Measuring in vivo changes in the drug metabolizing activity of cytochrome P450 (CYP) enzymes is critical to understanding and assessing drug-drug, drug-diet and drug-disease interactions. The sensitivity and specificity of ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) makes it an ideal tool for analyzing drugs and their metabolites in biological matrices, and has demonstrated utility in CYP phenotyping across varied applications. Published CYP phenotyping cocktail assays often require large plasma sample volumes (0.5-1 mL), have relatively low sensitivity and multi-step complex sample preparation and extraction procedures. Further, variability exists in the way that recovery and matrix effects are investigated and reported, and some studies fail to report these data altogether. Therefore, the aim of this study was to develop, validate and optimize a simplified assay for the probe drugs caffeine (metabolized by CYP1A2), omeprazole (CYP2C19), losartan (CYP2C9), dextromethorphan (CYP2D6), midazolam (CYP3A4) and their respective enzyme-specific metabolites in small volumes (100 μL) of human plasma, that addresses the issues noted. Analyte extraction involved protein precipitation with acetonitrile and solid-phase extraction (SPE). Samples were analyzed using an Agilent 1290 infinity LC system in tandem with 6460A triple quadrupole mass spectrometers. The assay met FDA guideline-recommended requirements for specificity, sensitivity (analyte LLOQs 0.78-23.4 ng/mL), accuracy (intra-day RE% nominal concentration 90.7-110.2%; inter-day RE% 87.0-110.5%) and precision (intra-day analyte RSD% 0.46-11.4%; inter-day RSD% 1.36-11.2%). Recovery and matrix effects were thoroughly investigated and excluded as potential interferers with assay performance. This assay has been used successfully to phenotype CYP activity in a human clinical trial participant. Importantly, the authors provide a contemporary commentary on commonly found issues in the CYP phenotyping cocktail assay literature, and make recommendations concerning best-practice approaches and the standardization of data reporting in this area.Copyright © 2020 Elsevier B.V. All rights reserved.
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