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Enhanced Lipids Removal From Biological Matrices to Prepare Samples for LC/MS/MS Analysis

Posters | 2017 | Agilent TechnologiesInstrumentation
Sample Preparation, Consumables, LC/MS, LC/MS/MS, LC/QQQ
Industries
Clinical Research
Manufacturer
Agilent Technologies

Summary

Significance of the Topic


Phospholipids are a major source of ion suppression and contamination in LC-MS/MS bioanalysis. Their effective removal is essential to achieve low detection limits, reliable quantitation, and prolonged instrument uptime in fields such as forensic toxicology, clinical research, and industrial quality control.

Study Objectives and Overview


This work evaluates the performance of Agilent Enhanced Matrix Removal–Lipid (EMR–Lipid) products in removing phospholipids from biological matrices. The aim is to demonstrate ultrafast, pass-through solid-phase extraction (SPE) cleanup that enhances LC-MS/MS analysis of representative drug compounds in human serum and other fluids.

Methodology and Instrumentation


Sample preparation involves in-situ protein precipitation followed by SPE cleanup using Captiva EMR–Lipid cartridges or 96-well plates. The sorbent combines size-exclusion and hydrophobic interactions to selectively bind major lipid classes. A typical protocol includes:
  • Aliquot sample and add internal standard solution
  • Precipitate proteins with acidified acetonitrile
  • Pass supernatant through EMR–Lipid sorbent under low vacuum
  • Collect eluate, evaporate, and reconstitute for LC-MS/MS

Instrumentation Used


  • Agilent 1290 Infinity UHPLC system
  • Agilent G6490 Triple Quadrupole MS
  • InfinityLab Poroshell 120 EC-C18 column, 150 x 2.1 mm, 2.7 µm
  • Mobile phase A: 5 mM ammonium acetate with 0.1% formic acid; B: 0.1% formic acid in acetonitrile

Key Results and Discussion


EMR–Lipid cleanup achieved over 99% removal of phospholipids across various matrices and anticoagulants, outperforming or matching alternative products. Post-column infusion profiles confirmed a marked reduction in matrix ion suppression. Calibration curves for test compounds displayed improved linearity (R2 increased from 0.986 to 0.993) when using EMR–Lipid cleanup. Intra- and inter-day accuracy and precision for nine representative drugs (including 5-Fluorouracil, Gemcitabine, Amphetamine, Metoprolol, Hydrocortisone, Warfarin, Androstenedione, Atorvastatin, Diclofenac) met standard QC criteria (accuracy within ±15%, RSD below 15%). Eluent clarity was visibly superior and cartridges resisted clogging even with high protein loads.

Benefits and Practical Applications


Benefits of the EMR–Lipid approach include:
  • Seamless integration into traditional protein precipitation workflows
  • Clog-free operation with optimized frit design
  • Highly selective lipid removal leading to reduced matrix effects
  • Exceptional quantitative performance facilitating easy method validation
  • Enhanced productivity via reduced downtime and prolonged column life

Future Trends and Potential Applications


Future developments may focus on full automation of EMR–Lipid cleanup in high-throughput formats, expansion to metabolomics and lipidomics, and adaptation for environmental and food analysis. Integration with online SPE and improved sorbent chemistries could further enhance selectivity for emerging analytes.

Conclusion


Agilent EMR–Lipid technology provides a robust, efficient solution for phospholipid removal in LC-MS/MS bioanalysis. The method delivers high recovery of target analytes, reliable accuracy and precision, and significant reduction of matrix interferences, supporting faster assay development and greater instrument longevity.

References


  1. Zhao L, Lucas D. Efficiency of Biological Fluid Matrix Removal using Captiva EMR-Lipid Cleanup. Agilent Technologies Application Note 5991-8006EN, 2017.
  2. Zhao L, Lucas D. Quantitative LC/MS/MS Analysis of Drugs in Human Serum With Agilent Captiva EMR-Lipid Cleanup. Agilent Technologies Application Note 5991-8007EN, 2017.
  3. US Food and Drug Administration. Guidance for Industry Bioanalytical Method Validation, 2001.

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