Evidence map›Paper›PMID 36792381›Full record

ArticleAddiction (Abingdon, England)2023

Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses.

Zhenxiang Gao, T John Winhusen, Maria Gorenflo, Udi E Ghitza, Pamela B Davis, David C Kaelber, Rong Xu

Open access · hybridAbstract read
In one paragraph

Article in Addiction (Abingdon, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
9.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

  1. Pooled it
  2. PGx-BasedCurrent neuropharmacology · 2026
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  10. Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 1 country.

Zhenxiang GaoCenter for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.ORCID 0000-0002-2223-5092
T John WinhusenCenter for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA.ORCID 0000-0002-3364-0739
Maria GorenfloCenter for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Udi E GhitzaCenter for the Clinical Trials Network (CCTN), National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID 0000-0001-9964-8066
Pamela B DavisCenter for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.ORCID 0000-0002-0013-8750
David C KaelberCenter for Clinical Informatics Research and Education, The Metro Health System, Cleveland, OH, USA.ORCID 0000-0001-7855-9515
Rong XuCenter for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.ORCID 0000-0003-3127-4795
Case Western Reserve University · USCleveland Clinic Lerner College of Medicine · USMetroHealth · USNational Institutes of Health · USUniversity of Cincinnati Medical Center · US

Funding

The Ohio Valley Node of the Clinical Trials Network (CTN-0153)UG1DA013732 · NIDA · UNIVERSITY OF CINCINNATI · PI T John WINHUSEN · 2015 to 2026
$42.5M
Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Clinical and Translational Science Collaborative of Northern Ohio, Catalyzing Linkages to Equity in Health (CLE Health)UM1TR004528 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI GRACE A MCCOMSEY · 2023 to 2026
$32.1M
NCATS NIH HHS UL1 TR002548NCATS NIH HHS UM1 TR004528NIDA NIH HHS UG1 DA013732
6 · The paper itself

Abstract

BACKGROUND AND

aimsCocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment.

designOur drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD.

settingThe study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases.

participantsA total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching. MEASUREMENTS: EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription.

findingsPatients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence.

conclusionsKetamine appears to be a potential repurposed drug for treatment of cocaine use disorder.

Indexed as

CocaineCocaine-Related DisordersKetamineSubstance-Related DisordersAntidepressive AgentsArtificial IntelligenceDrug RepositioningHumansMidazolamSerotonin Plasma Membrane Transport ProteinsAntidepressive AgentsCocaineKetamineMidazolamSerotonin Plasma Membrane Transport ProteinsSLC6A4 protein, humanArtificial intelligenceclinical corroborationcocaine use disorderdrug repurposingexpert evaluationketaminemechanism of action analyses

Identifiers

PMID36792381
PMCPMC10631254
OpenAlexW4320879294

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.