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Large-scale prediction and testing of drug activity on side-effect targets

Author

Listed:
  • Eugen Lounkine

    (Novartis Institutes for Biomedical Research)

  • Michael J. Keiser

    (SeaChange Pharmaceuticals Inc, 409 Illinois Street
    University of California, San Francisco, 1700 4th Street, Byers Hall Suite 508D, California 94158-2550, USA)

  • Steven Whitebread

    (Novartis Institutes for Biomedical Research)

  • Dmitri Mikhailov

    (Novartis Institutes for Biomedical Research)

  • Jacques Hamon

    (Novartis Institutes for Biomedical Research)

  • Jeremy L. Jenkins

    (Novartis Institutes for Biomedical Research)

  • Paul Lavan

    (Novartis Institutes for Biomedical Research)

  • Eckhard Weber

    (Novartis Institutes for Biomedical Research)

  • Allison K. Doak

    (University of California, San Francisco, 1700 4th Street, Byers Hall Suite 508D, California 94158-2550, USA)

  • Serge Côté

    (Novartis Institutes for Biomedical Research)

  • Brian K. Shoichet

    (University of California, San Francisco, 1700 4th Street, Byers Hall Suite 508D, California 94158-2550, USA)

  • Laszlo Urban

    (Novartis Institutes for Biomedical Research)

Abstract

Discovering the unintended ‘off-targets’ that predict adverse drug reactions is daunting by empirical methods alone. Drugs can act on several protein targets, some of which can be unrelated by conventional molecular metrics, and hundreds of proteins have been implicated in side effects. Here we use a computational strategy to predict the activity of 656 marketed drugs on 73 unintended ‘side-effect’ targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a drug–target–adverse drug reaction network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic oestrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme cyclooxygenase-1. The clinical relevance of this inhibition was borne out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.

Suggested Citation

  • Eugen Lounkine & Michael J. Keiser & Steven Whitebread & Dmitri Mikhailov & Jacques Hamon & Jeremy L. Jenkins & Paul Lavan & Eckhard Weber & Allison K. Doak & Serge Côté & Brian K. Shoichet & Laszlo U, 2012. "Large-scale prediction and testing of drug activity on side-effect targets," Nature, Nature, vol. 486(7403), pages 361-367, June.
  • Handle: RePEc:nat:nature:v:486:y:2012:i:7403:d:10.1038_nature11159
    DOI: 10.1038/nature11159
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    Cited by:

    1. Qiyao Luo & Liang Zhao & Jianxing Hu & Hongwei Jin & Zhenming Liu & Liangren Zhang, 2017. "The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
    2. Zheni Zeng & Yuan Yao & Zhiyuan Liu & Maosong Sun, 2022. "A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    4. Armaghan W Naik & Joshua D Kangas & Christopher J Langmead & Robert F Murphy, 2013. "Efficient Modeling and Active Learning Discovery of Biological Responses," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    5. Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    6. Julian E Fuchs & Susanne von Grafenstein & Roland G Huber & Christian Kramer & Klaus R Liedl, 2013. "Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-15, November.
    7. Ruibo Zhang & Daniel Nolte & Cesar Sanchez-Villalobos & Souparno Ghosh & Ranadip Pal, 2024. "Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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