Network-based in silico drug efficacy screening.

Journal: Nature communications
Published Date:

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.

Authors

  • Emre Guney
    Center for Complex Networks Research (CCNR) and Department of Physics, Northeastern University, 177 Huntington Avenue, 11th floor, Boston, Massachusetts 02115, USA.
  • Jörg Menche
    Center for Complex Networks Research (CCNR) and Department of Physics, Northeastern University, 177 Huntington Avenue, 11th floor, Boston, Massachusetts 02115, USA.
  • Marc Vidal
    Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, Massachusetts 02215, USA.
  • Albert-László Barábasi
    Center for Complex Networks Research (CCNR) and Department of Physics, Northeastern University, 177 Huntington Avenue, 11th floor, Boston, Massachusetts 02115, USA.