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BioMed Research International
Volume 2014 (2014), Article ID 258784, 9 pages
http://dx.doi.org/10.1155/2014/258784
Research Article

Network-Assisted Prediction of Potential Drugs for Addiction

1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
2Department of Biomedical Informatics, Vanderbilt University School of Medicine, 2525 West End Avenue, Suite 600, Nashville, TN 37203, USA
3Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA
4Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

Received 22 November 2013; Accepted 30 December 2013; Published 9 February 2014

Academic Editor: Yufei Huang

Copyright © 2014 Jingchun Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.