Format

Send to

Choose Destination
Biomed Res Int. 2014;2014:258784. doi: 10.1155/2014/258784. Epub 2014 Feb 9.

Network-assisted prediction of potential drugs for addiction.

Author information

1
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
2
Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA ; Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA ; Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.

Abstract

Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.

PMID:
24689033
PMCID:
PMC3932722
DOI:
10.1155/2014/258784
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Hindawi Limited Icon for PubMed Central
Loading ...
Support Center