Medicine, compound, cyst-reducing agent, and marker

Abstract

Provided are a medicine, a compound, a cyst-reducing agent, and a marker with which it is possible to treat autosomal dominant polycystic kidney disease (ADPKD).　The medicine of the present disclosure contains at least one of a histone deacetylase (HDAC) inhibitor and a TROP2 inhibitor, and is used for treating autosomal dominant polycystic kidney disease (ADPKD).

Description

Pharmaceuticals, compounds, cyst-shrinking agents, and markers

[0001] This disclosure relates to pharmaceuticals, compounds, cyst-reducing agents, and markers.

[0002] Various pharmaceuticals are being developed for the treatment of autosomal dominant polycystic kidney disease (ADPKD). Patent Document 1 discloses the use of thienopyridone derivatives or pharmaceutical compositions containing them in the treatment of ADPKD.

[0003] Special Publication No. 2023-510039

[0004] To date, various pharmaceuticals have been developed for the treatment of ADPKD, as disclosed in Patent Document 1. However, increasing the variety of pharmaceuticals is desirable from the perspective of expanding treatment options. Furthermore, the development of biomarkers that contribute to the diagnosis of ADPKD is also important.

[0005] Therefore, the purpose of this disclosure is to provide pharmaceuticals, compounds, cyst-reducing agents, and markers that can treat autosomal dominant polycystic kidney disease (ADPKD).

[0006] To achieve the aforementioned objectives, the medicament of this disclosure comprises at least one of a histone deacetylase (HDAC) inhibitor, a TROP2 inhibitor, and a PI3K inhibitor, and is used to treat autosomal dominant polycystic kidney disease (ADPKD).

[0007] The pharmaceuticals disclosed herein, including B-cell scavenging agents, are used to treat autosomal dominant polycystic kidney disease (ADPKD).

[0008] The markers of this disclosure are autosomal dominant polycystic kidney disease (ADPKD) markers comprising at least one selected from the group consisting of SFN, LAMB3, LAMC2, LAMA3, GABRP, SFRP2, TRIM29, SERPINE1, NNMT, TROP2, SLPI, KRT7, KRT17, KRT19, ITGB6, MMP7, CLDN4, and COL1A1.

[0009] The markers of this disclosure are autosomal dominant polycystic kidney disease (ADPKD) markers comprising at least one selected from the group consisting of ADAM8, NISCH, AKAP13, ZBP1, EIF4G3, PRRC2A, MCPH1, IFITM2, MEF2D, ATXN2L, ADGRG1, PTK2B, BSG, BRD2, RHBDL2, and HELZ2.

[0010] This is an autosomal dominant polycystic kidney disease (ADPKD) marker comprising at least one selected from the group consisting of BAG1, BAG6, SIRPB1, STXBP2, WDR18, SSC4, ZBP1, CCDC88B, NISCH, and ANKRD9.

[0011] The compounds of this disclosure or their pharmaceutically acceptable salts are represented by the following chemical formula (1): In the above chemical formula (1), X is a hydrogen atom or an oxygen atom, Y is a carbon atom or a heteroatom, Z is an aryl group which may have substituents, and L １ L is a single bond, a saturated or unsaturated C1-C6 hydrocarbon group which may have substituents, or an amide group (-CO-NH-), ２ L is a single bond or a saturated or unsaturated C1-C6 hydrocarbon group which may have substituents, ３ This is a covalent bond connecting X and the aromatic ring.

[0012] The compounds of this disclosure or their pharmaceutically acceptable salts are represented by the following chemical formula (2): In the chemical formula (2) above, W is a saturated or unsaturated hydrocarbon group having a cyclic structure, and the atoms constituting the hydrocarbon group may or may not contain heteroatoms, Ar １ Ar ２ , and Ar ３ Each of these structures contains an aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms, and they may be identical or different from each other.

[0013] The treatment prediction markers disclosed herein are autosomal dominant polycystic kidney disease (ADPKD) treatment prediction markers that include a gene associated with at least one selected from the group consisting of negative regulation of the vascular endothelial growth factor signaling pathway, regulation of apoptosis in smooth muscle cells, negative regulation of the cellular response to vascular endothelial growth factor stimulation, secondary palate formation, endoderm formation, positive regulation of leukocytosis, regulation of the vascular endothelial growth factor signaling pathway, and myelin formation.

[0014] The therapeutic predictive markers disclosed herein include mitochondrial translation elongation, mitochondrial translation initiation, mitochondrial translation termination, mitochondrial translation, protein repair, fibronectin matrix formation, RCbl transport in the body, androgen biosynthesis, nucleotide diphosphate and triphosphate interconversion, hyaluronic acid uptake and degradation, regulated necrosis, transcription-mediated nucleotide excision repair (TC-NER), and RHOBTB. This is a predictive marker for autosomal dominant polycystic kidney disease (ADPKD) treatment, comprising a gene associated with at least one selected from the group consisting of: the GTPase cycle, programmed cell death, membrane transport, regulation of necroptotic cell death, RIPK1-mediated controlled necrosis, gap-filling DNA repair synthesis and ligation in TC-NER, assembly of [2Fe-2S] clusters, regulation of protein transport to mitochondria, assembly of iron-sulfur clusters, assembly of metal-sulfur clusters, carboxylic acid degradation processes, maintenance of apical-basal cell polarity, maintenance of apical-basal polarity in epithelial cells, DNA repair, regulation that promotes the establishment of protein localization to telomeres, and regulation that promotes the establishment of protein localization to mitochondria.

[0015] This disclosure provides pharmaceuticals, compounds, cyst-reducing agents, and markers that can treat autosomal dominant polycystic kidney disease (ADPKD).

[0016] Figure 1 shows the results of gene expression in ADPKD tissue sections. Figures 1(A) and (B) show the results of spatial transcriptome analysis. Figure 1(C) shows the detailed analysis results of expressed genes. Figure 2 shows the results of other gene expression in ADPKD tissue sections. Figure 2(A) shows the results of spatial transcriptome analysis. Figure 2(B) shows SLPI gene expression in all clusters of renal cells. Figure 3 shows the results of a drug repository search conducted to explore therapeutic targets for ADPKD. Figure 4A shows the results of spatial transcriptome analysis of HDAC6 in cystic cells. Figure 4B shows the results of spatial transcriptome analysis of HDAC7 in cystic cells. Figure 4C shows the results of spatial transcriptome analysis of SIRT2 in cystic cells. Figure 4D shows the results of spatial transcriptome analysis of HDAC1 in cystic cells. Figure 4E shows the results of spatial transcriptome analysis of HDAC1 in cells from healthy individuals. Figure 4F shows the expression of HDAC6 in each cell. Figure 4G shows the expression of HDAC7 in each cell. Figure 4H shows the expression of SIRT2 in each cell. Figure 5 shows the results of differential gene expression analysis in patients with potentially treatable ADPKD. Figure 6A shows the results of pathway analysis (Reactome) of the top 500 upward regulatory genes in the Control group versus the ABCDE group. Figure 6B shows the results of pathway analysis (Reactome) of the top 500 downward regulatory genes in the Control group versus the ABCDE group. Figure 6C shows the results of pathway analysis (GO) of the top 500 downward regulatory genes in the Control group versus the ABCDE group. Figure 7A shows the results of gene expression in healthy individuals. Figure 7B shows the results of gene expression in ADPKD patients. Figure 7C shows an unknown cell cluster located in the cyst boundary region. Figure 8 shows the results of spatial transcriptome analysis of human kidney tissue affected by ADPKD. Figure 8(A) is a schematic diagram of the methodological framework.Figure 8(B) shows a UMAP visualization of an integrated spatial transcriptomics dataset annotated by cell type. Figure 8(C) is a map of spatial transcriptomics annotated by cell type. Figure 8(D) is a graph quantifying the number of cells for each annotated cell type. Figure 9 shows the expression of ADPKD-specific genes in diseased kidney tissue based on integrated analysis of single-cell and spatial transcriptomics. Figure 10A shows the results of examining key cyst-related genes in an independent ADPKD single-cell transcriptome dataset. Figure 10B shows the results of analyzing single-cell sequencing data from human iPSC-derived kidney ADPKD organoids. Figure 11 shows the roles of ADPKD-specific genes in epithelial remodeling, ECM organization, and proliferation signaling revealed by functional enrichment analysis. Figure 11(A) shows the results of enrichment analysis of GO biological processes. Figure 11(B) shows the results of intracellular components of GO. Figure 11(C) shows the results of GO molecular function analysis. Figure 11(D) shows the results from WikiPathway. Figure 12 shows that cystic cells in ADPKD originate from the distal convoluted tubule and have a transcriptional program that exhibits high proliferative activity. Figure 12(A) is a diagram showing spatial transcriptomics data obtained from ADPKD kidney tissue projected using UMAP and annotated for each cell type. Figure 12(B) shows a gradient of differentiation centered on cystic cell clusters, obtained by pseudo-temporal analysis of single cells. Figure 12(C) is an entropy map showing transcriptional instability. Figure 12(D) is a feature plot showing spatial enrichment of cell types associated with the distal convoluted tubule. Figure 12(E) shows the expression of loop of Henle. Figure 12(F) shows that COI is enriched in the cystic cell population. Figure 12(G) is a correlation heatmap showing a strong positive correlation between cyst-related genes and cell proliferation markers. Figure 13 shows the extracellular matrix-integrin signaling between cystic cells, endothelial cells, and distal tubular cells in ADPKD.Figure 13(A) is a coded diagram showing statistically significant intercellular interactions between annotated cell populations in ADPKD kidney tissue. Figure 13(B) is a dot plot showing ligand-receptor interaction pairs significantly enriched in COI-initiated signaling. Figure 14 is a diagram showing signatures associated with inflammation, coagulation, and proliferation of endothelial and distal tubular cells in ADPKD, based on functional enrichment analysis. Figure 14(A) is a Circos plot showing GO term enrichment for endothelial cells in ADPKD tissue. Figure 14(B) is a diagram showing top-level enrichment items of GO biological processes in endothelial cells. Figure 14(C) is a diagram showing the results of WikiPathways 2024 enrichment analysis in endothelial cells. Figure 14(D) is a diagram showing GO biological process enrichment in distal tubular cells. Figure 14(E) is a diagram showing the results of WikiPathways 2024 enrichment analysis in distal tubular cells. Figure 15A shows mutant genes specific to the low-risk group. Figure 15B shows mutant genes specific to the medium-risk group. Figure 15C shows mutant genes specific to the high-risk group. Figure 15D shows mutant genes specific to all risk groups. Figure 16 shows the number of each cell type in peripheral blood. Figures 16(A) to (E) show the number of CD4 cells, CD8 cells, Mo / Mac (monocyte / macrophage), megakaryocytes, and total B cells, respectively. Figure 17 shows the number of each cell type and predicted cytokine concentration in subsets of total B cells. Figures 17(A) to (E) show the number of total B cells, naive B cells, B-exhausted B cells, total memory B cells, and immature B cells, respectively. Figures 17(F) and (G) show the predicted concentrations of APRIL (A Proliferation-Inducing Ligand) cytokine and BAFF (B-cell Activating Factor) cytokine, respectively. Figure 18 shows the results of spatial transcriptome analysis in ADPKD tissue sections.Figure 19 is a graph showing the results of propidium iodide (PI) elimination and cell viability evaluation by flow cytometry 24 hours after CUDC-907 treatment. Figure 20 is a figure showing dose-dependent changes in cyst diameter and total cyst area (A: vehicle, B: 1 μM, C: 5 μM, D: 10 μM) and cyst morphological changes associated with CUDC-907 treatment. Figure 21 is another figure showing cyst morphological changes. Figure 22 is a graph showing RNA-seq analysis results showing that the expression of BAG6 (A), SIRPB1 (B), and WDR18 (C) was significantly increased in ADPKD patients compared to the control. Figure 23 is a graph showing RNA-seq analysis results showing that the expression levels of TSSC4 (A), ZBP1 (B), CCDC88B (C), NISCH (D), and ANKRD9 (E) were significantly elevated in ADPKD patients compared to the control. Figure 24 is a graph showing the results of RNA-seq analysis, indicating that the expression levels of STXBP2 (A) and BAG1 (B) were significantly lower in ADPKD patients compared to controls. Figure 25 is a Cox proportional hazards analysis result showing that the expression levels of the aforementioned genes were independently associated with the risk of progression in a demographically adjusted Cox proportional hazards model, and significant hazard ratios were observed in each category of the Mayo imaging classification. Figure 26 is a Cox proportional hazards analysis result showing that the expression levels of the aforementioned genes were significantly associated with the risk of progression in a Cox proportional hazards model adjusted for clinical and laboratory covariates, including htTKV, serum creatinine, BUN, albumin, eGFR, and Hb.

[0017] <Definitions> In this disclosure, "disease" means autosomal dominant polycystic kidney disease (ADPKD) unless otherwise specified.

[0018] In this disclosure, “biological sample” means a sample derived from a living organism, and a sample from which cells constituting the living organism can be extracted or isolated.

[0019] In this disclosure, “regenerative cell population” means a population of cells that have regenerative function. The regenerative function means the function of repairing or improving the function of tissues, organs, or tissues, and it is sufficient for the cell to perform any one of these functions. Specific examples of the regenerative function include the proliferation activity of vascular endothelial cells and / or the promotion of angiogenesis.

[0020] In this disclosure, “treatment” means therapeutic treatment and / or preventive treatment. In this disclosure, “treatment” means the treatment, cure, prevention, suppression, remission, improvement of a disease, condition, or disorder, or the cessation, suppression, reduction, or delay of the progression of a disease, condition, or disorder. In this disclosure, “prevention” means a reduction in the likelihood of developing a disease or condition, or a delay in the development of a disease or condition. The “treatment” may be, for example, treatment of a patient who develops the disease in question, or treatment of an animal model of the disease in question.

[0021] In this disclosure, “effective dose” means an amount sufficient to produce a therapeutic effect on the disease when administered to a patient with the disease.

[0022] In this disclosure, “Subject” means an animal or a cell, tissue, or organ of animal origin, and is used in a sense to include humans in particular. The animals mean humans and non-human animals. Examples of non-human animals include mammals such as mice, rats, rabbits, dogs, cats, cattle, horses, pigs, monkeys, dolphins, and sea lions.

[0023] The following is an explanation of this disclosure with examples, but this disclosure is not limited to the examples below and can be modified as desired. Furthermore, each explanation in this disclosure is interchangeable with one another unless otherwise specified. In this disclosure, when the expression "~" is used, it is used to include the numerical or physical value before and after it. Also, in this disclosure, the expression "A and / or B" includes "A only," "B only," and "both A and B."

[0024] In this disclosure, “medicine” may also mean, for example, an inhibitor, a preventive, a progression inhibitor, a progression halter, and / or an ameliorative agent. Furthermore, the medicines of this disclosure only need to significantly suppress the symptoms or progression of the disease compared to the absence of the medicines of this disclosure (non-administration conditions), and the disease may have progressed compared to the start of administration.

[0025] In this disclosure, the term "marker" may refer to at least one selected from, for example, a diagnostic marker, a marker for estimating the risk of developing or becoming ill, a marker for evaluating the risk of disease progression or progression, a prognostic marker, a marker for stratifying the severity of the disease, a marker for determining the effectiveness of treatment or prevention, and a marker for predicting treatment.

[0026] <Pharmaceuticals> The pharmaceuticals of this disclosure are pharmaceuticals used to treat autosomal dominant polycystic kidney disease (ADPKD), comprising at least one of a histone deacetylase (HDAC) inhibitor, a TROP2 inhibitor, and a PI3K inhibitor. The HDAC inhibitor includes, for example, an inhibitor that inhibits the activity of at least one enzyme selected from the group consisting of HDAC1, HDAC2, HDAC3, HDAC6, HDAC7, HDAC10, and SIRT2. The TROP2 inhibitor includes, for example, an inhibitor that inhibits the enzyme activity of TROP2. The PI3K inhibitor includes, for example, an inhibitor that inhibits the enzyme activity of PI3Kα, PI3Kβ, and PI3Kδ.

[0027] The HDAC inhibitor includes, for example, any of the following inhibitors (1) to (4): (1) an inhibitor that inhibits the enzyme activity of HDAC6 and HDAC7; (2) an inhibitor that inhibits the enzyme activity of HDAC6 and SIRT2; (3) an inhibitor that inhibits the enzyme activity of HDAC6, HDAC7, and SIRT2; (4) an inhibitor that inhibits the enzyme activity of HDAC6 and HDAC1.

[0028] The HDAC inhibitor includes, for example, at least one of vorinostat and trichostatin A (TSA).

[0029] As another aspect, the medicament of the present disclosure may be a medicament used for treating the above-mentioned ADPKD, which comprises a B cell depleting agent. The B cell depleting agent includes, for example, an agent targeting the CD20 antigen. The B cell depleting agent includes, for example, rituximab.

[0030] The method of using (administration conditions) the medicament of the present disclosure is not particularly limited. For example, the medicament may be administered to the administration target.

[0031] The administration target includes, for example, cells, tissues or organs. The administration target includes, for example, humans and non-human animals excluding humans. The non-human animals include, for example, mammalian animals such as mice, rats, rabbits, dogs, cats, cows, horses, pigs, monkeys, dolphins, and seals.

[0032] The cells are not particularly limited. For example, cells derived from humans and mice can be mentioned. The cells exclude, for example, human fertilized eggs, as well as cells in human embryos and human individuals.

[0033] The administration conditions of the medicament of the present disclosure are not particularly limited and can be determined appropriately according to the administration target. When the administration target is cells separated from a living body, for example, methods such as using transfection reagents, electroporation methods, and nanobubble methods can be mentioned. When the administration target is a living body, for example, parenteral administration, oral administration, etc. can be mentioned. The parenteral administration includes, for example, topical administration, subcutaneous administration, intravenous administration, etc. The administration conditions of the medicament of the present disclosure, such as the number of administrations and the dosage, are not particularly limited.

[0034] The form of the medicament of the present disclosure is not particularly limited. For example, it is an injection solution, an intravenous drip solution, an eye drop solution, an eye ointment, a skin ointment, a patch, an inhalant, a liquid, an aerosol, a pump spray, an oral preparation, etc.

[0035] In the medicament of the present disclosure, the compounding amount of the HDAC inhibitor is not particularly limited. In the medicament of the present disclosure, the compounding amount of the HDAC inhibitor is preferably included at a concentration that can achieve the above-mentioned administration conditions.

[0036] The pharmaceuticals of this disclosure may, for example, contain additives as needed, preferably pharmaceutically acceptable additives or pharmaceutically acceptable carriers. The additives are not particularly limited and include, for example, base ingredients, excipients, colorants, lubricants, binders, disintegrants, stabilizers, preservatives, flavoring agents such as fragrances, and so on. In the pharmaceuticals of this disclosure, the amount of additives is not particularly limited as long as it does not interfere with the function of the pharmaceuticals of this disclosure.

[0037] The pharmaceuticals of this disclosure may include, for example, the compounds or cyst-reducing agents of this disclosure as described later. The pharmaceuticals of this disclosure may be used, for example, in vivo or in vitro.

[0038] The pharmaceutical product of this disclosure is expected to be suitably used, for example, for diseases caused by ADPKD or for the treatment of ADPKD.

[0039] <Compound>

[0040] The compounds disclosed herein are compounds represented by the following chemical formula (1) or pharmaceutically acceptable salts thereof.

[0041] In the above chemical formula (1), X is a hydrogen atom or an oxygen atom.

[0042] In the chemical formula (1) above, Y is a carbon atom or a heteroatom. The heteroatom may be, for example, a nitrogen atom, an oxygen atom, or a sulfur atom.

[0043] In the chemical formula (1) above, Z is an aryl group which may have substituents. The substituent may be, for example, an alkyl group, an unsaturated aliphatic hydrocarbon group, an aryl group, a heteroaryl group, a halogen, a hydroxyl group (-OH), a mercapto group (-SH), or an alkylthio group (-SR, where R is an alkyl group).

[0044] In the above chemical formula (1), L １is a single bond, a C1-C6 saturated or unsaturated hydrocarbon group which may have a substituent, or an amide group (—CO—NH—). The substituent may be, for example, an alkyl group, an unsaturated aliphatic hydrocarbon group, an aryl group, a heteroaryl group, a halogen, a hydroxy group (—OH), a mercapto group (—SH), or an alkylthio group (—SR, where R is an alkyl group). The amide group can also be referred to as an amide bond, for example.

[0045] In the chemical formula (1), L ２ is a single bond, or a C1-C6 saturated or unsaturated hydrocarbon group which may have a substituent. The substituent may be, for example, an alkyl group, an unsaturated aliphatic hydrocarbon group, an aryl group, a heteroaryl group, a halogen, a hydroxy group (—OH), a mercapto group (—SH), or an alkylthio group (—SR, where R is an alkyl group).

[0046] In the chemical formula (1), L ３ is a covalent bond that connects X and the aromatic ring. The covalent bond is, for example, a single bond when X is a hydrogen atom, and a double bond when X is an oxygen atom.

[0047] The compound represented by the chemical formula (1) may be, for example, a compound represented by the following chemical formula (I) or (II).

[0048] Here, the compound represented by the chemical formula (I) inhibits the activities of, for example, HDAC6, HDAC7, and SIRT2. Also, the compound represented by the chemical formula (II) inhibits the activities of, for example, HDAC6 and HDAC1.

[0049] The compound of the present disclosure may be a compound represented by the following chemical formula (2) or a pharmaceutically acceptable salt thereof.

[0050] In the chemical formula (2) above, W is a saturated or unsaturated hydrocarbon group having a cyclic structure, and the hydrocarbon group may or may not contain heteroatoms among the atoms constituting the hydrocarbon group. The heteroatoms may be, for example, nitrogen atoms, oxygen atoms, or sulfur atoms. The hydrocarbon group may have, for example, a monocyclic structure, a fused ring structure, a bridging ring structure, or a spiro-ring structure. The hydrocarbon group may have, for example, a cyclic structure of a 3-20 membered ring, a 5-12 membered ring, a 5-7 membered ring, or a 6 membered ring. In the chemical formula (2) above, W may include, for example, cycloalkyl groups such as cyclopropyl, cyclobutyl, cyclopentyl, cyclohexyl, cycloheptyl, and cyclooctyl; cycloalkenyl groups such as cyclopentenyl, cyclohexenyl, and cyclohexadienyl; bridging cyclic groups such as norbornyl; fused cyclic groups such as indanyl and tetralinyl; and heterocyclic groups such as morpholinyl, piperidinyl, piperazinyl, tetrahydrofuranil, and tetrahydropyranil. In the above chemical formula (2), W is preferably, for example, morpholinyl.

[0051] In the above chemical formula (2), Ar １ Ar ２ , and Ar ３ Each of these structures contains an aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms, and may be the same as or different from each other. The aromatic ring may be, for example, a monocyclic or fused aromatic ring having 5 to 20 carbon atoms, and may be a 5-membered aromatic ring (e.g., thiophene ring, furan ring, pyrrole ring, imidazole ring, oxazole ring, thiazole ring, etc.), a 6-membered aromatic ring (e.g., benzene ring, pyridine ring, pyrimidine ring, pyrazine ring, pyridazine ring, triazine ring, etc.), or a fused aromatic ring (e.g., naphthalene ring, anthracene ring, phenanthrene ring, indene ring, quinoline ring, isoquinoline ring, indole ring, benzothiophene ring, benzofuran ring, carbazole ring, etc.). The heteroatom may be, for example, a nitrogen atom, an oxygen atom, or a sulfur atom.

[0052] In the above chemical formula (2), Ar １For example, is the six-membered aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms. In the chemical formula (2), Ar １ It is preferable that this is, for example, a pyridine ring.

[0053] In the above chemical formula (2), Ar ２ For example, is the condensed aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms. In the chemical formula (2), Ar ２ Preferably, this is, for example, a 7-thieno[3,2-d]pyrimidine ring.

[0054] In the above chemical formula (2), Ar ３ For example, is the six-membered aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms. In the chemical formula (2), Ar ３ Preferably, this is a pyrimidine ring, for example.

[0055] The compound represented by the chemical formula (2) may be, for example, the compound represented by the following chemical formula (III). The compound represented by the following chemical formula (III) may also be, for example, Fimepinostat (CUDC-907).

[0056] Here, the compound represented by the chemical formula (III) inhibits the activity of, for example, class I PI3Ks, HDAC1, HDAC2, HDAC3, and HDAC10.

[0057] The compounds represented by chemical formulas (1) and (2) may be, for example, isomers. Examples of such isomers include tautomers or stereoisomers. Examples of such tautomers or stereoisomers include all theoretically possible tautomers or stereoisomers. Furthermore, in this disclosure, the stereoconfiguration of each substituent is not particularly limited. The compounds represented by chemical formulas (1) and (2) may be, for example, hydrates or solvates of the compounds represented by chemical formulas (1) and (2) or pharmaceutically acceptable salts thereof.

[0058] The pharmaceutically acceptable salts are not particularly limited and include, for example, alkali metal salts such as sodium salts and potassium salts; alkaline earth metal salts such as calcium salts and magnesium salts; ammonium salts; aliphatic amine salts such as trimethylamine salt, triethylamine salt, dichlorohexylamine salt, ethanolamine salt, diethanolamine salt, triethanolamine salt, and brocaine salt; aralkylamine salts such as N,N-dibenzylethylenediamine; heterocyclic aromatic amine salts such as pyridine salt, picoline salt, quinoline salt, and isoquinoline salt; quaternary ammonium salts such as tetramethylammonium salt, tetraethylammonium salt, benzyltrimethylammonium salt, benzyltributylammonium salt, methyltrioctylammonium salt, and tetrabutylammonium salt; arginine salt, lysine salt, aspartate salt, and gluten salt. Examples include amino acid salts such as tamate; inorganic acid salts such as hydrochloride, sulfate, nitrate, phosphate, carbonate, bicarbonate, and perchlorate; acetate, propionate, succinate, glycolate, lactate, maleate, fumarate, tartrate, malate, citrate, ascorbate, hydroxymaleate, pyruvate, phenylacetate, benzoate, 4-aminobenzoate, anthranilate, 4-hydroxybenzoate, salicylate, 4-aminosalicylate, pamoate, gluconate, nicotinate, and other aliphatic organic acids or aromatic organic acid salts; and sulfonates such as methanesulfonate, isethionate, ethanesulfonate, benzenesulfonate, halobenzenesulfonate, toluenesulfonate, naphthalenesulfonate, sulfanilate, and cyclohexylsulfamate.

[0059] The compounds of this disclosure may be composed of, for example, multiple components.

[0060] The administration conditions for a pharmaceutical product containing the compound of this disclosure or a pharmaceutically acceptable salt thereof are not particularly limited, and the form of administration, timing of administration, dosage, etc., can be appropriately set depending on the type of target, etc. The target and administration conditions for a pharmaceutical product containing the compound of this disclosure or a pharmaceutically acceptable salt thereof can be described, for example, by referring to the description of the target and administration conditions for the pharmaceutical product of this disclosure.

[0061] Further descriptions of the compounds in this disclosure can be found, for example, by referring to the above-mentioned descriptions of the pharmaceuticals in this disclosure.

[0062] <Use of the compound or a pharmaceutically acceptable salt thereof> This disclosure relates to the use of the compound represented by chemical formula (1) or a pharmaceutically acceptable salt thereof for use in reducing the cyst or treating the ADPKD. This disclosure also relates to the use of the compound represented by chemical formula (1) or a pharmaceutically acceptable salt thereof for manufacturing a medicament for reducing the cyst or treating the ADPKD. This disclosure may, for example, rely on the descriptions of the medicament and the compound or pharmaceutically acceptable salt thereof of this disclosure.

[0063] <Cyst-Reducing Agent> The cyst-reducing agent of the present disclosure comprises the compound of the present disclosure or a pharmaceutically acceptable salt thereof. The cyst is, for example, a cyst occurring in the kidney. The cyst-reducing agent of the present disclosure may be used, for example, in vivo or in vitro. The compound of the present disclosure may consist of, for example, multiple components. In this case, the cyst-reducing agent of the present disclosure may also be called, for example, a cyst-reducing composition.

[0064] The administration conditions for the cyst-reducing agent of this disclosure are not particularly limited, and the administration form, timing, dosage, etc. can be appropriately set depending on the type of target, etc. The target and administration conditions for the cyst-reducing agent of this disclosure can be described by referring to, for example, the description of the target and administration conditions for the pharmaceutical product of this disclosure.

[0065] Further descriptions of the cyst-reducing agents of this disclosure can be found, for example, by referring to the descriptions of pharmaceuticals and compounds of this disclosure above.

[0066] <Cyst Reduction Method> The cyst reduction method of this disclosure uses, for example, the cyst reduction agent of this disclosure. The cyst reduction method of this disclosure is characterized by the use of the cyst reduction agent of this disclosure, and other steps and conditions are not particularly limited. The cyst reduction method of this disclosure can be described by reference to the description of the cyst reduction agent of this disclosure.

[0067] The cyst reduction method of this disclosure includes, for example, an administration step of administering the cyst reduction agent of this disclosure, specifically an administration step of administering the cyst reduction agent to a target. The cyst reduction agent may be administered in vitro or in vivo. For the target and administration conditions of the cyst reduction agent of this disclosure, refer, for example, to the description of the target and administration conditions for the cyst reduction agent of this disclosure.

[0068] <Markers> The markers of this disclosure are SFN (Stratifin), LAMB3 (Laminin Subunit Beta 3), LAMC2 (Laminin Subunit Gamma 2), LAMA3 (Laminin Subunit Alpha 3), GABRP (Gamma-Aminobutyric Acid Type A Receptor Pi Subunit), SFRP2 (Secreted Frizzled Related Protein 2), TRIM29 (Tripartite Motif Containing 29), SERPINE1 (Serpin Family E Member 1), NNMT (Nicotinamide N-Methyltransferase), TROP2 (trophoblastic cell surface antigen 2) (also sometimes referred to as TACSTD2 (Tumor Associated Calcium Signal Transducer 2)), SLPI (Secretory Leukocyte Peptidase Inhibitor), KRT7 (Keratin 7), and KRT17 (Keratin 7). 17) An ADPKD marker comprising at least one selected from the group consisting of KRT19 (Keratin 19), ITGB6 (Integrin Subunit Beta 6), MMP7 (Matrix Metallopeptidase 7), CLDN4 (Claudin 4), and COL1A1 (Collagen Type I Alpha 1 Chain).

[0069] Further, the markers of the present disclosure include ADAM8 (ADAM Metallopeptidase Domain 8), NISCH (Nischarin), AKAP13 (A-Kinase Anchoring Protein 13), ZBP1 (Z-DNA Binding Protein 1), EIF4G3 (Eukaryotic Translation Initiation Factor 4 Gamma 3), PRRC2A (Proline Rich Coiled-Coil 2A), MCPH1 (Microcephalin 1), IFITM2 (Interferon Induced Transmembrane Protein 2), MEF2D (Myocyte Enhancer Factor 2D), ATXN2L (Ataxin 2 Like), ADGRG1 (Adhesion G Protein-Coupled Receptor G1), PTK2B (Protein Tyrosine Kinase 2) Beta), BSG (Basigin), BRD2 (Bromodomain Containing 2), RHBDL2 (Rhomboid The ADPKD marker includes at least one selected from the group consisting of Like 2) and HELZ2 (Helicase With Zinc Finger 2).

[0070] Furthermore, the markers of this disclosure are ADPKD markers that include at least one selected from the group consisting of BAG1 (BCL2 Associated Athanogene 1), BAG6 (BCL2 Associated Athanogene 6), SIRPB1 (Signal Regulatory Protein Beta 1), STXBP2 (Syntaxin Binding Protein 2), WDR18 (WD Repeat Domain 18), SSC4 (Tumor Suppressor Candidate 4), ZBP1 (Z-DNA Binding Protein 1), CCDC88B (Coiled-Coil Domain Containing 88B), NISCH (Nischarin), and ANKRD9 (Ankyrin Repeat Domain 9).

[0071] The markers of this disclosure are ADPKD treatment predictive markers that include a gene associated with at least one selected from the group consisting of negative regulation of the vascular endothelial growth factor signaling pathway, regulation of the apoptotic process of smooth muscle cells, negative regulation of the cellular response to vascular endothelial growth factor stimulation, secondary palate formation, endoderm formation, positive regulation of leukocytosis, regulation of the vascular endothelial growth factor signaling pathway, and myelin formation.

[0072] The markers of this disclosure include mitochondrial translation elongation, mitochondrial translation initiation, mitochondrial translation termination, mitochondrial translation, protein repair, fibronectin matrix formation, RCbl transport in the body, androgen biosynthesis, nucleotide diphosphate and triphosphate interconversion, hyaluronic acid uptake and degradation, regulated necrosis, transcription-mediated nucleotide excision repair (TC-NER), and RHOBTB. This ADPKD treatment prediction marker includes a gene associated with at least one selected from the group consisting of the following: GTPase cycle, programmed cell death, membrane transport, regulation of necroptotic cell death, RIPK1-mediated controlled necrosis, gap-filling DNA repair synthesis and ligation in TC-NER, assembly of [2Fe-2S] clusters, regulation of protein transport to mitochondria, assembly of iron-sulfur clusters, assembly of metal-sulfur clusters, carboxylic acid degradation processes, maintenance of apical-basal cell polarity, maintenance of apical-basal polarity in epithelial cells, DNA repair, regulation that promotes the establishment of protein localization to telomeres, and regulation that promotes the establishment of protein localization to mitochondria.

[0073] The markers of this disclosure may be used alone, in combination with any of the markers of this disclosure, or in combination with at least one of other known ADPKD markers and treatment prediction markers. The markers of this disclosure can be used, for example, in testing and measurement methods for ADPKD.

[0074] <ADPKD Test Method> The ADPKD test method of this disclosure includes an evaluation step of evaluating the markers of this disclosure in a subject. The test method of this disclosure is characterized by evaluating the markers of this disclosure in the evaluation step, and other steps and conditions are not particularly limited. The ADPKD test method of this disclosure can be used to test for the likelihood of developing ADPKD. The ADPKD test method of this disclosure can be used with reference to the description of the markers of this disclosure.

[0075] In the test method of this disclosure, the subject may be, for example, a human, a non-human animal other than a human, and the non-human animal may be, as mentioned above, a mammal such as a mouse, rat, dog, monkey, rabbit, sheep, or horse.

[0076] In the test method of this disclosure, the type of biological sample is not particularly limited and may include, for example, body fluids, body fluid-derived cells, organs, tissues, or cells separated from a living organism. Examples of body fluids include blood samples, and specific examples include whole blood, serum, plasma, etc. Examples of body fluid-derived cells include blood-derived cells, and specifically, blood cells such as hematocytes, leukocytes, and lymphocytes. Examples of biological samples may be derived from organs where ADPKD can occur, such as the kidney.

[0077] The evaluation of the marker in the evaluation step may be, for example, an analysis of the presence or absence of the marker in the biological sample (qualitative analysis), an analysis of the amount of the marker (quantitative analysis), an evaluation of the presence or absence of the marker, or an evaluation of the degree of the marker.

[0078] In the evaluation step described above, the marker to be evaluated is any of the markers disclosed herein.

[0079] In the evaluation step described above, any of the markers of this disclosure may be used alone, in combination with any of the markers of this disclosure, or in combination with other known ADPKD markers.

[0080] If the marker to be evaluated in this disclosure is a gene, the evaluation of the marker in this disclosure can be carried out, for example, by measuring the expression level of the marker. The evaluation of expression is carried out, for example, based on the expression of the gene's mRNA. The mRNA expression may be measured, for example, by next-generation sequencing technology such as whole transcriptome sequencing (WTS). Specifically, for example, the mRNA expression can be analyzed by sequencing RNA extracted from a biological sample and performing a comprehensive gene expression analysis based on the obtained data. Examples of methods for evaluating mRNA expression include gene amplification methods utilizing reverse transcription reactions such as reverse transcription (RT)-PCR. Specifically, for example, this method involves synthesizing cDNA from mRNA by a reverse transcription reaction and amplifying the gene using the cDNA as a template. The evaluation methods for various markers are not particularly limited, and known methods can be adopted.

[0081] The test method of this disclosure may further include a step (test step) of testing the risk of developing ADPKD in the subject by comparing the evaluation results of the markers of this disclosure in the subject's biological sample (hereinafter also referred to as the "subject's biological sample") with a reference control. The reference values ​​are not particularly limited and may include, for example, the evaluation results of the markers of this disclosure in healthy individuals, ADPKD patients, and ADPKD patients at each stage of progression. In the case of prognosis evaluation, the reference may be, for example, the evaluation results of the markers of this disclosure before or after treatment (for example, immediately after treatment) in the same subject.

[0082] The aforementioned reference values ​​can be obtained, for example, using biological samples isolated from healthy individuals and / or ADPKD patients (hereinafter also referred to as "reference biological samples") as described above. In the case of prognosis evaluation, for example, in addition to or instead of the aforementioned reference biological samples, reference biological samples isolated from the same subject after treatment may be used. The aforementioned controls may be evaluated, for example, simultaneously with the subject's biological sample, or they may be evaluated beforehand. The latter is preferable because, for example, it becomes unnecessary to obtain controls each time the subject's biological sample is evaluated. It is preferable that the subject's biological sample and the reference biological sample are collected under the same conditions, for example, and that the markers of this disclosure are evaluated under the same conditions.

[0083] In the aforementioned testing process, the method for evaluating the subject's risk of developing ADPKD is not particularly limited and can be appropriately determined depending on the type of reference value. Specifically, if the evaluation result of the marker disclosed in the subject's biological sample is significantly different from the evaluation result of the marker disclosed in a reference biological sample of a healthy person, if it is the same as the evaluation result of the marker disclosed in a reference biological sample of an ADPKD patient (no significant difference), and / or if it is significantly different from the evaluation result of the marker disclosed in a reference biological sample of an ADPKD patient, the subject can be evaluated as being at risk or high risk of developing ADPKD, or not at risk or low risk. Furthermore, in the aforementioned testing process, the progression of ADPKD can be evaluated by comparing the evaluation result of the marker disclosed in the subject's biological sample with the evaluation result of the marker disclosed in a reference biological sample of an ADPKD patient for each progression stage. Specifically, if the subject's biological sample yields evaluation results comparable to, for example, the reference biological sample for any of the progression stages (i.e., there is no significant difference), the subject can be evaluated as potentially being at the progression stage.

[0084] In the aforementioned testing process, when evaluating the prognosis, for example, evaluation and judgment may be made in the same manner as described above, or the evaluation results of the markers disclosed in a reference biological sample of the same subject after treatment may be used as a reference value. Specifically, if the evaluation results of the markers disclosed in the subject's biological sample are significantly different from those of the control, the subject can be evaluated as having a risk of recurrence or worsening after treatment, or as having no risk or a low risk of recurrence. Also, if the evaluation results of the markers disclosed in the subject's biological sample are the same as the reference value (no significant difference), the subject can be evaluated as having no risk or a low risk of recurrence after treatment.

[0085] In this disclosure, for example, biological samples from the same subject may be collected over time, and the evaluation results of the markers of this disclosure in the biological samples may be compared. By doing so, for example, if the evaluation results change over time, it may be possible to determine whether the likelihood of developing the disease has increased, decreased, or is healing.

[0086] The test method of this disclosure may further include, for example, a step (administration step) of administering an ADPKD treatment drug to subjects who are evaluated as being at risk or highly at risk of developing ADPKD in the test step, or to subjects who are evaluated as being at risk of relapse or exacerbation after treatment. In this case, the test method of this disclosure can also be called a test and treatment method for ADPKD. The treatment drug may be, for example, the pharmaceutical and compound or pharmaceutically acceptable salt of this disclosure, but may also be a conventionally known treatment drug.

[0087] <Method for Predicting Treatment of ADPKD> The method for predicting treatment of ADPKD according to this disclosure includes an evaluation step of evaluating the treatment prediction markers disclosed herein in a subject. The method for predicting treatment of ADPKD according to this disclosure is characterized by evaluating the treatment prediction markers disclosed herein in the evaluation step, and other steps and conditions are not particularly limited. According to the method for predicting treatment of ADPKD according to this disclosure, the possibility of treating ADPKD can be predicted. The method for predicting treatment of ADPKD according to this disclosure can be made by reference to the description of the treatment prediction markers disclosed herein.

[0088] In the treatment prediction method of the present disclosure, the subject may be, for example, a human, a non-human animal other than a human, and the non-human animal may be, as mentioned above, a mammal such as a mouse, rat, dog, monkey, rabbit, sheep, or horse.

[0089] In the treatment prediction method of this disclosure, the type of biological sample is not particularly limited and may include, for example, body fluids, body fluid-derived cells, organs, tissues, or cells separated from a living organism. Examples of body fluids include blood samples, and specific examples include whole blood, serum, plasma, etc. Examples of body fluid-derived cells include blood-derived cells, and specifically, blood cells such as hematocytes, leukocytes, and lymphocytes. The biological sample may be derived from an organ where ADPKD can occur, such as the kidney.

[0090] The evaluation of the marker in the evaluation step may be, for example, an analysis of the presence or absence of the therapeutic prediction marker of the Disclosure in the biological sample (qualitative analysis), an analysis of the amount of the marker (quantitative analysis), an evaluation of the presence or absence of the marker, or an evaluation of the degree of the marker.

[0091] In the evaluation step described above, the marker to be evaluated is any of the markers disclosed herein.

[0092] In the evaluation step described above, any of the markers of this disclosure may be used alone, in combination with any of the markers of this disclosure, or in combination with other known ADPKD treatment prediction markers.

[0093] If the therapeutic prediction marker to be evaluated in this disclosure is a gene, the therapeutic prediction marker in this disclosure can be evaluated, for example, by measuring the expression level of the marker. The evaluation of expression is performed, for example, based on the expression of the gene's mRNA. The mRNA expression may be measured, for example, by next-generation sequencing technology such as whole transcriptome sequencing (WTS). Specifically, for example, the mRNA expression can be analyzed by sequencing RNA extracted from a biological sample and performing a comprehensive gene expression analysis based on the obtained data. Examples of methods for evaluating mRNA expression include gene amplification methods utilizing reverse transcription reactions such as reverse transcription (RT)-PCR. Specifically, for example, a method in which cDNA is synthesized from mRNA by a reverse transcription reaction and the cDNA is used as a template for gene amplification. The evaluation methods for various markers are not particularly limited, and known methods can be adopted.

[0094] The treatment prediction method of the present disclosure may further include a step (prediction step) of predicting the treatability of ADPKD in the subject by comparing the evaluation results of the treatment prediction markers of the present disclosure in the subject's biological sample (hereinafter also referred to as the "subject's biological sample") with a reference control. The reference values ​​are not particularly limited and may include, for example, the evaluation results of the treatment prediction markers of the present disclosure in healthy individuals, ADPKD patients, and ADPKD patients at each stage of progression. In the case of prognosis evaluation, the reference may be, for example, the evaluation results of the treatment prediction markers of the present disclosure before or after treatment (for example, immediately after treatment) of the same subject.

[0095] The aforementioned reference values ​​can be obtained, for example, using biological samples isolated from healthy individuals and / or ADPKD patients (hereinafter also referred to as "reference biological samples") as described above. In the case of prognosis evaluation, for example, in addition to or instead of the aforementioned reference biological samples, reference biological samples isolated from the same subject after treatment may be used. The aforementioned controls may be evaluated, for example, simultaneously with the subject's biological sample, or they may be evaluated beforehand. The latter is preferable because, for example, it becomes unnecessary to obtain controls each time the subject's biological sample is evaluated. It is preferable that the subject's biological sample and the reference biological sample are collected under the same conditions, for example, and that the evaluation of the treatment prediction markers of this disclosure is performed under the same conditions.

[0096] In the prediction step, the method for predicting the treatability of a subject's ADPKD is not particularly limited and can be appropriately determined depending on the type of reference value. Specifically, if the evaluation result of the treatment prediction marker disclosed in the subject's biological sample is significantly different from the evaluation result of the treatment prediction marker disclosed in the reference biological sample of a healthy person, if it is the same as the evaluation result of the treatment prediction marker disclosed in the reference biological sample of an ADPKD patient (no significant difference), and / or if it is significantly different from the evaluation result of the treatment prediction marker disclosed in the reference biological sample of an ADPKD patient, the subject can be evaluated as having a treatability or high probability of treatment for ADPKD, or as having no treatability or low probability of treatment.

[0097] The treatment prediction method of this disclosure may, for example, include a step of administering an ADPKD therapeutic drug (administration step) to subjects who have been evaluated in the prediction step as having a potential or high probability of being treatable for ADPKD. In this case, the treatment prediction method of this disclosure can also be called the treatment prediction and treatment method for ADPKD. The therapeutic drug may be, for example, the pharmaceutical product of this disclosure, but may also be a conventionally known therapeutic drug.

[0098] <ADPKD Test Reagent> The ADPKD test reagent of this disclosure includes the marker evaluation reagent of this disclosure and is used in the ADPKD disease risk test method of this disclosure. The test reagent of this disclosure is characterized by performing the ADPKD disease risk test based on the evaluation results of the marker of this disclosure, and other configurations and conditions are not particularly limited. The ADPKD disease risk test method of this disclosure can be performed simply using the test reagent of this disclosure. The test reagent of this disclosure only needs to be able to evaluate the marker of this disclosure and can be used, for example, in the test method of this disclosure. The test reagent of this disclosure can be used by reference to the description of the marker and test method of this disclosure.

[0099] The type of evaluation reagent is not particularly limited and can be appropriately set, for example, depending on the type of marker of this disclosure. The evaluation reagent may be, for example, a protein evaluation reagent or a gene mRNA evaluation reagent.

[0100] The test reagent of this disclosure may, for example, consist only of the evaluation reagent for the marker, or it may consist of other evaluation reagents for the marker. The marker to be measured by the evaluation reagent is, as described above, the marker of this disclosure.

[0101] The marker to be measured by the evaluation reagent may be any of the markers disclosed herein alone, in combination with any of the markers disclosed herein, or in combination with other known ADPKD markers.

[0102] The type of evaluation reagent for the marker can be appropriately set according to the type of marker.

[0103] If the marker is the protein, the evaluation reagent may be a binding substance that binds to the protein, and a specific example is an antibody. In this case, the test reagent of this disclosure preferably further includes a detection substance that detects the binding of the protein and the antibody. The detection substance may be, for example, a combination of a detectable labeled antibody against the antibody and a substrate for the label.

[0104] In this case, the detection reagent may be labeled with, for example, enzymes such as alkaline phosphatase (ALP) and luciferase; radioisotopes; particles; fluorescent substances such as fluorescent proteins; dyes such as pigments; luminescent substances; electron donors; chromogenic substrates for enzymes; haptens such as DNP (dinitrophenol) and TNP (trinitrophenol), vitamins such as biotin; etc. The particles may be, for example, metal particles such as gold and silver, latex particles such as colored latex particles, and magnetic particles. Examples of magnetic particles include solid-phase magnetic particles on which proteins are immobilized, and specific examples include anti-DNP antibody magnetic particles on which anti-DNP antibodies are immobilized, and StAvi solid-phase magnetic particles on which streptavidin (StAvi) is immobilized. The detection reagent may also be labeled with, for example, enzyme substrates, divalent iron (Fe) depending on the type of detection method. ２＋ It may be used in combination with reducing agents such as ). The substrate of the enzyme is not particularly limited and can be appropriately set depending on the enzyme. For example, when ALP is used as the enzyme, the substrate of the enzyme may be CDP-Star (registered trademark), NBT, etc.

[0105] If the marker is the gene, for example, the evaluation reagent may include a reverse transcription reagent for the mRNA and reagents used for library preparation, a sequencing reagent for comprehensively decoding the base sequence of mRNA used in whole transcriptome sequencing, or reagents used for designing and analyzing a specific gene panel (target gene analysis). The sequencing reagent may include, for example, reagents for efficiently extracting mRNA from a biological sample and reagents necessary for preparing a sequence library based on next-generation sequencing technology. The reagents used for designing and analyzing the specific gene panel may include, for example, primers, probes, or capture arrays for specifically capturing the target gene. These may be appropriately designed, for example, based on the gene sequence.

[0106] The test reagent of this disclosure may further include, for example, other components. These components include, for example, the carrier, the enzyme substrate, a buffer, a washing solution, and instructions for use. Each component of the test reagent of this disclosure may be housed in a separate container, or it may be housed in the same container, either mixed or unmixed. In the former case, the test reagent of this disclosure may also be called a test kit. If the test reagent of this disclosure includes the substrate, the substrate is housed, for example, in a separate container from the detection reagent.

[0107] <Method for Evaluating ADPKD Markers> The method for evaluating markers of this disclosure includes a step of evaluating the marker, and the marker includes the marker of this disclosure. Other steps and conditions in the evaluation method of this disclosure are not particularly limited. The evaluation method of this disclosure can be based on the descriptions of the marker, test method and test reagent of this disclosure.

[0108] <ADPKD Treatment Prediction Reagent> The ADPKD treatment prediction reagent of this disclosure includes the marker evaluation reagent of this disclosure and is used in the ADPKD treatment prediction method of this disclosure. The treatment prediction reagent of this disclosure is characterized by performing ADPKD treatment prediction based on the evaluation results of the marker of this disclosure, and other configurations and conditions are not particularly limited. The treatment prediction reagent of this disclosure allows for easy treatment prediction of ADPKD. The treatment prediction reagent of this disclosure only needs to be able to evaluate the marker of this disclosure and can be used, for example, in the treatment prediction method of this disclosure. The description of the marker and treatment prediction method of this disclosure can be incorporated into the description of the treatment prediction reagent of this disclosure.

[0109] The type of evaluation reagent is not particularly limited and can be appropriately set, for example, depending on the type of marker of this disclosure. The evaluation reagent may be, for example, a protein evaluation reagent or a gene mRNA evaluation reagent.

[0110] The treatment prediction reagent of this disclosure may, for example, include only the evaluation reagent for the marker, or it may include other evaluation reagents for the marker. The marker to be measured by the evaluation reagent is, as described above, the marker of this disclosure.

[0111] The marker to be measured by the evaluation reagent may be any of the markers disclosed herein alone, in combination with any of the markers disclosed herein, or in combination with other known ADPKD treatment prediction markers.

[0112] The type of evaluation reagent for the marker can be appropriately set according to the type of marker.

[0113] If the marker is the protein, the evaluation reagent may be a binding substance that binds to the protein, and a specific example is an antibody. In this case, the therapeutic prediction reagent of the present disclosure preferably further includes, for example, a detection substance that detects the binding of the protein and the antibody. The detection substance may be, for example, a combination of a detectable labeled antibody against the antibody and a substrate for the label.

[0114] In this case, the detection reagent may be labeled with, for example, enzymes such as alkaline phosphatase (ALP) and luciferase; radioisotopes; particles; fluorescent substances such as fluorescent proteins; dyes such as pigments; luminescent substances; electron donors; chromogenic substrates for enzymes; haptens such as DNP (dinitrophenol) and TNP (trinitrophenol), vitamins such as biotin; etc. The particles may be, for example, metal particles such as gold and silver, latex particles such as colored latex particles, and magnetic particles. Examples of magnetic particles include solid-phase magnetic particles on which proteins are immobilized, and specific examples include anti-DNP antibody magnetic particles on which anti-DNP antibodies are immobilized, and StAvi solid-phase magnetic particles on which streptavidin (StAvi) is immobilized. The detection reagent may also be labeled with, for example, enzyme substrates, divalent iron (Fe) depending on the type of detection method. ２＋ It may be used in combination with reducing agents such as ). The substrate of the enzyme is not particularly limited and can be appropriately set depending on the enzyme. For example, when ALP is used as the enzyme, the substrate of the enzyme may be CDP-Star (registered trademark), NBT, etc.

[0115] If the marker is the gene, for example, the evaluation reagent may include a reverse transcription reagent for the mRNA and reagents used for library preparation, a sequencing reagent for comprehensively decoding the base sequence of mRNA used in whole transcriptome sequencing, or reagents used for designing and analyzing a specific gene panel (target gene analysis). The sequencing reagent may include, for example, reagents for efficiently extracting mRNA from a biological sample and reagents necessary for preparing a sequence library based on next-generation sequencing technology. The reagents used for designing and analyzing the specific gene panel may include, for example, primers, probes, or capture arrays for specifically capturing the target gene. These may be appropriately designed, for example, based on the gene sequence.

[0116] The treatment prediction reagent of the present disclosure may further include, for example, other components. These components include, for example, the carrier, the enzyme substrate, a buffer, a washing solution, and other reagents, as well as instructions for use. Each component of the treatment prediction reagent of the present disclosure may be housed in a separate container, or it may be housed in the same container, either mixed or unmixed. In the former case, the treatment prediction reagent of the present disclosure may also be called a treatment prediction kit. If the treatment prediction reagent of the present disclosure includes the substrate, the substrate is housed, for example, in a separate container from the detection reagent.

[0117] <Method for Evaluating ADPKD Treatment Prediction Markers> The method for evaluating markers of the present disclosure includes a step of evaluating the marker, and the marker includes the marker of the present disclosure. Other steps and conditions in the evaluation method of the present disclosure are not particularly limited. The evaluation method of the present disclosure can be based on the descriptions of the marker, treatment prediction method, and treatment prediction reagent of the present disclosure.

[0118] <ADPKD Therapeutic Drugs and Methods for Treating ADPKD> The ADPKD therapeutic drugs of this disclosure may, for example, be the pharmaceuticals, compounds, or inhibitory substances that suppress the onset of ADPKD. The methods for treating ADPKD of this disclosure (hereinafter referred to as the "treatment methods") are characterized by including an administration step of administering the ADPKD therapeutic drug of this disclosure to a patient. The therapeutic drugs and treatment methods of this disclosure are characterized by suppressing the onset of ADPKD, and other configurations and conditions are not particularly limited. ADPKD can be treated according to the therapeutic drugs and treatment methods of this disclosure. The therapeutic drugs and treatment methods of this disclosure can be described by reference to the descriptions of the pharmaceuticals, compounds or pharmaceutically acceptable salts thereof, markers, test methods, treatment prediction methods, test reagents, and treatment prediction reagents of this disclosure.

[0119] The aforementioned disease-suppressing substances include, for example, substances that suppress the transcription of mRNA from the gene, substances that cleave the transcribed mRNA, and substances that suppress the translation of proteins from mRNA. Specific examples include RNA interference agents such as siRNA, shRNA, and miRNA, and expression-suppressing nucleic acid molecules such as antisense and ribozymes. The disease-suppressing substances may also include, for example, proteins and nucleic acids that constitute genome editing technology, or vectors that encode them. The proteins may include, for example, CRISPR (Clustered Regularly Interspaced Short Palindromic Examples of enzymes include Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9, Cas10, Csy1, Csy2, Csy3, Cse1, Cse2, Csc1, Csc2, Csa5, Csn2, Csm2, C Examples include sm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1, Csb2, Csb3, Csx17, Csx14, Csx10, Csx16, CsaX, Csx3, Csx1, Csx15, Csf1, Csf2, Csf3, Csf4, etc. The nucleic acids include, for example, crRNA and tracrRNA, or single-stranded nucleic acids in which these are linked via a linker. In this case, the disease-suppressing substance is designed, for example, to have a base sequence that anneals with the target sequence in crRNA that is complementary to the base sequence encoding the gene. The disease-suppressing substance may be used alone or in combination of two or more types.

[0120] The administration conditions for the ADPKD therapeutic agent in this disclosure are not particularly limited, and the administration form, timing, dosage, etc., can be appropriately set depending on the type of recipient, etc. The recipient and administration conditions for the ADPKD therapeutic agent in this disclosure can be described by referring to, for example, the description of recipient and administration conditions for the pharmaceutical product in this disclosure.

[0121] The present disclosure will be described in detail below using examples, but the present disclosure is not limited to the embodiments described in the examples. Unless otherwise specified, commercially available reagents and kits were used according to their respective protocols.

[0122] [Example 1] This study included 53 ADPKD patients. Each patient provided appropriate signed informed consent. 16 ml of peripheral blood was collected from all patients for whole transcriptome sequencing analysis. Two patients who had undergone kidney transplantation and whose kidney samples were stored in paraffin blocks were used for spatial transcriptome analysis. The paraffin blocks were cut to a thickness of 3 micrometers. QC checks were performed using a 4200 Tapestation system (Agilent Technologies, Inc.), resulting in DV-200 values ​​of 56 and 60. Subsequently, tissue sections, appropriately stained with hematoxylin and eosin, were inserted into 11 mm special transcriptome slides provided by 10x Genomics using a Visium instrument (10x Genomics).

[0123] The spatial transcriptome library was successfully constructed and sequenced using a NovaSeq 6000 sequencing system (Illumina, Inc.). Next, to analyze the internal content, the raw data, converted to HDF5 format using Cell Ranger (10x Genomics), was analyzed with a magnifying glass. The Seurat pipeline was also used afterward.

[0124] 1.1 Sample Quality Control RNA integrity was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.).

[0125] 1.2 Library Preparation for Transcriptome Sequencing Non-strand-Specific Library Messenger RNA was purified from total RNA using poly-T oligo-attach magnetic beads. After fragmentation, the first-strand cDNA was synthesized using random hexamer primers, followed by the synthesis of the second-strand cDNA. The library was prepared by end repair, A-tailing, adapter ligation, size selection, amplification, and purification. The library was quantified by Qubit (Thermo Fisher Scientific, Inc.) and real-time PCR, and the size distribution was detected using an Agilent 2100 Bioanalyzer.

[0126] Strand-specific library messenger RNA was purified from total RNA using poly-T oligo-attach magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers, and the second strand cDNA was synthesized using dUTP instead of dTTP. Directional libraries were prepared by end repair, A-tailing, adapter junction, size selection, amplification, and purification. They were quantified by Qubit and real-time PCR, and size distribution was detected using an Agilent 2100 Bioanalyzer.

[0127] 1.3 After clustering and quality control of the sequencing libraries, the different libraries were pooled based on effective concentration and target data volume, and then subjected to a NovaSeq 6000 sequencing system (Illumina, Inc.). The basic principle of sequencing is "Sequencing by Synthesis," in which fluorescently labeled dNTPs, DNA polymerase, and adapter primers are added to the sequencing flow cell and amplified. As each sequencing cluster extends its complementary strand, the addition of each fluorescently labeled dNTP releases a corresponding fluorescent signal. The sequencer captures these fluorescent signals and converts them into sequence peaks using computer software to obtain sequence information of the target fragment.

[0128] 2. Bioinformatics Analysis Pipeline 2.1 Data Quality Control Raw data (raw reads) in Fastq format were first processed using an in-house Perl script. In this step, reads containing adapters, reads containing poly-N, and low-quality reads were removed from the raw data to obtain clean data (clean reads). At the same time, the Q20, Q30, and GC content of the clean data were calculated. All subsequent analyses were performed based on high-quality clean data.

[0129] 2.2 Mapping Reads to the Reference Genome The reference genome and gene model annotation files were downloaded directly from the genome website. The reference genome index was constructed using Hisat2 v2.0.5, and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. Hisat2 was chosen as the mapping tool because it can generate a database of splice joins based on the gene model annotation files, resulting in better mapping results than other splicing mapping tools.

[0130] 2.3 Quantification of Gene Expression Level Characteristics Counts v1.5.0-p3 was used to count the number of reads mapped to each gene. The FPKM (Fragments Per Kilobase of exon per Million mapped reads) for each gene was calculated based on the gene length and the number of reads mapped to that gene. FPKM is currently the most common method for estimating gene expression levels, as it simultaneously considers the influence of sequencing depth and gene length on read counts.

[0131] 2.4 Differential Expression Analysis and DESeq2 Using Biological Replicates Differential expression analysis in two conditions / groups was performed using the DESeq2 R package (1.20.0). DESeq2 provides a statistical program for determining expression differences in digital gene expression data using a model based on a negative binomial distribution. The resulting p-values ​​were adjusted using the Benjamini-Hochberg method to control for false positives. A corrected p-value of 0.05 or less, and |log 2 (foldchange)| ≥ 1, was set as the threshold for significant expression difference.

[0132] For edgeR without biological replication, prior to differential gene expression analysis, the normalization coefficient was scaled for each sequencing library to eliminate differences in sequencing depth between samples, and the read count was adjusted using the edgeR R package (3.22.5). Subsequently, differential gene expression analysis was performed. The obtained p-values ​​were adjusted using the Benjamini-Hochberg method to control the error detection rate. A threshold for significant expression difference was set if the adjusted p-value was ≤0.05 and |log 2 (foldchange)| ≥ 1.

[0133] 2.5 Analysis of Differentially Expressed Genes by GO and KEGG GO (Gene Ontology) analysis of differentially expressed genes was performed using the R package clusterProfiler, corrected for gene length bias. GO items with a corrected p-value of less than 0.05 were considered significantly enriched by differentially expressed genes. KEGG is a database resource for understanding the advanced function and availability of biological systems such as cells, organisms, and ecosystems from molecular-level information such as large molecular datasets generated by genome sequencing and other high-throughput experimental techniques (http: / / www.genome.jp / kegg / ). The clusterProfiler package was used to verify the statistical enrichment of expressed genes in KEGG pathways. The Reactome database integrates various reactions and biological pathways of human model species. Reactome pathways with a corrected p-value of less than 0.05 were considered significantly enriched by expressed genes. The DO (Disease Ontology) database describes the function of human genes and diseases. DO pathways with adjusted p-values ​​less than 0.05 were considered significantly enriched by genes with altered expression levels. The DisGeNET database integrates human disease-related genes. DisGeNET pathways with adjusted p-values ​​less than 0.05 were considered significantly enriched by genes with altered expression levels. ClusterProfiler software was used to examine the statistical enrichment of genes with altered expression levels in Reactome pathways, DO pathways, and DisGeNET pathways.

[0134] 2.6 Gene Set Enrichment Analysis Gene set enrichment analysis (GSEA) is a computational method for determining whether a predefined set of genes shows significant and consistent differences between two biological states. Genes are ranked according to the degree of difference in expression levels between the two samples, and it is verified whether the predefined set of genes is enriched higher or lower on the list. Gene set enrichment analysis can also take into account subtle changes in expression. A local version of the GSEA analysis tool (http: / / www.broadinstitute.org / gsea / index.jsp) was used, and the GO, KEGG, Reactome, DO, and DisGeNET datasets were used individually in GSEA.

[0135] 2.7 SNP Analysis SNP calls were performed using GATK (v4.1.1.0) software. The raw vcf files were filtered using GATK's standard filtering method and other parameters (cluster:3; WindowSize:35; QD < 2.0; FS > 30.0; DP < 10).

[0136] 2.8 Alternative Splicing (AS) Analysis AS is an important mechanism that controls gene expression and protein diversity. rMATS (4.1.0) software was used to analyze AS events.

[0137] 2.9 Protein-protein interaction (PPI) analysis of genes with different expression levels. PPI analysis of genes with different expression levels was performed based on the STRING database, which summarizes known and predicted protein-protein interactions.

[0138] 2.10 Fusion Gene Analysis A fusion gene refers to a chimeric gene formed by the fusion of all or part of the sequences of two genes, generally resulting from chromosomal translocation, deletion, or other reasons. STAR-fusion software (1.9.0) was used to detect fusion genes. STAR-fusion is a software package that detects fusion transcripts using the fusion output results of STAR alignment, and includes STAR Alignment, STAR-Fusion.predict, and STAR-Fusion.filter. The accuracy of the results was ensured by using STAR-Fusion.filter to correct the prediction results of STAR-Fusion.

[0139] <Results> Figure 1 shows the results of gene expression in ADPKD tissue sections. Figures 1(A) and (B) show the results of spatial transcriptome analysis. Figure 1(C) shows the detailed analysis results of expressed genes. Analysis of cyst-specific genes revealed that TACSTD2 is highly expressed in the cyst boundary region, as shown in Figures 1(A) and (B). Further detailed analysis revealed that unknown clusters such as "6-1, 6-2, 6-3, 6-4, 6-5, 6-5.1" are highly expressed in TACSTD2, and these are revealed to be cyst-specific biomarkers (Figure 1(C)).

[0140] Next, Figure 2 shows the results of other gene expression in ADPKD tissue sections. Figure 2(A) shows the results of spatial transcriptome analysis. Figure 2(B) shows SLPI gene expression in all clusters of renal cells. As shown in Figure 2(A), high expression of the SLPI gene was shown in the cyst boundary region (indicated in parentheses in the figure). This indicates that the SLPI gene is actively involved in cyst formation. Furthermore, as shown in Figure 2(B), high expression of the SLPI gene was shown in "clusters 6-1 to 6-5.1".

[0141] Figure 7A shows the results of gene expression in healthy individuals. Figure 7B shows the results of gene expression in ADPKD patients. As shown in Figure 7A, healthy individuals do not express the genes KRT17, SFN, LAMB3, LAMC2, LAMA3, GABRP, SFRP2, and TRIM29. On the other hand, as shown in Figure 7B, ADPKD patients were found to specifically express these genes. Furthermore, as shown in Figure 7B, these genes were found to be located in the cyst boundary region. Figure 7C shows an unknown cell cluster located in the cyst boundary region. As shown in Figure 7C, all of these genes were found to be highly expressed in the unknown cell cluster.

[0142] Figure 3 shows the results of a drug repository search conducted to explore therapeutic targets for ADPKD. As shown in Figure 3, the non-selective HDAC inhibitors vorinostat and trichostatin A were shown to strongly suppress kidney tissue and cells, based on data from the drug repository.

[0143] Figures 4A-D show the search results for molecules highly expressed in cystic cells, representing the spatial transcriptome analysis results for HDAC6, HDAC7, SIRT2, and HDAC1, respectively. Figure 4E shows the spatial transcriptome analysis results for HDAC1 in cells from healthy individuals. As shown in Figure 4, HDAC6, HDAC7, SIRT2, and HDAC1 were found to be highly expressed in the kidney tissue of ADPKD patients. On the other hand, in healthy individuals, HDAC1 expression was minimal or almost absent.

[0144] Figures 4F to 4H show the expression levels of HDAC6, HDAC7, and SIRT2 in individual cells, respectively. The results from Figures 4A to 4H suggest that HDAC inhibitors containing any of the following inhibitors (1) to (4) are effective as pharmaceuticals used to treat ADPKD. (1) Inhibitors that inhibit the enzymatic activity of HDAC6 and HDAC7 (2) Inhibitors that inhibit the enzymatic activity of HDAC6 and SIRT2 (3) Inhibitors that inhibit the enzymatic activity of HDAC6, HDAC7, and SIRT2 (4) Inhibitors that inhibit the enzymatic activity of HDAC6 and HDAC1

[0145] For example, compound (I) below is known as an inhibitor that inhibits the enzymatic activity of HDAC6, HDAC7, and SIRT2, suggesting its potential usefulness in the treatment of ADPKD.

[0146] For example, compound (II) below is known as an inhibitor that inhibits the enzymatic activity of HDAC6 and HDAC1, suggesting its potential usefulness in the treatment of ADPKD.

[0147] Furthermore, inhibitors that inhibit the enzymatic activity of HDAC6 and SIRT2, as disclosed in the literature (Sinatra, Laura, et al. "Development of first-in-class dual Sirt2 / HDAC6 inhibitors as molecular tools for dual inhibition of tubulin deacetylation." Journal of Medicinal Chemistry 66.21 (2023): 14787-14814.), are suggested to be useful in the treatment of ADPKD.

[0148] Figure 5 shows the results of differential gene expression analysis in patients with treatable ADPKD. As shown in Figure 5, at least one gene selected from the group consisting of negative regulation of the vascular endothelial growth factor signaling pathway, regulation of the apoptotic process in smooth muscle cells, negative regulation of the cellular response to vascular endothelial growth factor stimulation, secondary palate formation, endoderm formation, positive regulation of leukocytosis, regulation of the vascular endothelial growth factor signaling pathway, and myelin formation was found to be a gene associated with the treatability of ADPKD. By examining the presence or absence of expression of these genes, it is possible, for example, to stratify patients before initiating treatment and identify which patients will respond well to treatment. This is thought to be useful for personalized medicine.

[0149] Figure 6A shows the results of pathway analysis (Reactome) of the top 500 upregulatory genes in the Control group versus the ABCDE group. Figure 6B shows the results of pathway analysis (Reactome) of the top 500 downregulatory genes in the Control group versus the ABCDE group. Figure 6C shows the results of pathway analysis (GO) of the top 500 downregulatory genes in the Control group versus the ABCDE group. The ABCDE group was classified according to the Mayo classification based on TKV (total kidney volume) growth.

[0150] As shown in Figure 6A, genes related to mitochondrial translation elongation, initiation, termination, protein repair, fibronectin matrix formation, RCbl transport in the body, androgen biosynthesis, interconversion of nucleotide diphosphates and triphosphates, and hyaluronic acid uptake and degradation were found to be biomarkers that show a good response to ADPKD treatment.

[0151] Furthermore, as shown in Figures 6B and 6C, it was found that a gene associated with at least one selected from the group consisting of controlled necrosis, transcription-linked nucleotide excision repair (TC-NER), RHOBTB GTPase cycle, programmed cell death, membrane transport, regulation of necroptotic cell death, RIPK1-mediated controlled necrosis, gap-filling DNA repair synthesis and ligation in TC-NER, [2Fe-2S] cluster assembly, regulation of protein transport to mitochondria, iron-sulfur cluster assembly, metal-sulfur cluster assembly, carboxylic acid degradation process, maintenance of apical-basal cell polarity, maintenance of apical-basal polarity in epithelial cells, DNA repair, regulation that promotes the establishment of protein localization to telomeres, and regulation that promotes the establishment of protein localization to mitochondria serves as a biomarker indicating a poor response (i.e., a poor response) to ADPKD treatment.

[0152] By examining the presence or absence of these gene expressions, it may be possible to stratify patients before initiating treatment and identify which patients are likely to respond well to treatment. This could then be used for personalized medicine.

[0153] [Example 2] All participants gave written informed consent for participation and organization provision in accordance with the Declaration of Helsinki. This study was approved by the Tokushukai Group Joint Ethics Committee and conducted in accordance with the principles outlined in the Declaration of Helsinki.

[0154] 1. Preparation of Visium FFPE Samples: RNA integrity from formalin-fixed, paraffin-embedded (FFPE) human kidney tissue was evaluated using the RNeasy FFPE Kit (Qiagen, catalog number: 73504), strictly following the manufacturer's instructions. Subsequently, RNA quality was assessed using an Agilent Bioanalyzer (Agilent Technologies, Inc.), and only samples with a DV200 value exceeding 50% were selected. Then, 5 μm thick tissue sections were mounted on 10 × Visium Spatial Gene Expression Slides. After deparaffinization of the tissue, the samples were stained with hematoxylin and eosin (H&E), and imaged using a microscope (Keyence Corporation, catalog number: BZ-X810) for comprehensive slide visualization. The post-imaging procedure included decrosslinking, probe hybridization, release, and extension. Subsequently, a library was constructed using the Single Index Kit TS Set A (10x Genomics, product name: PN-3000511) according to the manufacturer's guidelines. Library quality was verified using the Agilent Bioanalyzer (Agilent Technologies, Inc.). The prepared library was subjected to paired-end sequencing (2 × 150 bp) using the Illumina NovaSeq 6000 system (Illumina).

[0155] 2. Data Availability Pre-processed scRNA-seq data from healthy human kidney tissue, obtained from the Human Cell Atlas portal (https: / / explore.data.humancellatlas.org / projects / 77c13c40-a598-4036-807f-be09209ec2dd), was used for downstream analysis. The original version of this dataset is publicly available under accession number GSE202109. Gene expression profiles for the entire human tissue were obtained from the NCBI (National Center for Biotechnology Information) database using transcriptome data reported in RPKM (Reads Per Kilobase of exon per Million mapped reads) units and logarithmically transformed before analysis. The original bulk RNA sequencing data is available at "www.proteinatlas.org / about / download". SnRNA-seq data from ADPKD patients was obtained from the GEO database under accession number GSE185948.

[0156] 3. Spatial Data Preprocessing and Analysis Expression analysis, along with mapping, counting, and clustering, was performed using Space Ranger software version 2.0 with the GRCh38 (Genome Reference Consortium Human Build 38) reference genome. After initial quality control and filtering, a total of 2881 high-quality cells were retained for further analysis. The spatial localization patterns of different cell populations were visualized and analyzed using Loupe Browser (version 8.1.2, 10x Genomics). The Scanpy library (version 1.10.3) implemented in Python was used for processing and analyzing scRNA-seq data. Scanpy was used for data quality control, scaling, transformation, dimensionality reduction, expression analysis, and visualization. In data preprocessing, potential doublets were identified and removed using the scanpy.pp.scrublet() function. As a result of this preprocessing step, a total of 2759 cells were retained. The data were normalized and scaled using the scanpy.pp.normalize_per_cell() function. Subsequently, the nearest neighbor graph was constructed using the scanpy.pp.neighbors() function. Dimensionality reduction was performed using the Uniform Manifold Approximation and Projection (UMAP) algorithm implemented with the scanpy.tl.umap() function. Spatially degraded gene expression profiles from tissue sections were analyzed using the R Seurat package (version 5.1.0).

[0157] 4. Cell Annotation Annotation of clustered cell populations was performed using a combination of automated prediction with reference transcriptome atreze, particularly Azimuth28 and CellTypist (version 1.6.3), the “All Immune Low” model for immune cells, and expert curation based on marker genes established from the literature (Lake, BB et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619, 585-594 (2023)). Identification of biomarker genes for target cells was performed using differential expression analysis with Loupe Browser (version 8.1.2, 10x Genomics) and gene ranking based on logistic regression using Scanpy's rank_genes_groups() function, with a significance threshold of p < 0.05 for both methods. To characterize the biological, molecular functions, and cellular components of the identified gene signatures, GO and pathway enrichment analyses were performed using Enrichr 30. Significant terms (p < 0.05) were selected and interpreted the potential roles of these genes in cellular processes and signaling pathways.

[0158] 5. Ligand-Receptor Analysis Using the LIANA+ (version 1.5.0) framework in Python, we implemented the CellPhoneDB32 method to further investigate the interactions between target cells and other clusters through ligand-receptor analysis. Intercellular interactions with p < 0.05 were excluded from subsequent analysis. Subsequently, the interaction frequencies between each cell type pair were quantified, and the resulting interaction network was visualized using the pyCirclize (version 1.9.0) library in Python. Ligand-receptor interactions were also visualized using the liana.plot.dotplot() function.

[0159] 6. Cell Orbit Analysis The trajectory of a single cell was calculated using Palantir (version 1.3.6). The analysis was initiated by selecting a starting cell and performing pseudo-time inference using the run_palantir() function. Terminal states were selected using the palantir.utils.find_terminal_states() function based on the annotated cell type (DCT, LOH, COI), and differentiation probabilities were calculated using Markov chain modeling. In addition to the pseudo-time and differentiation probabilities for each terminal state, cellular entropy, which quantifies the degree of plasticity, was also calculated. Vector fields representing the state transitions of the transcriptome were inferred using Dynamo (version 1.4.1), and these transitions were visualized in low-dimensional space using UMAP, enabling detailed modeling of cell fate dynamics.

[0160] 7. Statistical Data Analysis Correlation analysis between COI biomarkers and proliferation genes was performed using the Python libraries Pingouin and Scipy. Pearson's correlation coefficient and Spearman's correlation coefficient were calculated, and the strength of the association and statistical significance were evaluated along with their respective p-values.

[0161] <Results> 1. Spatial Transcriptome Profiling of Human ADPKD Tissue Figure 8 shows the results of spatial transcriptome analysis of human kidney tissue affected by ADPKD. Figure 8(A) is a schematic diagram of the methodological framework. Figure 8(B) is a visualization of the integrated spatial transcriptomics dataset annotated by cell type using UMAP. Figure 8(C) is a map of spatial transcriptomics annotated by cell type. Figure 8(D) is a graph quantifying the number of cells for each annotated cell type. In Figure 8(D), the horizontal axis is the abbreviation for cell tumor, and the vertical axis is the count. In Figure 8, the cell type abbreviations refer to PT (proximal tubule), LOH (loop of Henle), PC (chief cell), DCT (distal convoluted tubule), IC-B (type B interstitial cell), ParEpiCell (parietal epithelial cell), Mes (mesangial cell), Peri (pericyte), PODO (podocyte), ENDO (endothelial cell), Fib Mat (matrix fibroblast), Fib / Mes (mesangial cell-to-fibroblast transition cell), SM / Fib (smooth muscle cell-to-fibroblast transition cell), B cell (B cell), T cell (T cell), DC (dendritic cell), Mac (macrophage), and Res Mac (resident macrophage).

[0162] Unsupervised clustering of transcriptome spots (visualized with UMAP) revealed populations of renal cell types, which were then annotated using known marker genes from published kidney studies (Figure 8(B), (C)). As a result, all major renal cell types, including immune cells, vascular cells, stromal fibroblasts, and multiple tubular epithelial subtypes, were identified within the ADPKD section. Of particular note was one cell group that stood out in the boundary region of cystic structures, which we call the "cells of interest (COI)" cluster. These cells, although not shown in the figure, were mainly localized around the separation walls of expanding microcysts. In the ADPKD sample, proximal tubular cells formed the largest cluster, followed by distal convoluted tubular cells, novel cyst-associated COIs, and loop of Henle cells (Figure 8(D)). Spatial mapping revealed that the epithelial cells covering the cyst (the newly discovered COI) were concentrated around the cyst, unlike other tubular and interstitial cell clusters (Figure 8(C)). This suggests the existence of a unique cell population associated with cyst formation. To clarify the characteristics of this new cell population, differential gene expression analysis was performed on the COI cluster.

[0163] 2. Identification of ADPKD-Specific Genes Figure 9 shows the expression of ADPKD-specific genes in diseased kidney tissue based on integrated analysis of single-cell and spatial transcriptomics. Figure 10A shows the results of examining key cyst-related genes in an independent ADPKD single-cell transcriptome dataset. Figure 10B shows the results of analyzing single-cell sequencing data from human iPSC-derived kidney ADPKD organoids.

[0164] Logistic regression ranking tests (false detection rate < 0.05) identified 10 biomarker genes as significantly enriched in the COI cluster compared to all other clusters. These genes showed marked upexpression in the cystic epithelial region of ADPKD tissue, but were virtually absent in healthy kidney tissue. For validation, the expression of these genes in ADPKD spatial data was compared with the publicly available healthy kidney scRNA-seq dataset (GSE202109). Since these biomarkers were hardly expressed or not expressed at all in healthy kidney data across all cell types, it was confirmed that the high expression of these genes is specific to the ADPKD cystic environment (Figure 9). In healthy reference cells, most clusters showed near-zero expression (plotted in baseline color), with only a few genes, such as KRT19, MMP7, TROP2, CLDN4, and KRT7, showing very sparse, low-level signals (rarely up to score 1).

[0165] To support this finding, a comparative analysis was performed using a publicly available mononuclear RNA sequencing (snRNA-seq) dataset of healthy kidney tissue (GSE185948). This revealed the absence of 10 ADPKD-specific genes, confirming the disease-specific expression profile (Figure 10A).

[0166] In contrast, in the cells covering ADPKD cysts, moderate to consistently high expression was observed for all 10 genes, mainly in the extensive areas surrounding and enclosing the smaller cysts. Interestingly, H&E stained images of ADPKD tissue sections (Figure 9) revealed that some small cysts contained thin septa or partition-like structures within their lumen. These appeared as delicate bands of epithelial cells extending across parts of the cyst's interior, effectively dividing the cyst into subcompartments.

[0167] Next, single-cell sequencing data from human iPSC-derived kidney ADPKD organoids were analyzed, revealing that the genes identified in Figure 10 were also expressed in these organoids (Figure 10B). Furthermore, the expression of the TROP2 gene was upregulated, indicating that targeting this gene would be effective. This suggests that, for example, Trodelvy®, which has activity against TROP2, may be an effective treatment for ADPKD.

[0168] The spatial transcriptome data presented in this disclosure provide a molecular background for these structures. Notably, the septa of microcysts contain cells expressing cyst-specific biomarkers. For example, COL1A1, NNMT, and MMP7 are co-expressed in adjacent regions, suggesting shared regulatory pathways. Taken together, these findings provide a comprehensive view that the cells covering the cysts represent a distinct set of genes involved in the formation of complex multicellular structures such as septa within developing cysts.

[0169] 3. Functional enrichment and pathway network of cyst-related genes in ADPKD. Figure 11 shows the roles of functional enrichment analysis of ADPKD-specific genes in epithelial remodeling, ECM organization, and proliferation signaling. Figure 11(A) shows the results of enrichment analysis of GO biological processes. Figure 11(B) shows the results of intracellular components of GO. Figure 11(C) shows the results of molecular function analysis of GO. Figure 11(D) shows the results of WikiPathways.

[0170] Next, to understand the functional roles of these cyst-related genes, enrichment analyses were performed on GO and WikiPathways. Figure 11 shows the network of biomarker genes connected to the significantly enriched pathways.

[0171] First, enrichment analysis of GO biological processes revealed several relevant themes (Figure 11(A)): control of cell migration (e.g., GO:0030334, GO:0030335), epithelial development / differentiation (e.g., GO:0030855, GO:0060429), and tissue morphogenesis (e.g., GO:1905331, GO:1900028). These processes are consistent with known mechanisms in ADPKD (abnormal cell migration / invasion, abnormal epithelial differentiation, cystic morphogenesis). For example, KRT19, KRT17, COL1A1, MMP7, and CLDN4 each mapped to numerous terms in these categories, suggesting they have multifaceted effects on bladder cell behavior.

[0172] In the GO intracellular components, the higher-level enrichment items were clustered into three major functional groups (Figure 11(B)): cytoskeletal elements (e.g., intermediate filaments, actin cytoskeleton), intercellular junction complexes (e.g., desmosomes, tight junctions), and the extracellular matrix. This reflects the nature of the identified genes (encoding cytoskeletal keratin, tight junction proteins, and collagen matrix proteins) and suggests that the cells covering the cyst undergo structural and adhesion changes.

[0173] In the GO molecular function analysis, there were few significant terms, but a notable association was metalloendopeptidase activity mainly related to MMP7, which was consistent with the role of MMP7 as a matrix-degrading enzyme (Figure 11(C)).

[0174] Finally, WikiPathways (Figure 11(D)) showed that at least six of the biomarkers crossed pathways highly relevant to the pathogenesis of ADPKD. These included cell-matrix adhesion and integrin signaling pathways (e.g., WP306, WP3932), growth factor receptor signaling pathways (e.g., EGFR, TGF-β pathways; WP560, WP5158), inflammation and stress responses (e.g., TNF / NF-κB pathways; WP453, WP2324), and epigenetic regulation suggestive of chromatin changes (WP704, WP3967). Among the genes, COL1A1 was the most closely connected node in the pathway network, particularly highlighting cell-matrix interactions and adhesion pathways. ITGB6, on the other hand, was the second most closely connected node, linked to growth factor and matrix-related pathways. These analyses support the idea that the cells lining the bladder are not quiescent but molecularly active, involved in pathways that promote proliferation, fibrosis, and remodeling in the vesicokidney.

[0175] 4. Analysis of spatial differentiation state reveals the origin and proliferative capacity of ADPKD cystic cells.

[0176] Figure 12 shows that cystic cells in ADPKD originate from the distal convoluted tubule and possess a transcriptional program that exhibits high proliferative capacity. Figure 12(A) is a diagram showing spatial transcriptomics data obtained from ADPKD kidney tissue projected using UMAP and annotated for each cell type. Figure 12(B) shows a gradient of differentiation centered on cystic cell clusters, obtained by pseudo-temporal analysis of single cells. Figure 12(C) is an entropy map showing transcriptional instability. Figure 12(D) is a feature plot showing spatial enrichment of cell types associated with the distal convoluted tubule. Figure 12(E) shows the expression of loop of Henle. Figure 12(F) shows the enrichment of COI in the cystic cell population. Figure 12(G) is a correlation heatmap showing a strong positive correlation between cyst-related genes and cell proliferation markers.

[0177] Analysis combining pseudotime and origin cells showed that the majority of bladder epithelial cells originated from the distal convoluted tubule, with only a small contribution from the loop of Henle (Figure 12(A), (B)). This is consistent with single-cell atlas data described in the ADPKD literature (Li, Q. et al. Heterogeneity of cell composition and origin identified by single-cell transcriptomics in renal cysts of patients with autosomal dominant polycystic kidney disease. Theranostics 11, 10064-10073 (2021)), indicating that bladder epithelium may arise from multiple nephron segments, particularly the distal segment and collecting duct.

[0178] The high transcriptional entropy observed in bladder cells signifies increased heterogeneity and variability in gene expression, typical of cells undergoing dedifferentiation or state transitions (Figure 12(C)–(F)). Such entropy-driven changes are analogous to the concept in cancer and tissue remodeling, as well as the model in which epithelial cells enter a metastable transitional state during disease progression. In ADPKD, this plasticity may cause cystic cells to detach from their original tubular identity and adopt a pro-cystocystic phenotype.

[0179] A strong positive correlation was observed between cyst-related markers (KRT7, SLPI, ITGB6, MMP7, etc.) and growth regulators (MKI67, RRM1, CDK1, RPA1), indicating that cystic cells are actively entering the cell cycle (Figure 12(G)). This supports macroscopic observations in ADPKD, where epithelial proliferation promotes cyst enlargement.

[0180] In ADPKD, distal convoluted tubular cells undergo transcriptional destabilization and dedifferentiation, transitioning to a hyperplastic state and actively remodeling the microenvironment through matrix-related and immune-related gene programs. This multifaceted cellular change appears to play a central role in cystic enlargement and progressive tissue degeneration.

[0181] 5. Intercellular signaling network driven by cyst-derived cells in ADPKD. Figure 13 shows extracellular matrix-integrin signaling between cyst cells and endothelial and distal tubular cells in ADPKD. Figure 13(A) is a coded diagram showing statistically significant intercellular interactions between annotated cell populations in ADPKD kidney tissue. Figure 13(B) is a dot plot showing ligand-receptor interaction pairs significantly enriched in COI-initiated signaling.

[0182] Analysis of intercellular signaling revealed that bladder-derived cells actively engage in paracrine signaling with multiple neighboring cell types, with particularly strong interactions observed with endothelial cells, distal tubular cells, and B cells (Figure 13(A)). These interactions are controlled by extracellular matrix (ECM)-integrin ligand-receptor pairs such as COL1A1-ITGA2 / ITGB1, FN1-ITGB1, and LAMC1-ITGA2 / ITGB1 (Figure 13(B)). These interactions suggest that bladder cells regulate the microenvironment by secreting ECM components, which in turn activates integrin-mediated pathways in neighboring cells.

[0183] Such stromal-epithelial crosstalk likely contributes to the fibrous remodeling and vascular changes commonly seen in ADPKD. In particular, COL1A1 and FN1 are well-known fibrosis-associated ligands, and their interaction with endothelial integrins may promote capillary thinning, endothelial dysfunction, and neovascular remodeling, key features of progressive ADPKD. Interactions with DCT cells, including common integrin signaling, support transcriptome evidence that many cysts originate from this nephron segment. Cystic cells may enhance surrounding DCT-like features through such pathways.

[0184] Interestingly, communication with B cells may indicate the regulation of the immune microenvironment, possibly involving antigen presentation, inflammatory signaling, or chronic immune cell recruitment. Although not extensively studied in ADPKD, immune-epithelial crosstalk is increasingly being shown to play a significant role in cyst progression and inflammation. Overall, these results suggest that COI cells are not passive bystanders but actively restructure the local microenvironment, influencing adjacent epithelial, stromal, endothelial, and immune cells through extracellular matrix-integrin signaling, thereby promoting fibrosis and cyst enlargement.

[0185] 6. Different Immunological, Coagulation, and Proliferative Programs in Endothelial and Distal Tubular Cells in ADPKD Figure 14 shows the signatures associated with inflammation, coagulation, and proliferation of endothelial and distal tubular cells in ADPKD, as determined by functional enrichment analysis. Figure 14(A) is a Circos plot showing GO term enrichment for endothelial cells in ADPKD tissue. Figure 14(B) shows the top-level enrichment items of GO biological processes in endothelial cells. Figure 14(C) shows the results of WikiPathways 2024 enrichment analysis in endothelial cells. Figure 14(D) shows the enrichment of GO biological processes in distal tubular cells. Figure 14(E) shows the results of WikiPathways 2024 enrichment analysis in distal tubular cells.

[0186] Endothelial cells in ADPKD tissue showed enrichment of GO terms related to immune activation, including NK cell toxicity regulation and complement and coagulation pathways (Figure 14). The involvement of endothelial cells in coagulation has been reported in the context of vascular injury, where complement components are known to enhance endothelial cell activation and thrombus formation. Enrichment of type II interferon and p53 signaling pathways indicates an inflammatory and stress-responsive endothelial phenotype, consistent with known endothelial dysfunction in ADPKD and vascular complications in this disease. Distal tubular cells, on the other hand, showed GO-based enrichment in migration, proliferation, and signaling, reflecting an active role in cystic expansion. Marked activation of the PI3K-Akt-mTOR pathway in these cells is consistent with extensive literature demonstrating this axis as a driver of epithelial hyperproliferation and cystic proliferation in PKD. Involvement of local adhesion pathways suggests ECM interactions and cytoskeletal dynamics that promote cystic morphogenesis. The association with nephrotic syndrome and glomerulosclerosis suggests that molecular stress responses are shared between distal tubular cells and other renal pathologies.

[0187] These findings, taken together, support a model in which endothelial cells and distal tubular cells actively form the cystic microenvironment through robust signaling with cystic epithelium via ligand-receptor interactions. Endothelial cells contribute to immune, vascular, and inflammatory signaling, while distal tubular cells promote cyst formation through enhanced proliferation and activation of the PI3K-Akt pathway. Insights into these mechanisms highlight the potential of therapeutic strategies targeting vascular inflammation, proliferation signaling, and cell-matrix interactions in ADPKD.

[0188] [Example 3] ADPKD progression-related genes were identified through genotypic and phenotypic analysis.

[0189] First, peripheral blood samples were collected from 51 ADPKD patients, and whole RNA sequencing was performed on all patients. Subsequently, the patients were classified into the following three groups: High-risk group: Patients corresponding to Mayo classification group E (n=24) Risk group: Patients corresponding to Mayo classification groups C and D (n=8) Low-risk group: Patients corresponding to Mayo classification groups A and B (n=9) Gene mutation analysis (indel and SNP analysis) was performed on all patients. Then, the top 2000 genes with the highest mutation frequency were selected for each patient.

[0190] Next, we used a Cox proportional hazards model to evaluate the association between genes and increased total kidney volume (TKV) in each group (high-risk, intermediate-risk, and low-risk) and in all groups.

[0191] <Results> Figure 15A shows mutant genes specific to the low-risk group. Figure 15B shows mutant genes specific to the medium-risk group. Figure 15C shows mutant genes specific to the high-risk group. Figure 15D shows mutant genes specific to all risk groups.

[0192] As shown in Figure 15A, the mutation gene specific to the low-risk group was ADAM8, with a hazard ratio (HR) of 20.11. The mutation gene specific to the medium-risk group was shown in Figure 15B. Furthermore, the mutation gene specific to the high-risk group was shown in Figure 15C. Finally, the mutation gene specific to all risk groups was shown in Figure 15D.

[0193] Here, the functions and roles of risk group-related genes in the progression of ADPKD are shown in Table 1 below.

[0194] The results above demonstrate changes in immune cells in ADPKD patients. Therefore, to prove the changes in immune cells in ADPKD, we performed a Cibersort analysis.

[0195] Figure 16 shows the number of each cell type in peripheral blood. Figures 16(A) to (E) show the number of CD4 cells, CD8 cells, Mo / Mac (monocyte / macrophage), megakaryocytes, and total B cells, respectively. Figure 17 shows the number of each subset of total B cells and the predicted concentrations of cytokines. Figures 17(A) to (E) show the number of total B cells, naive B cells, B-exhausted B cells, total memory B cells, and immature B cells, respectively. Figures 17(F) and (G) show the predicted concentrations of APRIL (A Proliferation-Inducing Ligand) cytokine and BAFF (B-cell Activating Factor) cytokine, respectively.

[0196] In Figures 16 and 17(A) to (E), the horizontal axis shows the control and Mayo classification, and the vertical axis shows the percentage of cells in ADPKD. In Figures 17(F) and (G), the horizontal axis shows the control and ADPKD patients, and the vertical axis shows the predicted concentration.

[0197] As shown in Figure 16, the total number of B cells in peripheral blood increased by approximately twofold. Furthermore, as shown in Figure 17, it was confirmed that a subset of B cells also increased in a manner dependent on APRIL and BAFF cytokines.

[0198] Figure 18 shows the results of spatial transcriptome analysis in ADPKD tissue sections. Similar to Example 1, spatial transcriptome analysis of ADPKD patient samples revealed that various subsets of B cells significantly infiltrated the cyst boundaries, indicating that they play an important role in cyst progression.

[0199] Here, rituximab is known as a monoclonal antibody that targets a protein called CD20, which is present on the surface of B cells, a type of white blood cell. By binding to CD20, rituximab helps destroy B cells. Therefore, given that ADPKD patients overexpress B cells, rituximab is considered an effective treatment for ADPKD.

[0200] [Example 4] In this example, the cyst-reducing effect of pharmaceuticals containing HDAC inhibitors and / or TROP2 inhibitors was evaluated using a three-dimensional Matrigel cyst formation assay with human kidney cell lines.

[0201] 1. Reagents and Materials In this example, Accutase (AT104), Matrigel® Growth Factor Reduced (GFR; Corning, 356231), recombinant human epidermal growth factor (rhEGF; AF-100-15), dimethyl sulfoxide (DMSO; D2650), hydrocortisone (H0888), ITS Liquid Media Supplement (insulin-transferrin-selenium; I3146), forskolin (067-02191), and fimepinostat (CUDC-907; HY-13522-50MG) were used. DMEM (ATCC, 30-2002) and fetal bovine serum (FBS; ATCC, 30-2020) were also included as basal media.

[0202] 2. Cell lines and culture conditions Human kidney cell lines WT-9-12 (ATCC CRL-2833, PKD 1 mutation patients proximal and distal tubule epithelial cells) and WT-9-7 (ATCC CRL-2830, PKD 1 mutation patients proximal and distal tubule epithelial cells) were maintained under standard humidified culture conditions at 37°C. The cells were cultured under normal conditions in DMEM supplemented with FBS and ITS. The cells were passaged at non-confluent density using Accutase and used in 3D assays.

[0203] 3. Three-dimensional cyst formation assay (WT-9-12 and WT-9-7) A 3D cyst formation assay was performed using Matrigel® GFR in a 96-well format. Matrigel® GFR was thawed on ice, and to prevent premature polymerization, all handling of Matrigel® GFR and the Matrigel® GFR-cell mixture was performed using pre-cooled tips and tubes. WT-9-12 or WT-9-7 cells were harvested with Accutase, counted, and then resuspended in cooled Matrigel® GFR to prepare a homogeneous Matrigel® GFR-cell suspension.

[0204] 50 μL of Matrigel® GFR-cell mixture containing 10,000 cells was dispensed in a dome shape into each well of a 96-well plate. The plate was incubated at 37°C for 30 minutes to achieve complete gelation. If the dome remained soft, the gelation time was extended up to a maximum of 60 minutes. After polymerization, the domes were overlaid with working medium containing DMEM supplemented with ITS, forskolin (final concentration 10 μM), and rhEGF (final concentration 20 μg / mL). For rhEGF, 20 μg was used in the laboratory to prepare a working stock / aliquot according to the supplier's instructions, and then rhEGF was added to the working medium and applied for cyst induction. The working medium was changed every 48 hours. Cysts were allowed to develop until day 11, and well-formed cystic structures were consistently observed.

[0205] 4. Drug Treatment (CUDC-907 and sacituzumab govitecan / Trodelvy) The drug efficacy test was initiated on day 11 after cyst formation. For fimepinostat (CUDC-907), treatment groups were set at 1, 5, and 10 μM, and a solvent control group was also included. CUDC-907 was prepared in DMSO and diluted in the working medium immediately before use. The solvent control contained the corresponding DMSO concentration.

[0206] For sacituzumab govitecan (IMMU-132; Trodelvy), cysts were treated using the same schedule and workflow, with concentration groups of 1, 5, 10, 20, 30, and 50 μM, including a corresponding solvent control group. For both compounds, the treatment medium was changed every 48 hours, and cysts were monitored longitudinally throughout the treatment period.

[0207] 5. Image acquisition and blinded quantitative bright-field images were acquired every 48 hours using an inverted microscope from the start of treatment (day 11) throughout the treatment period. For quantification, images were analyzed blindly in ImageJ. Group identity was masked during measurement. Cyst proliferation / response was quantified from images using a consistent measurement method (e.g., 2D projected cyst area) applied uniformly to all wells and time points. Two independent replicates were compared for each condition.

[0208] 6. Cytotoxicity Assay by PI Staining WT-9-12 cells were seeded in fibronectin-precoated 24-well plates and adhered under standard culture conditions (37 °C, 5% CO2). Cells were treated with CUDC-907 at final concentrations of 1, 5, and 10 μM, or with a solvent control (solvent concentration matched), and incubated for 24 hours. After treatment, cells were harvested, washed with FACS buffer, and stained with propidium iodide (PI; BioLegend) according to the manufacturer's instructions. PI staining was performed immediately before acquisition, and samples were immediately analyzed using a Beckman Coulter DxFLEX (13-color) flow cytometer. PI-positive events were considered non-viability, and cell viability was calculated as the percentage of PI-negative cells in the gated cell population. Flow cytometry data were processed using FlowJo (BD FlowJo Portal). A consistent gating strategy was applied to all samples (cells were identified by FSC / SSC, and debris was excluded). The survival rate results were reported as relative values ​​to the solvent-treated group.

[0209] 7. Statistical Analysis Statistical analysis and figure creation were performed using GraphPad Prism 10. Data were presented in a format suitable for the dataset (e.g., mean ± SD / SEM). A p-value threshold (e.g., p < 0.05) was used to define statistical significance, and specific statistical methods were selected based on the experimental design and distribution assumptions.

[0210] <Results> (1) Cell viability by PI exclusion and flow cytometry

[0211] Fimepinostat (CUDC-907) exhibits strong inhibitory activity against class I PI3Ks and class I / II HDACs, and its IC25 is positive for PI3Kα, PI3Kβ, and PI3Kδ. 50 These values ​​are 19, 54, and 39 nM respectively, for the ICs HDAC1, HDAC2, HDAC3, and HDAC10. 50 These values ​​were 1.7, 5.0, 1.8, and 2.8 nM, respectively.

[0212] Figure 19 is a graph showing the results of evaluating cell viability 24 hours after CUDC-907 treatment by propidium iodide (PI) elimination and flow cytometry. In Figure 19, the horizontal axis represents the sample, and the vertical axis represents the percentage of PI-negative cells. As shown in Figure 19, the results of evaluating cell viability by PI elimination and flow cytometry after 24 hours of CUDC-907 treatment showed that the percentage of viable cells in the vehicle, 1, 5, and 10 μM treatment groups were 95.60%, 92.27%, 88.84%, and 92.46%, respectively. Only the 5 μM treatment group showed a statistically significant difference compared to the vehicle treatment group. These results indicate that CUDC-907 treatment is safe and non-toxic.

[0213] (2) Decrease in cyst diameter and total cyst area, and morphological changes. Figure 20 shows dose-dependent changes in cyst diameter and total cyst area (A: vehicle, B: 1 μM, C: 5 μM, D: 10 μM) and morphological changes of cysts associated with CUDC-907 treatment. In Figure 20(E), the horizontal axis shows the sample and the vertical axis shows the diameter (φ, μm) of ADPKD cysts. As shown in Figures 20(A) to (E), CUDC-907 treatment was associated with a dose-dependent decrease in cyst diameter and total cyst area, while vehicle-treated cultures showed increased cyst proliferation. Furthermore, as shown in Figures 20(F) and (G), CUDC-907-treated cysts showed significant morphological changes, including cell wall breakdown / disintegration and perforation-like findings (indicated by arrows in the figure), which was consistent with the loss of cyst integrity at 1, 5, and 10 μM.

[0214] Figure 21 is another diagram showing morphological changes in the cysts. As shown in Figure 21, the cysts in the 1 μM (Figure 21(A)) and 5 μM (Figure 21(B)) treatment groups showed significant morphological changes.

[0215] (3) RNA-seq gene expression analysis Figure 22 is a graph showing the results of RNA-seq analysis, which indicates that the expression of BAG6 (A), SIRPB1 (B), and WDR18 (C) was significantly increased in ADPKD patients compared to the control. In Figure 22, the horizontal axis represents the subjects (ADPKD patients or control), and the vertical axis represents the log2 converted value of the expression level of each gene. As shown in Figure 22, RNA-seq revealed that the expression of BAG6, SIRPB1, and WDR18 was significantly increased in ADPKD patients compared to the control. Together with the detected genetic mutations, these data suggest dysregulation / activation of related molecular pathways and support the idea that these genes could be candidate markers for ADPKD.

[0216] Figure 23 is a graph showing the results of RNA-seq analysis, illustrating that the expression levels of TSSC4 (A), ZBP1 (B), CCDC88B (C), NISCH (D), and ANKRD9 (E) were significantly elevated in ADPKD patients compared to controls. In Figure 23, the horizontal axis represents the subjects (ADPKD patients or control), and the vertical axis represents the log2 transformed values ​​of each gene expression level. The expression levels of TSSC4, ZBP1, CCDC88B, NISCH, and ANKRD9 were significantly elevated in ADPKD patients compared to controls, indicating dysregulation of related molecular pathways and supporting the possibility that these genes could be candidate markers for ADPKD.

[0217] Figure 24 is a graph showing the results of RNA-seq analysis, which indicates that the expression levels of STXBP2 (A) and BAG1 (B) were significantly lower in ADPKD patients compared to controls. In Figure 24, the horizontal axis represents the subjects (ADPKD patients or control), and the vertical axis represents the log2 converted expression levels of each gene. The expression levels of STXBP2 and BAG1 were significantly lower in ADPKD patients compared to controls, suggesting decreased activity in these pathways and supporting the possibility that these genes could be candidate markers for ADPKD.

[0218] (5) Association with progression risk according to Cox proportional hazards model Figure 25 shows the results of a Cox proportional hazards analysis, which indicates that the expression levels of the genes were independently associated with progression risk in a demographically adjusted Cox proportional hazards model, and significant hazard ratios were observed in each category of the Mayo imaging classification. In a demographically adjusted Cox proportional hazards model, the expression levels of these genes were independently associated with progression risk, and significant hazard ratios were observed across each category of the Mayo imaging classification (low, medium, high). These results support the possibility of using this gene expression signature for risk stratification and prognosis assessment.

[0219] Next, we evaluated the prognostic associations of these genes using Cox proportional hazards models adjusted for key clinical and laboratory covariates, including height-adjusted total kidney volume (htTKV), serum creatinine, blood urea nitrogen (BUN), albumin, eGFR, and hemoglobin (Hb).

[0220] Figure 26 shows the results of a Cox proportional hazards analysis, adjusted for clinical and laboratory covariates including htTKV, serum creatinine, BUN, albumin, eGFR, and Hb, demonstrating that the expression levels of the aforementioned genes were significantly associated with the risk of progression. In these fully adjusted analyses, the expression levels of all genes remained significantly associated with the risk of progression, and significant hazard relationships were observed across Mayo imaging classification strata (low-risk, intermediate-risk, and high-risk groups). Taken together, these findings suggest that gene expression profiling of this panel may be useful for risk stratification and assessment of ADPKD progression, complementing established clinical and imaging predictors.

[0221] Although the present disclosure has been described above with reference to embodiments and examples, the present disclosure is not limited to the above embodiments and examples. Various modifications to the structure and details of the present disclosure are possible, which can be understood by those skilled in the art within the scope of the present disclosure.

[0222] <Notes> Some or all of the above embodiments and examples may be described as follows, but are not limited to the following. (Note 1) A pharmaceutical product used to treat autosomal dominant polycystic kidney disease (ADPKD), comprising at least one of a histone deacetylase (HDAC) inhibitor, a TROP2 inhibitor, and a PI3K inhibitor. (Note 2) The pharmaceutical product according to Note 1, wherein the HDAC inhibitor comprises an inhibitor that inhibits the activity of at least one enzyme selected from the group consisting of HDAC1, HDAC2, HDAC3, HDAC6, HDAC7, HDAC10, and SIRT2, the TROP2 inhibitor comprises an inhibitor that inhibits the enzyme activity of TROP2, and the PI3K inhibitor comprises an inhibitor that inhibits the enzyme activity of PI3Kα, PI3Kβ, and PI3Kδ. (Note 3) The pharmaceutical product according to Note 2, wherein the HDAC inhibitor comprises any of the inhibitors (1) to (4) below. (1) Inhibitors that inhibit the enzyme activity of HDAC6 and HDAC7 (2) Inhibitors that inhibit the enzyme activity of HDAC6 and SIRT2 (3) Inhibitors that inhibit the enzyme activity of HDAC6, HDAC7, and SIRT2 (4) Inhibitors that inhibit the enzyme activity of HDAC6 and HDAC1 (Note 4) A pharmaceutical product according to any one of Notes 1 to 3, wherein the HDAC inhibitor comprises at least one of vorinostat and trichostatin A (TSA). (Note 5) A pharmaceutical product used to treat autosomal dominant polycystic kidney disease (ADPKD), comprising a B cell scavenger. (Note 6) A pharmaceutical product according to Note 5, wherein the B cell scavenger comprises a drug that targets the CD20 antigen. (Note 7) A pharmaceutical product according to Note 5 or 6, wherein the B cell scavenger comprises rituximab. (Note 8) Autosomal dominant polycystic kidney disease (ADPKD) marker comprising at least one selected from the group consisting of SFN, LAMB3, LAMC2, LAMA3, GABRP, SFRP2, TRIM29, SERPINE1, NNMT, TROP2, SLPI, KRT7, KRT17, KRT19, ITGB6, MMP7, CLDN4, and COL1A1.(Note 9) An autosomal dominant polycystic kidney disease (ADPKD) marker comprising at least one selected from the group consisting of ADAM8, NISCH, AKAP13, ZBP1, EIF4G3, PRRC2A, MCPH1, IFITM2, MEF2D, ATXN2L, ADGRG1, PTK2B, BSG, BRD2, RHBDL2, and HELZ2. (Note 10) An autosomal dominant polycystic kidney disease (ADPKD) marker comprising at least one selected from the group consisting of BAG1, BAG6, SIRPB1, STXBP2, WDR18, SSC4, ZBP1, CCDC88B, NISCH, and ANKRD9. (Note 11) A compound represented by the following chemical formula (1) or a pharmaceutically acceptable salt thereof. (Chemical formula 1). In the above chemical formula (1), X is a hydrogen atom or an oxygen atom, Y is a carbon atom or a heteroatom, Z is an aryl group which may have substituents, and L １ L is a single bond, a saturated or unsaturated C1-C6 hydrocarbon group which may have substituents, or an amide group (-CO-NH-), ２ L is a single bond or a saturated or unsaturated C1-C6 hydrocarbon group which may have substituents, ３ This is a covalent bond connecting X and the aromatic ring. (Note 12) The compound represented by the above chemical formula (1) is the compound represented by the following chemical formula (I), as described in Note 11, or a pharmaceutically acceptable salt thereof. (Chemical Formula I) (Note 13) A compound represented by the following chemical formula (2) or a pharmaceutically acceptable salt thereof. (Chemical Formula 2) In the chemical formula (2) above, W is a saturated or unsaturated hydrocarbon group having a cyclic structure, and the atoms constituting the hydrocarbon group may or may not contain heteroatoms, Ar １ Ar ２ , and Ar ３Each of these structures contains an aromatic ring, and the atoms constituting the aromatic ring may or may not contain heteroatoms, and may be identical or different from each other. (Note 14) The compound according to claim 13 or a pharmaceutically acceptable salt thereof, wherein the compound represented by chemical formula (2) is the compound represented by the following chemical formula (III). The compound according to claim 13 or a pharmaceutically acceptable salt thereof. (Chemical Formula III) (Note 15) A cyst-reducing agent comprising a compound described in any of Notes 11 to 14 or a pharmaceutically acceptable salt thereof. (Note 16) The cyst-reducing agent according to Note 15, wherein the cyst is a cyst occurring in the kidney. (Note 17) A pharmaceutical product used to treat autosomal dominant polycystic kidney disease (ADPKD), comprising a compound described in any of Notes 11 to 14 or a pharmaceutically acceptable salt thereof. (Note 18) A predictive marker for autosomal dominant polycystic kidney disease (ADPKD) comprising a gene associated with at least one selected from the group consisting of negative regulation of the vascular endothelial growth factor signaling pathway, regulation of the apoptotic process of smooth muscle cells, negative regulation of the cellular response to vascular endothelial growth factor stimulation, secondary palate formation, endoderm formation, positive regulation of leukocytosis, regulation of the vascular endothelial growth factor signaling pathway, and myelin formation. (Note 19) Mitochondrial translation elongation, mitochondrial translation initiation, mitochondrial translation termination, mitochondrial translation, protein repair, fibronectin matrix formation, RCbl transport in the body, androgen biosynthesis, interconversion of nucleotide diphosphates and triphosphates, hyaluronic acid uptake and degradation, regulated necrosis, transcription-mediated nucleotide excision repair (TC-NER), RHOBTB Autosomal dominant polycystic kidney disease (ADPKD) treatment predictive markers comprising a gene associated with at least one selected from the group consisting of: GTPase cycle, programmed cell death, membrane transport, regulation of necroptotic cell death, RIPK1-mediated controlled necrosis, gap-filling DNA repair synthesis and ligation in TC-NER, [2Fe-2S] cluster assembly, regulation of protein transport to mitochondria, iron-sulfur cluster assembly, metal-sulfur cluster assembly, carboxylic acid degradation processes, maintenance of apical-basal cell polarity, maintenance of apical-basal polarity in epithelial cells, DNA repair, regulation that promotes the establishment of protein localization to telomeres, and regulation that promotes the establishment of protein localization to mitochondria.

[0223] As described above, this disclosure provides pharmaceuticals, compounds, cyst-reducing agents, and markers that can treat autosomal dominant polycystic kidney disease (ADPKD). Therefore, this disclosure is extremely useful, for example, in the clinical and biochemical fields related to autosomal dominant polycystic kidney disease (ADPKD).

[0224] This application claims priority based on Japanese Patent Application No. 2025-010995, filed on 24 January 2025, and Japanese Patent Application No. 2025-119157, filed on 15 July 2025, and incorporates all of their disclosures herein.

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