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Nat Commun. 2016 Feb 1;7:10331. doi: 10.1038/ncomms10331.

Network-based in silico drug efficacy screening.

Guney E1,2, Menche J1,3, Vidal M2,4, Barábasi AL1,2,3,5.

Author information

Center for Complex Networks Research (CCNR) and Department of Physics, Northeastern University, 177 Huntington Avenue, 11th floor, Boston, Massachusetts 02115, USA.
Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, Massachusetts 02215, USA.
Center for Network Science, Central European University, Nador utca 9, 1051 Budapest, Hungary.
Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA.
Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, Massachusetts 02115, USA.


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.

[Indexed for MEDLINE]
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