Drug repositioning assistance system and drug repositioning assistance method

Abstract

According to the present invention, a system: acquires a dataset composed of gene expression data of a sample belonging to either of two groups; generates a gene network representing interactions between a plurality of genes by referring to a pathway database; selects one gene from among the plurality of genes; extracts a path by following the gene network downstream with the selected gene as a starting point; calculates path scores by executing enrichment analysis by using, as inputs, a list of genes included in the path and the dataset; generates a ranked gene list by ranking the plurality of genes on the basis of the path scores of the plurality of genes; and calculates drug scores by executing the enrichment analysis by using, as inputs, a list of target genes of drugs stored in a drug database and the ranked gene list.

Description

Drug Repositioning Support System and Drug Repositioning Support Method Incorporation by reference

[0001] This application claims the priority of Japanese Patent Application No. 2025 - 026473 filed on February 21, 2025, and incorporates its content by reference into this application.

[0002] The present invention relates to a technology for supporting drug repositioning.

[0003] Drug repositioning is a development method for finding drugs effective for another disease from existing drugs effective for a certain disease. It has attracted attention because the cost, time, and risk are significantly lower than those of new drug development.

[0004] To identify new therapeutic uses of existing drugs, it is important to accurately identify genes targeted by specific diseases.

[0005] Heonjong Han, Sangyoung Lee, and Insuk Lee, ”NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets”, Mol. Cells 2019; 42(8): 579-588

[0006] Conventionally, target genes have been selected based on gene expression differences between disease samples and normal samples. Also, the method described in Non-Patent Document 1 is known. Non-Patent Document 1 describes a method for selecting target genes based on modified gene ranking considering gene networks.

[0007] An object of the present invention is to provide an analysis method for accurately identifying target genes and evaluating the effectiveness of drugs.

[0008] A representative example of the invention disclosed in this application is as follows: In other words, the drug repositioning support system comprises a processor, a storage device connected to the processor, and a network interface connected to the processor, and is accessiblely connected to a pathway database for managing pathways representing interactions between genes and a drug database for managing a list of genes targeted by a drug. The processor acquires a dataset consisting of gene expression data containing information on the expression levels of multiple genes of a sample belonging to one of two groups, and generates a directed graph representing the interactions of the multiple genes as a gene network by referring to the pathway database. It selects one gene from the multiple genes, extracts a path by tracing the gene network downstream from the selected gene, calculates the enrichment score of the path as a path score by performing enrichment analysis using the list of genes included in the path and the dataset, ranks the multiple genes based on the path score of each of the multiple genes to generate a ranked gene list, calculates the enrichment score of the drug as a drug score by performing enrichment analysis using the list of genes targeted by the drug stored in the drug database and the ranked gene list, and outputs the drug score.

[0009] According to the present invention, target genes can be selected with high precision, and the efficacy of drugs can be evaluated. Other issues, configurations, and effects will be clarified by the following description of the examples.

[0010] This figure shows an example of the system configuration of Example 1. This figure shows an example of the hardware configuration of the computer constituting the drug repositioning support system of Example 1. This figure shows an example of a pathway managed in the pathway DB of Example 1. This figure shows an example of the data structure of the drug DB of Example 1. This is a flowchart illustrating an example of the processing performed by the drug repositioning support system of Example 1. This figure shows an example of the method for calculating the path score of Example 1. This figure shows an example of the method for calculating the drug score of Example 1. This figure shows an example of the drug evaluation results generated by the drug repositioning support system of Example 1.

[0011] The embodiments of the present invention will be described below with reference to the drawings. However, the present invention is not to be construed as being limited to the embodiments described below. It will be readily apparent to those skilled in the art that the specific configuration can be modified without departing from the spirit or intent of the present invention.

[0012] In the configuration of the invention described below, identical or similar components or functions are denoted by the same reference numerals, and redundant descriptions are omitted.

[0013] The designations "First," "Second," "Third," etc., used in this specification are for the purpose of identifying constituent elements and do not necessarily limit their number or order.

[0014] Figure 1 shows an example of the system configuration of Example 1. Figure 2 shows an example of the hardware configuration of the computer that constitutes the drug repositioning support system 100 of Example 1. Figure 3 shows an example of a pathway managed in the pathway DB of Example 1. Figure 4 shows an example of the data structure of the drug DB of Example 1.

[0015] The system consists of a drug repositioning support system 100, a pathway database 110, and a drug database 111. The pathway database 110 and the drug database 111 are publicly available databases. The drug repositioning support system 100 is connected to the pathway database 110 and the drug database 111 via a network, allowing access to them.

[0016] The drag repositioning support system 100 consists of a computer 200 as shown in Figure 2. The computer 200 has a processor 201, a main memory 202, a secondary memory 203, and a network interface 204. Each hardware element is connected to the others via a bus 205.

[0017] The processor 201 executes a program stored in the main memory 202. By executing processing according to the program, the processor 201 operates as a functional unit (module) that realizes a specific function. In the following description, when the processing is described with a functional unit as the subject, it indicates that the processor 201 is executing a program that realizes that functional unit.

[0018] The main memory 202 is a memory unit that stores the program executed by the processor 201 and the information used by the program. The main memory 202 is also used as a work area.

[0019] The secondary storage device 203 is a large-capacity storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive). The programs and information stored in the main memory 202 may also be stored in the secondary storage device 203. In this case, the processor 201 reads the programs and information from the secondary storage device 203 and loads them into the main memory 202.

[0020] The network interface 204 is an interface for connecting to a network.

[0021] The computer 200 may also have input devices such as a keyboard, mouse, and touch panel, as well as output devices such as a display.

[0022] Pathway DB110 is a database that manages pathways, which represent interactions between genes. As shown in Figure 3, pathways are represented as directed graphs consisting of nodes representing genes and edges representing interactions. Pathway DB110 stores, for example, information about nodes and information about edges.

[0023] Drug DB 111 is a database for managing target genes of existing drugs. Drug DB 111 stores a table 400 that stores entries consisting of a drug 401 and a gene 402, as shown in Figure 4, for example. Drug 401 is a field that stores the name of an existing drug, etc. Gene 402 is a field that stores the target gene of an existing drug.

[0024] The drug repositioning support system 100 includes a gene network construction unit 101, a path evaluation unit 102, a ranking determination unit 103, and a drug evaluation unit 104. The functional units of the drug repositioning support system 100 may be combined into a single functional unit, or a single functional unit may be divided into multiple functional units according to its function.

[0025] Figure 5 is a flowchart illustrating an example of the processing performed by the drug repositioning support system 100 of Example 1. Figure 6 is a diagram illustrating an example of the method for calculating the path score in Example 1. Figure 7 is a diagram illustrating an example of the method for calculating the drug score in Example 1. Figure 8 is a diagram illustrating an example of the drug evaluation results generated by the drug repositioning support system 100 of Example 1.

[0026] When the drug repositioning support system 100 receives an execution instruction along with the gene expression dataset 120, it starts the process described below.

[0027] The gene expression dataset 120 is a dataset composed of gene expression data that stores the expression levels of multiple genes in a single sample, or statistical values ​​calculated from those expression levels. The gene expression dataset 120 includes gene expression data from two groups: patients with a certain disease and healthy individuals.

[0028] The gene network construction unit 101 refers to the pathway DB 110 and generates a gene network composed of genes included in the gene expression dataset 120 (step S101). The gene network, like pathways, is represented as a directed graph.

[0029] The path evaluation unit 102 selects one gene from among the genes included in the gene network (step S102). In this embodiment, genes are selected in order from the upstream of the gene network. If the gene network has the shape shown in Figure 3, genes are selected in order from GENE1 or GENE2.

[0030] The path evaluation unit 102 extracts paths by tracing the genes downstream of the gene network, starting from the selected gene (step S103). For example, if GENE4 is selected as the selected gene in the gene network shown in Figure 3, a path consisting of GENE5 and GENE6 and a path consisting of GENE7 and GENE8 are extracted.

[0031] The path evaluation unit 102 calculates the enrichment score of the gene list corresponding to the path as the path score by performing GSEA (Gene SET Enrichment Analysis) using the gene expression dataset 120 and the gene set corresponding to the path (step S104). Specifically, the path score is calculated by the following procedure.

[0032] (S104-1) The path evaluation unit 102 generates a first ranked gene list by ranking the genes included in the gene expression data based on differential expression levels.

[0033] (S104-2) The path evaluation unit 102 uses the first-ranked gene list to calculate the enrichment score of the gene set corresponding to each path as the path score. In Figure 6, the gene set corresponding to path 1 has a higher path score than the gene set corresponding to path 2. The first-ranked gene list shown in Figure 6 is arranged from left to right in descending order of differential expression level.

[0034] (S104-3) The path evaluation unit 102 associates the maximum value among the path scores of each path with the selected gene and stores it in the work area.

[0035] Genes functionally important to a particular disease process are thought to not only exhibit dysregulation themselves, but also tend to cause significant dysregulation in downstream genes within the functional gene network. In this example, genes are scored while also considering differences in the expression of downstream genes. When multiple paths exist, adopting the maximum path score allows for an accurate estimation of genes that may cause significant dysregulation in downstream genes. Non-patent document 1 considers adjacent genes and does not perform scoring based on the hypothesis described above.

[0036] The path evaluation unit 102 determines whether processing has been completed for all genes included in the gene network (step S105).

[0037] If processing is not complete for all genes included in the gene network, the path evaluation unit 102 returns to step S102 and performs the same processing.

[0038] Once processing is complete for all genes included in the gene network, the ranking unit 103 generates a second ranked gene list by ranking the genes based on their path scores (step S106).

[0039] The drug evaluation unit 104 calculates the drug score for each drug by performing GSEA using the drug DB 111 and the second-ranked gene list, thereby determining the enrichment score of the list of genes targeted by each drug (step S107). In Figure 7, the gene set corresponding to drug 1 has a higher pass score than the gene set corresponding to drug 2. The second-ranked gene list shown in Figure 7 is arranged from left to right in descending order of pass score.

[0040] The drug evaluation unit 104 generates and outputs drug evaluation results, which include data linking drugs and drug scores (step S108). The drug evaluation results are, for example, a list as shown in Figure 8.

[0041] Note that the present invention is not limited to the above-described embodiments, and various modifications are included. Further, for example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Also, it is possible to add, delete, or replace a part of the configuration of each embodiment with other configurations.

[0042] Further, each of the above configurations, functions, processing units, processing means, etc. may be realized by hardware, for example, by designing a part or all of them with an integrated circuit or the like. Also, the present invention can be realized by a program code of software that realizes the functions of the embodiments. In this case, a storage medium recording the program code is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing it constitute the present invention. As a storage medium for supplying such a program code, for example, a flexible disk, CD-ROM, DVD-ROM, hard disk, SSD (Solid State Drive), optical disk, magneto-optical disk, CD-R, magnetic tape, non-volatile memory card, ROM, etc. are used.

[0043] Also, the program code for realizing the functions described in this embodiment can be implemented in a wide range of programs or script languages such as assembler, C / C++, perl, Shell, PHP, Python, Java, etc.

[0044] Further, by distributing the program code of software that realizes the functions of the embodiments via a network, it can be stored in a storage means such as a hard disk or memory of a computer or a storage medium such as a CD-RW or CD-R, and a processor included in the computer reads and executes the program code stored in the storage means or the storage medium.

[0045] In the above-described embodiment, the control lines and information lines shown are those deemed necessary for explanation and do not necessarily represent all control lines and information lines in the actual product. All components may be interconnected.

Claims

1. A drug repositioning support system comprising a processor, a storage device connected to the processor, and a network interface connected to the processor, and being accessiblely connected to a pathway database for managing pathways representing interactions between genes and a drug database for managing a list of genes targeted by a drug, wherein the processor acquires a dataset consisting of gene expression data containing information on the expression levels of multiple genes of a sample belonging to one of two groups, generates a directed graph representing the interactions of the multiple genes as a gene network by referring to the pathway database, selects one gene from the multiple genes, extracts a path by tracing the gene network downstream from the selected gene, calculates an enrichment score for the path as a path score by performing enrichment analysis using a list of genes included in the path and the dataset, ranks the multiple genes based on the path score of each of the multiple genes to generate a ranked gene list, calculates an enrichment score for the drug as a drug score by performing enrichment analysis using a list of genes targeted by the drug stored in the drug database and the ranked gene list, and outputs the drug score.

2. A drug repositioning support system according to claim 1, wherein the processor generates the ranked gene list using the largest path score when there are two or more paths for the selected gene.

3. A computer-based drug repositioning support method, wherein the computer comprises a processor, a storage device connected to the processor, and a network interface connected to the processor, and is accessiblely connected to a pathway database for managing pathways representing interactions between genes and a drug database for managing a list of genes targeted by a drug, the drug repositioning support method comprising: a first step in which the processor obtains a dataset consisting of gene expression data containing information on the expression levels of multiple genes of a sample belonging to one of two groups; a second step in which the processor generates a directed graph representing the interactions of the multiple genes as a gene network by referring to the pathway database; a third step in which the processor selects one gene from the multiple genes, extracts a path by tracing the gene network downstream from the selected gene, and calculates an enrichment score for the path as a path score by performing enrichment analysis using a list of the genes included in the path and the dataset; and a fourth step in which the processor ranks the multiple genes based on the path score of each of the multiple genes to generate a ranked gene list. A drug repositioning support method characterized by comprising: a fifth step in which the processor calculates an enrichment score for the drug as a drug score by performing enrichment analysis using a list of target genes for the drug and a ranking gene list stored in the drug database; and a sixth step in which the processor outputs the drug score.

4. A drug repositioning support method according to claim 3, wherein the fourth step comprises the step of the processor generating the ranked gene list using the largest path score when there are two or more paths for the selected gene.

Images