4.7 Article

Variability in Mass Spectrometry-based Quantification of Clinically Relevant Drug Transporters and Drug Metabolizing Enzymes

Journal

MOLECULAR PHARMACEUTICS
Volume 14, Issue 9, Pages 3142-3151

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.molpharmaceut.7b00364

Keywords

drug transporters; drug metabolizing enzymes; membrane proteins; protein quantification; targeted proteomics; label-free proteomics

Funding

  1. Swedish Research Council [2822, 5715]
  2. Swiss National Science Foundation [P200P3_154635, P2EZP3_148644]
  3. German Research Foundation (DFG/Gottfried Wilhelm Leibniz Prize)
  4. German Federal Ministry of Education and Research (BMBF) [FKZ 0315742, FKZ 031A142, 03IPT612X]
  5. Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester
  6. Swiss National Science Foundation (SNF) [P2EZP3_148644] Funding Source: Swiss National Science Foundation (SNF)

Ask authors/readers for more resources

Many different methods are used for mass-spectrometry-based protein quantification in pharmacokinetics and systems pharmacology. It has not been established to what extent the results from these various methods are comparable. Here, we compared six different mass spectrometry-based proteomics methods by measuring the expression of clinically relevant drug transporters and metabolizing enzymes in human liver. Mean protein concentrations were in general quantified to similar levels by methods using whole tissue lysates. Methods using subcellular membrane fractionation gave incomplete enrichment of the proteins. When the enriched proteins were adjusted to levels in whole tissue lysates, they were on average 4 fold lower than those quantified directly in whole tissue lysates. The differences in protein levels were propagated into differences in predictions of hepatic clearance. In conclusion, caution is needed when comparing and applying quantitative proteomics data obtained with different methods, especially since membrane fractionation is common practice for protein quantification used in drug clearance predictions.

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