Literature DB >> 22533568

Recent advances in metabolite identification and quantitative bioanalysis by LC-Q-TOF MS.

Cen Xie1, Dafang Zhong, Kate Yu, Xiaoyan Chen.   

Abstract

The need for rapid, sensitive and effective identification and quantitation of drugs and metabolites to accelerate drug discovery and development has given MS its central position in drug metabolism and pharmacokinetic research. This review attempts to orient the readers with respect to hybrid Q-TOF MS, which enables accurate mass measurement and generates information-rich datasets. The key properties of the Q-TOF MS system, including mass accuracy, resolution, scan speed and dynamic range, are herein discussed. Developments on tandem separation techniques (e.g., UHPLC(®) and ion mobility spectrometry), data acquisition and data-mining methods (e.g., mass defect, product/neutral loss, isotope pattern filters and background subtraction) that facilitate qualitative and quantitative analysis are then examined. The performance and versatility of LC-Q-TOF MS are thoroughly illustrated by its applications in metabolite identification and quantitative bioanalysis. Future perspectives are also discussed.

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Year:  2012        PMID: 22533568     DOI: 10.4155/bio.12.43

Source DB:  PubMed          Journal:  Bioanalysis        ISSN: 1757-6180            Impact factor:   2.681


  10 in total

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Review 5.  Mass spectrometry of natural products: current, emerging and future technologies.

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9.  A Comprehensive Study to Identify Major Metabolites of an Amoxicillin-Sulbactam Hybrid Molecule in Rats and Its Metabolic Pathway Using UPLC-Q-TOF-MS/MS.

Authors:  Fei-Ke Zhao; Ren-Bin Shi; Yu-Bin Sun; Shuang-Yun Yang; Liang-Zhu Chen; Bing-Hu Fang
Journal:  Metabolites       Date:  2022-07-18

10.  Drug Metabolite Cluster-Based Data-Mining Method for Comprehensive Metabolism Study of 5-hydroxy-6,7,3',4'-tetramethoxyflavone in Rats.

Authors:  Yuqi Wang; Xiaodan Mei; Zihan Liu; Jie Li; Xiaoxin Zhang; Shuang Lang; Long Dai; Jiayu Zhang
Journal:  Molecules       Date:  2019-09-09       Impact factor: 4.411

  10 in total

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