Eicosanoids comprise a class of bioactive lipids derived from a unique group of essential fatty acids that mediate a variety of important physiological functions. Owing to the structural diversity of these lipids, their analysis in biological samples is often a major challenge. Advancements in mass spectrometric have been helpful for the characterization and quantification of these molecular lipid species in complex matrices. However, there are technical limitations to this approach, including low-abundant and/or poorly ionizable lipids. Using high-resolution multiple-reaction monitoring (MRMHR), we were able to develop a targeted bioanalytical method for eicosanoid quantification. For this, we optimized the LC-MS/MS conditions and evaluated several parameters, including linearity, limits of quantification, matrix effects and recovery yields. For validation purposes, we looked at the method’s precision and accuracy. A library of high-resolution fragmentation spectra for eicosanoids was developed. Our comprehensive dataset meets benchmark standards for targeted analysis, having been derived using best-practice workflows and rigorous quality assessments. As such, our method has applications for determining complex eicosanoid profiles in the biomedical field.
1. Monensin A, an important antibiotic ionophore that is primarily employed to treat coccidiosis, selectively complexes and transports sodium cations across lipid membranes and displays a variety of biological properties. 2. In this study, we evaluated the fungi Cunninghamella echinulata var. elegans ATCC 8688A, Cunninghamella elegans NRRL 1393 ATCC 10028B and human hepatic microsomes as CYP-P450 models to investigate the in vitro metabolism of monensin A and compare the products with the metabolites produced in vivo. 3. Mass spectrometry analysis of the products from these model systems revealed the formation of three metabolites: 3-O-demethyl monensin A, 12-hydroxy monensin A and 12-hydroxy-3-O-demethyl monensin A. We identified these products by tandem mass spectrometry and through comparison with the in vivo metabolites. 4. This analysis demonstrated that the model systems produce the same metabolites found in in vivo studies, thus they could be used to predict the metabolism of monensin A. Furthermore, we verified that liquid chromatography coupled to mass spectrometry is a powerful tool to study the in vitro metabolism of drugs, because it allows the successful identifications of several derivatives from different metabolic models.
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