Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Journal: Nature communications
PMID: 33589615

Abstract

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Authors

  • Steve Rodriguez
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
  • Clemens Hug
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
  • Petar Todorov
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Sciences, Harvard Medical SchoolBoston, MA.
  • Nienke Moret
    Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA.
  • Sarah A Boswell
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
  • Kyle Evans
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
  • George Zhou
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA.
  • Nathan T Johnson
    Worcester Polytechnic Institute, Bioinformatics and Computational Biology Program, Worcester, Massachusetts 01609, USA.
  • Bradley T Hyman
  • Peter K Sorger
    Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA peter_sorger@hms.harvard.edu.
  • Mark W Albers
    Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA. albers.mark@mgh.harvard.edu.
  • Artem Sokolov
    Harvard Medical School, Boston, MA 02115, USA.

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