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Multidimensional LC-MS/MS enables simultaneous quantification of intact human insulin and five recombinant analogs in human plasma

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This work provides a multidimensional method for the simultaneous, direct quantification of intact human insulin and five insulin analogs in human plasma. This investigation solves both the selectivity and sensitivity problems encountered for accurate quantification of insulins in plasma since the former is not possible with conventional assays and the latter with conventional LC-MS/MS. The method uses a mixed-mode SPE and a multidimensional LC method including a solid-core particle column containing an anion exchange stationary phase. Matrix factors for all analogs were calculated in 6 sources of human plasma and CVs of the matrix factors were <15% in all cases supporting the selectivity of the method, while achieving LLOQs of 50-200 pg/mL (1.4-5.6 μIU/mL) for each insulin from 250 μL of human plasma. The average accuracy for the standard curve points in extracted human plasma was 99-100%. Average inter- and intraday accuracies for QC samples were 98% and 94%, respectively. Average inter- and intraday precisions for QC samples were 7.5 and 5.3%, respectively. Patient samples were analyzed in a blind study and results concurred with their diabetes multidosing regimes. The study also demonstrated that the presence of high levels of human insulin and bovine insulin does not interfere with quantification of any of the analyzed analogs. We propose this method for the accurate pharmacokinetic monitoring of diabetic patients, for sport antidoping and forensic toxicology analysis.

Original languageEnglish
JournalAnalytical Chemistry
Volume86
Issue number1
Pages (from-to)694-702
Number of pages9
ISSN0003-2700
DOIs
Publication statusPublished - 7 Jan 2014
Externally publishedYes

    Research areas

  • Amino Acid Sequence, Animals, Cattle, Chromatography, Liquid/methods, Humans, Insulin/analogs & derivatives, Mass Spectrometry/methods, Molecular Sequence Data, Tandem Mass Spectrometry/methods

ID: 54960798