Complementarity of two proteomic data analysis tools in the identification of drug-metabolising enzymes and transporters in human liver.

Several software packages are available for the analysis of proteomic LC-MS/MS data, including commercial (e.g. Mascot/Progenesis LC-MS) and open access software (e.g. MaxQuant). In this study, Progenesis and MaxQuant were used to analyse the same data set from human liver microsomes (n = 23). Comparison focussed on the total number of peptides and proteins identified by the two packages. For the peptides exclusively identified by each software package, distribution of peptide length, hydrophobicity, molecular weight, isoelectric point and score were compared. Using standard cut-off peptide scores, we found an average of only 65% overlap in detected peptides, with surprisingly little consistency in the characteristics of peptides exclusively detected by each package. Generally, MaxQuant detected more peptides than Progenesis, and the additional peptides were longer and had relatively lower scores. Progenesis-specific peptides tended to be more hydrophilic and basic relative to peptides detected only by MaxQuant. At the protein level, we focussed on drug-metabolising enzymes (DMEs) and transporters, by comparing the number of unique peptides detected by the two packages for these specific proteins of interest, and their abundance. The abundance of DMEs and SLC transporters showed good correlation between the two software tools, but ABC showed less consistency. In conclusion, in order to maximise the use of MS datasets, we recommend processing with more than one software package. Together, Progenesis and MaxQuant provided excellent coverage, with a core of common peptides identified in a very robust way.

Proteomic quantification of receptor tyrosine kinases involved in the development and progression of colorectal cancer liver metastasis.

Introduction: Alterations in expression and activity of human receptor tyrosine kinases (RTKs) are associated with cancer progression and in response to therapeutic intervention. Methods: Thus, protein abundance of 21 RTKs was assessed in 15 healthy and 18 cancerous liver samples [2 primary and 16 colorectal cancer liver metastasis (CRLM)] matched with non-tumorous (histologically normal) tissue, by a validated QconCAT-based targeted proteomic approach. Results: It was demonstrated, for the first time, that the abundance of EGFR, INSR, VGFR3 and AXL, is lower in tumours relative to livers from healthy individuals whilst the opposite is true for IGF1R. EPHA2 was upregulated in tumour compared with histologically normal tissue surrounding it. PGFRB levels were higher in tumours relative to both histologically normal tissue surrounding tumour and tissues taken from healthy individuals. The abundances of VGFR1/2, PGFRA, KIT, CSF1R, FLT3, FGFR1/3, ERBB2, NTRK2, TIE2, RET, and MET were, however, comparable in all samples. Statistically significant, but moderate correlations were observed (Rs > 0.50, p < 0.05) for EGFR with INSR and KIT. FGFR2 correlated with PGFRA and VGFR1 with NTRK2 in healthy livers. In non-tumorous (histologically normal) tissues from cancer patients, there were correlations between TIE2 and FGFR1, EPHA2 and VGFR3, FGFR3 and PGFRA (p < 0.05). EGFR correlated with INSR, ERBB2, KIT and EGFR, and KIT with AXL and FGFR2. In tumours, CSF1R correlated with AXL, EPHA2 with PGFRA, and NTRK2 with PGFRB and AXL. Sex, liver lobe and body mass index of donors had no impact on the abundance of RTKs, although donor age showed some correlations. RET was the most abundant of these kinases in non-tumorous tissues (~35%), while PGFRB was the most abundant RTK in tumours (~47%). Several correlations were also observed between the abundance of RTKs and proteins relevant to drug pharmacokinetics (enzymes and transporters). Discussion: DiscussionThis study quantified perturbation to the abundance of several RTKs in cancer and the value generated in this study can be used as input to systems biology models defining liver cancer metastases and biomarkers of its progression.

Proteomic quantification of perturbation to pharmacokinetic target proteins in liver disease.

Model-based assessment of drug pharmacokinetics in liver disease requires quantification of abundance and disease-related changes in hepatic enzymes and transporters. This study aimed to assess performance of three label-free methods [high N (HiN), intensity-based absolute quantification (iBAQ) and total protein approach (TPA)] against QconCAT-based targeted data in healthy and diseased (cancer and cirrhosis) liver tissue. Measurements were compared across methods and disease-to-control ratios provided a 'disease perturbation factor' (DPF) for each protein. Mean label-free measurements of targets correlated well (Pearson's coefficient, r = 0.91-0.98 p < 0.001) and with targeted data (r = 0.65-0.95, p < 0.001). Concordance with targeted data was generally moderate (Lin's concordance coefficient, ρc = 0.46-0.92), depending on methodology. Moderate precision and accuracy were observed for label-free methods (average fold error, AFE = 1.44-1.68; absolute average fold error, AAFE = 2.44-3.23). The DPF reconciled the data and indicated downregulated expression in cancer and cirrhosis, consistent with an inflammatory effect. HiN estimated perturbation consistently with targeted data (AFEHiN = 1.07, AAFEHiN = 1.57), whereas iBAQ overestimated (AFEiBAQ = 0.81, AAFEiBAQ = 1.67) and TPA underestimated (AFETPA = 1.37, AAFETPA = 1.65) disease effect. Progression from mild to severe cirrhosis was consistent with progressive decline in expression, reproduced by HiN but overestimated by iBAQ and underestimated by TPA (AFEHiN = 0.98, AFEiBAQ = 0.60, AFETPA = 1.24). DPF data confirmed non-uniform disease effect on drug-elimination pathways and progressive impact of disease severity. SIGNIFICANCE: This study demonstrated good correlation and moderate concordance between intensity-based label-free proteomic methods (HiN, iBAQ and TPA) and targeted data. Label-free measurements tended to overestimate abundance, but differences were reconciled using a disease perturbation factor (DPF) for each protein. With targeted data as a reference, HiN defined disease perturbation and the impact of disease progression consistently, indicating that the use of 'razor' peptides for quantification against an exogenous standard provides biologically sensible quantitative fingerprints of disease. Disease-driven perturbations in expression relative to healthy baseline are incorporated into drug kinetic models used to predict drug exposure in disease populations where clinical studies may not be feasible.

Quantitative Proteomics of Hepatic Drug-Metabolizing Enzymes and Transporters in Patients With Colorectal Cancer Metastasis.

The impact of liver cancer metastasis on protein abundance of 22 drug-metabolizing enzymes (DMEs) and 25 transporters was investigated using liquid chromatography-tandem accurate mass spectrometry targeted proteomics. Microsomes were prepared from liver tissue taken from 15 healthy individuals and 18 patients with cancer (2 primary and 16 metastatic). Patient samples included tumors and matching histologically normal tissue. The levels of cytochrome P450 (CYPs 2B6, 2D6, 2E1, 3A4, and 3A5) and uridine 5'-diphospho-glucuronosyltransferases (UGTs 1A1, 1A6, 1A9, 2B15, 2B4, and 2B7) were lower in histologically normal tissue from patients relative to healthy controls (up to 6.6-fold) and decreased further in tumors (up to 21-fold for CYPs and 58-fold for UGTs). BSEP and MRPs were also suppressed in histologically normal (up to 3.1-fold) and tumorous tissue (up to 6.3-fold) relative to healthy individuals. Abundance of OCT3, OAT2, OAT7, and OATPs followed similar trends (up to 2.9-fold lower in histologically normal tissue and up to 16-fold lower in tumors). Abundance of NTCP and OCT1 was also lower (up to 9-fold). Interestingly, monocarboxylate transporter MCT1 was more abundant (3.3-fold) in tumors, the only protein target to show this pattern. These perturbations could be attributed to inflammation. Interindividual variability was substantially higher in patients with cancer. Proteomics-informed physiologically-based pharmacokinetic (PBPK) models of 50 drugs with different attributes and hepatic extraction ratios (Simcyp) showed substantially lower drug clearance with cancer-specific parameters compared with default parameters. In conclusion, this study provides values for decreased abundance of DMEs and transporters in liver cancer, which enables using population-specific abundance for these patients in PBPK modeling.

A family of QconCATs (Quantification conCATemers) for the quantification of human pharmacological target proteins.

We have developed a family of QconCAT standards for the absolute quantification of pharmacological target proteins in a variety of human tissues. The QconCATs consist of concatenated proteotypic peptides, are designed in silico, and expressed in E. coli in media enriched with [13C6] arginine and [13C6] lysine to generate stable isotope-labeled multiplexed absolute quantification standards. The so-called MetCAT (used to quantify cytochrome P450 (CYP) and glucuronosyltransferase (UGT) enzymes), the liver TransCAT (used to quantify plasma-membrane drug transporters) and the brain TransCAT (used to quantify transporters expressed in the blood-brain barrier) were previously reported. We now report new QconCATs for the quantification of non-UGT non-CYP drug metabolizing enzymes (NuncCAT) and receptor tyrosine kinases (KinCAT). We have also redesigned the liver TransCAT, replacing problematic peptides and the N-terminal tag, for better characterization and expression. All these QconCATs showed high purity, high labelling efficiency with stable 13C isotope (>95%), and high sequence coverage (>88%). They represent a close-knit family of standards for quantifying pharmacokinetic targets, together with a more distant cousin, the KinCAT, used to quantify pharmacodynamic targets. SIGNIFICANCE: Multiplexed determination of absolute protein abundances using quantitative conCATemers (QconCATs) has already been successfully demonstrated in different human tissues. We have previously reported two QconCATs; MetCAT and TransCAT, for the quantification of key enzymes (cytochrome P450 enzymes (CYP) and glucuronosyltransferases (UGT)) and drug transporters. To build on these reports, application of the QconCAT methodology for the determination of non-UGT non-CYP enzymes and receptor tyrosine kinases (RTKs) in human tissue is reported here. This report focuses on development and characterization of two QconCAT constructs for the quantification of 24 enzymes and 21 RTKs. We demonstrate that the developed QconCATs have high purity, high incorporation efficiency and low peptide miscleavage upon proteolysis. Application of these QconCATs for reliable quantification of target proteins was achieved in human liver.

Label-Free but Still Constrained: Assessment of Global Proteomic Strategies for the Quantification of Hepatic Enzymes and Transporters.

Building and refining pharmacology models require "system" data derived from tissues and in vitro systems analyzed by quantitative proteomics. Label-free global proteomics offers a wide scope of analysis, allowing simultaneous quantification of thousands of proteins per sample. The data generated from such analysis offer comprehensive protein expression profiles that can address existing gaps in models. In this study, we assessed the performance of three widely used label-free proteomic methods, "high N" ion intensity approach (HiN), intensity-based absolute quantification (iBAQ) and total protein approach (TPA), in relation to the quantification of enzymes and transporters in 27 human liver microsomal samples. Global correlations between the three methods were highly significant (R2 > 0.70, P < 0.001, n = 2232 proteins). Absolute abundances of 57 pharmacokinetic targets measured by standard-based label-free methods (HiN and iBAQ) showed good agreement, whereas the TPA overestimated abundances by two- to threefold. Relative abundance distribution of enzymes was similar for the three methods, while differences were observed with TPA in the case of transporters. Variability (CV) was similar across methods, with consistent between-sample relative quantification. The back-calculated amount of protein in the samples based on each method was compared with the nominal protein amount analyzed in the proteomic workflow, revealing overall agreement with data from the HiN method with bovine serum albumin as standard. The findings herein present a critique of label-free proteomic data relevant to pharmacokinetics and evaluate the possibility of retrospective analysis of historic datasets. SIGNIFICANCE STATEMENT: This study provides useful insights for using label-free methods to generate abundance data applicable for populating pharmacokinetic models. The data demonstrated overall correlation between intensity-based label-free proteomic methods (HiN, iBAQ and TPA), whereas iBAQ and TPA overestimated the total amount of protein in the samples. The extent of overestimation can provide a means of normalization to support absolute quantification. Importantly, between-sample relative quantification was consistent (similar variability) across methods.

Proteomics of colorectal cancer liver metastasis: A quantitative focus on drug elimination and pharmacodynamics effects.

AIMS: This study aims to quantify drug-metabolising enzymes, transporters, receptor tyrosine kinases (RTKs) and protein markers (involved in pathways affected in cancer) in pooled healthy, histologically normal and matched cancerous liver microsomes from colorectal cancer liver metastasis (CRLM) patients. METHODS: Microsomal fractionation was performed and pooled microsomes were prepared. Global and accurate mass and retention time liquid chromatography-mass spectrometry proteomics were used to quantify proteins. A QconCAT (KinCAT) for the quantification of RTKs was designed and applied for the first time. Physiologically based pharmacokinetic (PBPK) simulations were performed to assess the contribution of altered abundance of drug-metabolising enzymes and transporters to changes in pharmacokinetics. RESULTS: Most CYPs and UGTs were downregulated in histologically normal relative to healthy samples, and were further reduced in cancer samples (up to 54-fold). The transporters, MRP2/3, OAT2/7 and OATP2B1/1B3/1B1 were downregulated in CRLM. Application of abundance data in PBPK models for substrates with different attributes indicated substantially lower (up to 13-fold) drug clearance when using cancer-specific instead of default parameters in cancer population. Liver function markers were downregulated, while inflammation proteins were upregulated (by up to 76-fold) in cancer samples. Various pharmacodynamics markers (e.g. RTKs) were altered in CRLM. Using global proteomics, we examined proteins in pathways relevant to cancer (such as metastasis and desmoplasia), including caveolins and collagen chains, and confirmed general over-expression of such pathways. CONCLUSION: This study highlights impaired drug metabolism, perturbed drug transport and altered abundance of cancer markers in CRLM, demonstrating the importance of population-specific abundance data in PBPK models for cancer.

Hepatic Scaling Factors for In Vitro-In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis.

In vitro-in vivo extrapolation (IVIVE) linked with physiologically based pharmacokinetics (PBPK) modeling is used to predict the fates of drugs in patients. Ideally, the IVIVE-PBPK models should incorporate systems information accounting for characteristics of the specific target population. There is a paucity of such scaling factors in cancer, particularly microsomal protein per gram of liver (MPPGL) and cytosolic protein per gram of liver (CPPGL). In this study, cancerous and histologically normal liver tissue from 16 patients with colorectal liver metastasis were fractionated to microsomes and cytosol. Protein content was measured in homogenates, microsomes, and cytosol. The loss of microsomal protein during fractionation was accounted for using corrections based on NADPH cytochrome P450 reductase activity in different matrices. MPPGL was significantly lower in cancerous tissue (24.8 ± 9.8 mg/g) than histologically normal tissue (39.0 ± 13.8 mg/g). CPPGL in cancerous tissue was 42.1 ± 12.9 mg/g compared with 56.2 ± 16.9 mg/g in normal tissue. No correlations between demographics (sex, age, and body mass index) and MPPGL or CPPGL were apparent in the data. The generated scaling factors together with assumptions regarding the relative volumes of cancerous versus noncancerous tissue were used to simulate plasma exposure of drugs with different extraction ratios. The PBPK simulations revealed a substantial difference in drug exposure (area under the curve), up to 3.3-fold, when using typical scaling factors (healthy population) instead of disease-related parameters in cancer population. These indicate the importance of using population-specific scalars in IVIVE-PBPK for different disease states. SIGNIFICANCE STATEMENT: Accuracy in predicting the fate of drugs from in vitro data using IVIVE-PBPK depends on using correct scaling factors. The values for two of such scalars, namely microsomal and cytosolic protein per gram of liver, is not known in patients with cancer. This study presents, for the first time, scaling factors from cancerous and matched histologically normal livers. PBPK simulations of various metabolically cleared drugs demonstrate the necessity of population-specific scaling for model-informed precision dosing in oncology.

Mass spectrometry-based abundance atlas of ABC transporters in human liver, gut, kidney, brain and skin.

ABC transporters (ATP-binding cassette transporter) traffic drugs and their metabolites across membranes, making ABC transporter expression levels a key factor regulating local drug concentrations in different tissues and individuals. Yet, quantification of ABC transporters remains challenging because they are large and low-abundance transmembrane proteins. Here, we analysed 200 samples of crude and membrane-enriched fractions from human liver, kidney, intestine, brain microvessels and skin, by label-free quantitative mass spectrometry. We identified 32 (out of 48) ABC transporters: ABCD3 was the most abundant in liver, whereas ABCA8, ABCB2/TAP1 and ABCE1 were detected in all tissues. Interestingly, this atlas unveiled that ABCB2/TAP1 may have TAP2-independent functions in the brain and that biliary atresia (BA) and control livers have quite different ABC transporter profiles. We propose that meaningful biological information can be derived from a direct comparison of these data sets.

Toxicity assessment and comparison of the land snail's Cornu aspersum responses against CuO nanoparticles and ZnO nanoparticles.

The goal of the present study was to examine the effects of ZnO NPs and CuO NPs on Cornu aspersum land snail, enlightening their cytotoxic profile. ZnO NPs and CuO NPs were synthesized and thoroughly characterized. Α series of concentrations of either ZnO NPs or CuO NPs were administered in the feed of snails for 20 days. Thereafter, neutral red retention assay was conducted, in order to estimate NRRT50 values. Subsequently, snails were fed with NPs concentrations slightly lower than the concentrations that were corresponding to the NRRT50 values, i.e. 3 mg·L-1 ZnO NPs and 6 mg·L-1 CuO NPs, for 1, 5, 10 and 20 days. Both NPs agglomerates were detected in hemocytes by Transmission Electron Microscopy. Moreover, both effectors resulted to toxicity in the snails' hemocytes. The latter was shown by changes in the NRRT50 values, increased reactive oxygen species (ROS) production, lipid peroxidation, DNA integrity loss, protein carbonyl content, ubiquitin conjugates and cleaved caspases conjugates levels compared to the untreated animals. Although ZnO NPs exhibited higher toxicity, as indicated by the NRRT50 values, both NPs affected similarly a wide range of the cellular parameters mentioned above. The latter parameters could constitute sensitive biomarkers in biomonitoring studies of terrestrial environment against nanoparticles.

Quantitative mass spectrometry-based proteomics in the era of model-informed drug development: Applications in translational pharmacology and recommendations for best practice.

Quantitative translation of the fate and action of a drug in the body is facilitated by models that allow extrapolation of in vitro measurements (such as the rate of metabolism, active transport across membranes, inhibition of enzymes and receptor occupancy) to in vivo consequences (intensity and duration of drug effects). These models use various physiological parameters, including data that describe the expression levels of pharmacologically relevant enzymes, transporters and receptors in tissues and in vitro systems. Immunoquantification approaches have traditionally been used to determine protein expression levels, generally providing relative quantification data with compromised selectivity and reproducibility. More recently, the development of several quantitative proteomic techniques, fuelled by advances in state-of-the-art mass spectrometry, has led to generating a wealth of qualitative and quantitative data. These data are currently used for various quantitative systems pharmacology applications, with the ultimate goal of conducting virtual clinical trials to inform clinical studies, especially when assessments are difficult to conduct on patients. In this review, we explore available quantitative proteomic methods, discuss their main applications in translational pharmacology and offer recommendations for selecting and implementing proteomic techniques.