Repositioning drug discovery method based on integration of a plurality of transcriptome data sets and drug target information

Abstract

The invention relates to a repositioning drug discovery method based on integration of a plurality of transcriptome data sets and drug target information. The method comprises: step one, for any disease, a plurality of transcriptome data sets containing phenotype information are obtained from an openomics database; step two, correlation statistic values of all genes and phenotypes in all transcriptome data sets are calculated based on a statistical analysis method and quantitative combination is carried out on the correlation statistic values to obtain a combination value; step three, a systembiology network is constructed; step four, an importance score of each gene is calculated based on a node sorting algorithm; step five, a target gene list of a to-be-tested drug is obtained; and stepsix, the importance of the gene of the to-be-tested drug is detected and analyzed by statistics and whether the to-be-tested drug is a repositioning drug is predicted. The method having advantages ofclear principle, high efficiency, low cost and wide application range can be applied to screening and research and development of repositioning drugs for various diseases, especially the malignant tumor.

Description

technical field [0001] The invention relates to the technical field of biomedicine, in particular to a relocation drug discovery method based on the integration of multiple transcriptome data sets and drug target information. Background technique [0002] Drug repositioning, commonly known as "reuse of old drugs", refers to the discovery, verification and application of new indications for drugs that have been used in clinical practice (including those that are in use, those that are no longer used, and those that have received clinical evaluations). : Pritchard JE, O'Mara TA, Glubb DM. Enhancing the Promise of Drug Repositioning through Genetics. Front Pharmacol, 2017, 8:896). Traditional new drug development needs to go through multiple links such as target confirmation, lead compound discovery and optimization, preclinical research, phase I-III clinical trials, marketing and post-marketing evaluation, which requires a lot of manpower, material resources and time, and the ...

AI Technical Summary

Problems solved by technology

However, the above two methods have significant limitations

Drug-disease relationship discovery based on the comparison of gene expression profile patterns relies on the expression profile change pattern data obtained from a large number of experiments before and after drug action, and it is difficult to cover a large number of diseases and drugs due to cost constraints; in addition, the above data generally come from There is a certain gap between the expression profile of the real human tissue and the in vitro experiment of the cell line

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