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• W2164315135 abstract "Isomers cannot be differentiated from each other solely based on accurate mass measurement of the compound. A liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/Q-TOFMS) method was used to systematically fragment a large group of different isomers. Two software programs were used to characterize in silico mass fragmentation of compounds in order to identify characteristic fragments. The software programs employed were ACD/MS Fragmenter (ACD Labs Toronto, Canada), which uses general fragmentation rules to generate fragments based on the structure of a compound, and SmartFormula3D (Bruker Daltonics), which assigns fragments from a mass spectra and calculates the molecular formulae for the ions using accurate mass data. From an in-house toxicology database of 874 drug substances, 48 isomer groups comprising 111 compounds, for which a reference standard was available, were found. The product ion spectra were processed with the two software programs and 1–3 fragments were identified for each compound. In 82% of the cases, the fragment could be identified with both software programs. Only 10 isomer pairs could not be differentiated from each other based on their fragments. These compounds were either diastereomers or position isomers undergoing identical fragmentation. Accurate mass data could be utilized with both software programs for structural elucidation of the fragments. Mean mass accuracy and isotopic pattern match values (SigmaFit; Bruker Daltonics Bremen, Germany) were 0.9 mDa and 24.6 mSigma, respectively. The study introduces a practical approach for preliminary compound identification in a large target database by LC/Q-TOFMS without necessarily possessing reference standards. Copyright © 2010 John Wiley & Sons, Ltd." @default.
• W2164315135 created "2016-06-24" @default.
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• W2164315135 date "2010-05-27" @default.
• W2164315135 modified "2023-09-28" @default.
• W2164315135 title "Differentiation of structural isomers in a target drug database by LC/Q-TOFMS using fragmentation prediction" @default.
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• W2164315135 doi "https://doi.org/10.1002/dta.134" @default.
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