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.

Physical and chemical screening of honey samples available in the Saudi market: An important aspect in the authentication process and quality assessment.

Honey is becoming accepted as a reputable and effective therapeutic agent by practitioners of conventional medicine and by the general public. It has many biological activities and has been effectively used in the treatment of many diseases, e.g. gastrointestinal diseases, skin diseases, cancer, heart diseases, and neurological degeneration. Honey is an excellent source of energy containing mainly carbohydrates and water, as well as, small amounts of organic acids, vitamins, minerals, flavonoids, and enzymes. As a natural product with a relatively high price, honey has been for a long time a target for adulteration. The authenticity of honey is of great importance from commercial and health aspects. The study of the physical and chemical properties of honey has been increasingly applied as a certification process for the purpose of qualification of honey samples. The current work focusses on studying the authenticity of various types of honey sold in Riyadh market (24 samples). For this purpose, physical properties (pH, hydroxylmethylfurfural HMF, and pollen test) were measured. Besides, sugar composition was evaluated using Fehling test and an HPLC method. Elemental analysis was carried out using inductively coupled plasma (ICP). In addition, the presence of drug additives was assessed by means of GC-MS. The obtained results were compared with the Saudi Arabian standards, Codex Alimentarius Commission (2001), and harmonized methods of the international honey commission.

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.

Evaluating the safety and efficacy of the leukotriene receptor antagonist montelukast as adjuvant therapy in obese patients with type 2 diabetes mellitus: A double-blind, randomized, placebo-controlled trial.

Background: Type 2 diabetes mellitus (T2DM) is common with obesity. Metformin is a first-line therapy for this condition. However, it has only a minor impact on weight loss in some patients. Aim: This study aimed to evaluate the effectiveness, tolerability, and safety of combining montelukast therapy with metformin in obese diabetic patients. Methods: One hundred obese diabetic adult patients were recruited and randomized into two equal groups. Group 1 received placebo plus metformin 2 g/d, and Group 2 received 2 g/d metformin plus 10 mg/d montelukast. Demographic, anthropometric measurements (e.g., body weight, body mass index [BMI], and visceral adiposity index), lipid profile, diabetes control measures (fasting blood glucose, glycated hemoglobin [HbA1c], and homeostatic model assessment for insulin resistance [HOMA-IR]), adiponectin, and inflammatory markers (e.g., TNF-α, IL-6, and leukotriene B4) were assessed and reported for each group at baseline and after 12 weeks of treatment. Results: Both interventions significantly reduced all the measured parameters, except for adiponectin and HDL-C, levels of which increased compared to baseline data (p < 0.001). The montelukast group significantly improved in all parameters compared to the placebo group (ANCOVA test p < 0.001). The percentage changes in BMI, HbA1c, HOMA-IR, and inflammatory markers were 5%, 9%, 41%, and 5%-30%, respectively, in the placebo group compared to 8%, 16%, 58%, and 50%-70%, respectively, in the montelukast group. Conclusion: Montelukast adjuvant therapy was superior to metformin-only therapy in diabetes control and weight loss, most likely due to its increased insulin sensitivity and anti-inflammatory properties. The combination was tolerable and safe throughout the study duration. Clinical Trial Registration: [Clinicaltrial.gov], identifier [NCT04075110].

Altered Bioavailability and Pharmacokinetics in Crohn's Disease: Capturing Systems Parameters for PBPK to Assist with Predicting the Fate of Orally Administered Drugs.

BACKGROND AND OBJECTIVE: Crohn's disease (CD) is a chronic inflammatory bowel disease that affects a wide age range. Hence, CD patients receive a variety of drugs over their life beyond those used for CD itself. The changes to the integrity of the intestine and its drug metabolising enzymes and transporters (DMETs) can alter the oral bioavailability of drugs. However, there are other changes in systems parameters determining the fate of drugs in CD, and understanding these is essential for dose adjustment in patients with CD. METHODS: The current analysis gathered all the available clinical data on the kinetics of drugs in CD (by March 2021), focusing on orally administered small molecule drugs. A meta-analysis of the systems parameters affecting oral drug pharmacokinetics was conducted. The systems information gathered on intestine, liver and blood proteins and other physiological parameters was incorporated into a physiologically based pharmacokinetic (PBPK) platform to create a virtual population of CD patients, with a view for guiding dose adjustment in the absence of clinical data in CD. RESULTS: There were no uniform trends in the reported changes in reported oral bioavailability. The nature of the drug as well as the formulation affected the direction and magnitude of variation in kinetics in CD patients relative to healthy volunteers. Even for the same drug, the reported changes in exposure varied, possibly due to a lack of distinction between the activity states of CD. The highest alteration was seen with S-verapamil and midazolam, 8.7- and 5.3-fold greater exposure, respectively, in active CD patients relative to healthy volunteers. Only one report was available on liver DMETs in CD, and indicated reduced CYP3A4 activity. In a number of reports, mRNA expression of DMETs in the ileum and colon of CD patients was measured, focussing on P-glycoprotein (p-gp) transporter and CYP3A4 enzyme, and showed contradictory results. No data were available on protein expression in duodenum and jejunum despite their dominant role in oral drug absorption. CONCLUSION: There are currently inadequate dedicated clinical or quantitative proteomic studies in CD to enable predictive PBPK models with high confidence and adequate verification. The PBPK models for CD with the available systems parameters were able to capture the major physiological influencers and the gaps to be filled by future research. Quantification of DMETs in the intestine and the liver in CD is warranted, alongside well-defined clinical drug disposition studies with a number of index drugs as biomarkers of changes in DMETs in these patients, to avoid large-scale dedicated studies for every drug to determine the effects of disease on the drug's metabolism and disposition and the consequential safety and therapeutic concerns.

Quantitative Assessment of the Impact of Crohn's Disease on Protein Abundance of Human Intestinal Drug-Metabolising Enzymes and Transporters.

Crohn's disease affects the mucosal layer of the intestine, predominantly ileum and colon segments, with the potential to affect the expression of intestinal enzymes and transporters, and consequently, oral drug bioavailability. We carried out a quantitative proteomic analysis of inflamed and non-inflamed ileum and colon tissues from Crohn's disease patients and healthy donors. Homogenates from samples in each group were pooled and protein abundance determined by liquid chromatography-mass spectrometry (LC-MS). In inflamed Crohn's ileum, CYP3A4, CYP20A1, CYP51A1, ADH1B, ALPI, FOM1, SULT1A2, SULT1B1 and ABCB7 showed ≥10-fold reduction in abundance compared with healthy baseline. By contrast, only MGST1 showed ≥10 fold reduction in inflamed colon. Ileal UGT1A1, MGST1, MGST2, and MAOA levels increased by ≥2 fold in Crohn's patients, while only ALPI showed ≥2 fold increase in the colon. Counter-intuitively, non-inflamed ileum had a higher magnitude of fold change than inflamed tissue when compared with healthy tissue. Marked but non-uniform alterations were observed in the expression of various enzymes and transporters in ileum and colon compared with healthy samples. Modelling will allow improved understanding of the variable effects of Crohn's disease on bioavailability of orally administered drugs.

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.

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.

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.

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.