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

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          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.

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          Most cited references77

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans.

              (2015)
              Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Journal
                9304118
                2264
                Addiction
                Addiction
                Addiction (Abingdon, England)
                0965-2140
                1360-0443
                4 November 2023
                July 2023
                23 February 2023
                08 November 2023
                : 118
                : 7
                : 1307-1319
                Affiliations
                [1 ]Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
                [2 ]Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
                [3 ]Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
                [4 ]Center for the Clinical Trials Network (CCTN), National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Bethesda, MD, USA
                [5 ]Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
                [6 ]Center for Clinical Informatics Research and Education, The Metro Health System, Cleveland, OH, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                Zhenxiang Gao: Conceptualization-equal; formal analysis-lead; methodology-lead; software-lead; validation-lead; visualization-lead; writing — original draft-lead; writing — review & editing-equal. T. John Winhusen: Conceptualization (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); project administration (equal); supervision (equal); writing—original draft (equal); writing—review and editing (equal). Maria Gorenflo: Formal analysis (equal); methodology (equal); visualization (equal); writing—original draft (equal); writing—review and editing (equal). Udi Ghitza: Formal analysis (equal); writing—original draft (equal); writing—review and editing (equal). Pamela B. Davis: Formal analysis (supporting); writing—original draft (supporting); writing—review and editing (supporting). David C. Kaelber: Resources (lead). Rong Xu: Conceptualization-lead; formal analysis-equal; funding acquisition-lead; project administration-equal; software-equal; supervision-lead; writing — original draft-equal; writing — review & editing-equal.

                Correspondence T. John Winhusen, Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA. winhust@ 123456ucmail.uc.edu , Rong Xu, Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. rxx@ 123456case.edu
                Author information
                http://orcid.org/0000-0002-2223-5092
                http://orcid.org/0000-0002-3364-0739
                http://orcid.org/0000-0001-9964-8066
                http://orcid.org/0000-0002-0013-8750
                http://orcid.org/0000-0001-7855-9515
                http://orcid.org/0000-0003-3127-4795
                Article
                NIHMS1940319
                10.1111/add.16168
                10631254
                36792381
                9c372919-f9f5-48da-8c53-e95a32a877a1

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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                Categories
                Article

                Clinical Psychology & Psychiatry
                artificial intelligence,clinical corroboration,cocaine use disorder,drug repurposing,expert evaluation,ketamine,mechanism of action analyses

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