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

Abstract

Background and aims: Cocaine 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.

Design: Our 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.

Setting: The 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.

Participants: A 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.

Findings: 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) = 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.

Conclusions: Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.

Keywords: Artificial intelligence; clinical corroboration; cocaine use disorder; drug repurposing; expert evaluation; ketamine; mechanism of action analyses.

FIGURE 1

The pipeline of the drug repurposing strategy for cocaine use disorder. (a) Our knowledge graph-based drug discovery system modeled multi-type interactions from various biomedical databases to rank candidate drugs for cocaine use disorder (CUD) treatment. (b) The CTN-0114 advisory committee reviewed the top-ranked drugs and selected ketamine for further clinical evaluation. (c) Electronic health record (EHR)-based analysis provided clinical corroborations of ketamine for CUD treatment. (d) The genetic and functional-level analyses showed that ketamine directly targets multiple CUD genes and pathways.

FIGURE 2

Flow-charts of retrospective case–control cohort design.

FIGURE 3

Hazard ratios for remission from cocaine use disorder (CUD) in patients prescribed with ketamine compared with propensity score-matched patients prescribed with other anesthetics.

FIGURE 4

Hazard ratios for remission from cocaine use disorder (CUD) in patients with depression prescribed with ketamine compared with propensity score-matched patients prescribed with other antidepressants or midazolam.

FIGURE 5

Clinical efficacy of ketamine for cocaine use disorder (CUD) remission in two non-overlapping cohorts of CUD patients.