Cell membrane chromatography (CMC) derived from pathological tissues is ideal for screening specific components acting on specific diseases from complex medicines owing to the maximum simulation of in vivo drug-receptor interactions. However, there are no pathological tissue-derived CMC models that have ever been developed, as well as no visualized affinity comparison of potential active components between normal and pathological CMC columns. In this study, a novel comparative normal/failing rat myocardium CMC analysis system based on online column selection and comprehensive two-dimensional (2D) chromatography/monolithic column/time-of-flight mass spectrometry was developed for parallel comparison of the chromatographic behaviors on both normal and pathological CMC columns, as well as rapid screening of the specific therapeutic agents that counteract doxorubicin (DOX)-induced heart failure from Acontium carmichaeli (Fuzi). In total, 16 potential active alkaloid components with similar structures in Fuzi were retained on both normal and failing myocardium CMC models. Most of them had obvious decreases of affinities on failing myocardium CMC compared with normal CMC model except for four components, talatizamine (TALA), 14-acetyl-TALA, hetisine, and 14-benzoylneoline. One compound TALA with the highest affinity was isolated for further in vitro pharmacodynamic validation and target identification to validate the screen results. Voltage-dependent K(+) channel was confirmed as a binding target of TALA and 14-acetyl-TALA with high affinities. The online high throughput comparative CMC analysis method is suitable for screening specific active components from herbal medicines by increasing the specificity of screened results and can also be applied to other biological chromatography models.
Comparative normal/failing rat myocardium cell membrane chromatographic analysis system for screening specific components that counteract doxorubicin-induced heart failure from Acontium carmichaeli.
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作者:Chen Xiaofei, Cao Yan, Zhang Hai, Zhu Zhenyu, Liu Min, Liu Haibin, Ding Xuan, Hong Zhanying, Li Wuhong, Lv Diya, Wang Lirong, Zhuo Xianyi, Zhang Junping, Xie Xiang-Qun, Chai Yifeng
| 期刊: | Analytical Chemistry | 影响因子: | 6.700 |
| 时间: | 2014 | 起止号: | 2014 May 20; 86(10):4748-57 |
| doi: | 10.1021/ac500287e | ||
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