KR20160144065A - System and method for drug repositioning based on gene expression features of disease genes - Google Patents
System and method for drug repositioning based on gene expression features of disease genes Download PDFInfo
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- KR20160144065A KR20160144065A KR1020150080336A KR20150080336A KR20160144065A KR 20160144065 A KR20160144065 A KR 20160144065A KR 1020150080336 A KR1020150080336 A KR 1020150080336A KR 20150080336 A KR20150080336 A KR 20150080336A KR 20160144065 A KR20160144065 A KR 20160144065A
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Abstract
Description
The present invention relates to a system and a method for predicting drug regeneration using disease gene expression characteristics.
Drug regeneration is a method of drug development that develops into new drugs by identifying new medicinal effects of drugs that have already failed to commercialize because of reasons other than stability in the market or clinical stage. Despite the fact that the average R & D cost for one new drug is more than $ 1 billion, there are 250 candidates to be in pre-clinical phase among 5,000 ~ 10,000 candidate drugs, 5 types of clinical trials, Only. It is also known that the development period usually takes about 10-17 years. However, drug re-creation can identify the effect of a new drug on the same drug target from an existing drug or a failed drug, or identify a new target and identify a therapeutic effect for a new disease. And reduce costs and time, which is 25 to 35 percent of new drug R & D costs. In addition, drug regeneration can greatly improve the success rate of new drug development. Generally, the success rate of new drug development is 10% at the time of initial administration, but Shinyang re-creation is more than double at 25%. An example of a typical drug regeneration is sildenafil, a PDE5 inhibitor developed to treat angina. Sildenafil was discarded as ineffective in clinical trials, but was found to be an ideal remedy for erectile dysfunction. It was re-created as Viagra (VIAGRA), effective on pulmonary arterial hypertension and regenerated as REVATIO.
One method of regenerating such drugs is a computerized method for analyzing the similarities of drug and drug targets, drug-disease similarities, and side-effect similarities. There is a method of regenerating a computer using a gene expression analysis method of disease and drug reaction that provides information at an enterprise level under specific conditions of a cell. The connectivity map, which measures gene expression patterns after treatment with low molecular weight drugs, is a large-scale library that integrates gene expression information for each drug response. Most methods for analyzing such data include disease gene expression data and drug response genes The reverse linkage analysis of highly expressed or underexpressed genes in the expression data makes it possible to predict treatable drugs. However, there is no way of predicting treatable drugs through understanding the mechanism of the disease.
In conclusion, the problem of analysis method of gene expression data is summarized as follows. First, we did not consider the mechanism of disease and drug because we analyzed using upper gene. Second, there is a heterogeneity in the expression of different genes in the same drug or the same disease every time the amount of gene expression is measured.
A problem to be solved by the present invention is to provide a system and method for predicting drug regeneration using disease gene expression characteristics. More specifically, in predicting drug regeneration, a disease gene in a higher rank is selected using disease gene expression data and drug reaction gene expression data, and then ranking-based reverse association of an upper disease gene extracted from disease and drug reaction gene expression data A method for predicting drug re-generation based on a disease gene expression feature, and a computer-readable recording medium, which enable predicting drug re-creation through a method of prioritizing drugs corresponding to disease gene expression data by analyzing the disease gene expression data.
The system and method according to an embodiment of the present invention enable disease genes considering the mechanism of disease to be used and enable comparison of gene expression characteristics of disease genes in drug reaction gene expression data and disease gene expression data, To predict new drug efficacy.
First, the present invention can understand the mechanism of action of diseases and drugs through drug regeneration using disease genes and increase the accuracy. Second, by identifying disease-causing mechanisms that may be different through disease-associated gene expansion, they increase the likelihood of resolving heterogeneity among samples measured in the same disease or drug.
1 is a block diagram of a drug regeneration prediction system using disease gene expression characteristics according to an embodiment of the present invention.
Figure 2 is a disease gene extension module according to one embodiment of the present invention.
3 is a disease profile generation module according to an embodiment of the present invention.
4 is a drug reaction profile generation module according to an embodiment of the present invention.
Figure 5 is a disease-drug profile evaluation module according to one embodiment of the present invention.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.
Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise.
1 is a block diagram of a drug regeneration prediction system using disease gene expression characteristics according to an embodiment of the present invention.
1, the drug regeneration prediction system includes a "disease gene extension module" for collecting and expanding a known disease-related gene, a "disease profile creation module" for generating a disease profile from disease gene expression data, a drug reaction gene expression database Drug profile evaluation module "that prioritizes the drug by comparing and evaluating the disease profile and drug-specific drug response profile to generate a drug-specific drug response profile from the drug-response profile.
Figure 2 is a disease gene extension module according to one embodiment of the present invention.
Referring to FIG. 2, a disease gene database constructed by collecting disease gene information from six public disease gene related DBs (Cancer Gene Census, HuGE Navigator, KEGG DISEASE, OMIM, PharmGKB Disease and Genetic Association Database) The "drug-target database" constructed by collecting disease gene information from the public drug-target database (therapeutic target database, KEGG Drug, DrugBank), the disease gene information of the disease gene database and the drug-target database based on Entrez Gene ID Integrate disease gene information.
In addition, a "protein-protein interaction database" constructed by collecting protein-protein interaction data for disease gene expansion, and a collection of four signaling pathway related database (Cell Map, NCI_Nature, Reactome, KEGG pathway) Signaling path database ".
Based on a database of protein-protein interaction databases, we select one-hop-related protein of integrated disease gene and provide expanded disease gene by selecting protein in the same signaling pathway of integrated disease gene based on signaling pathway database.
3 is a disease profile generation module according to an embodiment of the present invention.
3, disease gene expression data composed of diseased tissue / normal tissue was collected from Gene Expression Omnibus (GEO) to construct a "disease gene expression database" "Normalization of expression amount of disease gene expression data " is performed to perform tile normalization. It provides a disease profile consisting of ranking information of the top 50, 100, and 150 disease genes with the ranking and low expression levels of the top 50, 100, and 150 disease genes with high expression levels.
4 is a drug reaction profile generation module according to an embodiment of the present invention.
Referring to FIG. 4, a drug reaction gene expression data composed of about 1300 drug-treated / non-treated drug was collected from the Connecitivy Map to construct a "drug reaction gene expression database" And performs "normalization of expression amount of disease gene expression data" to perform quantile normalization. It provides a disease profile consisting of ranking information of the top 50, 100, and 150 disease genes with the ranking and low expression levels of the top 50, 100, and 150 disease genes with high expression levels to compare with their disease profile.
Figure 5 is a disease-drug profile evaluation module according to one embodiment of the present invention.
Referring to FIG. 5, a Kolmogorov-Smirnov test is used to quantify the disease profile and the drug-related drug response profile and the reverse linkage. The method of quantification using the Kolmogorovs Smirnov test is as follows. Provide drug candidates in the order of the drug with the lowest negative number.
KS e = KS (Kolmogorov-Smirnov) scores with high or low profile scores
n = total number of genes
t = the number of genes in a high or low gene set
j = Rank of the gene in the gene set entered as input
V (j) = rank of the jth gene among all genes
ES e = 0 (if KS_up and KS_down have the same sign)
Otherwise, follow the above values.
As described above, the present invention can improve the accuracy and the understanding of the mechanism of diseases and drugs by regenerating drugs using disease genes. Second, by identifying disease-causing mechanisms that may be different through disease-associated gene expansion, they increase the likelihood of resolving heterogeneity among samples measured in the same disease or drug.
The embodiments of the present invention described above are not implemented only by the apparatus and method, but may be implemented through a program for realizing the function corresponding to the configuration of the embodiment of the present invention or a recording medium on which the program is recorded.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, It belongs to the scope of right.
Claims (1)
A disease profile generation module that generates a disease profile from disease gene expression data, a drug reaction profile generation module that generates a drug reaction profile by drug from the drug reaction gene expression database,
A disease-drug profile assessment module that prioritizes drugs by comparing and evaluating disease profiles and drug-specific drug response profiles
A drug regeneration prediction system.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200116801A (en) * | 2019-04-02 | 2020-10-13 | 주식회사 엘지화학 | Method for selecting biomarkers by utilizing drug repositioning |
| KR20210119334A (en) * | 2020-03-24 | 2021-10-05 | (의료)길의료재단 | Method for prediction of drug target gene for treating and preventing diseases |
| WO2023277423A1 (en) * | 2021-07-02 | 2023-01-05 | (의료)길의료재단 | Method for predicting new drug-target genes for treatment and prevention of diseases |
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2015
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200116801A (en) * | 2019-04-02 | 2020-10-13 | 주식회사 엘지화학 | Method for selecting biomarkers by utilizing drug repositioning |
| KR20210119334A (en) * | 2020-03-24 | 2021-10-05 | (의료)길의료재단 | Method for prediction of drug target gene for treating and preventing diseases |
| WO2023277423A1 (en) * | 2021-07-02 | 2023-01-05 | (의료)길의료재단 | Method for predicting new drug-target genes for treatment and prevention of diseases |
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