In Vitro, In Vivo and In Silico Characterization of a Novel Kappa-Opioid Receptor Antagonist

药效团 生物信息学 κ-阿片受体 化学 兴奋剂 体内 药理学 阿片受体 受体 敌手 对接(动物) 类阿片 放射性配体 配体(生物化学) 体外 立体化学 生物化学 生物 医学 护理部 生物技术 基因
作者
Kristina Puls,Aina-Leonor Olivé-Marti,Szymon Pach,Birgit Pinter,Filippo Erli,Gerhard Wolber,Mariana Spetea
出处
期刊:Pharmaceuticals [Multidisciplinary Digital Publishing Institute]
卷期号:15 (6): 680-680 被引量:4
标识
DOI:10.3390/ph15060680
摘要

Kappa-opioid receptor (KOR) antagonists are promising innovative therapeutics for the treatment of the central nervous system (CNS) disorders. The new scaffold opioid ligand, Compound A, was originally found as a mu-opioid receptor (MOR) antagonist but its binding/selectivity and activation profile at the KOR and delta-opioid receptor (DOR) remain elusive. In this study, we present an in vitro, in vivo and in silico characterization of Compound A by revealing this ligand as a KOR antagonist in vitro and in vivo. In the radioligand competitive binding assay, Compound A bound at the human KOR, albeit with moderate affinity, but with increased affinity than to the human MOR and without specific binding at the human DOR, thus displaying a preferential KOR selectivity profile. Following subcutaneous administration in mice, Compound A effectively reverse the antinociceptive effects of the prototypical KOR agonist, U50,488. In silico investigations were carried out to assess the structural determinants responsible for opioid receptor subtype selectivity of Compound A. Molecular docking, molecular dynamics simulations and dynamic pharmacophore (dynophore) generation revealed differences in the stabilization of the chlorophenyl moiety of Compound A within the opioid receptor binding pockets, rationalizing the experimentally determined binding affinity values. This new chemotype bears the potential for favorable ADMET properties and holds promise for chemical optimization toward the development of potential therapeutics.

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
1秒前
白紫寒完成签到,获得积分10
1秒前
勇敢的风发布了新的文献求助10
1秒前
排骨大王完成签到 ,获得积分10
2秒前
3秒前
俭朴听南完成签到,获得积分10
4秒前
4秒前
4秒前
桐桐应助Morin采纳,获得10
5秒前
美满平松完成签到 ,获得积分10
5秒前
Jasper应助pansy采纳,获得30
5秒前
Jasper应助fishss采纳,获得10
7秒前
8秒前
余铸海发布了新的文献求助10
8秒前
8秒前
lcz发布了新的文献求助10
9秒前
星辰大海应助zjq采纳,获得10
9秒前
ghdltkdtn56完成签到,获得积分10
9秒前
SciGPT应助TYT采纳,获得10
10秒前
TYH_235发布了新的文献求助10
11秒前
12秒前
dddddw完成签到,获得积分10
13秒前
充电宝应助赵伟豪采纳,获得10
13秒前
Lee_Peng发布了新的文献求助10
14秒前
14秒前
汤圆完成签到,获得积分10
14秒前
15秒前
lailight完成签到,获得积分10
16秒前
cz发布了新的文献求助10
17秒前
17秒前
Lucas应助waker采纳,获得10
18秒前
18秒前
落后猕猴桃完成签到,获得积分10
19秒前
难过龙猫完成签到,获得积分10
20秒前
20秒前
20秒前
22秒前
可爱的函函应助老杨采纳,获得10
22秒前
科研通AI6.2应助小豆芽芽采纳,获得10
23秒前
23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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6361439
求助须知:如何正确求助?哪些是违规求助? 8175188
关于积分的说明 17221423
捐赠科研通 5416250
什么是DOI,文献DOI怎么找? 2866218
邀请新用户注册赠送积分活动 1843512
关于科研通互助平台的介绍 1691443