JP7629611B2 - Biomarker for predicting drug efficacy, reagent for predicting drug efficacy, kit for predicting drug efficacy, method for predicting drug efficacy, biomarker for predicting tissue fluid recovery ability, reagent for predicting tissue fluid recovery ability, kit for predicting tissue fluid recovery ability, method for predicting tissue fluid recovery ability - Google Patents

Description

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 estimating tissue fluid recovery ability, a reagent for estimating tissue fluid recovery ability, a kit for estimating tissue fluid recovery ability, and a method for estimating tissue fluid recovery ability.

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 acts antagonistically against antidiuretic hormone (vasopressin) at the V2 receptor in the renal collecting duct, thereby inhibiting the reabsorption of water 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.

However, it is known that administration of tolvaptan is ineffective in some patients, and therefore, in clinical practice, a method for predicting the therapeutic effect in advance in 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.

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

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.

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.

It is believed that when vascular formation is relatively insufficient compared to a healthy subject, it becomes difficult for the vascular system to collect accumulated tissue fluid, and the ability of the vascular system to collect tissue fluid is relatively reduced. Therefore, it was suggested that in subjects in whom specific SNPs were detected, the function of transporting tissue fluid to the kidney via the vascular system is reduced because the collection of accumulated tissue fluid by the vascular system is insufficient.
Therefore, it was predicted that patients in whom specific SNPs were detected tend to have a congenital low ability to recover tissue fluid through the blood vessels, and tissue fluid that has accumulated due to organ failure is difficult to recover through the blood vessels, leading to a tendency for the patient to have a constitution in which tissue fluid that should be recovered is difficult to transport to the kidney. Thus, the results of GWAS suggest that specific SNPs are related to the formation of blood vessels and the ability of the blood vessels 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.

That is, the present invention has the following aspects.
[1] A biomarker for predicting the efficacy of a drug that drains body fluids accumulated in an organ, the biomarker being 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, rs680165 27, rs10800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191 498017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs5 48655480, rs9818982, rs73093151, rs28722753, rs190430047, rs14667713 4, rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs2 8724305, rs139082715, rs202039738, rs558310023, rs373193142, rs310839 9, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs 142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs11756 5657, rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117952 795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs28 371759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, r s148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs695674 0, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, rs 150413109, rs149631544, rs188579453, rs138430375, rs146943578, rs1173 77672, rs4739103, rs76335247, rs139552329, rs143804156, rs2991364, rs 9299186, rs4978937, rs143816412, rs16907787, rs150949929, rs141204966 , rs150684513, rs143150122, rs190968040, rs79875703, rs78332808, rs151 314241, rs74236102, rs141277618, rs12227766, rs17012289, rs73166235, r s73166243, rs56127528, rs17487644, rs11290772, rs145849286, rs139361 259, rs141696046, rs147493082, rs149200875, rs7150903, rs79594269, rs1 40034212, rs186766499, rs11852611, rs118115242, rs6496797, rs4541027, rs285753, rs79740453, rs3760436, rs11361451, rs115523756, rs60619936, rs142571079, rs146099575, rs62103373, rs183728329, rs764818965, rs36 9720664, rs76726076, rs2071643, rs137967792, rs56689668, rs5201, rs520 2, rs2070097, rs5904376, rs781940941, rs781941435, rs4898372, rs736272 55, rs3761527, rs3761529, rs138461346, rs12559136, rs4898458, rs151275 155, rs142251323, rs186986494, rs2872601, rs5945370, rs183746106, rs59 45368, rs5987180, rs55702376, rs5987179, rs111399454, rs7886319, rs705 2686, rs5945169, rs150224573, rs145658816, rs141108459, rs113549204, rs148460019, rs186585558, rs2071126, rs5945367, and rs782472424,
The genomic region is 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] The biomarker for predicting drug efficacy according to [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 according to [1] or [2], wherein the drug is a V2 receptor antagonist.
[4] A reagent for predicting 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 the 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, the kit comprising the drug efficacy prediction reagent according to [4].
[6] A method for predicting efficacy of a drug for draining body fluids accumulated in an organ, comprising detecting a biomarker for predicting efficacy according to any one of [1] to [3] for a base sequence of a nucleic acid collected from a subject.
[7] The method for predicting a drug efficacy according to [6], further comprising the step of predicting the drug efficacy based on the following clinical factors in addition to the detection of 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.
[8] A biomarker for predicting the ability of blood vessels to recover tissue fluid, the biomarker being 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, rs680165 27, rs10800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191 498017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs5 48655480, rs9818982, rs73093151, rs28722753, rs190430047, rs14667713 4, rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs2 8724305, rs139082715, rs202039738, rs558310023, rs373193142, rs310839 9, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs 142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs11756 5657, rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117952 795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs28 371759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, r s148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs695674 0, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, rs 150413109, rs149631544, rs188579453, rs138430375, rs146943578, rs1173 77672, rs4739103, rs76335247, rs139552329, rs143804156, rs2991364, rs 9299186, rs4978937, rs143816412, rs16907787, rs150949929, rs141204966 , rs150684513, rs143150122, rs190968040, rs79875703, rs78332808, rs151 314241, rs74236102, rs141277618, rs12227766, rs17012289, rs73166235, r s73166243, rs56127528, rs17487644, rs11290772, rs145849286, rs139361 259, rs141696046, rs147493082, rs149200875, rs7150903, rs79594269, rs1 40034212, rs186766499, rs11852611, rs118115242, rs6496797, rs4541027, rs285753, rs79740453, rs3760436, rs11361451, rs115523756, rs60619936, rs142571079, rs146099575, rs62103373, rs183728329, rs764818965, rs36 9720664, rs76726076, rs2071643, rs137967792, rs56689668, rs5201, rs520 2, rs2070097, rs5904376, rs781940941, rs781941435, rs4898372, rs736272 55, rs3761527, rs3761529, rs138461346, rs12559136, rs4898458, rs151275 155, rs142251323, rs186986494, rs2872601, rs5945370, rs183746106, rs59 45368, rs5987180, rs55702376, rs5987179, rs111399454, rs7886319, rs705 2686, rs5945169, rs150224573, rs145658816, rs141108459, rs113549204, rs148460019, rs186585558, rs2071126, rs5945367, and rs782472424,
The genomic region is a biomarker for predicting tissue fluid recovery ability, which is the position 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] The biomarker for predicting tissue fluid recovery ability according to [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 according to [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 of tissue fluid to recover, according to 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 according to [11].
[13] A method for predicting the recovery ability of tissue fluid by blood vessels, comprising detecting a biomarker for predicting the recovery ability of tissue fluid according to any one of [8] to [10] in a base sequence of a nucleic acid collected from a subject.
[14] The method for predicting tissue fluid recovery ability according to [13], which predicts tissue fluid recovery ability via blood vessels based on the following clinical factors in addition to the detection of 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.

According to one aspect of the present invention, a biomarker for predicting efficacy is provided that can predict in advance with high accuracy the efficacy of a drug that expels accumulated body fluids caused by organ failure.

FIG. 1 is a schematic diagram showing the signal transduction pathway of downstream genes of the SVEP1 gene. FIG. 1 is a schematic diagram showing the signal transduction pathway when gene expression of downstream genes of the SVEP1 gene is decreased. FIG. 1 shows an outline of the study design in the examples. FIG. 1 is a Manhattan plot showing the results of the GWAS. FIG. 1 shows the results of searching for eQTL for rs2991364 using GTEx. FIG. 1 shows the results of a search for SVEP1 gene expression levels in the risk allele group of rs2991364 using the ICGC database. FIG. 13 shows the results of a search for ITGA9 gene expression levels in the risk allele group of rs2991364 using the ICGC database. 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. FIG. 1 shows the results of a search for gene expression levels of Tie1 in the risk allele group of rs2991364 using the ICGC database. FIG. 1 shows the results of a search for the gene expression levels of Ang-2 in the risk allele group of rs2991364 using the ICGC database. 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.

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" means two sets of genomic sequences, one set inherited from each of the gametes (sperm or egg). Humans have 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 sequence, it is sometimes expressed as "homozygous", and when they have different sequences, it is sometimes expressed as "heterozygous".
In this specification, an allele consisting of one or more base sequences (α) detected on 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 similarly. When a base is missing on a specific genomic region, it is described as "allele-".
"Risk allele" means an allele that increases the probability of disease, illness, or symptoms among multiple types of single nucleotide polymorphisms detected in a genome 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 (for example, about twice as high) than that in a person carrying allele A, allele G is the risk allele, that is, the allele that increases the risk of disease.
Furthermore, a single nucleotide polymorphism in which the base sequence 5'-..."AA"...-3' is frequently detected in a specific genomic region is referred to as an "allele AA." Other base sequences consisting of two or more bases 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" is an abbreviation for Genome Wide Association Study, and is also called genome-wide association analysis.
"SNP" is an abbreviation for Single Nucleotide Polymorphism, and is referred to as "single nucleotide polymorphism" in this specification. A plurality of SNPs may be collectively referred to as "SNPs."
"Subject" means a human subject of study.
The numerical range indicated by "to" means that the numerical range includes the numerical range before and after it as the lower limit and upper limit.

<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. An example of the V2 receptor antagonist is tolvaptan (product name "Samsca", Otsuka Pharmaceutical Co., Ltd.). Tolvaptan is a drug that forcibly discharges ascites, which is body fluid 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.

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.

"rs number"
rs77769596, rs116951693, rs199623596, rs141544115, rs78650683, rs617 73260, rs16827413, rs3736931, rs77980226, rs1885744, rs68016527, rs10 800602, rs4915165, rs149057569, rs78506769, rs144622901, rs191498017 , rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs5486554 80, rs9818982, rs73093151, rs28722753, rs190430047, rs146677134, rs14 7398967, rs201727204, rs146161796, rs146974727, rs76934337, rs287243 05, rs139082715, rs202039738, rs558310023, rs373193142, rs3108399, rs 146184416, rs149351859, rs117186452, rs192877030, rs368145706, rs1420 73533, rs144330279, rs185364332, rs78306822, rs73746613, rs117565657 , rs142078510, rs536782924, rs13358143, rs117217176, rs145513135, rs1 38786151, rs75479599, rs138025072, rs73416655, rs9358200, rs11795279 5, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, rs2837 1759, rs34784390, rs12721627, rs4646474, rs368125720, rs200112828, rs 148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs695674 0, rs140143507, rs144155862, rs190856909, rs142154029, rs138882192, r s150413109, rs149631544, rs188579453, rs138430375, rs146943578, rs117 377672, rs4739103, rs76335247, rs139552329, rs143804156, rs2991364, r s9299186, rs4978937, rs143816412, rs16907787, rs150949929, rs1412049 66, rs150684513, rs143150122, rs190968040, rs79875703, rs78332808, rs 151314241, rs74236102, rs141277618, rs12227766, rs17012289, rs7316623 5, rs73166243, rs56127528, rs17487644, rs11290772, rs145849286, rs139 361259, rs141696046, rs147493082, rs149200875, rs7150903, rs79594269 , rs140034212, rs186766499, rs11852611, rs118115242, rs6496797, rs454 1027, rs285753, rs79740453, rs3760436, rs11361451, rs115523756, rs6061 9936, rs142571079, rs146099575, rs62103373, rs183728329, rs764818965 , rs369720664, rs76726076, rs2071643, rs137967792, rs56689668, rs5201 , rs5202, rs2070097, rs5904376, rs781940941, rs781941435, rs4898372, r s73627255, rs3761527, rs3761529, rs138461346, rs12559136, rs4898458, r s151275155, rs142251323, rs186986494, rs2872601, rs5945370, rs183746 106, rs5945368, rs5987180, rs55702376, rs5987179, rs111399454, rs78863 19, rs7052686, rs5945169, rs150224573, rs145658816, rs141108459, rs11 3549204, rs148460019, rs186585558, rs2071126, rs5945367, rs782472424.

"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.

The single nucleotide polymorphisms detected in these genomic regions are as follows:
chr2:57009383:C>CT, chr4:56445490:T>TTTTG, chr4:77343844:GCCCTCT> 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.

The 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 single nucleotide polymorphism selected from the group consisting of the single nucleotide polymorphisms (1) to (192) below.

(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, and is 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 allele G is detected more frequently than allele T.
(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.

(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 ATAAATAAAT 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 allele T is detected more frequently than the allele TTTTG.
(39): A single nucleotide polymorphism detected in the genomic region “chr4:77343844”, which is an insertion type single nucleotide polymorphism 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.

(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 the T allele is detected more frequently than the C allele.
(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 allele G is detected more frequently than allele T.
(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”, which is a deletion-type single nucleotide polymorphism in which the T allele is detected more frequently than the TA allele.

(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 -.

(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”, which 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.

(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 allele T is detected more frequently than allele G.
(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 allele T is detected more frequently than allele G.
(113): A single nucleotide polymorphism detected in the genomic region “chr11:81185465”, in which allele T is detected more frequently than allele C.
(114): A single nucleotide polymorphism identified by rs78332808, in which allele T is detected more frequently than allele G.
(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 allele C.
(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.

(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 allele T is detected more frequently than allele G.
(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”, which 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.

(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 the T allele is detected more frequently than the C allele.
(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.

(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 allele T is detected more frequently than allele G.
(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.

(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 allele G is detected more frequently than allele T.
(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): 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 allele A.

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

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, since 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.

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 T is held 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 efficacy of the drug is so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected. 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 exhibiting 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.

(Mechanism of action)
The biomarkers for predicting drug efficacy of the present invention described above are single nucleotide polymorphisms that were detected at a significantly high frequency in a patient group in which the drug efficacy was relatively insufficient as a result of performing GWAS on a patient group in which a drug was administered and the degree of drug efficacy was confirmed, as described in the Examples below. Therefore, when the biomarkers for predicting drug efficacy that are single nucleotide polymorphisms are detected in a nucleic acid 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.

(Application)
The biomarker for predicting drug efficacy 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 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.

<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).

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 1 to several, 1 to 5, or 1 to 3.

The base length of the nucleic acid (A) is not particularly limited as long as it can specifically form a complementary pair with a genomic 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.
With regard to the design, synthesis, etc. of the nucleic acid (A), reference can be made to books such as Molecular Cloning, Third Edition, Cold Spring Harbor Laboratory Press, New York.

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.

The optional components in the drug efficacy prediction reagent are not particularly limited. For example, components that contribute to improving the preservation state of the nucleic acid (A) are possible.

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 drug efficacy prediction biomarker of the present invention, is detected. Therefore, a specific complementary pair can be formed with a genomic region containing the above-mentioned drug efficacy prediction biomarker 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 exhibits a genotype that has an allele corresponding to the single nucleotide polymorphism. As a result, it is possible to predict in advance with high accuracy that the drug efficacy will be so insufficient that improvement in fluid retention due to organ failure is unlikely.

<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.

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.

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. Therefore, the drug efficacy prediction kit of the present invention can predict with high accuracy in advance that the drug efficacy is so insufficient that improvement in fluid retention caused by organ failure is unlikely.

<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.

"Detecting a biomarker for predicting drug efficacy" means detecting whether or not a subject has a genotype that possesses 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 achieved.

[1]: A genotype that possesses allele A of rs77769596.
[2]: A genotype that possesses the T allele of rs116951693.
[3]: A genotype that possesses allele A of rs199623596.
[4]: A genotype that possesses allele A of rs141544115.
[5]: A genotype that possesses allele C of rs78650683.
[6]: A genotype that possesses the T allele of rs61773260.
[7]: A genotype that possesses allele C of rs16827413.
[8]: A genotype that possesses allele A of rs3736931.
[9]: A genotype that possesses allele C of rs77980226.
[10]: A genotype that possesses allele C of rs1885744.
[11]: A genotype that possesses the CCC allele of rs68016527.
[12]: A genotype that possesses allele A of rs10800602.
[13]: A genotype that possesses the T allele of rs4915165.
[14]: A genotype that possesses allele C of rs149057569.
[15]: A genotype that possesses allele G of rs78506769.
[16]: A genotype that possesses allele C detected in the genomic region “chr2:57009383”.
[17]: A genotype that possesses allele C of rs144622901.
[18]: A genotype that possesses allele G of rs191498017.
[19]: A genotype that possesses allele G of rs117878160.
[20]: A genotype that possesses allele C of rs2861960.

[21]: A genotype that possesses allele C of rs6738731.
[22]: A genotype that possesses the T allele of rs139777869.
[23]: A genotype that possesses allele C of rs7606583.
[24]: A genotype that possesses allele C of rs548655480.
[25]: A genotype that possesses allele G of rs9818982.
[26]: A genotype that possesses allele C of rs73093151.
[27]: A genotype that possesses allele C of rs28722753.
[28]: A genotype that possesses the T allele of rs190430047.
[29]: A genotype that possesses allele A of rs146677134.
[30]: A genotype that possesses allele A of rs147398967.
[31]: A genotype that possesses the allele ATAAATAAAT of rs201727204.
[32]: A genotype that possesses allele A of rs146161796.
[33]: A genotype that possesses the T allele of rs146974727.
[34]: A genotype that possesses allele A of rs76934337.
[35]: A genotype that possesses allele G of rs28724305.
[36]: A genotype that possesses allele C of rs139082715.
[37]: A genotype that possesses the T allele of rs202039738.
[38]: A genotype that possesses the allele T detected in the genomic region “chr4:56445490”.
[39]: A genotype that possesses the allele GCCCTCT detected in the genomic region “chr4:77343844”.
[40]: A genotype that possesses the allele T detected in the genomic region “chr4:77985714”.

[41]: A genotype that possesses allele A of rs558310023.
[42]: A genotype that possesses the ATAGATA allele of rs373193142.
[43]: A genotype that possesses allele A of rs3108399.
[44]: A genotype that possesses the allele CTTACCTTACCT of rs146184416.
[45]: A genotype that possesses the T allele of rs149351859.
[46]: A genotype that possesses the T allele of rs117186452.
[47]: A genotype that possesses allele G of rs192877030.
[48]: A genotype that possesses the allele GG of rs368145706.
[49]: A genotype that possesses allele A of rs142073533.
[50]: A genotype that possesses allele G of rs144330279.
[51]: A genotype that possesses the allele T detected in the genomic region “chr4:184681293”.
[52]: A genotype that possesses allele G of rs185364332.
[53]: A genotype that possesses allele C of rs78306822.
[54]: A genotype that possesses the T allele of rs73746613.
[55]: A genotype that possesses the T allele of rs117565657.
[56]: A genotype that possesses allele G of rs142078510.
[57]: A genotype that possesses allele A of rs536782924.
[58]: A genotype that possesses allele G of rs13358143.
[59]: A genotype that possesses allele C of rs117217176.
[60]: A genotype that possesses allele T detected in the genomic region “chr5:106033177”.

[61]: A genotype that possesses allele C of rs145513135.
[62]: A genotype that possesses the T allele of rs138786151.
[63]: A genotype that possesses allele A of rs75479599.
[64]: A genotype that possesses allele G of rs138025072.
[65]: A genotype that possesses allele A of rs73416655.
[66]: A genotype that possesses allele A of rs9358200.
[67]: A genotype that possesses allele C of rs117952795.
[68]: A genotype that possesses allele A of rs187188904.
[69]: A genotype that possesses allele A of rs17279589.
[70]: A genotype that possesses the T allele of rs116255027.
[71]: A genotype that possesses allele C of rs41279866.
[72]: A genotype that possesses allele C of rs2242480.
[73]: A genotype that possesses allele A of rs28371759.
[74]: A genotype that possesses the T allele of rs34784390.
[75]: A genotype that possesses allele G of rs12721627.
[76]: A genotype that possesses allele C of rs4646474.
[77]: A genotype that possesses the rs368125720 allele.
[78]: A genotype that possesses the rs200112828 allele.
[79]: A genotype that possesses the T allele of rs148391916.
[80]: A genotype that possesses allele A of rs57649937.

[81]: A genotype that possesses allele G of rs77535773.
[82]: A genotype that possesses the T allele of rs149607532.
[83]: A genotype that possesses allele G of rs76148287.
[84]: A genotype that possesses allele C of rs6956740.
[85]: A genotype that possesses the allele CACACACACAT detected in the genomic region "chr7:119762819".
[86]: A genotype that possesses the T allele of rs140143507.
[87]: A genotype that possesses allele A of rs144155862.
[88]: A genotype that possesses allele C of rs190856909.
[89]: A genotype that possesses the T allele of rs142154029.
[90]: A genotype that possesses allele C of rs138882192.
[91]: A genotype that possesses the allele GA detected in the genomic region “chr7:126519252”.
[92]: A genotype that possesses allele C of rs150413109.
[93]: A genotype that possesses the T allele of rs149631544.
[94]: A genotype that possesses allele G of rs188579453.
[95]: A genotype that possesses allele A of rs138430375.
[96]: A genotype that possesses allele A of rs146943578.
[97]: A genotype that possesses allele A of rs117377672.
[98]: A genotype that possesses allele C of rs4739103.
[99]: A genotype that possesses allele G of rs76335247.
[100]: A genotype that possesses allele G of rs139552329.

[101]: A genotype that possesses allele C of rs143804156.
[102]: A genotype that possesses the T allele of rs2991364.
[103]: A genotype that possesses allele C of rs9299186.
[104]: A genotype that possesses allele C of rs4978937.
[105]: A genotype that possesses the T allele of rs143816412.
[106]: A genotype that possesses allele C of rs16907787.
[107]: A genotype that possesses the T allele of rs150949929.
[108]: A genotype that possesses allele G of rs141204966.
[109]: A genotype that possesses allele C of rs150684513.
[110]: A genotype that possesses allele C of rs143150122.
[111]: A genotype that possesses the T allele of rs190968040.
[112]: A genotype that possesses the T allele of rs79875703.
[113]: A genotype that possesses the allele T detected in the genomic region “chr11:81185465”.
[114]: A genotype that possesses the T allele of rs78332808.
[115]: A genotype that possesses allele A of rs151314241.
[116]: A genotype that possesses allele C of rs74236102.
[117]: A genotype that possesses the rs141277618 allele.
[118]: A genotype that possesses the T allele of rs12227766.
[119]: A genotype that possesses allele A of rs17012289.
[120]: A genotype that possesses allele G of rs73166235.

[121]: A genotype that possesses allele C of rs73166243.
[122]: A genotype that possesses the T allele of rs56127528.
[123]: A genotype that possesses the T allele of rs17487644.
[124]: A genotype that possesses the CCC allele of rs11290772.
[125]: A genotype that possesses the T allele of rs145849286.
[126]: A genotype that possesses the T allele of rs139361259.
[127]: A genotype that possesses allele A of rs141696046.
[128]: A genotype that possesses allele A detected in the genomic region “chr13:96979189”.
[129]: A genotype that possesses allele G of rs147493082.
[130]: A genotype that possesses allele A of rs149200875.
[131]: A genotype that possesses allele A of rs7150903.
[132]: A genotype that possesses allele C of rs79594269.
[133]: A genotype that possesses allele G of rs140034212.
[134]: A genotype that possesses allele G of rs186766499.
[135]: A genotype that possesses the allele GTGTA detected in the genomic region “chr14:95984290”.
[136]: A genotype that possesses allele A of rs11852611.
[137]: A genotype that possesses allele G of rs118115242.
[138]: A genotype that possesses the T allele of rs6496797.
[139]: A genotype that possesses allele C of rs4541027.
[140]: A genotype that possesses allele G of rs285753.

[141]: A genotype that possesses allele C of rs79740453.
[142]: A genotype that possesses allele C of rs3760436.
[143]: A genotype that possesses allele C of rs11361451.
[144]: A genotype that possesses the T allele of rs115523756.
[145]: A genotype that possesses allele C of rs60619936.
[146]: A genotype that possesses the T allele of rs142571079.
[147]: A genotype that possesses allele A of rs146099575.
[148]: A genotype that possesses the T allele of rs62103373.
[149]: A genotype that possesses allele G of rs183728329.
[150]: 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]: A genotype that possesses allele G of rs76726076.
[154]: A genotype that possesses allele C of rs2071643.
[155]: A genotype that possesses allele C of rs137967792.
[156]: A genotype that possesses the T allele of rs56689668.
[157]: A genotype that possesses allele A of rs5201.
[158]: A genotype that possesses allele C of rs5202.
[159]: A genotype that possesses allele A of rs2070097.
[160]: A genotype that possesses the CCC allele of rs5904376.

[161]: A genotype that possesses allele C of rs781940941.
[162]: A genotype that possesses the T allele of rs781941435.
[163]: A genotype that possesses allele A of rs4898372.
[164]: A genotype that possesses allele G of rs73627255.
[165]: A genotype that possesses the T allele of rs3761527.
[166]: A genotype that possesses allele G of rs3761529.
[167]: A genotype that possesses allele G of rs138461346.
[168]: A genotype that possesses allele G of rs12559136.
[169]: A genotype that possesses allele A of rs4898458.
[170]: A genotype that possesses allele C of rs151275155.
[171]: A genotype that possesses allele G of rs142251323.
[172]: A genotype that possesses allele A of rs186986494.
[173]: A genotype that possesses allele G of rs2872601.
[174]: A genotype that possesses allele C of rs5945370.
[175]: A genotype that possesses allele G of rs183746106.
[176]: A genotype that possesses allele C of rs5945368.
[177]: A genotype that possesses allele C of rs5987180.
[178]: A genotype that possesses the T allele of rs55702376.
[179]: A genotype that possesses allele A of rs5987179.
[180]: A genotype that possesses allele G of rs111399454.

[181]: A genotype that possesses allele A of rs7886319.
[182]: A genotype that possesses allele G of rs7052686.
[183]: A genotype that possesses allele A of rs5945169.
[184]: A genotype that possesses allele G of rs150224573.
[185]: A genotype that possesses allele A of rs145658816.
[186]: A genotype that possesses the T allele of rs141108459.
[187]: A genotype that possesses allele A of rs113549204.
[188]: A genotype that possesses allele A of rs148460019.
[189]: A genotype that possesses allele G of rs186585558.
[190]: A genotype that possesses allele G of rs2071126.
[191]: A genotype that possesses allele C of rs5945367.
[192]: A genotype that possesses the rs782472424 allele.

For example, in the case of rs2991364 with a single nucleotide polymorphism of (102), i.e., 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 efficacy of the drug is so insufficient that improvement of fluid retention due to organ failure is unlikely to be expected in the subject.
In the other genotypes shown above in genotypes [1] to [192], in the genotypes of the target alleles of frequently detected gene polymorphisms, it is predicted that the efficacy of the drug will be so insufficient that improvement of fluid retention due to organ failure in the subject is unlikely to be achieved.

In the method for predicting a drug efficacy of the present invention, it is detected whether or not a subject shows 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 or more genotypes of [1] to [192], since this is expected to enable advance prediction of the efficacy of a drug with even greater accuracy.

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.

In the method for predicting a drug efficacy of the present invention, 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, since this further improves the accuracy of prediction.
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.

Among these clinical factors, at least one selected from the group consisting of serum urea nitrogen, creatinine, sodium, and α-fetoprotein is preferred, and at least one selected from the group consisting of serum urea nitrogen and sodium is more preferred, as this will further improve the accuracy of prediction.

"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.

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 in which a biomarker for predicting a drug efficacy of the present invention can be detected. The length of the "genomic region in which 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.

The race and population of the subjects are not limited to Japanese people and the Japanese population. The subjects may be non-Japanese mongoloids or other races (e.g., Caucasoids, etc.). However, considering that genetically similar populations such as the Japanese population and the Chinese and Korean populations show similar trends in the types and allele frequencies of single nucleotide polymorphisms, the subjects are preferably mongoloids such as Japanese, Chinese, Korean, etc.; Caucasoids, more preferably mongoloids, and even more preferably Japanese.

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. Examples of the method for detecting a biomarker for predicting efficacy include a method using primers and probes specific to an allele, amplification by PCR, and detection of a polymorphism in the PCR amplified product by luminescence, as well as the methods exemplified in paragraph 0014 of 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 a sample nucleic acid in advance by PCR or the like.
When detecting a large number of samples, the time required for detection can be reduced, and therefore preferred methods include those using primers and probes specific to alleles, amplifying by PCR, and detecting polymorphisms in the PCR amplified products by luminescence; the TaqMan-PCR method, the Invader method, a method using FRET, the ASP-PCR method, a MALDI-TOF/MS method using a primer extension method, the RCA method, a method using a DNA chip or microarray, and the DigiTag2 method.

As the primer and the probe, DNA, RNA, peptide nucleic acid, etc. are 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>.

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.
When the drug efficacy prediction method predicts that the drug efficacy is so insufficient that improvement of fluid retention caused by organ failure is unlikely, the drug efficacy prediction biomarker is detected, and then administration of a drug such as a V2 receptor antagonist is not selected, thereby reducing unnecessary financial burden on patients and the burden associated with hospitalization.
On the other hand, if the drug efficacy prediction method predicts that the drug efficacy is sufficient to be expected 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.

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.

<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.

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, 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>.

(Mechanism of action)
The biomarker for predicting tissue fluid recovery ability of the present invention described above is a single nucleotide polymorphism that was detected at a significantly high frequency in a patient group in which a drug was administered and the degree of drug efficacy was confirmed, as shown in the Examples described later.
Among the above-mentioned single nucleotide polymorphisms (1) to (192), it was found that those present on the intron of a gene called SVEP1, such as (102), (103), and (104), are included. SVEP1 is a gene that is believed 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, it was hypothesized that, among these single nucleotide polymorphisms, for example, in the risk allele of rs2991364, the expression levels of genes that contribute to vascular formation, such as the FOXC2 gene, a downstream signal of the SVEP1 gene, are 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.

(Application)
The biomarker for predicting the recovery ability of tissue fluid 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 tissue fluid recovery ability of the present invention can be applied to, for example, 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 of the present invention described below. Details of the use of the biomarker for predicting tissue fluid recovery ability of the present invention will be described in detail in the sections below titled <Reagent for predicting tissue fluid recovery ability>, <Kit for predicting tissue fluid recovery ability>, and <Method for predicting tissue 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.

<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 a 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>.

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 <Drug efficacy prediction method>, it is possible to determine whether or not a subject exhibits a genotype that possesses an allele corresponding to the single nucleotide polymorphism. As a result, it is possible to predict whether or not the ability to recover tissue fluid by blood vessels is naturally reduced.

<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.

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 a 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 a probe, and may further comprise containers, collection instruments, and the like, and may further comprise an instruction manual.

The kit for predicting tissue fluid recovery ability of the present invention includes the reagent for predicting tissue fluid recovery ability of the present invention. Therefore, the kit for predicting tissue fluid recovery ability of the present invention can predict whether or not the ability of blood vessels to recover tissue fluid is congenitally reduced.

<Method for predicting interstitial fluid recovery ability>
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 a drug such as a V2 receptor antagonist, thereby reducing the financial burden on the patient and the burden associated with unnecessary hospitalization.

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 drug efficacy>.
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>.

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.

<Explanation of Abbreviations>
Case group: Patients whose weight increased due to administration of tolvaptan, and whose efficacy as a pharmacological agent was insufficient. Control group: Patients whose weight decreased by 1.5 kg/week or more due to administration of tolvaptan, and whose efficacy as a pharmacological agent was sufficient. 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 a power of 10. For example, "2.4E-07" means 2.4×10 ⁻⁷ .

Study design
An outline of the research design performed in this example is shown in Figure 3. As shown in Figure 3, this example describes the following three validations: "1. Discovery GWAS", "2. Replication study", and "3. Combined analysis".

"1. Discovery GWAS"
The study subjects were 253 patients who were administered tolvaptan for refractory ascites due to liver cirrhosis at a collaborative research facility and who agreed to participate in the genetic study. Of these, GWAS was performed on 25 patients in the case group and 156 patients in the control group.

"2. Replication study"
In a cohort independent of the above, a GWAS was conducted on 55 case cases in which tolvaptan was administered to treat refractory ascites due to liver cirrhosis and 177 control cases, and this was used as a replication study.

"3. Combined analysis"
Association analysis was performed using all case and control groups from the Discovery GWAS and Replication study. In addition, multivariate analysis including clinical factors was performed.

<SNP typing and functional analysis>
Genome-wide SNP typing was performed on blood-derived genomic DNA obtained from subjects using the Affymetrix Axiom ASI1 array. 411,709 SNPs were analyzed, filtered for SNP call rate < 0.95, MAF < 0.05, and Hardy-Weinberg equilibrium test of 0.001 or less.
In the replication study, SNP typing including the candidate SNPs (p<10 ⁻⁴ : 183 SNPs) obtained in the GWAS was performed by the DigiTag2 method.
Allele frequencies between patient and control groups in association analyses and cohort studies were compared using the chi-square test.

<Discovery GWAS results>
GWAS was performed on 411,709 marker SNPs distributed across the entire genome, with 25 case cases and 156 control cases. As a result of comparing the allele frequency of each SNP between the two groups, 134 SNPs were found to be significantly associated with tolvaptan treatment effect (p< ^10-4 ). Among these, several SNPs that reached the significant level were found near the SVEP1 gene, KIAA1109 gene, and miR181 gene. These 134 SNPs were extracted as candidates for related genes (Figure 4).

4 is a Manhattan plot showing the results of GWAS, in which chromosome 1, chromosome 2, ... chromosome 22, and chromosome X are plotted on the horizontal axis from the left, and -log ₁₀ (P value) is plotted on the vertical axis.
An example of the related gene candidates shown in FIG. 4 will be described.
SNPs were identified at multiple sites near miR181 (chromosome 1q32.1) (P<5×10 ⁻⁸ ). It has been reported that miR181 promotes cell survival and metabolism by suppressing PTEN.
SNPs were identified at multiple sites 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 sites (3 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. It has been reported that knockout mice exhibit severe edema due to abnormal lymphatic vessel development and die shortly after birth (Circ Res. 2017; 120: 1276-1288).

Furthermore, a GWAS using genome-wide imputation data identified 49 SNPs as multiple association candidates in addition to the three regions mentioned above. A total of 183 SNPs, consisting of these 49 SNPs and the 134 SNPs mentioned above, were extracted as association candidates based on the results of Discovery GWAS.

<Replication study results>
In the Replication study, 183 SNPs extracted from the results of Discovery GWAS and 9 SNPs that showed extremely significant differences (P<10 ⁻⁶ ) while not meeting the conditions of linkage disequilibrium were examined by the DigiTag2 method using another cohort (55 cases in the Case group, 177 cases in the Control group). The results of the DigiTag2 method are shown in Table 7. Here, the 9 SNPs are the single nucleotide polymorphisms (7), (12), (43), (47), (55), (87), (103), (120), and (144) among the single nucleotide polymorphisms (1) to (192) described above. These 9 SNPs were excluded from the 183 SNPs because they did not satisfy the conditions for linkage disequilibrium in GWAS, but they were the 9 SNPs that showed a significant difference of P<10 ^-6 .
That is, these nine SNPs are: (7): a single nucleotide polymorphism identified by rs number rs16827413; (12): a single nucleotide polymorphism identified by rs number rs10800602; (43): a single nucleotide polymorphism identified by rs number rs3108399; (47): a single nucleotide polymorphism identified by rs number rs192877030; (55): a single nucleotide polymorphism identified by rs number rs (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; and (144): a single nucleotide polymorphism identified by the rs number rs115523756.

As shown in Table 7, three SNPs (rs2991364, rs9299186, and rs4978937) were identified as associated SNPs that reached a significant level.

<Results of combined analysis>
The results of the association analysis using all case and control groups from the Discovery GWAS and Replication study are shown in Table 8.

As shown in Table 8, rs2991364, rs9299186, and rs4978937 were detected at a higher frequency in the case group than in the control group. In particular, rs2991364 reached a genome-wide significance level of 3.5519 (OR) and 2.01×10 ⁻⁸ (P value) by association analysis using all the case groups and control groups of the Discovery GWAS and Replication study, and was identified as the most promising candidate among the 192 SNPs. Here, rs2991364 is a C/T polymorphism SNP in an intron on the SVEP1 gene of chromosome 9 (9q32).
Table 9 shows the results of multivariate analysis including clinical factors.

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.

<Analysis using the GTEx database>
The GTEx (https://gtexportal.org/home/) project is conducting large-scale investigations into the effects of genetic mutations on gene expression (expression QTL (eQTL)) using various human tissue samples, and has published the results as a database. Using GTEx, we searched for eQTL for rs2991364, one of the candidate SNPs in this study. The results are shown in Figure 5. As shown in Figure 5, it was found that SVEP1 gene expression in the cerebellum was significantly reduced in the risk allele group (P value: 1.9 x ^10-6 ).

<Analysis using ICGC database>
Next, to examine the effect of decreased SVEP1 gene expression on downstream signal expression, whole genome sequence and RNA sequence data of liver tissue were obtained from public databases, and downstream signal gene expression differential analysis was performed according to rs2991364 polymorphism. The liver data was selected from non-cancerous 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 from 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 the expression level between the rs2991364 polymorphisms was observed. However, 10 cases in which SVEP1 gene expression was significantly decreased among the risk allele group of rs2991364 were extracted and compared with the major allele group (37 cases), and the expression of ITGA9, Angioprotein2, and FOXC2, which are downstream signal genes of SVEP1, was significantly decreased (Figures 1, 6-10).

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) furthermore, a decrease in the SVEP1 gene causes a decrease in the expression of downstream genes, in particular the expression of the FOXC2 gene, which is closely involved in the vascular network 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, and 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 the possibility that the vascular network and vascular remodeling mechanism are vulnerable. This result supports the validity of the hypothesis that the 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.

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 mechanisms.
According to the mechanism of this hypothesis, the drug that drains the accumulated body fluid caused by organ failure is not limited to tolvaptan, and it is also possible that the administration effect of tolvaptan is difficult to obtain in rs2991364 risk allele carriers, as long as it is a vasopressin V2 receptor antagonist. That is, as shown in Figure 11, when tissue fluid accumulates in an organ due to organ failure, rs2991364 risk allele carriers have fragile vascular formation, so that it is difficult to collect tissue fluid by the vascular system from birth, and it is difficult to collect tissue fluid by the vascular system in the first place, and as a result, it is considered 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 antagonist that promotes water excretion in the kidney is administered, the pharmacological effect of vasopressin V2 receptor antagonist tends to be difficult to be recognized.
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 effect of tolvaptan in cardiac edema.

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

In the present invention, the contents of the papers, published patent publications, etc. explicitly stated in this specification may be incorporated by citation in their entirety.

According to the present invention, the efficacy of a drug for draining accumulated body fluid caused by organ failure can be predicted in advance with high accuracy.
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 (7)

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) . In addition to the single nucleotide polymorphism identified by the rs number: rs2991364,
The method for predicting a drug efficacy according to claim 1, comprising detecting in combination 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 (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, rs1914 98017, rs117878160, rs2861960, rs6738731, rs139777869, rs7606583, rs54 8655480, rs9818982, rs73093151, rs28722753, rs190430047, rs146677134 , rs147398967, rs201727204, rs146161796, rs146974727, rs76934337, rs2 8724305, rs139082715, rs202039738, rs558310023, rs373193142, rs31083 99, rs146184416, rs149351859, rs117186452, rs192877030, rs368145706, r s142073533, rs144330279, rs185364332, rs78306822, rs73746613, rs1175 65657, rs142078510, rs536782924, rs13358143, rs117217176, rs14551313 5, rs138786151, rs75479599, rs138025072, rs73416655, rs9358200, rs117 952795, rs187188904, rs17279589, rs116255027, rs41279866, rs2242480, r s28371759, rs34784390, rs12721627, rs4646474, rs368125720, rs2001128 28, rs148391916, rs57649937, rs77535773, rs149607532, rs76148287, rs69 56740, rs140143507, rs144155862, rs190856909, rs142154029, rs1388821 92, rs150413109, rs149631544, rs188579453, rs138430375, rs146943578, r s117377672, rs4739103, rs76335247, rs139552329, rs143804156, rs92991 86, rs4978937, rs143816412, rs16907787, rs150949929, rs141204966, rs1 50684513, rs143150122, rs190968040, rs79875703, rs78332808, rs151314 241, rs74236102, rs141277618, rs12227766, rs17012289, rs73166235, rs73 166243, rs56127528, rs17487644, rs11290772, rs145849286, rs139361259 , rs141696046, rs147493082, rs149200875, rs7150903, rs79594269, rs140 034212, rs186766499, rs11852611, rs118115242, rs6496797, rs4541027, r s285753, rs79740453, rs3760436, rs11361451, rs115523756, rs60619936, r s142571079, rs146099575, rs62103373, rs183728329, rs764818965, rs369 720664, rs76726076, rs2071643, rs137967792, rs56689668, rs5201, rs520 2, rs2070097, rs5904376, rs781940941, rs781941435, rs4898372, rs73627 255, rs3761527, rs3761529, rs138461346, rs12559136, rs4898458, rs15127 5155, rs142251323, rs186986494, rs2872601, rs5945370, rs183746106, rs 5945368, rs5987180, rs55702376, rs5987179, rs111399454, rs7886319, rs7 052686, 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. 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 in combination a single nucleotide polymorphism identified by at least one or more rs numbers 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, which include a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes) . The drug efficacy prediction method according to any one of claims 1 to 3, wherein the drug is a V2 receptor antagonist (excluding a method for diagnosing a human, which includes a step of determining a human's physical condition or a prescription based on a physical condition for medical purposes) . The method for predicting efficacy of a drug according to any one of claims 1 to 4, which predicts efficacy of the drug based on the following clinical factors in addition to detecting the single nucleotide polymorphism identified by the RS number: rs2991364 (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) :
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. 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. 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.

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