Screening strategy for the rapid detection of in vitro generated glutathione conjugates using high‐performance liquid chromatography and low‐resolution mass spectrometry in combination with LightSight® software for data processing

2009 
The knowledge of drug metabolism in the early phases of the drug discovery process is vital for minimising compound failure at later stages. As chemically reactive metabolites may cause adverse drug reactions, it is generally accepted that avoiding formation of reactive metabolites increases the chances of success of a molecule. In order to generate this important information, a screening strategy for the rapid detection of invitro generated reactive metabolites trapped by glutathione has been developed. The bioassay incorporated the use of native glutathione and its close analogue the glutathione ethyl ester. The generic conditions for detecting glutathione conjugates that undergo constant neutral loss of 129 Da were optimised using a glutathione-based test mix of four compounds. The final liquid chromatography/tandem mass spectrometry constant neutral loss method used low-resolution settings and a scanning window of 200 amu. Data mining was rapidly and efficiently performed using LightSight® software. Unambiguous identification of the glutathione conjugates was significantly facilitated by the analytical characteristics of the conjugate pairs formed with glutathione and glutathione ethyl ester, i.e. by chromatographic retention time and mass differences. The reliability and robustness of the screening strategy was tested using a number of compounds known to form reactive metabolites. Overall, the developed screening strategy provided comprehensive and reliable identification of glutathione conjugates and is well suited for rapid routine detection of trapped reactive metabolites. This new approach allowed the identification of a previously unreported diclofenac glutathione conjugate. Copyright © 2009 John Wiley & Sons, Ltd.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    35
    References
    15
    Citations
    NaN
    KQI
    []