氯胺酮的抗抑郁作用以及AMPA谷氨酸受体的作用和NMDA受体拮抗作用之外的其他机制。

Antidepressant effects of ketamine and the roles of AMPA glutamate receptors and other mechanisms beyond NMDA receptor antagonism.

作者信息

Aleksandrova Lily R, Phillips Anthony G, Wang Yu Tian

机构信息

From the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada (Aleksandrova, Wang); and the Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada (Aleksandrova, Phillips).

出版信息

J Psychiatry Neurosci. 2017 Jun;42(4):222-229. doi: 10.1503/jpn.160175.

Abstract

The molecular mechanisms underlying major depressive disorder remain poorly understood, and current antidepressant treatments have many shortcomings. The recent discovery that a single intravenous infusion of ketamine at a subanesthetic dose had robust, rapid and sustained antidepressant effects in individuals with treatment-resistant depression inspired tremendous interest in investigating the molecular mechanisms mediating ketamine's clinical efficacy as well as increased efforts to identify new targets for antidepressant action. We review the clinical utility of ketamine and recent insights into its mechanism of action as an antidepressant, including the roles of -methyl-D-aspartate receptor inhibition, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor upregulation, activation of downstream synaptogenic signalling pathways and the production of an active ketamine metabolite, hydroxynorketamine. Emerging knowledge of the molecular mechanisms underlying both ketamine's positive therapeutic and detrimental side effects will aid the development of a new generation of much-needed superior antidepressant agents.

摘要

重度抑郁症潜在的分子机制仍未得到充分理解,并且目前的抗抑郁治疗存在许多缺点。最近有一项发现,即对难治性抑郁症患者单次静脉注射亚麻醉剂量的氯胺酮可产生强大、快速且持续的抗抑郁作用,这激发了人们对研究介导氯胺酮临床疗效的分子机制的极大兴趣,也促使人们加大力度寻找抗抑郁作用的新靶点。我们综述了氯胺酮的临床应用价值以及近期对其作为抗抑郁药作用机制的见解,包括 N-甲基-D-天冬氨酸受体抑制、α-氨基-3-羟基-5-甲基-4-异恶唑丙酸受体上调、下游突触生成信号通路的激活以及活性氯胺酮代谢物羟基去甲氯胺酮的产生所起的作用。关于氯胺酮积极治疗作用和有害副作用背后分子机制的新认识,将有助于开发新一代急需的更优质抗抑郁药物。

引用本文的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索