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Predicting new molecular targets for known drugs

Author

Listed:
  • Michael J. Keiser

    (Department of Pharmaceutical Chemistry,
    Graduate Group in Bioinformatics, University of California San Francisco, 1700 4th Street, San Francisco, California 94143-2550, USA)

  • Vincent Setola

    (NIMH Psychoactive Drug Screening Program)

  • John J. Irwin

    (Department of Pharmaceutical Chemistry,)

  • Christian Laggner

    (Department of Pharmaceutical Chemistry,)

  • Atheir I. Abbas

    (Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA)

  • Sandra J. Hufeisen

    (The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina 27759, USA)

  • Niels H. Jensen

    (The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina 27759, USA)

  • Michael B. Kuijer

    (NIMH Psychoactive Drug Screening Program)

  • Roberto C. Matos

    (NIMH Psychoactive Drug Screening Program)

  • Thuy B. Tran

    (NIMH Psychoactive Drug Screening Program)

  • Ryan Whaley

    (NIMH Psychoactive Drug Screening Program)

  • Richard A. Glennon

    (School of Pharmacy, Medical College of Virginia Campus, Virginia Commonwealth University, 410 North 12th Street, PO Box 980540, Richmond, Virginia 23298-0540, USA)

  • Jérôme Hert

    (Department of Pharmaceutical Chemistry,)

  • Kelan L. H. Thomas

    (Department of Pharmaceutical Chemistry,
    University of Michigan Health System, 1500 East Medical Center Drive, Ann Arbor, Michigan 48109, USA)

  • Douglas D. Edwards

    (Department of Pharmaceutical Chemistry,)

  • Brian K. Shoichet

    (Department of Pharmaceutical Chemistry,)

  • Bryan L. Roth

    (NIMH Psychoactive Drug Screening Program
    The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina 27759, USA)

Abstract

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (

Suggested Citation

  • Michael J. Keiser & Vincent Setola & John J. Irwin & Christian Laggner & Atheir I. Abbas & Sandra J. Hufeisen & Niels H. Jensen & Michael B. Kuijer & Roberto C. Matos & Thuy B. Tran & Ryan Whaley & Ri, 2009. "Predicting new molecular targets for known drugs," Nature, Nature, vol. 462(7270), pages 175-181, November.
    • Handle: RePEc:nat:nature:v:462:y:2009:i:7270:d:10.1038_nature08506
    DOI: 10.1038/nature08506
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    Citations

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    Cited by:

    1. Xing Chen & Ming-Xi Liu & Qing-Hua Cui & Gui-Ying Yan, 2012. "Prediction of Disease-Related Interactions between MicroRNAs and Environmental Factors Based on a Semi-Supervised Classifier," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    2. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    3. Richard D Smith & Jing Lu & Heather A Carlson, 2017. "Are there physicochemical differences between allosteric and competitive ligands?," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-18, November.
    4. Dong-Sheng Cao & Yi-Zeng Liang & Zhe Deng & Qian-Nan Hu & Min He & Qing-Song Xu & Guang-Hua Zhou & Liu-Xia Zhang & Zi-xin Deng & Shao Liu, 2013. "Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-12, April.
    5. Bin Chen & Ying Ding & David J Wild, 2012. "Assessing Drug Target Association Using Semantic Linked Data," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-10, July.
    6. Zheng Hong, 2019. "A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-9, October.
    7. Kejian Wang & Jiazhi Sun & Shufeng Zhou & Chunling Wan & Shengying Qin & Can Li & Lin He & Lun Yang, 2013. "Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.

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