Dealing With Variable Drug Exposure Due to Variable Hepatic Metabolism: A Proof-of-Concept Application of Liquid Biopsy in Renal Impairment.

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics (PK), affecting drug exposure, and in pharmacodynamics (PD), affecting response achieved at the same drug concentration at the site of action. Although liquid biopsy for assessing different levels of molecular drug targets has yet to be established, individual characterization of drug elimination pathways using liquid biopsy has recently been demonstrated. The feasibility of applying this approach in conjunction with modeling tools to guide individual dosing remains unexplored. In this study, we aimed to individualize physiologically-based pharmacokinetic (PBPK) models based on liquid biopsy measurements in plasma from 25 donors with different grades of renal function who were previously administered oral midazolam as part of a microdose cocktail. Virtual twin models were constructed based on demographics, renal function, and hepatic expression of relevant pharmacokinetic pathways projected from liquid biopsy output. Simulated exposure (AUC) to midazolam was in agreement with observed data (AFE = 1.38, AAFE = 1.78). Simulated AUC variability with three dosing approaches indicated higher variability with uniform dosing (14-fold) and stratified dosing (13-fold) compared with individualized dosing informed by liquid biopsy (fivefold). Further, exosome screening revealed mRNA expression of 532 targets relevant to drug metabolism and disposition (169 enzymes and 361 transporters). Data related to these targets can be used to further individualize PBPK models for pathways relevant to PK of other drugs. This study provides additional verification of liquid biopsy-informed PBPK modeling approaches, necessary to advance strategies that seek to achieve precise dosing from the start of treatment.

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.

Toward systems-informed models for biologics disposition: covariates of the abundance of the neonatal Fc Receptor (FcRn) in human tissues and implications for pharmacokinetic modelling.

Biologics are a fast-growing therapeutic class, with intertwined pharmacokinetics and pharmacodynamics, affected by the abundance and function of the FcRn receptor. While many investigators assume adequacy of classical models, such as allometry, for pharmacokinetic characterization of biologics, advocates of physiologically-based pharmacokinetics (PBPK) propose consideration of known systems parameters that affect the fate of biologics to enable a priori predictions, which go beyond allometry. The aim of this study was to deploy a systems-informed modelling approach to predict the disposition of Fc-containing biologics. We used global proteomics to quantify the FcRn receptor [p51 and ß2-microglobulin (B2M) subunits] in 167 samples of human tissue (liver, intestine, kidney and skin) and assessed covariates of its expression. FcRn p51 subunit was highest in liver relative to other tissues, and B2M was 1-2 orders of magnitude more abundant than FcRn p51 across all sets. There were no sex-related differences, while higher expression was confirmed in neonate liver compared with adult liver. Trends of expression in liver and kidney indicated a moderate effect of body mass index, which should be confirmed in a larger sample size. Expression of FcRn p51 subunit was approximately 2-fold lower in histologically normal liver tissue adjacent to cancer compared with healthy liver. FcRn mRNA in plasma-derived exosomes correlated moderately with protein abundance in matching liver tissue, opening the possibility of use as a potential clinical tool. Predicted effects of trends in FcRn abundance in healthy and disease (cancer and psoriasis) populations using trastuzumab and efalizumab PBPK models were in line with clinical observations, and global sensitivity analysis revealed endogenous IgG plasma concentration and tissue FcRn abundance as key systems parameters influencing exposure to Fc-conjugated biologics.

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.

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.

Examining Physiologically Based Pharmacokinetic Model Assumptions for Cross-Tissue Similarity of Activity per Unit of Enzyme: The Case Example of Uridine 5'-Diphosphate Glucuronosyltransferase.

The default assumption during in vitro in vivo extrapolation (IVIVE) to predict metabolic clearance in physiologically based pharmacokinetics (PBPK) is that protein expression and activity have the same relationship in various tissues. This assumption is examined for uridine 5'-diphosphate glucuronosyltransferases (UGTs), a case example where expression and hence metabolic activity are distributed across various tissues. Our literature analysis presents overwhelming evidence of a greater UGT activity per unit of enzyme (higher kcat) in kidney and intestinal tissues relative to liver (greater than 200-fold for UGT2B7). This analysis is based on application of abundance values reported using similar proteomic techniques and within the same laboratory. Our findings call into question the practice of assuming similar kcat during IVIVE estimations as part of PBPK and call for a systematic assessment of the kcat of various enzymes across different organs. The analysis focused on compiling data for probe substrates that were common for two or more of the studied tissues to allow for reliable comparison of cross-tissue enzyme kinetics; this meant that UGT enzymes included in the study were limited to UGT1A1, 1A3, 1A6, 1A9, and 2B7. Significantly, UGT1A9 (n = 24) and the liver (n = 27) were each found to account for around half of the total dataset; these were found to correlate with hepatic UGT1A9 data found in 15 of the studies, highlighting the need for more research into extrahepatic tissues and other UGT isoforms. SIGNIFICANCE STATEMENT: During physiologically based pharmacokinetic modeling (in vitro in vivo extrapolation) of drug clearance, the default assumption is that the activity per unit of enzyme is the same in all tissues. The analysis provides preliminary evidence that this may not be the case and that renal and intestinal tissues may have almost 250-fold greater uridine 5'-diphosphate glucuronosyltransferase activity per unit of enzyme than liver tissues.

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.

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.

Liquid Biopsy for Patient Characterization in Cardiovascular Disease: Verification against Markers of Cytochrome P450 and P-Glycoprotein Activities.

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics together with subsequent pharmacodynamic differences. Liquid biopsy is a valuable new approach to diagnose disease prior to the appearance of clinical signs and symptoms, potentially circumventing invasive tissue biopsies. However, the possibility of quantitative grading of biomarkers, as opposed to simply confirming their presence or absence, is relatively new. In this study, we aimed to verify expression measurements of cytochrome P450 (CYP) enzymes and the transporter P-glycoprotein (P-gp) in liquid biopsy against genotype and activity phenotype (assessed by the Geneva cocktail approach) in 30 acutely ill patients with cardiovascular disease in a hospital setting. After accounting for exosomal shedding, expression in liquid biopsy correlated with activity phenotype for CYP1A2, CYP2B6, CYP2C9, CYP3A, and P-gp (r = 0.44-0.70, P ≤ 0.05). Although genotype offered a degree of stratification, large variability (coefficient of variation (CV)) in activity (up to 157%) and expression in liquid biopsy (up to 117%) was observed within each genotype, indicating a mismatch between genotype and phenotype. Further, exosome screening revealed expression of 497 targets relevant to drug metabolism and disposition (159 enzymes and 336 transporters), as well as 20 molecular drug targets. Although there were no functional data available to correlate against these large-scale measurements, assessment of disease perturbation from healthy baseline was possible. Verification of liquid biopsy against activity phenotype is important to further individualize modeling approaches that aspire to achieve precision dosing from the start of drug treatment without the need for multiple rounds of dose optimization.

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition.

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition. Graphical abstract.

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.

Non-uniformity of Changes in Drug-Metabolizing Enzymes and Transporters in Liver Cirrhosis: Implications for Drug Dosage Adjustment.

Liver cirrhosis is a chronic disease that affects the liver structure, protein expression, and overall metabolic function. Abundance data for drug-metabolizing enzymes and transporters (DMET) across all stages of disease severity are scarce. Levels of these proteins are crucial for the accurate prediction of drug clearance in hepatically impaired patients using physiologically based pharmacokinetic (PBPK) models, which can be used to guide the selection of more precise dosing. This study aimed to experimentally quantify these proteins in human liver samples and assess how they can impact the predictive performance of the PBPK models. We determined the absolute abundance of 51 DMET proteins in human liver microsomes across the three degrees of cirrhosis severity (n = 32; 6 mild, 13 moderate, and 13 severe), compared to histologically normal controls (n = 14), using QconCAT-based targeted proteomics. The results revealed a significant but non-uniform reduction in the abundance of enzymes and transporters, from control, by 30-50% in mild, 40-70% in moderate, and 50-90% in severe cirrhosis groups. Cancer and/or non-alcoholic fatty liver disease-related cirrhosis showed larger deterioration in levels of CYP3A4, 2C8, 2E1, 1A6, UGT2B4/7, CES1, FMO3/5, EPHX1, MGST1/3, BSEP, and OATP2B1 than the cholestasis set. Drug-specific pathways together with non-uniform changes of abundance across the enzymes and transporters under various degrees of cirrhosis necessitate the use of PBPK models. As case examples, such models for repaglinide, dabigatran, and zidovudine were successful in recovering disease-related alterations in drug exposure. In conclusion, the current study provides the biological rationale behind the absence of a single dose adjustment formula for all drugs in cirrhosis and demonstrates the utility of proteomics-informed PBPK modeling for drug-specific dose adjustment in liver cirrhosis.

Quantitative Proteomic Map of Enzymes and Transporters in the Human Kidney: Stepping Closer to Mechanistic Kidney Models to Define Local Kinetics.

The applications of translational modeling of local drug concentrations in various organs had a sharp increase over the last decade. These are part of the model-informed drug development initiative, adopted by the pharmaceutical industry and promoted by drug regulatory agencies. With respect to the kidney, the models serve as a bridge for understanding animal vs. human observations related to renal drug disposition and any consequential adverse effects. However, quantitative data on key drug-metabolizing enzymes and transporters relevant for predicting renal drug disposition are limited. Using targeted and global quantitative proteomics, we determined the abundance of multiple enzymes and transporters in 20 human kidney cortex samples. Nine enzymes and 22 transporters were quantified (8 for the first time in the kidneys). In addition, > 4,000 proteins were identified and used to form an open database. CYP2B6, CYP3A5, and CYP4F2 showed comparable, but generally low expression, whereas UGT1A9 and UGT2B7 levels were the highest. Significant correlation between abundance and activity (measured by mycophenolic acid clearance) was observed for UGT1A9 (Rs = 0.65, P = 0.004) and UGT2B7 (Rs = 0.70, P = 0.023). Expression of P-gp ≈ MATE-1 and OATP4C1 transporters were high. Strong intercorrelations were observed between several transporters (P-gp/MRP4, MRP2/OAT3, and OAT3/OAT4); no correlation in expression was apparent for functionally related transporters (OCT2/MATEs). This study extends our knowledge of pharmacologically relevant proteins in the kidney cortex, with implications on more prudent use of mechanistic kidney models under the general framework of quantitative systems pharmacology and toxicology.

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.

Proteomic Quantification of Changes in Abundance of Drug-Metabolizing Enzymes and Drug Transporters in Human Liver Cirrhosis: Different Methods, Similar Outcomes.

Model-based assessment of the effects of liver disease on drug pharmacokinetics requires quantification of changes in enzymes and transporters responsible for drug metabolism and disposition. Different proteomic methods are currently used for protein quantification in tissues and in vitro systems, each with specific procedures and requirements. The outcome of quantitative proteomic assays using four different methods (one targeted and three label-free) applied to the same sample set was compared in this study. Three pooled cirrhotic liver microsomal samples corresponding to cirrhosis with nonalcoholic fatty liver disease, biliary disease, or cancer and a control microsomal pool were analyzed using quantification concatemer-based targeted proteomics, the total protein approach (TPA), high three ion intensity (Hi3) approach, and intensity-based absolute quantification (iBAQ) to determine the absolute and relative abundance in disease compared with control. The relative abundance data provided a "disease perturbation factor" (DPF) for each target protein. Absolute and relative abundances generated by standard-based label-free methods (iBAQ and Hi3) showed good agreement with targeted proteomics (limited bias and scatter), but TPA (standard-free method) overestimated absolute abundances by approximately 2-fold. The DPF was consistent between different proteomic methods but varied between enzymes and transporters, indicating discordance of effects of cirrhosis on various metabolism-related proteins. The DPF ranged from no change (e.g., for glucuronosyltransferase-1A6 in nonalcoholic fatty liver disease group) to less than 0.3 (e.g., carboxylesterases-1 in cirrhosis of biliary origin). SIGNIFICANCE STATEMENT: This study demonstrated that relative changes in enzymes and transporters (DPF) are independent of the quantitative proteomic methods used. Standard-based label-free methods, such as high three ion intensity (Hi3) and intensity-based absolute quantification (iBAQ) methods, were less biased and more precise than the total protein approach (TPA) when compared with targeted data. The DPF reconciled differences across proteomic methods observed with absolute levels. Using this approach, differences were revealed in the expression of enzymes/transporters in cirrhosis associated with different etiologies.

Inhibition of aggregation of amyloid-ß through covalent modification with benzylpenicillin; potential relevance to Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) is correlated with the misfolding and aggregation of amyloid-beta protein (Aß). Here we report that the antibiotic benzylpenicillin (BP) can specifically bind to Aß, modulate the process of aggregation and supress its cytotoxic effect, initially via a reversible binding interaction, followed by covalent bonding between specific functional groups (nucleophiles) within the Aß peptide and the beta-lactam ring. Mass spectrometry and computational docking supported covalent modification of Aß by BP. BP was found to inhibit aggregation of Aß as revealed by the Thioflavin T (ThT) fluorescence assay and atomic force microscopy (AFM). In addition, BP treatment was found to have a cytoprotective activity against Aß-induced cell cytotoxicity as shown by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell toxicity assay. The specific interaction of BP with Aß suggests the possibility of structure-based drug design, leading to the identification of new drug candidates against AD. Moreover, good pharmacokinetics of beta-lactam antibiotics and safety on long-time use make them valuable candidates for drug repurposing towards neurological disorders such as AD.

Label-Free Quantitative Proteomics and Substrate-Based Mass Spectrometry Imaging of Xenobiotic Metabolizing Enzymes in Ex Vivo Human Skin and a Human Living Skin Equivalent Model.

We report for the first time label-free quantification of xenobiotic metabolizing enzymes (XME), transporters, redox enzymes, proteases, and nucleases in six human skin explants and a three-dimensional living skin equivalent model from LabSkin. We aimed to evaluate the suitability of LabSkin as an alternative to animal testing for the development of topical formulations. More than 2000 proteins were identified and quantified from total cellular protein. Alcohol dehydrogenase 1C, the most abundant phase I XME in human skin, and glutathione S-transferase pi 1, the most abundant phase II XME in human skin, were present in similar abundance in LabSkin. Several esterases were quantified and esterase activity was confirmed in LabSkin using substrate-based mass spectrometry imaging. No cytochrome P450 (P450) activity was observed for the substrates tested, in agreement with the proteomics data, where the cognate P450s were absent in both human skin and LabSkin. Label-free protein quantification allowed insights into other related processes such as redox homeostasis and proteolysis. For example, the most abundant antioxidant enzymes were thioredoxin and peroxiredoxin-1. This systematic determination of functional equivalence between human skin and LabSkin is a key step toward the construction of a representative human in vitro skin model, which can be used as an alternative to current animal-based tests for chemical safety and for predicting dosage of topically administered drugs. SIGNIFICANCE STATEMENT: The use of label-free quantitative mass spectrometry to elucidate the abundance of xenobiotic metabolizing enzymes, transporters, redox enzymes, proteases, and nucleases in human skin enhance our understanding of the skin physiology and biotransformation of topical drugs and cosmetics. This will help to develop mathematical models to predict drug metabolism in human skin and to develop more robust in vitro engineered human skin tissue as alternatives to animal testing.

Liquid Biopsy Enables Quantification of the Abundance and Interindividual Variability of Hepatic Enzymes and Transporters.

Variability in individual capacity for hepatic elimination of therapeutic drugs is well recognized and is associated with variable expression and activity of liver enzymes and transporters. Although genotyping offers some degree of stratification, there is often large variability within the same genotype. Direct measurement of protein expression is impractical due to limited access to tissue biopsies. Hence, determination of variability in hepatic drug metabolism and disposition using liquid biopsy (blood samples) is an attractive proposition during drug development and in clinical practice. This study used a multi-"omic" strategy to establish a liquid biopsy technology intended to assess hepatic capacity for metabolism and disposition in individual patients. Plasma exosomal analysis (n = 29) revealed expression of 533 pharmacologically relevant genes at the RNA level, with 147 genes showing evidence of expression at the protein level in matching liver tissue. Correction of exosomal RNA expression using a novel shedding factor improved correlation against liver protein expression for 97 liver-enriched genes. Strong correlation was demonstrated for 12 key drug-metabolizing enzymes and 4 drug transporters. The developed test allowed reliable patient stratification, and in silico trials demonstrated utility in adjusting drug dose to achieve similar drug exposure between patients with variable hepatic elimination. Accordingly, this approach can be applied in characterization of volunteers prior to enrollment in clinical trials and for patient stratification in clinical practice to achieve more precise individual dosing.