A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.

Journal: Malaria journal
Published Date: Apr 11, 2018

Abstract

BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed.

Authors

  • Yasaman KalantarMotamedi
    Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
  • Richard T Eastman
    Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD, 20852, USA.
  • Rajarshi Guha
    National Center for Advancing Translational Science, Rockville, MD, USA.
  • Andreas Bender
    Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK ab454@cam.ac.uk.

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