CAS: 210353-53-0; (Z)-7-(3-(Aminomethyl)-4-(Methoxyimino)Pyrrolidin-1-yl)-1-Cyclopropyl-6-Fluoro-4-Oxo-1,4-Dihydro-1,8-Naphthyridine-3-Carboxylic Acid Methanesulfonic Acid Salt
该化合物是一种合成的氟己酮抗生素,具有广泛频谱抗菌活性,主要用于治疗呼吸道感染,包括肺炎和支气管炎,抑制细菌DNA凝酶和四型异构酶,这些对细菌DNA复制和转录至关重要;该物质的特点是其溶性在水和有机溶剂中的溶解性,有利于吸收和生物利用;Gemifloxain mesylate具有有利的药用植物基因特征,允许每日一次使用;其化学结构包括一个双环核,含氟素原子,有助于防止各种克色阳性和克丁酸细菌;其间盐形态增强其稳定性和溶性;其常见的副作用可能包括胃肠扰动和对...
- 英文名称(Z)-7-(3-(Aminomethyl)-4-(Methoxyimino)Pyrrolidin-1-yl)-1-Cyclopropyl-6-Fluoro-4-Oxo-1,4-Dihydro-1,8-Naphthyridine-3-Carboxylic Acid Methanesulfonic Acid Salt
- 中文名称甲磺酸吉米沙星
- IUPAC名称(Z)-7-(3-(aminomethyl)-4-(methoxyimino)pyrrolidin-1-yl)-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydro-1,8-naphthyridine-3-carboxylic acid--methanesulfonic acid (1/1)
- 其它别名Gemifloxacin Mesilate [mart.] ; Mesilato De Gemifloxacina ; 2-Methyl-2-Phenylpropanamide # ; (+/-)-7-(3-(Aminomethyl)-4-Oxo-1-Pyrrolidinyl)-1-Cyclopropyl-6-Fluoro-1,4-Dihydro-4-Oxo-1,8-Naphthyridine-3-Carboxylic Acid, 7(Sup 4)-(Z)-(O-Methyloxime), Monomet
- CAS编号210353-53-0
- MFCD编号:MFCD08277637
- EINECS号:988-321-0
- 分子式:C19H24FN5O7S分子量:485.49
- 产品CID: 1088366
- 同类组份化合物CAS175463-14-6 (free base)210353-53-0 (mesylate)
- 产品分类原料药 → 人工合成抗感染类药 → 喹诺酮类药
物理性质
MSDS等安全信息
欧盟法规
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methanesulfonic acid gemifloxacin 7-(3-aminomethyl-4-methoxyimino-pyrrolidine-1-yl)-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydro-[1,8]-naphthyridine-3-carboxylic acid合成工艺路线路线简述
海关参考信息
- 2905110000-甲醇
2912110000-甲醛
2915110000-甲酸
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主要参考文献
1: Foroughipour M, Nezamzadeh-Ejhieh A. CaTiO3/g-C3N4 heterojunction-based composite photocatalyst: Part I: Experimental design, kinetics, and scavenging agents' effects in photocatalytic degradation of gemifloxacin. Chemosphere. 2023 May 24;334:139019. doi: 10.1016/j.chemosphere.2023.139019. Epub ahead of print. 13(7):1234. doi: 10.3390/diagnostics13071234.
3: Faisal M, Ahmed J, Jalalah M, Alsareii SA, Alsaiari M, Harraz FA. Rapid elimination of antibiotic gemifloxacin mesylate and methylene blue over Pt nanoparticles dispersed chitosan/g-C3N4 ternary visible light photocatalyst. Environ Sci Pollut Res Int. 2023 May;30(22):61710-61725. doi: 10.1007/s11356-023-26456-w. Epub 2023 Mar 18. 30(1):2185180. doi: 10.1080/10717544.2023.2185180.
5: Jang MG, Cha S, Kim S, Lee S, Lee KE, Shin KH. Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database. Expert Opin Drug Saf. 2023 Feb
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