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Optimization and validation of a GC-MS quantitative method for the determination of an extended estrogenic profile in human urine: Variability intervals in a population of healthy women.

Authors
  • Alladio, Eugenio1, 2
  • Amante, Eleonora1, 2
  • Bozzolino, Cristina1
  • Vaglio, Sara1
  • Guzzetti, Giusy1
  • Gerace, Enrico2
  • Salomone, Alberto1, 2
  • Vincenti, Marco1, 2
  • 1 Dipartimento di Chimica, Università degli Studi di Torino, Torino, Italy. , (Italy)
  • 2 Centro Regionale Antidoping e di Tossicologia "A. Bertinaria", Orbassano (TO), Italy. , (Italy)
Type
Published Article
Journal
Biomedical Chromatography
Publisher
Wiley (John Wiley & Sons)
Publication Date
Feb 01, 2021
Volume
35
Issue
2
Identifiers
DOI: 10.1002/bmc.4967
PMID: 32803777
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

An analytical method based on GC-MS was developed for the determination of a wide panel of urinary estrogens, together with their principal metabolites. Because of the low concentration of estrogens in urine, an efficient sample pre-treatment was optimized by a design of experiment (DoE) procedure to achieve satisfactory sensitivity. A second DoE was built for the optimization of the chromatographic run, with the purpose of reaching the most efficient separation of analytes with potentially interfering ions and similar chromatographic properties. The method was fully validated using a rigorous calibration strategy: from several replicate analyses of blank urine samples spiked with the analytes, calibration models were built with particular attention to the study of heteroscedasticity and quadraticity. Other validation parameters, including the limit of detection, intra-assay precision and accuracy, repeatability, selectivity, specificity, and carry-over, were obtained using the same set of data. Further experiments were performed to evaluate matrix effect and extraction recovery. Then the urinary estrogen profiles of 138 post-menopausal healthy women were determined. These profiles provide a representation of physiological concentration ranges, which, in forthcoming studies, will be matched on the base of multivariate statistics with the urinary estrogenic profile of women with breast or ovarian cancer. © 2020 John Wiley & Sons, Ltd.

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