A computational method for drug repositioning using publicly available gene expression data

BMC Bioinformatics

Research

KM Shabana¹  KA Abdul Nazeer¹  Meeta Pradhan²  Mathew Palakal² 

[1] Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, India;IU School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA;

关键词: drug repositioning;    computational drug discovery;    gene expression data;   

DOI  :  10.1186/1471-2105-16-S17-S5

来源: Springer

【 摘 要 】

MotivationThe identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. The advent of high throughput microarray technology has enabled comprehensive monitoring of transcriptional response associated with various disease states and drug treatments. This data can be used to characterize disease and drug effects and thereby give a measure of the association between a given drug and a disease. Several computational methods have been proposed in the literature that make use of publicly available transcriptional data to reposition drugs against diseases.MethodIn this work, we carry out a data mining process using publicly available gene expression data sets associated with a few diseases and drugs, to identify the existing drugs that can be used to treat genes causing lung cancer and breast cancer.ResultsThree strong candidates for repurposing have been identified- Letrozole and GDC-0941 against lung cancer, and Ribavirin against breast cancer. Letrozole and GDC-0941 are drugs currently used in breast cancer treatment and Ribavirin is used in the treatment of Hepatitis C.

【 授权许可】

CC BY   
© Shabana et al. 2015