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Identification of Penicillin G Metabolites in Human Serum by Liquid Chromatography-Tandem Mass Spectrometry
|關鍵字:||Tandem Mass Spectrometry;串聯質譜;Penicillin G;Metabolites;盤尼西林G;代謝物||出版社:||化學系所||引用:||Reference  A. D. Deshpande, K. G. Baheti,N. R. Chatterjee, Degradation of β-lactam antibiotics, Current Science, 87 (2004) 1684.  J. P. Hou,J. W. Poole, beta-lactam antibiotics: Their physicochemical properties and biological activities in relation to structure, Journal of Pharmaceutical Sciences, 60 (1971) 503.  H. C. Neu, ß-Lactam Antibiotics: Structural Relationships Affecting in Vitro Activity and Pharmacologic Properties, Reviews of Infectious Diseases, 8 (1986) S237.  N. A. Rosário,A. S. Grumach, Allergy to beta-lactams in pediatrics: a practical approach, Jornal de Pediatria, 82 (2006) S181.  F. Sáchez-Sancho, E. Perez-Inestrosa, R. Suau, M. I. Montañez, C. Mayorga, M. J. Torres, A. Romano,M. Blanca, Synthesis, characterization and immunochemical evaluation of cephalosporin antigenic determinants, Journal of Molecular Recognition, 16 (2003) 148.  J. Frumin,J. C. 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盤尼西林G及其代謝產物導致人體過敏反應，為其用藥限制最主要的原因。至今對其代謝產物機制仍有許多不明白的地方，造成在臨床上無法確切和有效的預防過敏反應的產生。故本實驗將採用具有高選擇性及高靈敏度的數據依據性液相層析串聯質譜技術 (Data-dependent LC-MSn) 對人體血清中盤尼西林G及其微量代謝物進行鑑定分析，試圖找出目前尚未發現之微量代謝物，推測其代謝途徑，並藉由MassWorks軟體對推測結果進行精確分子量的測量。根據實驗結果成功檢測出七個盤尼西林G代謝產物，包括二個已知代謝物 (Penicilloate, C16H21N2O5S 和 Penilloate, C15H21N2O3S) 及五個未知代謝物 (C19H25N2O7S、C19H27N2O7S、C16H21N2O6S、C22H29N2O11S和C22H31N4O8S3)。本實驗同時證實Data-dependent LC-MSn於人體藥物代謝物偵測的可行性，所得之實驗結果將作為盤尼西林G微量代謝物於過敏反應機制的研究的參考依據。
Penicillin G (PCN G) was the first antibiotic found and widely applied for human bacterial disease; however, hypersensitivity reactions to penicillin are due to different metabolites formed in vivo when the antibiotic is administered. Several penicillin metabolites in human allergic reaction have been studied and assessed by using penicillin skin test. Unfortunately, the skin test still may give false-negative results and bring the risk of unsafety, such as anaphylactic reactions. Currently, LC-MS and LC-MS/MS were applied to characterize drug metabolites, but they remain time-consuming processes. Frequently, the data obtained are insufficient to locate the site of metabolism on a candidate molecule. Data-dependent LC-MSn is a powerful tool to provide large amounts of the necessary structural information regarding each analyte in one chromatographic run, thereby allowing for a more detailed characterization of the metabolites. Furthermore, it is highly sensitive and selective to detect trace metabolites even in a complex matrix. In the study, Data-dependent LC-MSn was utilized to identify trace metabolites of PCN G in human serum. From the results, in addition to the known metabolites, such as penicilloate and penilloate, the trace unknown metabolites of PCN G metabolites were successfully identified. The structures were determined as the proposed unknown metabolites have been further confirmed by using MassWorks.
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