Improved detection of reactive metabolites with a bromine-containing glutathione analog using mass defect and isotope pattern matching

2010 ◽  
Vol 24 (9) ◽  
pp. 1241-1250 ◽  
Author(s):  
André LeBlanc ◽  
Tze Chieh Shiao ◽  
René Roy ◽  
Lekha Sleno
2017 ◽  
Vol 51 (3) ◽  
pp. 1518-1526 ◽  
Author(s):  
Ana Ballesteros-Gómez ◽  
Joaquín Ballesteros ◽  
Xavier Ortiz ◽  
Willem Jonker ◽  
Rick Helmus ◽  
...  

2021 ◽  
Author(s):  
Marothu Vamsi Krishna ◽  
Kantamaneni Padmalatha ◽  
Gorrepati Madhavi

Metabolic stability of a compound is an important factor to be considered during the early stages of drug discovery. If the compound has poor metabolic stability, it never becomes a drug even though it has promising pharmacological characteristics. For example, a drug is quickly metabolized in the body; it does not have sufficient in vivo exposure levels and leads to the production of toxic, non-active or active metabolites. A drug is slowly metabolized in the body it could remain longer periods in the body and lead to unwanted adverse reactions, toxicity or may cause drug interactions. Metabolic stability assay is performed to understand the susceptibility of the compound to undergo biotransformation in the body. Intrinsic clearance of the compound is measured by metabolic stability assays. Different in vitro test systems including liver microsomes, hepatocytes, S9 fractions, cytosol, recombinant expressed enzymes, and cell lines are used to investigate the metabolic stability of drugs. Metabolite profiling is a vital part of the drug discovery process and LC–MS plays a vital role. The development of high-resolution (HR) MS technologies with improved mass accuracy, in conjunction with novel data processing techniques, has significantly improved the metabolite detection and identification process. HR-MS based data acquisition (ion intensity-dependent acquisition, accurate-mass inclusion list-dependent acquisition, isotope pattern-dependent acquisition, pseudo neutral loss-dependent acquisition, and mass defect-dependent acquisition) and data mining techniques (extracted ion chromatogram, product ion filter, mass defect filter, isotope pattern filter, neutral loss filter, background subtraction, and control sample comparison) facilitate the drug metabolite identification process.


1969 ◽  
Vol 61 (1_Suppl) ◽  
pp. S120
Author(s):  
M. D. Cain ◽  
K. J. Catt ◽  
J. P. Coghlan

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