Gao Zhenxiang, Winhusen T John, Gorenflo Maria P, Dorney Ian, Ghitza Udi E, Kaelber David C, Xu Rong
Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Addiction. 2025 Apr;120(4):732-744. doi: 10.1111/add.16715. Epub 2024 Nov 17.
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.
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.
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.
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.
There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.
苯丙胺类兴奋剂是全球第二大常用非法药物,但美国食品药品监督管理局(FDA)尚未批准用于治疗苯丙胺类兴奋剂使用障碍(ATSUD)的药物。本研究的目的是利用一个整合了基于人工智能(AI)的药物预测、临床确证和作用机制分析的药物发现框架,来识别可重新用于治疗ATSUD的FDA批准药物。
首先利用基于AI的知识图谱模型,对FDA批准药物在治疗ATSUD方面的潜在疗效进行排序。在排名前十的候选药物中,氯胺酮是一种新的候选药物,很少有研究考察其对ATSUD的影响。因此,我们进行了一项回顾性队列研究,使用美国电子健康记录(EHR)数据评估氯胺酮与ATSUD缓解之间的关联。最后,我们在ATSUD的背景下分析了氯胺酮的潜在作用机制。
2019年1月至2022年6月期间接受麻醉(n = 3663)或被诊断为抑郁症(n = 4328)的ATSUD患者。结局指标是药物处方后一年内ATSUD缓解的诊断。
与其他麻醉剂相比,ATSUD患者使用氯胺酮进行麻醉与更高的ATSUD缓解率相关:风险比(HR)= 1.58,95%置信区间(CI)= 1.15 - 2.17。在将氯胺酮与抗抑郁药以及安非他酮/米氮平进行比较时,患有抑郁症的ATSUD患者也得到了类似结果,HR分别为1.51(95% CI = 1.14 - 2.01)和1.68(95% CI = 1.18 - 2.38)。功能分析表明,氯胺酮靶向多种与ATSUD相关的途径,包括神经活性配体 - 受体相互作用和苯丙胺成瘾。
临床医生开具的氯胺酮与苯丙胺类兴奋剂使用障碍患者较高的缓解率之间似乎存在关联。