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Network-Assisted Prediction of Potential Drugs for Addiction

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 fo...

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Detalles Bibliográficos
Autores principales: Sun, Jingchun, Huang, Liang-Chin, Xu, Hua, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932722/
https://www.ncbi.nlm.nih.gov/pubmed/24689033
http://dx.doi.org/10.1155/2014/258784
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author Sun, Jingchun
Huang, Liang-Chin
Xu, Hua
Zhao, Zhongming
author_facet Sun, Jingchun
Huang, Liang-Chin
Xu, Hua
Zhao, Zhongming
author_sort Sun, Jingchun
collection PubMed
description 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.
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spelling pubmed-39327222014-03-31 Network-Assisted Prediction of Potential Drugs for Addiction Sun, Jingchun Huang, Liang-Chin Xu, Hua Zhao, Zhongming Biomed Res Int Research Article 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. Hindawi Publishing Corporation 2014 2014-02-09 /pmc/articles/PMC3932722/ /pubmed/24689033 http://dx.doi.org/10.1155/2014/258784 Text en Copyright © 2014 Jingchun Sun et al. https://creativecommons.org/licenses/by/3.0/ 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.
spellingShingle Research Article
Sun, Jingchun
Huang, Liang-Chin
Xu, Hua
Zhao, Zhongming
Network-Assisted Prediction of Potential Drugs for Addiction
title Network-Assisted Prediction of Potential Drugs for Addiction
title_full Network-Assisted Prediction of Potential Drugs for Addiction
title_fullStr Network-Assisted Prediction of Potential Drugs for Addiction
title_full_unstemmed Network-Assisted Prediction of Potential Drugs for Addiction
title_short Network-Assisted Prediction of Potential Drugs for Addiction
title_sort network-assisted prediction of potential drugs for addiction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3932722/
https://www.ncbi.nlm.nih.gov/pubmed/24689033
http://dx.doi.org/10.1155/2014/258784
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