Identifying fast-onset antidepressants using rodent models

心理学 啮齿动物模型 啮齿动物 医学 精神药理学 神经科学 精神科 生物 内科学 生态学
作者
Marcia J. Ramaker,Stephanie C. Dulawa
出处
期刊:Molecular Psychiatry [Springer Nature]
卷期号:22 (5): 656-665 被引量:161
标识
DOI:10.1038/mp.2017.36
摘要

Depression is a leading cause of disability worldwide and a major contributor to the burden of suicide. A major limitation of classical antidepressants is that 2–4 weeks of continuous treatment is required to elicit therapeutic effects, prolonging the period of depression, disability and suicide risk. Therefore, the development of fast-onset antidepressants is crucial. Preclinical identification of fast-onset antidepressants requires animal models that can accurately predict the delay to therapeutic onset. Although several well-validated assay models exist that predict antidepressant potential, few thoroughly tested animal models exist that can detect therapeutic onset. In this review, we discuss and assess the validity of seven rodent models currently used to assess antidepressant onset: olfactory bulbectomy, chronic mild stress, chronic forced swim test, novelty-induced hypophagia (NIH), novelty-suppressed feeding (NSF), social defeat stress, and learned helplessness. We review the effects of classical antidepressants in these models, as well as six treatments that possess fast-onset antidepressant effects in the clinic: electroconvulsive shock therapy, sleep deprivation, ketamine, scopolamine, GLYX-13 and pindolol used in conjunction with classical antidepressants. We also discuss the effects of several compounds that have yet to be tested in humans but have fast-onset antidepressant-like effects in one or more of these antidepressant onset sensitive models. These compounds include selective serotonin (5-HT)2C receptor antagonists, a 5-HT4 receptor agonist, a 5-HT7 receptor antagonist, NMDA receptor antagonists, a TREK-1 receptor antagonist, mGluR antagonists and (2R,6R)-HNK. Finally, we provide recommendations for identifying fast-onset antidepressants using rodent behavioral models and molecular approaches.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
1秒前
赘婿应助Cam采纳,获得30
3秒前
RyanNeo完成签到,获得积分10
4秒前
此然发布了新的文献求助10
5秒前
温暖的颜演完成签到 ,获得积分10
6秒前
wesley完成签到,获得积分10
9秒前
wh关闭了wh文献求助
10秒前
sen123完成签到,获得积分10
12秒前
霸霸发布了新的文献求助10
12秒前
Jocelyn完成签到,获得积分10
13秒前
2025迷完成签到 ,获得积分10
14秒前
老年学术废物完成签到 ,获得积分10
15秒前
千空应助萧萧采纳,获得10
17秒前
digiwood完成签到,获得积分10
17秒前
19秒前
2385697574完成签到,获得积分10
19秒前
好学的泷泷完成签到 ,获得积分10
20秒前
weibo发布了新的文献求助10
20秒前
mike2012完成签到 ,获得积分10
21秒前
24秒前
姜昊彤完成签到,获得积分10
24秒前
free完成签到,获得积分10
26秒前
陶醉的惜梦完成签到,获得积分20
27秒前
儒雅的豁完成签到,获得积分10
27秒前
lucky完成签到,获得积分10
27秒前
HaoHao04完成签到 ,获得积分10
28秒前
快乐大炮发布了新的文献求助10
29秒前
笑对人生完成签到 ,获得积分10
29秒前
lucky发布了新的文献求助10
30秒前
梁正凤完成签到,获得积分10
30秒前
ccyh完成签到,获得积分20
31秒前
31秒前
科研通AI2S应助予秋采纳,获得10
35秒前
Cam发布了新的文献求助30
36秒前
FBQZDJG2122完成签到,获得积分10
39秒前
花花完成签到,获得积分10
40秒前
43秒前
英勇的幻露完成签到,获得积分10
44秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 生物化学 化学工程 物理 计算机科学 复合材料 内科学 催化作用 物理化学 光电子学 电极 冶金 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6021799
求助须知:如何正确求助?哪些是违规求助? 7636171
关于积分的说明 16166946
捐赠科研通 5169597
什么是DOI,文献DOI怎么找? 2766509
邀请新用户注册赠送积分活动 1749547
关于科研通互助平台的介绍 1636615