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Network-based in silico drug efficacy screening

Author

Listed:
  • Emre Guney

    (Northeastern University
    Dana-Farber Cancer Institute)

  • Jörg Menche

    (Northeastern University
    Center for Network Science, Central European University)

  • Marc Vidal

    (Dana-Farber Cancer Institute
    Harvard Medical School)

  • Albert-László Barábasi

    (Northeastern University
    Dana-Farber Cancer Institute
    Center for Network Science, Central European University
    Brigham and Women's Hospital, Harvard Medical School)

Abstract

The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.

Suggested Citation

  • Emre Guney & Jörg Menche & Marc Vidal & Albert-László Barábasi, 2016. "Network-based in silico drug efficacy screening," Nature Communications, Nature, vol. 7(1), pages 1-13, April.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms10331
    DOI: 10.1038/ncomms10331
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    Cited by:

    1. Daniel Couch & Zhenning Yu & Jin Hyun Nam & Carter Allen & Paula S Ramos & Willian A da Silveira & Kelly J Hunt & Edward S Hazard & Gary Hardiman & Andrew Lawson & Dongjun Chung, 2019. "GAIL: An interactive webserver for inference and dynamic visualization of gene-gene associations based on gene ontology guided mining of biomedical literature," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
    2. JungHo Kong & Doyeon Ha & Juhun Lee & Inhae Kim & Minhyuk Park & Sin-Hyeog Im & Kunyoo Shin & Sanguk Kim, 2022. "Network-based machine learning approach to predict immunotherapy response in cancer patients," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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