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Related Concept Videos

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
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Very complex internal standard response variation in LC-MS/MS bioanalysis: root cause analysis and impact assessment.

Nico C van de Merbel1,2, Remco A Koster1, Corey Ohnmacht3

  • 1Bioanalytical Laboratory, PRA Health Sciences, Amerikaweg 18, 9407 TK, Assen, The Netherlands.

Bioanalysis
|October 1, 2019
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Summary

Internal standards (ISs) are crucial for accurate quantitative bioanalytical LC-MS/MS methods. This study explores complex IS response variations, offering a decision tree to identify root causes like injection errors or sample degradation.

Keywords:
LC–MSinternal standardmatrix effectsresponse variabilityroot cause analysis

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Area of Science:

  • Analytical Chemistry
  • Bioanalysis
  • Mass Spectrometry

Background:

  • Internal standards (ISs) are vital for quantitative bioanalytical methods.
  • ISs correct for analytical response variations due to experimental conditions.
  • Variability in IS response can indicate issues in sample handling or analysis.

Purpose of the Study:

  • To investigate complex variations in internal standard (IS) responses.
  • To identify analytical and matrix-related root causes of extreme IS variability.
  • To propose a decision tree for diagnosing IS variability issues.

Main Methods:

  • Case study analysis of complex IS response variations.
  • Identification of analytical problems (e.g., injection errors, sample inhomogeneity).
  • Assessment of matrix-related issues (e.g., degradation, ionization efficiency).

Main Results:

  • Complex IS variability can stem from injection errors, sample inhomogeneity, degradation, and increased ionization efficiency.
  • These issues significantly impact the reliability of quantitative bioanalytical methods.
  • A systematic approach is needed to diagnose the source of variability.

Conclusions:

  • Extreme internal standard variability in LC-MS/MS methods signals underlying analytical or matrix problems.
  • A structured diagnostic approach, like the proposed decision tree, is essential.
  • Addressing these root causes ensures the development of robust and reliable bioanalytical methods.