坐骨神经
TRPV1型
背根神经节
钠通道
伤害
医学
麻醉
电生理学
感觉系统
左旋布比卡因
瞬时受体电位通道
药理学
解剖
止痛药
钠
化学
内科学
神经科学
受体
背
生物
有机化学
作者
Cheng Zhou,Lei Tang,Qinqin Yin,Linghui Yang,Deying Gong,Yi Kang,Hangxue Cao,Jing Fan,Yujun Zhang,Dong Qian,Qianqian Zhang,Bowen Ke,Jin Liu,Wensheng Zhang,Jun Yang
出处
期刊:Regional Anesthesia and Pain Medicine
[BMJ]
日期:2020-04-12
卷期号:45 (6): 412-418
被引量:2
标识
DOI:10.1136/rapm-2019-101057
摘要
Background and objective Long-acting nociceptive-selective regional anesthesia has remained an elusive clinical goal. We aspired to identify a novel compound that would produce nociceptive-selective regional anesthesia through the transient receptor potential vanilloid 1 (TRPV1) channels. Methods We designed and synthesized a novel compound (LL-a) that penetrates the cell membrane through TRPV1 channels and binds to voltage-gated sodium channels. The regional anesthetic effect of LL-a was evaluated in a rodent sciatic nerve block model. Electrophysiological recording was applied to test the inhibition of LL-a on voltage-gated sodium channel currents. Results LL-a inhibited sodium channel currents on the dorsal root ganglion neurons of mice and this action was diminished by TRPV1 channel knockout. In a sciatic nerve block model of a rat, 0.2% and 0.4% (w/v) LL-a produced selective sensory block with median (IQR) durations of 42.0 (24.0, 48.0) and 72.0 (69.0, 78.0) hours, respectively. No motor block was found for 0.2% LL-a. 0.4% LL-a produced a motor block with a median (IQR) duration of 3.0 (0.0, 6.0) hours. This selective sensory block was not observed on TRPV1 knockout mice. As a positive control, 0.5% and 0.75% levobupivacaine produced a non-selective sciatic nerve block with median (IQR) durations of 2.8 (2.6, 2.8) and 3.8 (3.8, 4.8) hours, respectively. No systemic or local irritation was observed during injection of LL-a and sensory and motor function completely recovered for all the animals. Conclusions LL-a is a potential novel local anesthetic for long-lasting nociceptive-selective analgesia.
科研通智能强力驱动
Strongly Powered by AbleSci AI