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How does the order of sample analysis influence the matrix effect during LC-MS bioanalysis?

Elżbieta Gniazdowska1, Joanna Giebułtowicz2, Piotr J Rudzki3

  • 1Łukasiewicz Research Network - Industrial Chemistry Institute, Pharmaceutical Analysis Laboratory, 8 Rydygiera, 01-793 Warsaw, Poland; Department of Drug Chemistry, Doctoral School, Medical University of Warsaw, 61 Żwirki i Wigury, 02-091 Warsaw, Poland.

Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences
|July 20, 2023
PubMed
Summary
This summary is machine-generated.

The order of sample analysis significantly impacts matrix effect variability in bioanalytical methods. An interleaved analysis scheme is more sensitive than block schemes for detecting matrix effects, crucial for reliable drug development.

Keywords:
Bioanalytical method validationChemometricsLC-MSLipemic plasmaMatrix effectMatrix factorPhospholipids

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Area of Science:

  • Analytical Chemistry
  • Pharmacology
  • Bioanalysis

Background:

  • Liquid chromatography-mass spectrometry is vital for drug development and personalized therapy.
  • The matrix effect, caused by interfering biological compounds, can compromise quantitative results in bioanalysis.
  • Evaluating the matrix effect is essential for validating bioanalytical methods.

Purpose of the Study:

  • To compare the sensitivity of interleaved versus block experimental designs in detecting matrix effect variability.
  • To investigate factors influencing matrix effect variability, including chromatographic elution, plasma type, co-elution, and carry-over.

Main Methods:

  • Utilized chemometric methods, specifically Principal Component Analysis (PCA) and Partial Least-Squares Discriminant Analysis (PLS-DA).
  • Evaluated matrix effect variability using interleaved and block sample analysis schemes.
  • Assessed the influence of chromatographic elution, plasma type (normal, lipemic, hemolyzed), and phospholipid carry-over.

Main Results:

  • Both interleaved and block schemes showed comparable, yet statistically different, matrix effects.
  • The interleaved scheme demonstrated higher sensitivity in detecting matrix effect variability.
  • Chemometric analysis indicated that lipemic samples under isocratic conditions are most susceptible to the matrix effect.
  • Variability in matrix lots, particularly lipemic plasma, can affect method reliability.

Conclusions:

  • The order of sample analysis significantly influences matrix effect quantification and must be reported for experimental repeatability.
  • The interleaved experimental design is more effective for detecting matrix effect variability.
  • Lipemic plasma and specific chromatographic conditions increase susceptibility to matrix effects, necessitating evaluation of multiple plasma sources.