Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.

Journal: Addiction (Abingdon, England)
PMID: 39552271

Abstract

BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)-based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA-approved drugs that can be repurposed for treating ATSUD.

Authors

  • Zhenxiang Gao
    Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
  • T John Winhusen
    Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
  • Maria P Gorenflo
    Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
  • Ian Dorney
    Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
  • Udi E Ghitza
    National Institutes of Health, National Institutes on Drug Abuse, Rockville, MD, USA.
  • David C Kaelber
    Center for Clinical Informatics Research and Education, The Metro Health System, Cleveland, OH, USA.
  • Rong Xu

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