4.6 Article

Quantitative evaluation of the matrix effect in bioanalytical methods based on LC-MS: A comparison of two approaches

Journal

JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
Volume 155, Issue -, Pages 314-319

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jpba.2018.03.052

Keywords

Matrix effect; Matrix factor; Liquid chromatography/mass spectrometry; Bioanalytical method validation; Pharmacokinetics

Funding

  1. European Union (European Regional Development Fund) under the Polish Innovative Economy Operational Programme [UDA-POIG.01.03.01-14-069/08, UDA-POIG.01.03.01-14-062/09]
  2. National Centre for Research and Development of the Republic of Poland [INNOTECH-K1/IN1/14/159003/NCBR/12, INNOTECH-K2/IN2/65/182982/NCBR/13]
  3. Jagiellonskie Centrum Innowacji Sp. z o.o., Poland
  4. Biopharm Ltd., Poland [PBS1/B7/7/2012]
  5. ICN Polfa Rzeszow S.A., Rzeszow, Poland

Ask authors/readers for more resources

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for studying pharmacokinetics and toxicokinetics. Reliable bioanalysis requires the characterization of the matrix effect, i.e. influence of the endogenous or exogenous compounds on the analyte signal intensity. We have compared two methods for the quantitation of matrix effect. The CVs(%) of internal standard normalized matrix factors recommended by the European Medicines Agency were evaluated against internal standard normalized relative matrix effects derived from Matuszewski et al. (2003). Both methods use post-extraction spiked samples, but matrix factors require also neat solutions. We have tested both approaches using analytes of diverse chemical structures. The study did not reveal relevant differences in the results obtained with both calculation methods. After normalization with the internal standard, the CV(%) of the matrix factor was on average 0.5% higher than the corresponding relative matrix effect. The method adopted by the European Medicines Agency seems to be slightly more conservative in the analyzed datasets. Nine analytes of different structures enabled a general overview of the problem, still, further studies are encouraged to confirm our observations. (C) 2018 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available