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Drug repurposing looks for new cost\u2010effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA\u2010approved drugs for CUD treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Our drug repurposing strategy combines artificial intelligence (AI)\u2010based 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\u2010based 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\u2010ketamine anesthesia or antidepressants\/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine\u2019s potential mechanisms of action in the context of CUD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>The study utilized TriNetX to access EHRs from more than 90 million patients world\u2010wide. Genetic\u2010 and functional\u2010level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>A total of 7742 CUD patients who received anesthesia (3871 ketamine\u2010exposed and 3871 anesthetic\u2010controlled) and 7910 CUD patients with depression (3955 ketamine\u2010exposed and 3955 antidepressant\u2010controlled) were identified after propensity score\u2010matching.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>EHR analysis outcome was a CUD remission diagnosis within 1\u2009year of drug prescription.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR)\u2009=\u20091.98, 95% confidence interval (CI)\u2009=\u20091.42\u20132.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR\u2009=\u20094.39, 95% CI\u2009=\u20092.89\u20136.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD\u2010associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand\u2010receptor interaction, cAMP signaling and cocaine abuse\/dependence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/add.16168","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T21:48:20Z","timestamp":1676497700000},"page":"1307-1319","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence\u2010based prediction, expert evaluation, clinical corroboration and mechanism of action analyses"],"prefix":"10.1111","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2223-5092","authenticated-orcid":false,"given":"Zhenxiang","family":"Gao","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence in Drug Discovery, School of Medicine Case Western Reserve University  Cleveland OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3364-0739","authenticated-orcid":false,"given":"T. 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