nucleotide polymorphism identified by rs3761529, in which allele G is detected more frequently than allele A. (167): A single nucleotide polymorphism identified by rs138461346, in which allele G is detected more frequently than allele A. (168): A single nucleotide polymorphism identified by rs12559136, in which allele G is detected more frequently than allele A. (169): A single nucleotide polymorphism identified by rs4898458, in which allele A is detected more frequently than allele G. (170): A single nucleotide polymorphism identified by rs151275155, in which allele C is detected more frequently than allele T. (171): A single nucleotide polymorphism identified by rs142251323, in which allele G is detected more frequently than allele A. (172): A single nucleotide polymorphism identified by rs186986494, in which allele A is detected more frequently than allele C. (173): A single nucleotide polymorphism identified by rs2872601, in which allele G is detected more frequently than allele A. (174): A single nucleotide polymorphism identified by rs5945370, in which allele C is detected more frequently than allele T. (175): A single nucleotide polymorphism identified by rs183746106, in which allele G is detected more frequently than allele A. (176): A single nucleotide polymorphism identified by rs5945368, in which allele C is detected more frequently than allele G and allele T. (177): A single nucleotide polymorphism identified by rs5987180, in which allele C is detected more frequently than allele T. (178): A single nucleotide polymorphism identified by rs55702376, in which allele T is detected more frequently than allele C. (179): A single nucleotide polymorphism identified by rs5987179, in which allele A is detected more frequently than allele G and allele T. (180): A single nucleotide polymorphism identified by rs111399454, in which allele G is detected more frequently than allele A.

[0033] (181): A single nucleotide polymorphism identified by rs7886319, in which allele A is detected more frequently than allele C, allele G, and allele T. (182): A single nucleotide polymorphism identified by rs7052686, in which the G allele is detected more frequently than the T allele. (183): A single nucleotide polymorphism identified by rs5945169, in which allele A is detected more frequently than allele C. (184): A single nucleotide polymorphism identified by rs150224573, in which allele G is detected more frequently than allele A. (185): A single nucleotide polymorphism identified by rs145658816, in which allele A is detected more frequently than allele C. (186): A single nucleotide polymorphism identified by rs141108459, in which allele T is detected more frequently than allele C. (187): A single nucleotide polymorphism identified by rs113549204, in which allele A is detected more frequently than allele G. (188): A single nucleotide polymorphism identified by rs148460019, in which allele A is detected more frequently than allele T. (189): A single nucleotide polymorphism identified by rs186585558, in which allele G is detected more frequently than allele A. (190): A single nucleotide polymorphism identified by rs2071126, in which allele G is detected more frequently than allele A. (191): A single nucleotide polymorphism identified by rs5945367, in which allele C is detected more frequently than allele T. (192): This is a single nucleotide polymorphism identified by rs782472424, which is a deletion-type single nucleotide polymorphism in which the - allele is detected more frequently than the A allele.

[0034] The biomarker for predicting drug efficacy of the present invention is at least one single nucleotide polymorphism or a combination of at least two or more single nucleotide polymorphisms among (1) to (192). When the biomarker for predicting drug efficacy of the present invention is a combination of at least two or more single nucleotide polymorphisms among (1) to (192), it is expected that the drug efficacy can be predicted in advance with even greater accuracy.

[0035] In particular, as the biomarker for predicting drug efficacy of the present invention, among the single nucleotide polymorphisms (1) to (192), at least one selected from the group consisting of (102), (103), and (104) is preferred because the inventors have made progress in analyzing experimental data. That is, as the biomarker for predicting drug efficacy of the present invention, a single nucleotide polymorphism specified by at least one or more rs numbers selected from the group consisting of rs2991364, rs9299186, and rs4978937 is preferred, and a single nucleotide polymorphism specified by rs2991364 is more preferred.

[0036] The biomarker for predicting drug efficacy of the present invention is a biomarker used to predict whether or not the efficacy of a drug is lower than the originally expected level by using the detection of the above-mentioned single nucleotide polymorphisms (1) to (192) as an index. Therefore, in subjects who have alleles corresponding to the above-mentioned single nucleotide polymorphisms (1) to (192), it is predicted that the drug efficacy is so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected. Here, "having an allele corresponding to a single nucleotide polymorphism of (1) to (192)" means, for example, that when the single nucleotide polymorphism is (102), the allele is T on a predetermined genome region. For example, when the single nucleotide polymorphism is rs2991364 of (102), the allele is C or T, and the target allele is T. Therefore, when the genotype is in the order of T / T>T / C>C / C, it is predicted that the drug efficacy is so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected in the subject. In this case, the subject shows a genotype having the allele T of rs299136. The same applies to other single nucleotide polymorphisms. Therefore, in subjects with a genotype having an allele corresponding to the above-mentioned single nucleotide polymorphisms (1) to (192), it is predicted that the efficacy of the drug will be so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected.

[0037] (Mechanism of action) The biomarkers for predicting drug efficacy of the present invention described above are single nucleotide polymorphisms that were detected at significantly higher frequencies in a patient group in which a drug was administered and the degree of efficacy was confirmed as a result of performing GWAS on a patient group in which the drug efficacy was relatively insufficient, as described in the Examples below. Therefore, when the biomarkers for predicting drug efficacy that are single nucleotide polymorphisms are detected in nucleic acids as samples collected from subjects, it can be predicted in advance with high accuracy that the drug efficacy is so insufficient that improvement of fluid retention due to organ failure is unlikely to be achieved.

[0038] (Application) The biomarker for predicting drug efficacy of the present invention can be applied to, for example, prior prediction of the efficacy of V2 receptor antagonists such as vasopressin. The biomarker for predicting drug efficacy of the present invention can be applied to, for example, the reagent for predicting drug efficacy, the kit for predicting drug efficacy, and the method for predicting drug efficacy of the present invention described below. The detailed uses of the biomarker for predicting drug efficacy of the present invention will be described in detail in the sections <Reagent for predicting drug efficacy>, <Kit for predicting drug efficacy>, and <Method for predicting drug efficacy> described below. Specific examples of the edema to which the present invention is applied include generalized edema and localized edema. The cause of edema is not particularly limited. Examples of generalized edema include renal edema, cardiac edema, hepatic edema, nutritional edema, drug-induced edema, edema due to endocrine disease, and idiopathic edema. Examples of localized edema include venous edema, lymphatic edema, inflammatory edema, and angioneurotic edema. However, specific examples of edema are not limited to these examples.

[0039] <Reagents for predicting drug efficacy> The drug efficacy prediction reagent of the present invention is a reagent for predicting the efficacy of a drug that discharges body fluids accumulated in an organ. The drug efficacy prediction reagent of the present invention contains a nucleic acid (A) specific to a genomic region in which a single nucleotide polymorphism, which is the above-mentioned biomarker for drug efficacy prediction of the present invention, is detected. The drug efficacy prediction reagent of the present invention may further contain any component other than the nucleic acid (A).

[0040] The nucleic acid (A) is a reagent for predicting a drug efficacy of the present invention that specifically binds to a genomic region in which a single nucleotide polymorphism, which is the above-mentioned biomarker for predicting a drug efficacy of the present invention, is detected. The nucleic acid (A) can also be said to be the above-mentioned primer and probe. That is, the nucleic acid (A) specifically forms a complementary pair with a genomic region where a biomarker for predicting a drug efficacy can be detected. The nucleic acid (A) has a sequence complementary to a genomic region where a biomarker for predicting drug efficacy can be detected. However, the nucleic acid (A) may have some mismatches as long as a specific complementary pair can be formed. The degree of mismatch may be one to several, one to five, or one to three.

[0041] The base length of the nucleic acid (A) is not particularly limited as long as it can specifically form a complementary pair with a genome region where a biomarker for predicting drug efficacy can be detected. For example, in the case of a probe, it may be 15 to 150 bases long, or 16 to 100 bases long. For example, in the case of a primer, it may be 10 to 70 bases long, 15 to 60 bases long, or 20 to 50 bases long. The nucleic acid (A) can be synthesized, for example, by the phosphodiester method. For the design, synthesis, etc. of nucleic acid (A), reference can be made to books such as Molecular Cloning, Third Edition, Cold Spring Harbor Laboratory Press, New York.

[0042] The nucleic acid (A) may be labeled in advance with a labeling substance. By labeling with the labeling substance, for example, the biomarker for predicting a drug efficacy of the present invention may be detected using the amount of label in a PCR amplification product as an index. As the labeling substance, for example, those exemplified in paragraph 0021 of WO 2017 / 191720 can be used. As a labeling method, for example, the methods exemplified in paragraph 0020 of WO 2017 / 191720 can be used.

[0043] The optional components in the drug efficacy prediction reagent are not particularly limited, and may be, for example, components that contribute to improving the storage state of the nucleic acid (A).

[0044] The drug efficacy prediction reagent of the present invention contains a nucleic acid specific to a genomic region in which a single nucleotide polymorphism, which is a biomarker for predicting drug efficacy of the present invention, is detected. Therefore, a specific complementary pair can be formed with a genomic region containing the above-mentioned biomarker for predicting drug efficacy on a genomic region in a sample collected from a subject. Therefore, for example, by detecting a single nucleotide polymorphism by the detection method described in the section <Drug efficacy prediction method> below, it is possible to determine whether or not a subject shows a genotype having an allele corresponding to the single nucleotide polymorphism. As a result, it is possible to predict with high accuracy in advance that the drug efficacy is so insufficient that improvement of fluid retention due to organ failure is difficult to expect.

[0045] <Drug efficacy prediction kit> The drug efficacy prediction kit of the present invention is a kit for predicting the drug efficacy of a drug that expels body fluids accumulated in an organ. The drug efficacy prediction kit of the present invention includes the above-mentioned drug efficacy prediction reagent of the present invention.

[0046] The kit for predicting drug efficacy of the present invention may include the nucleic acid (A) described above in the section <Reagent for predicting drug efficacy>. The details and preferred aspects of the nucleic acid (A) may be the same as those described above in the section <Reagent for predicting drug efficacy>. The drug efficacy prediction kit of the present invention may further comprise reagents such as DNA polymerase, restriction enzymes, buffer solutions, and color-developing reagents which are necessary when using the nucleic acid (A) as a primer and a probe, and may further comprise containers, collection tools, and the like, and may further comprise an instruction manual.

[0047] The drug efficacy prediction kit of the present invention includes a drug efficacy prediction reagent for detecting the drug efficacy prediction biomarker of the present invention, and therefore, the drug efficacy prediction kit of the present invention can predict in advance with high accuracy that the drug efficacy will be so insufficient that improvement of fluid retention caused by organ failure is unlikely.

[0048] <Drug efficacy prediction method> The method for predicting a drug's efficacy of the present invention is a method for predicting the drug's efficacy for expelling body fluids accumulated in organs. In the method for predicting a drug efficacy of the present invention, the biomarker for predicting a drug efficacy of the present invention is detected in the base sequence of a nucleic acid collected from a subject. In the method for predicting a drug efficacy of the present invention, when an allele corresponding to the single nucleotide polymorphisms (1) to (192) described above is detected, the drug efficacy is predicted to be lower than that originally expected. That is, in a subject having an allele corresponding to the single nucleotide polymorphisms (1) to (192) described above, the drug efficacy is predicted to be so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected.

[0049] "Detecting a biomarker for predicting drug efficacy" means detecting whether or not a subject has a genotype having an allele corresponding to the single nucleotide polymorphisms (1) to (192) described above. Therefore, in the drug efficacy prediction method of the present invention, if a subject has any of the genotypes (1) to (192) below, it can be predicted that the drug efficacy will be so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected.

[0050] [1]: This is a genotype that possesses allele A of rs77769596. [2]: This is a genotype that possesses the T allele of rs116951693. [3]: This is a genotype that possesses allele A of rs199623596. [4]: This is a genotype that possesses allele A of rs141544115. [5]: This is a genotype that possesses allele C of rs78650683. [6]: This is a genotype that possesses the T allele of rs61773260. [7]: This is a genotype that possesses allele C of rs16827413. [8]: This is a genotype that possesses allele A of rs3736931. [9]: This is a genotype that possesses allele C of rs77980226.

[10] : This is a genotype that possesses allele C of rs1885744.

[11] : This is a genotype that possesses the CCC allele of rs68016527.

[12] : This is a genotype that possesses allele A of rs10800602.

[13] : This is a genotype that possesses the T allele of rs4915165.

[14] : This is a genotype that possesses allele C of rs149057569.

[15] : This is a genotype that possesses allele G of rs78506769.

[16] : This is a genotype that possesses allele C detected in the genomic region “chr2:57009383”.

[17] : This is a genotype that possesses allele C of rs144622901.

[18] : This is a genotype that possesses allele G of rs191498017.

[19] : This is a genotype that possesses the G allele of rs117878160.

[20] : This is a genotype that possesses allele C of rs2861960.

[0051]

[21] : This is a genotype that possesses allele C of rs6738731.

[22] : This is a genotype that possesses the T allele of rs139777869.

[23] : This is a genotype that possesses allele C of rs7606583.

[24] : This is a genotype that possesses allele C of rs548655480.

[25] : This is a genotype that possesses allele G of rs9818982.

[26] : This is a genotype that possesses allele C of rs73093151.

[27] : This is a genotype that possesses allele C of rs28722753.

[28] : This is a genotype that possesses the T allele of rs190430047.

[29] : This is a genotype that possesses allele A of rs146677134.

[30] : This is a genotype that possesses allele A of rs147398967.

[31] : This is the genotype that possesses the ATAATAAAAT allele of rs201727204.

[32] : This is a genotype that possesses allele A of rs146161796.

[33] : This is a genotype that possesses the T allele of rs146974727.

[34] : This is a genotype that possesses allele A of rs76934337.

[35] : This is a genotype that possesses allele G of rs28724305.

[36] : This is a genotype that possesses allele C of rs139082715.

[37] : This is a genotype that possesses the T allele of rs202039738.

[38] : This is a genotype that possesses the T allele detected in the genomic region “chr4:56445490.”

[39] : This is a genotype that possesses the allele GCCCTCT detected in the genomic region “chr4:77343844.”

[40] : This is a genotype that possesses the T allele detected in the genomic region “chr4:77985714.”

[0052]

[41] : This is a genotype that possesses allele A of rs558310023.

[42] : This is a genotype that carries the ATAGATA allele of rs373193142.

[43] : This is a genotype that possesses allele A of rs3108399.

[44] : This is a genotype that possesses the CTTACCTTACCT allele of rs146184416.

[45] : This is a genotype that possesses the T allele of rs149351859.

[46] : This is a genotype that possesses the T allele of rs117186452.

[47] : This is a genotype that possesses allele G of rs192877030.

[48] : This is a genotype that possesses the GG allele of rs368145706.

[49] : This is a genotype that possesses allele A of rs142073533.

[50] : This is a genotype that possesses allele G of rs144330279.

[51] : This is a genotype that possesses the T allele detected in the genomic region “chr4:184681293.”

[52] : This is a genotype that possesses allele G of rs185364332.

[53] : This is a genotype that possesses allele C of rs78306822.

[54] : This is a genotype that possesses the T allele of rs73746613.

[55] : This is a genotype that possesses the T allele of rs117565657.

[56] : This is a genotype that carries the G allele of rs142078510.

[57] : This is a genotype that possesses allele A of rs536782924.

[58] : This is a genotype that possesses allele G of rs13358143.

[59] : This is a genotype that possesses allele C of rs117217176.

[60] : This is a genotype that possesses the T allele detected in the genomic region “chr5:106033177.”

[0053]

[61] : This is a genotype that possesses allele C of rs145513135.

[62] : This is a genotype that possesses the T allele of rs138786151.

[63] : This is a genotype that possesses allele A of rs75479599.

[64] : This is a genotype that possesses allele G of rs138025072.

[65] : This is a genotype that possesses allele A of rs73416655.

[66] : This is a genotype that possesses allele A of rs9358200.

[67] : This is a genotype that possesses allele C of rs117952795.

[68] : This is a genotype that possesses allele A of rs187188904.

[69] : This is a genotype that possesses allele A of rs17279589.

[70] : This is a genotype that carries the T allele of rs116255027.

[71] : This is a genotype that possesses allele C of rs41279866.

[72] : This is a genotype that possesses allele C of rs2242480.

[73] : This is a genotype that possesses allele A of rs28371759.

[74] : This is a genotype that carries the T allele of rs34784390.

[75] : This is a genotype that possesses allele G of rs12721627.

[76] : This is a genotype that possesses allele C of rs4646474.

[77] : This is a genotype that possesses the rs368125720 allele.

[78] : This is a genotype that possesses the rs200112828 allele.

[79] : This is a genotype that carries the T allele of rs148391916.

[80] : This is a genotype that possesses allele A of rs57649937.

[0054]

[81] : This is a genotype that possesses allele G of rs77535773.

[82] : This is a genotype that possesses the T allele of rs149607532.

[83] : This is a genotype that possesses allele G of rs76148287.

[84] : This is a genotype that possesses allele C of rs6956740.

[85] : This is a genotype that possesses the allele CACACACACAT detected in the genomic region "chr7:119762819."

[86] : This is a genotype that carries the T allele of rs140143507.

[87] : This is a genotype that possesses allele A of rs144155862.

[88] : This is a genotype that possesses allele C of rs190856909.

[89] : This is a genotype that carries the T allele of rs142154029.

[90] : This is a genotype that possesses allele C of rs138882192.

[91] : This is a genotype that possesses the allele GA detected in the genomic region “chr7:126519252.”

[92] : This is a genotype that possesses allele C of rs150413109.

[93] : This is a genotype that possesses the T allele of rs149631544.

[94] : This is a genotype that possesses allele G of rs188579453.

[95] : This is a genotype that possesses allele A of rs138430375.

[96] : This is a genotype that possesses allele A of rs146943578.

[97] : This is a genotype that possesses allele A of rs117377672.

[98] : This is a genotype that possesses allele C of rs4739103.

[99] : This is a genotype that possesses allele G of rs76335247.

[100] : This is a genotype that carries the G allele of rs139552329.

[0055]

[101] : This is a genotype that possesses allele C of rs143804156.

[102] : This is a genotype that carries the T allele of rs2991364.

[103] : This is a genotype that carries allele C of rs9299186.

[104] : This is a genotype that carries allele C of rs4978937.

[105] : This is a genotype that carries the T allele of rs143816412.

[106] : This is a genotype that carries allele C of rs16907787.

[107] : This is a genotype that carries the T allele of rs150949929.

[108] : This is a genotype that possesses allele G of rs141204966.

[109] : This is a genotype that carries allele C of rs150684513.

[110] : This is a genotype that carries allele C of rs143150122.

[111] : This is a genotype that carries the T allele of rs190968040.

[112] : This is a genotype that carries the T allele of rs79875703.

[113] : This is a genotype that possesses the T allele detected in the genomic region “chr11:81185465.”

[114] : This is a genotype that carries the T allele of rs78332808.

[115] : This is a genotype that possesses allele A of rs151314241.

[116] : This is a genotype that possesses allele C of rs74236102.

[117] : A genotype that possesses the rs141277618 allele.

[118] : This is a genotype that carries the T allele of rs12227766.

[119] : This is a genotype that possesses allele A of rs17012289.

[120] : This is a genotype that carries the G allele of rs73166235.

[0056]

[121] : This is a genotype that possesses allele C of rs73166243.

[122] : This is a genotype that carries the T allele of rs56127528.

[123] : This is a genotype that carries the T allele of rs17487644.

[124] : This is a genotype that possesses the CCC allele of rs11290772.

[125] : This is a genotype that carries the T allele of rs145849286.

[126] : This is a genotype that carries the T allele of rs139361259.

[127] : This is a genotype that possesses allele A of rs141696046.

[128] : This is a genotype that possesses allele A detected in the genomic region "chr13:96979189".

[129] : This is a genotype that possesses allele G of rs147493082.

[130] : This is a genotype that possesses allele A of rs149200875.

[131] : This is a genotype that possesses allele A of rs7150903.

[132] : This is a genotype that carries allele C of rs79594269.

[133] : This is a genotype that carries allele G of rs140034212.

[134] : This is a genotype that carries allele G of rs186766499.

[135] : This is a genotype that possesses the allele GTGTA detected in the genomic region “chr14:95984290.”

[136] : This is a genotype that possesses allele A of rs11852611.

[137] : This is a genotype that carries allele G of rs118115242.

[138] : This is a genotype that carries the T allele of rs6496797.

[139] : This is a genotype that carries allele C of rs4541027.

[140] : This is a genotype that carries the G allele of rs285753.

[0057]

[141] : This is a genotype that carries allele C of rs79740453.

[142] : This is a genotype that carries allele C of rs3760436.

[143] : This is a genotype that possesses allele C of rs11361451.

[144] : This is a genotype that carries the T allele of rs115523756.

[145] : This is a genotype that carries allele C of rs60619936.

[146] : This is a genotype that carries the T allele of rs142571079.

[147] : This is a genotype that possesses allele A of rs146099575.

[148] : This is a genotype that carries the T allele of rs62103373.

[149] : This is a genotype that carries the G allele of rs183728329.

[150] : This is a genotype that possesses allele A detected in the genomic region "chr22:29908953".

[151] : A genotype that possesses the rs764818965 allele.

[152] : A genotype that possesses the rs369720664 allele.

[153] : This is a genotype that carries the G allele of rs76726076.

[154] : This is a genotype that carries allele C of rs2071643.

[155] : This is a genotype that carries allele C of rs137967792.

[156] : This is a genotype that carries the T allele of rs56689668.

[157] : This is a genotype that carries allele A of rs5201.

[158] : This is a genotype that carries allele C of rs5202.

[159] : This is a genotype that possesses allele A of rs2070097.

[160] : This is a genotype that carries the CCC allele of rs5904376.

[0058]

[161] : This is a genotype that possesses allele C of rs781940941.

[162] : This is a genotype that carries the T allele of rs781941435.

[163] : This is a genotype that possesses allele A of rs4898372.

[164] : This is a genotype that carries the G allele of rs73627255.

[165] : This is a genotype that carries the T allele of rs3761527.

[166] : This is a genotype that carries the G allele of rs3761529.

[167] : This is a genotype that carries allele G of rs138461346.

[168] : This is a genotype that carries the G allele of rs12559136.

[169] : This is a genotype that possesses allele A of rs4898458.

[170] : This is a genotype that carries allele C of rs151275155.

[171] : This is a genotype that carries allele G of rs142251323.

[172] : This is a genotype that possesses allele A of rs186986494.

[173] : This is a genotype that carries the G allele of rs2872601.

[174] : This is a genotype that carries allele C of rs5945370.

[175] : This is a genotype that carries allele G of rs183746106.

[176] : This is a genotype that carries allele C of rs5945368.

[177] : This is a genotype that carries allele C of rs5987180.

[178] : This is a genotype that carries the T allele of rs55702376.

[179] : This is a genotype that possesses allele A of rs5987179.

[180] : This is a genotype that carries allele G of rs111399454.

[0059]

[181] : This is a genotype that possesses allele A of rs7886319.

[182] : This is a genotype that carries the G allele of rs7052686.

[183] : This is a genotype that possesses allele A of rs5945169.

[184] : This is a genotype that carries allele G of rs150224573.

[185] : This is a genotype that possesses allele A of rs145658816.

[186] : This is a genotype that carries the T allele of rs141108459.

[187] : This is a genotype that possesses allele A of rs113549204.

[188] : This is a genotype that possesses allele A of rs148460019.

[189] : This is a genotype that carries allele G of rs186585558.

[190] : This is a genotype that carries the G allele of rs2071126.

[191] : This is a genotype that carries allele C of rs5945367.

[192] : A genotype that possesses the rs782472424 allele.

[0060] For example, in the case of rs2991364 with a single nucleotide polymorphism of (102), that is, in the case of the genotype of

[102] , the allele is C or T, and the target allele is T. Therefore, in the order of the genotypes T / T>C / T>C / C, it is predicted that the drug efficacy will be insufficient to the extent that improvement in fluid retention due to organ failure is unlikely to be expected in the subject. In the other genotypes listed above (genotypes [1] to

[192] ), in the genotypes of the target alleles of frequently detected gene polymorphisms, the efficacy of the drug is predicted to be so insufficient that improvement of fluid retention due to organ failure is unlikely to be achieved in the subject.

[0061] In the method for predicting a drug efficacy of the present invention, it is detected whether or not a subject exhibits at least one or more genotypes of [1] to

[192] or a combination of at least two or more genotypes of [1] to

[192] . It is preferable to detect whether or not a subject exhibits a combination of at least two of the genotypes [1] to

[192] , since this is expected to enable advance prediction of the efficacy of a drug with even greater accuracy.

[0062] In particular, among the genotypes [1] to

[192] , at least one genotype selected from the group consisting of

[102] ,

[103] and

[104] is preferred, as it provides better prediction accuracy, and genotype

[102] is more preferred.

[0063] In the method for predicting a drug efficacy of the present invention, in order to improve the accuracy of prediction, it is preferable to predict the drug efficacy based on the following clinical factors in addition to detecting at least one of the single nucleotide polymorphisms (1) to (192) described above. Clinical factors: at least one selected from age, sex, height, weight, Child-Pugh classification, platelet count, albumin, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum urea nitrogen, creatinine, sodium, ammonia, and α-fetoprotein.

[0064] Among these clinical factors, at least one selected from the group consisting of serum urea nitrogen, creatinine, sodium, and α-fetoprotein is preferable, and at least one selected from the group consisting of serum urea nitrogen and sodium is more preferable, because it provides better prediction accuracy.

[0065] "Predicting the efficacy of a drug based on clinical factors" means predicting that the efficacy of a drug is lower than that originally expected when the value of the clinical factor is greater than or smaller than a reference value. Specifically, for example, by combining data on single nucleotide polymorphisms and data on clinical factors using a statistical method such as multivariate analysis and performing multivariate analysis including clinical factors, it is expected that the efficacy of a drug can be predicted in advance with even greater accuracy. The reference value may be a value above which the efficacy of the drug is predicted to be lower than the originally expected level, or a value below which the efficacy of the drug is predicted to be lower than the originally expected level. The reference value may be, for example, a cutoff value in a statistical method such as multivariate analysis.

[0066] Nucleic acid collected from a subject can be prepared by extracting genomic DNA from a sample collected from the subject's blood, saliva, lymph, urine, sweat, skin cells, mucosal cells, hair, etc., and purifying the genomic DNA. The extraction method and purification method are not particularly limited. Genomic DNA of any length can be used as a sample nucleic acid, so long as it contains a genomic region where a biomarker for predicting a drug efficacy of the present invention can be detected. The length of the "genomic region where a biomarker for predicting a drug efficacy can be detected" may be, for example, 16 to 500 bases long, or 18 to 200 bases long.

[0067] The race and population of the subject are not limited to Japanese and the Japanese population. The subject may be a mongoloid other than Japanese or another race (e.g., Caucasoid, etc.). However, considering that genetically similar populations such as the Chinese population and the Korean population show similar trends in the types and allele frequencies of single nucleotide polymorphisms, the subject is preferably a mongoloid such as Japanese, Chinese, or Korean; Caucasoid, more preferably a mongoloid, and even more preferably a Japanese.

[0068] The method for detecting a biomarker for predicting efficacy is not particularly limited as long as it is a method for detecting a single nucleotide polymorphism that is commonly used. For example, the method for detecting a biomarker for predicting efficacy can be a method using primers and probes specific to an allele, amplification by PCR, and detection of the polymorphism of the PCR amplified product by luminescence, as well as the methods exemplified in paragraph 0014 of International Publication WO 2017 / 191720. The detection methods may be used alone or in combination. Alternatively, the biomarkers for predicting drug efficacy may be detected after amplifying the sample nucleic acid in advance by PCR or the like. When detecting a large number of samples, methods that use primers and probes specific to alleles, amplify the alleles, and detect polymorphisms in the PCR amplified products by luminescence are preferred, since this reduces the time required for detection; for example, the TaqMan-PCR method, the Invader method, a method that utilizes FRET, the ASP-PCR method, the MALDI-TOF / MS method using primer extension, the RCA method, a method that uses a DNA chip or microarray, and the DigiTag2 method.

[0069] As the primers and probes, DNA, RNA, peptide nucleic acid, etc. may be selected depending on the detection method. For example, the conditions for forming a complementary pair may be appropriately set with reference to Molecular Cloning (Third Edition, Cold Spring Harbor Laboratory Press, New York). The nucleic acid (A) described above in the section <Reagent for predicting drug efficacy> can be used as the primer and the probe. Therefore, the details and preferred aspects of the primer and the probe can be the same as those of the nucleic acid (A) described above in the section <Reagent for predicting drug efficacy>.

[0070] In the method for predicting a drug efficacy of the present invention, the biomarker for predicting a drug efficacy of the present invention is detected in the base sequence of a nucleic acid collected from a subject. Therefore, when the biomarker for predicting a drug efficacy, which is a single nucleotide polymorphism, is detected in the nucleic acid as a sample collected from a subject, it can be predicted in advance with high accuracy that the drug efficacy is so insufficient that improvement of fluid retention due to organ failure is unlikely to be achieved. If the drug efficacy prediction method predicts that the drug efficacy is so insufficient that improvement of fluid retention caused by organ failure is unlikely, then after detecting the drug efficacy prediction biomarker, the administration of drugs such as V2 receptor antagonists is not selected, which reduces the financial burden on patients and the burden associated with unnecessary hospitalization. On the other hand, if the drug efficacy prediction method predicts that the drug efficacy is sufficient to improve fluid retention caused by organ failure, administration of a drug such as a V2 receptor antagonist is selected, which allows selective administration of the drug to patients who can be expected to see a sufficient therapeutic effect from the drug.

[0071] The drug efficacy prediction method of the present invention can also be said to be a method for predicting the drug efficacy of a drug for discharging body fluids accumulated in an organ. In the method for predicting the drug efficacy of a drug for discharging body fluids accumulated in an organ, the biomarker for predicting drug efficacy of the present invention is detected for the base sequence of a nucleic acid collected from a subject, as in the drug efficacy prediction method of the present invention. The drug efficacy prediction method of the present invention can also be said to be a method of collecting data for predicting the drug efficacy of a drug for draining body fluids accumulated in an organ. In the method of collecting data for predicting the drug efficacy of a drug for draining body fluids accumulated in an organ, the biomarker for predicting drug efficacy of the present invention is detected for the base sequence of a nucleic acid collected from a subject, as in the drug efficacy prediction method of the present invention.

[0072] <Biomarkers for predicting tissue fluid recovery> The biomarker for predicting the recovery ability of tissue fluid of the present invention is a biomarker for predicting the recovery ability of tissue fluid by blood vessels. Examples of blood vessels include blood vessels connecting the liver and kidneys and lymphatic vessels connecting the liver and kidneys.

[0073] The biomarker for predicting tissue fluid recovery ability of the present invention is at least one single nucleotide polymorphism selected from the group consisting of a single nucleotide polymorphism identified by a specific rs number registered in the SNP database of the National Center for Biotechnology Information and a single nucleotide polymorphism detected in a specific genomic region. The biomarkers for predicting interstitial fluid recovery ability of the present invention are the single nucleotide polymorphisms (1) to (192) described in the section <Biomarkers for predicting drug efficacy>. Therefore, the details and preferred aspects of the biomarkers for predicting interstitial fluid recovery ability are the same as those described in the section <Biomarkers for predicting drug efficacy>.

[0074] (Mechanism of action) The biomarkers for predicting tissue fluid recovery ability of the present invention described above are single nucleotide polymorphisms that were detected at significantly higher frequencies in patient groups in which a drug was administered and the degree of efficacy was confirmed, as shown in the Examples described later. Among the above-mentioned single nucleotide polymorphisms (1) to (192), it was found that some of them, such as (102), (103), and (104), are present on the intron of a gene called SVEP1. SVEP1 is a gene that is thought to contribute to the formation of vasculature, such as lymphatic vessels and blood vessels, during the developmental stage of organisms such as humans. For example, it has been reported that SVEP1 knockout mice are prone to severe edema and die shortly after birth (Circ Res. 2017; 120: 1276-1288). In addition, as shown in Figure 1, downstream signals of SVEP1 include the FOXC2 (Forkhead box protein C2) gene, which is involved in vascular network and valve formation and is one of the causative genes of congenital lymphedema. It has also been reported that the control of FOXC2 gene expression may be involved in the remodeling mechanism of lymphatic vessels (J Clin Invest.2016;126(7):2437-2451). Considering the above reports, we hypothesized that, for example, in the risk allele of rs2991364, the expression level of genes that contribute to the formation of blood vessels, such as the FOXC2 gene, a downstream signal of the SVEP1 gene, is reduced, weakening the vascular network and vascular remodeling mechanism (Figure 2). Therefore, it can be inferred that the accumulated tissue fluid is difficult to collect by the blood vessels, and the ability of the blood vessels to collect tissue fluid is naturally reduced.

[0075] (Application) The biomarker for predicting tissue fluid recovery ability of the present invention can be applied to, for example, predicting in advance the efficacy of V2 receptor antagonists such as vasopressin. The biomarker for predicting interstitial fluid recovery ability of the present invention can be applied to, for example, the reagent for predicting interstitial fluid recovery ability, the kit for predicting interstitial fluid recovery ability, and the method for predicting interstitial fluid recovery ability of the present invention described below. The detailed uses of the biomarker for predicting interstitial fluid recovery ability of the present invention will be described in detail in the sections below titled <Reagent for predicting interstitial fluid recovery ability>, <Kit for predicting interstitial fluid recovery ability>, and <Method for predicting interstitial fluid recovery ability>. The subject of application is assumed to be edema. Specific examples of edema include systemic edema and localized edema. The cause of edema is not particularly limited. Examples of systemic edema include renal edema, cardiac edema, hepatic edema, nutritional edema, drug-induced edema, edema due to endocrine disease, and idiopathic edema. Examples of localized edema include venous edema, lymphatic edema, inflammatory edema, and angioneurotic edema. However, specific examples of edema are not limited to these examples.

[0076] <Reagent for predicting tissue fluid recovery ability> The reagent for predicting tissue fluid recovery ability of the present invention is a reagent for predicting the tissue fluid recovery ability of blood vessels. The reagent for predicting tissue fluid recovery ability of the present invention contains a nucleic acid specific to a genomic region in which a single nucleotide polymorphism, which is a biomarker for predicting drug efficacy of the present invention, is detected. Details and preferred embodiments of the nucleic acid specific to the genomic region in which a single nucleotide polymorphism is detected, which is a biomarker for predicting drug efficacy, are the same as those of the nucleic acid (A) described above in the section <Reagent for predicting drug efficacy>.

[0077] The reagent for predicting tissue fluid recovery ability of the present invention contains a nucleic acid specific to a genomic region in which a single nucleotide polymorphism, which is a biomarker for predicting tissue fluid recovery ability of the present invention, is detected. Therefore, a specific complementary pair can be formed with a genomic region containing a biomarker for predicting tissue fluid recovery ability on a genomic region in a sample collected from a subject. Therefore, for example, by detecting a single nucleotide polymorphism by the detection method described in the section <Method for predicting drug efficacy>, it can be determined whether or not the subject shows a genotype that has an allele corresponding to the single nucleotide polymorphism. As a result, it can be predicted whether or not the ability to recover tissue fluid by blood vessels is naturally reduced.

[0078] <Tissue fluid recovery estimation kit> The kit for predicting the recovery ability of tissue fluid is a kit for predicting the recovery ability of tissue fluid by blood vessels. The kit for predicting recovery ability of the present invention includes the above-mentioned reagent for predicting recovery ability of the present invention.

[0079] The kit for predicting recovery ability of tissue fluid of the present invention may include the nucleic acid (A) described above in the section <Reagent for predicting drug efficacy>. Details and preferred aspects of the nucleic acid (A) may be the same as those described above in the section <Reagent for predicting drug efficacy>. The kit for predicting the recovery ability of tissue fluid of the present invention may further comprise reagents such as DNA polymerase, restriction enzymes, buffer solutions, and color-developing reagents which are necessary when using nucleic acid (A) as a primer and probe, and may further comprise containers, collection tools, and the like, and may further comprise an instruction manual.

[0080] The kit for predicting interstitial fluid recovery ability of the present invention includes the reagent for predicting interstitial fluid recovery ability of the present invention. Therefore, the kit for predicting interstitial fluid recovery ability of the present invention can predict whether or not an individual has a congenital reduced ability to recover interstitial fluid via blood vessels.

[0081] <Method for predicting tissue fluid recovery> The method for predicting tissue fluid recovery ability of the present invention is a method for predicting the recovery ability of tissue fluid by blood vessels. In the method for predicting tissue fluid recovery ability of the present invention, a biomarker for predicting tissue fluid recovery ability of the present invention is detected in the base sequence of a nucleic acid collected from a subject. Details and preferred embodiments of the method for detecting biomarkers in subjects, samples, and tissue fluids are the same as those described above in the section <Method for predicting drug efficacy>. In the method for predicting interstitial fluid recovery ability of the present invention, a biomarker for predicting recovery ability is detected in the base sequence of a nucleic acid collected from a subject. Therefore, when the biomarker for predicting interstitial fluid recovery ability, which is a single nucleotide polymorphism, is detected in a nucleic acid sample collected from a subject, it can be predicted that the ability of blood vessels to recover interstitial fluid is naturally reduced. When the method for predicting interstitial fluid recovery predicts that the ability of blood vessels to recover interstitial fluid is congenitally reduced, appropriate measures are taken after detecting the biomarker for predicting interstitial fluid recovery, such as not administering drugs such as V2 receptor antagonists. This can reduce the financial burden on patients and the burden associated with unnecessary hospitalization.

[0082] The method for predicting tissue fluid recovery of the present invention can also be said to be a method for predicting tissue fluid recovery by blood vessels. In the method for predicting tissue fluid recovery by blood vessels, the biomarker for predicting tissue fluid recovery of the present invention is detected in the base sequence of a nucleic acid collected from a subject in the same manner as described above in the section <Method for predicting efficacy of a drug>. The method for predicting tissue fluid recovery of the present invention can also be said to be a method for collecting data for predicting tissue fluid recovery by blood vessels. In the method for collecting data for predicting tissue fluid recovery by blood vessels, the biomarker for predicting tissue fluid recovery of the present invention is detected from the base sequence of nucleic acid collected from a subject in the same manner as described above in the section <Method for predicting efficacy of a drug>. EXAMPLES

[0083] The present invention will be described in more detail below with reference to examples, but the present invention is not limited to the following description.

[0084] <Explanation of Abbreviations> Case group: Patients who gained weight due to the administration of tolvaptan, and whose efficacy as tolvaptan was insufficient. Control group: Patients who experienced a weight loss of 1.5 kg or more per week due to the administration of tolvaptan and who showed sufficient efficacy of tolvaptan. MAF: minor allele frequency P-value: p-value OR: odds ratio Child(A / B / C): Number of cases in each grade of the Child-Pugh classification GTEx: Genotype-Tissue Expression ICGC:International Cancer Genome Consortium SVEP1:sushi,von Willebrand factor type A,EGF and pentraxin domain containing 1 ITGA9: Integrin alpha9 "E" means the power of 10. For example, "2.4E-07" means 2.4 × 10 -7 .

[0085] <Study Design> Figure 3 shows the outline of the study design conducted in this example. As shown in Figure 3, in this example, the following three verifications, "1. Discovery GWAS", "2. Replication study", and "3. Combined analysis", will be described.

[0086] "1. Discovery GWAS" The patient group under study consisted of 253 patients who had given consent for genetic research among those who had received tolvaptan for intractable ascites due to liver cirrhosis at a collaborative research facility. Among these, GWAS was performed on 25 patients in the Case group and 156 patients in the Control group.

[0087] "2. Replication study" In an independent cohort from the above, GWAS was performed on 55 cases in the Case group and 177 cases in the Control group who had received tolvaptan for intractable ascites due to liver cirrhosis, and it was used as a Replication study.

[0088] "3. Combined analysis" A combined analysis was performed using all the Case groups and Control groups from the Discovery GWAS and the Replication study. In addition, a multivariate analysis including clinical factors was performed.

[0089] <SNP Typing and Functional Analysis> Genome-wide SNP typing was performed on genomic DNA derived from blood obtained from the subjects using the Affymetrix Axiom ASI1 array. A total of 411,709 SNPs that passed filtering with an SNP call rate < 0.95, minor allele frequency (MAF) < 0.05, and a p-value of less than 0.001 in the Hardy-Weinberg equilibrium test were analyzed. For the replication study, SNP typing including candidate SNPs (p < 10 -4 : 183 SNPs) obtained from the GWAS was performed using the DigiTag2 method. Allele frequencies in the patient and control groups in the association analysis and cohort study were compared using the chi-squared test.

[0090] <Results of the Discovery GWAS> A GWAS was performed on 411,709 marker SNPs distributed across the genome for 25 cases and 156 controls. As a result of comparing the allele frequencies of each SNP between the two groups, 134 SNPs were found to have a significant association (p < 10 -4 ) with the torasemide treatment effect. Among these, multiple SNPs reaching a significant level were found in the vicinity of the SVEP1 gene, KIAA1109 gene, and miR181 gene. These 134 SNPs were extracted as candidate associated genes (Figure 4).

[0091] Figure 4 is a Manhattan plot representing the results of the GWAS. In Figure 4, the chromosomes 1, 2, ···, 22, and X chromosome are plotted on the horizontal axis in order from the left, and -log 10 (p-value) is plotted on the vertical axis. An example of the candidate associated genes shown in Figure 4 will be described. Multiple SNPs were identified in the vicinity of miR181 (chromosome 1q32.1) (P < 5 × 10 -8 ). miR181 has been reported to promote cell survival and metabolism by suppressing PTEN. SNPs were identified at multiple locations near KIAA1109 (chromosome 4q27) (P = 5×10 -7 ). KIAA1109 encodes a protein, but its structure is unknown. SNPs in the KIAA1109 / IL2 / IL21 gene region have been reported to be associated with autoimmune diseases such as celiac disease, Crohn's disease, and rheumatoid arthritis. SNPs were identified at multiple locations (three points) near SVEP1 (chromosome 9q31.3) (P = 7×10 -7 ). It encodes a ligand for integrin α9β1. SVEP1 has been reported to be necessary for bone formation, skeletal muscle differentiation, and epithelial formation. There is a report that knockout mice exhibit severe edema due to abnormal lymphatic vessel development and die soon after birth (Circ Res. 2017;120:1276 - 1288).

[0092] Furthermore, by GWAS using genome - wide imputation data, 49 SNPs were found as multiple related candidates in addition to the above three regions. A total of 183 SNPs, including these 49 SNPs and the above - mentioned 134 SNPs, were extracted as related candidates according to the results of the Discovery GWAS.

[0093] <Results of Replication study> In the Replication study, 192 SNPs, which were the sum of 183 SNPs extracted according to the results of the Discovery GWAS and 9 SNPs that showed a very significant difference (P < 10 -6 ) although they did not meet the condition of linkage disequilibrium, were verified by the DigiTag2 method using another cohort (55 cases in the Case group and 177 cases in the Control group). The verification results by the DigiTag2 method are shown in Table 7. Here, the 9 SNPs are single - nucleotide polymorphisms of (7), (12), (43), (47), (55), (87), (103), (120), and (144) among the above - mentioned single - nucleotide polymorphisms (1) - (192). These 9 SNPs did not meet the condition of linkage disequilibrium in the GWAS and were excluded from the 183 SNPs, but the significant difference was P < 10-6 Nine SNPs that were prominent were extracted. That is, these nine SNPs are: (7): a single nucleotide polymorphism identified by the rs number rs16827413, (12): a single nucleotide polymorphism identified by the rs number rs10800602, (43): a single nucleotide polymorphism identified by the rs number rs3108399, (47): a single nucleotide polymorphism identified by the rs number rs192877030, (55): a single nucleotide polymorphism identified by the rs number rs117565657, (87): a single nucleotide polymorphism identified by the rs number rs144155862, (103): a single nucleotide polymorphism identified by the rs number rs9299186, (120): a single nucleotide polymorphism identified by the rs number rs73166235, (144): a single nucleotide polymorphism identified by the rs number rs115523756.

[0094]

Table 7

[0095] As shown in Table 7, three SNPs (rs2991364, rs9299186, rs4978937) were identified as related SNPs that reached the significance level.

[0096] <Results of Combined analysis> Table 8 shows the results of the association analysis using all Case groups and Control groups in the Discovery GWAS and Replication study.

[0097]

Table 8

[0098] As shown in Table 8, rs2991364, rs9299186, and rs4978937 were detected with significantly higher frequencies in the Case group compared to the Control group. In particular, rs2991364 reached genome-wide significance levels with a hazard ratio (OR): 3.5519 and a P-value of 2.01×10 -8 in the association analysis using all Case and Control groups in the Discovery GWAS and Replication study, and was identified as the most promising candidate among 192 SNPs. Here, rs2991364 is a C / T polymorphic SNP in the intron on the SVEP1 gene of chromosome 9 (9q32). Table 9 shows the results of the multivariate analysis including clinical factors.

[0099]

Table 9

[0100] As shown in Table 9, BUN (OR: 1.00, P<0.05), Na (OR: 1.00, P = 0.01), and rs2991364 (OR: 3.32, P = 0.000242) were extracted as independent factors. These BUN, Na, and rs2991364 were suggested to be significantly associated with the Case group.

[0101] <Analysis using the GTEx database> The GTEx (https: / / gtexportal.org / home / ) project has extensively investigated the impact of genetic mutations on gene expression (expression QTL (eQTL)) using various human tissue specimens and made the data publicly available as a database. In GTEx, the eQTL of rs2991364, one of the candidate SNPs in this study, was searched. The results are shown in Figure 5. As shown in Figure 5, it was found that the expression of the SVEP1 gene was significantly decreased (P-value: 1.9×10 -6 ) in the risk allele group in the cerebellum.

[0102] <Analysis using the ICGC database> Next, to examine the effect of decreased SVEP1 gene expression on downstream signal expression, we obtained whole genome sequence and RNA sequence data of liver tissue from public databases and performed differential analysis of downstream signal gene expression by rs2991364 polymorphism. Liver data was selected from non-cancerous tissue data of hepatitis C-related liver cancer cases conducted in Japan from cancer genome research data registered and published in ICGC (https: / / icgc.org / ). In a total of 83 cases of ICGC data (risk allele group CT / TT mutation: 21 cases, major allele group CC mutation: 62 cases), the difference in the expression level of the SVEP1 gene between the rs2991364 polymorphisms was examined, but no significant decrease in expression level was observed between the rs2991364 polymorphisms. However, when 10 cases in which SVEP1 gene expression was significantly decreased were extracted from the risk allele group of rs2991364 and a comparative analysis was performed with the major allele group (37 cases), the expression of ITGA9, Angioprotein2, and FOXC2, which are downstream signal genes of SVEP1, was significantly decreased (Figure 1, 6-10).

[0103] Analysis using the GTEx and ICGC databases revealed that, although the phenotype of rs2991364 differs depending on the tissue, 1) it affects SVEP1 gene expression at least in the cerebellum, and 2) a decrease in the SVEP1 gene leads to a decrease in the expression of downstream genes, particularly the FOXC2 gene, which is closely involved in vascular networks and valve formation. As mentioned above, the FOXC2 gene is involved in vascular network and valve formation, and is one of the causative genes of congenital lymphedema. It has been reported that the control of its gene expression may be involved in the lymphatic remodeling mechanism (J Clin Invest. 2016; 126 (7): 2437-2451). Therefore, in the risk allele of rs2991364, the expression level of the FOXC2 gene is reduced, strongly suggesting that the vascular network and vascular remodeling mechanism may be weakened. This result supports the validity of the hypothesis that accumulated tissue fluid is difficult to recover by the blood vessels, and that the ability of the blood vessels to recover tissue fluid is naturally reduced.

[0104] The results of the examples described above appear to support the hypothesis that risk allele groups are naturally predisposed to poor tissue fluid recovery due to vulnerability of the vascular network and vascular remodeling mechanism. According to the mechanism of this hypothesis, drugs that drain accumulated fluid due to organ failure are not limited to tolvaptan, and similarly, vasopressin V2 receptor antagonists may be less effective in rs2991364 risk allele carriers. In other words, as shown in Figure 11, when tissue fluid accumulates in an organ due to organ failure, rs2991364 risk allele carriers have weak vascular formation, so it is difficult for the vascular system to collect tissue fluid, and it is difficult for the vascular system to collect tissue fluid in the first place, and as a result, it is thought that they have a constitution that cannot transport tissue fluid sufficiently to the kidney. Therefore, it was suggested that no matter how much vasopressin V2 receptor antagonists that promote water excretion in the kidney are administered, the efficacy of vasopressin V2 receptor antagonists tends to be difficult to be observed. Furthermore, recent results of a large-scale GWAS have reported that SVEP1 gene mutations are one of the risk factors for cardiovascular disease (N Engl J Med.2016 Mar 24;374(12):1134-1144.). The SVEP1 gene is thought to be a gene related to fluid retention and cardiovascular disease, and it has been suggested that rs2991364, which affects SVEP1 gene expression, may be a SNP marker that predicts the effectiveness of tolvaptan in cardiac edema.

[0105] From the results of the examples described above, it is believed that by detecting the 192 SNPs identified by GWAS, it is possible to predict with high accuracy the efficacy of drugs that expel accumulated body fluids caused by organ failure.

[0106] In the present invention, the contents of papers, published patent publications, and the like explicitly stated in this specification may be incorporated by citation in their entirety. [Industrial Applicability]

[0107] According to the present invention, it is possible to predict in advance with high accuracy the efficacy of a drug for draining accumulated body fluid caused by organ failure. According to the present invention, it is possible to infer whether or not the ability of blood vessels to collect tissue fluid is congenitally reduced.

Claims

1. A method for predicting efficacy of a drug for draining body fluid accumulated in an organ, comprising: A drug efficacy prediction method for detecting a single nucleotide polymorphism identified by rs number rs2991364 registered in the SNP database of the National Center for Biotechnology Information on August 1, 2019, in the base sequence of nucleic acid collected from a subject (excluding methods for diagnosing humans, including a step of determining a human's physical condition or a prescription based on a physical condition, for medical purposes).

2. In addition to the single nucleotide polymorphism identified by the rs number: rs2991364, The method for predicting a drug efficacy according to claim 1, wherein at least one single nucleotide polymorphism selected from the group consisting of single nucleotide polymorphisms identified by the following rs numbers and single nucleotide polymorphisms detected in the following genomic regions, which were registered in the SNP database of the National Center for Biotechnology Information on August 1, 2019, is detected in combination (excluding methods for diagnosing humans, which include a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes). rs numbers: rs77769596, rs116951693, rs199623596, rs141544115, rs78650683, rs61773260, rs16827413, rs3736931, rs77980226, rs1885744, rs68016527, rs10800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191498017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs548655480, rs9818982, rs73093151, rs28722753, rs190430047, rs146677134, rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs28724305, rs139082715, rs202039738, rs558310023, rs373193142, rs3108399, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs117565657, rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117952795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs28371759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, rs148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs6956740, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, rs150413109, rs149631544, rs188579453, rs138430375rs146943578、rs117377672、rs4739103、rs76335247、rs139552329、rs143804156、rs9299186、rs4978937、rs143816412、rs16907787、rs150949929、rs141204966、rs150684513、rs143150122、rs190968040、rs79875703、rs78332808、rs151314241、rs74236102、rs141277618、rs12227766、rs17012289、rs73166235、rs73166243、rs56127528、rs17487644、rs11290772、rs145849286、rs139361259、rs141696046、rs147493082、rs149200875、rs7150903、rs79594269、rs140034212、rs186766499、rs11852611、rs118115242、rs6496797、rs4541027、rs285753、rs79740453、rs3760436、rs11361451、rs115523756、rs60619936、rs142571079、rs146099575、rs62103373、rs183728329、rs764818965、rs369720664、rs76726076、rs2071643、rs137967792、rs56689668、rs5201、rs5202、rs2070097、rs5904376、rs781940941、rs781941435、rs4898372、rs73627255、rs3761527、rs3761529、rs138461346、rs12559136、rs4898458、rs151275155、rs142251323、rs186986494、rs2872601、rs5945370、rs183746106、rs5945368、rs5987180、rs55702376、rs5987179、rs111399454、rs7886319、rs7052686、rs5945169、rs150224573、rs145658816、rs141108459、rs113549204、rs148460019、rs186585558、rs2071126、rs5945367 and rs782472424, respectively. Position of each base in the genomic regions: chr2:57009383, chr4:56445490, chr4:77343844, chr4:77985714, chr4:184681293, chr5:106033177, chr7:119762819, chr7:126519252, chr11:81185465, chr13:96979189, chr14:95984290 and chr22:29908953.

3. In addition to the single nucleotide polymorphism identified by the rs number: rs2991364, The method for predicting a drug efficacy according to claim 1, further comprising detecting a single nucleotide polymorphism identified by at least one rs number selected from the group consisting of rs9299186 and rs4978937, which were registered in the SNP database of the National Center for Biotechnology Information on August 1, 2019 (excluding methods for diagnosing humans, including a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes).

4. A method for predicting a drug efficacy according to any one of claims 1 to 3, wherein the drug is a V2 receptor antagonist (excluding a method for diagnosing a human being, which includes a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes).

5. The method for predicting efficacy of the drug according to any one of claims 1 to 4 (excluding methods for diagnosing humans, including a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes) in addition to detecting a single nucleotide polymorphism identified by the rs number: rs2991364, and predicting the efficacy of the drug based on the following clinical factors: Clinical factors: at least one selected from age, sex, height, weight, Child-Pugh classification, platelet count, albumin, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum urea nitrogen, creatinine, sodium, ammonia, and α-fetoprotein.

6. A reagent for use in the drug efficacy prediction method according to any one of claims 1 to 5, A reagent for predicting drug efficacy, comprising a nucleic acid specific to a genomic region in which a single nucleotide polymorphism identified by the rs number: rs2991364 is detected.

7. A kit for predicting the efficacy of a drug for draining body fluids accumulated in an organ, comprising: A kit for predicting drug efficacy, comprising the reagent for predicting drug efficacy according to claim 6.

Images