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The integration of data across randomized controlled trials (RCTs) testing interventions and treatments for substance use disorder (SUD) offers a rich opportunity for improving the evidence base and analytic methods used in SUD research. With over 50 completed trials of the National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network (CTN) available for secondary analysis and harmonization, the possibilities are extensive, but the effort to harmonize and document datasets demands complex analytic formulation and methodology. This commentary discusses strengths and challenges of data harmonization, sharing clinical and data science considerations based on four exemplar studies that harmonized data across multiple CTN trials. We offer recommendations for others planning data harmonization for secondary analysis, discuss guiding principles for research data management, outline suggestions to bridge gaps in the context of the CTN, and finally frame considerations for using state-of-the-art tools such as generative AI and integration of data from clinical trials and electronic health records to enhance the promise of data harmonization.
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.
Design and setting: An AI-based knowledge graph model was first utilized to prioritize FDA-approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.
Participants and measurements: ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.
Findings: Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15-2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14-2.01) and 1.68 (95% CI = 1.18-2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD-associated pathways including neuroactive ligand-receptor interaction and amphetamine addiction.
Conclusions: There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.
Related protocols: CTN-0114