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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.

The liver plays an important role in metabolism and elimination of xenobiotics, including drugs. Determination of concentrations of proteins involved in uptake, distribution, metabolism, and excretion of xenobiotics is required to understand and predict elimination mechanisms in this tissue. In this work, we have fractionated homogenates of snap -frozen human liver by differential centrifugation and performed quantitative mass spectrometry -based proteomic analysis of each fraction. Concentrations of proteins were calculated by the "total protein approach". A total of 4586 proteins were identified by at least five peptides and were quantified in all fractions. We found that the xenobiotics transporters of the canalicular and basolateral membranes were differentially enriched in the subcellular fractions and that phase I and II metabolizing enzymes, the cytochrome P450s and the UDP glucuronyl transferases, have complex subcellular distributions. These findings show that there is no simple way to scale the data from measurements in arbitrarily selected membrane fractions using a single scaling factor for all the proteins of interest. This study also provides the first absolute quantitative subcellular catalog of human liver proteins obtained from frozen tissue specimens. Our data provide quantitative insights into the sub cellular distribution of proteins and can be used as a guide for development of fractionation procedures.

Human liver microsomes (HLM) and human hepatocytes (HH) are important in vitro systems for studies of intrinsic drug clearance (CL_int) in the liver. However, the CL_int values are often in disagreement for these two systems. Here, we investigated these differences in a side-by-side comparison of drug metabolism in HLM and HH prepared from 15 matched donors. Protein expression and intracellular unbound drug concentration (Kp_uu) effects on the CL_int were investigated for five prototypical probe substrates (bupropion–CYP2B6, diclofenac–CYP2C9, omeprazole–CYP2C19, bufuralol–CYP2D6, and midazolam–CYP3A4). The samples were donor-matched to compensate for inter-individual variability but still showed systematic differences in CL_int. Global proteomics analysis outlined differences in HLM from HH and homogenates of human liver (HL), indicating variable enrichment of ER-localized cytochrome P450 (CYP) enzymes in the HLM preparation. This suggests that the HLM may not equally and accurately capture metabolic capacity for all CYPs. Scaling CL_int with CYP amounts and Kp_uu could only partly explain the discordance in absolute values of CL_int for the five substrates. Nevertheless, scaling with CYP amounts improved the agreement in rank order for the majority of the substrates. Other factors, such as contribution of additional enzymes and variability in the proportions of active and inactive CYP enzymes in HLM and HH, may have to be considered to avoid the use of empirical scaling factors for prediction of drug metabolism.

Rosuvastatin is a frequently used probe to study transporter-mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time-concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293-transfected cells, and developed a semimechanistic model with a bottom-up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two-fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (Cmax; 76%; AFE: 1.05), and terminal half-life (t1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.