Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

In Vitro to In Vivo Scalars for Drug Clearance in Nonalcoholic Fatty Liver and Steatohepatitis.

In vitro-in vivo extrapolation (IVIVE) allows prediction of clinical outcomes across populations from in vitro data using specific scalars tailored to the biologic characteristics of each population. This study experimentally determined scalars for patients with varying degrees of nonalcoholic fatty liver disease (NAFLD), ranging from fatty liver to nonalcoholic steatohepatitis (NASH) and cirrhosis. Microsomal, S9, and cytosol fractions were extracted from 36 histologically normal and 66 NAFLD livers (27 nonalcoholic fatty liver [NAFL], 13 NASH, and 26 NASH with cirrhosis). Corrected microsomal protein per gram liver (MPPGL) progressively decreased with disease severity (26.8, 27.4, and 24.3 mg/g in NAFL, NASH, and NASH/cirrhosis, respectively, compared with 35.6 mg/g in normal livers; ANOVA, P < 0.001). Homogenate, S9, and cytosolic protein showed a consistent trend of decline in NASH/cirrhosis relative to normal control (post-hoc t test, P < 0.05). No differences across the groups were observed in homogenate, S9, cytosolic, and microsomal protein content in matched kidney samples. MPPGL-based scalars that combine protein content with liver size revealed that the reduction in MPPGL in NAFL and NASH was compensated by the reported increase in liver size (relative scalar ratios of 0.96 and 0.99, respectively), which was not the case with NASH/cirrhosis (ratio of 0.63), compared with healthy control. Physiologically based pharmacokinetics-informed global sensitivity analysis of the relative contribution of IVIVE scalars (hepatic CYP3A4 abundance, MPPGL, and liver size) to variability in exposure (area under the curve) to three CYP3A substrates (alprazolam, midazolam, and ibrutinib) revealed enzyme abundance as the most significant parameter, followed by MPPGL, whereas liver volume was the least impactful factor. SIGNIFICANCE STATEMENT: Nonalcoholic fatty liver disease-specific scalars necessary for extrapolation from in vitro systems to liver tissue are lacking. These are required in clearance prediction and dose selection in nonalcoholic fatty liver and steatohepatitis populations. Previously reported disease-driven changes have focused on cirrhosis, with no data on the initial stages of liver disease. The authors obtained experimental values for microsomal, cytosolic, and S9 fractions and assessed the relative impact of microsomal scalars on predicted exposure to substrate drugs using physiologically based pharmacokinetics.

Quantitative Proteomics for Translational Pharmacology and Precision Medicine: State of The Art and Future Outlook.

Over the past 20 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. A symposium at the 25th North American ISSX meeting, in Boston, in September 2023, was held to explore current and emerging applications of quantitative proteomics in translational pharmacology and strategies for improved integration into model-informed drug development based on practical experience of each of the presenters. A summary of the talks and discussions is presented in this perspective alongside future outlooks that were outlined for future meetings. Significance Statement This perspective explores current and emerging applications of quantitative proteomics in translational pharmacology and precision medicine, and outlines outlooks for improved integration into model-informed drug development.

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine.

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

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.

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.

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.

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.

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.

Application of proteomic data in the translation of in vitro observations to associated clinical outcomes.

Translation of information on drug exposure and effect is facilitated by in silico models that enable extrapolation of in vitro measurements to in vivo clinical outcomes. These models integrate drug-specific data with information describing physiological processes and pathological changes, including alterations to proteins involved in drug absorption, distribution and elimination. Over the past 15 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. In this review, we explore current and emerging applications of targeted and global (untargeted) proteomics in translational pharmacology as well as strategies for improved integration into model-based drug development.

Scaling Factors for Clearance in Adult Liver Cirrhosis.

In vitro to in vivo extrapolation (IVIVE) enables prediction of in vivo clinical outcomes related to drug exposure in various populations from in vitro data. Prudent IVIVE requires scalars specific to the biologic characteristics of the system in each population. This study determined experimentally for the first time scalars in liver samples from patients with varying degrees of cirrhosis. Microsomal and cytosolic fractions were extracted from 13 noncirrhotic and 32 cirrhotic livers (six mild, 13 moderate, and 13 severe, based on Child-Pugh score). Fractional protein content was determined, and cytochrome P450 reductase activity was used to correct for microsomal protein loss. Although the median microsomal protein per gram liver (MPPGL) in mild, moderate, and severe cirrhosis (26.2, 32.4, and 30.8 mg⋅g-1, respectively) seemed lower than control livers (36.6 mg⋅g-1), differences were not statistically significant (Kruskal-Wallis test, P > 0.05). Corresponding values for cytosolic protein per gram liver were 88.2, 67.9, 62.2, and 75.4 (mg⋅g-1) for mild, moderate, and severe cirrhosis and control livers, respectively, with statistically lower values for severe versus controls (Mann-Whitney P = 0.006). Cirrhosis associated with cancer showed lower MPPGL (24.8 mg⋅g-1) than cirrhosis associated with cholestasis (38.3 mg⋅g-1, P = 0.003). Physiologically based pharmacokinetic simulations with disease-specific scalars captured cirrhosis impact on exposure to alfentanil, metoprolol, midazolam, and ethinylestradiol. These experimentally-determined scalars should alleviate the need for indirect scaling using functional liver volume. Scaling factors in cirrhosis might be a reflection of the etiology rather than the disease severity. Hence, bundling various cirrhotic conditions under the same umbrella when predicting hepatic impairment impact should be revisited. SIGNIFICANCE STATEMENT: Cirrhosis-specific scalars required for extrapolation from microsomal or cytosolic in vitro systems to liver tissue are lacking. These scalars can help in predicting drug clearance and selection of dosage regimens for cirrhosis populations. Attempts to consider potential changes have been empirical and ignored the potential impact of the cause of cirrhosis. We obtained experimental values for these scalars for the first time and assessed their impact on predicted exposure to various substrate drugs using physiologically-based pharmacokinetics simulations.

Quantitative Proteomics of Clinically Relevant Drug-Metabolizing Enzymes and Drug Transporters and Their Intercorrelations in the Human Small Intestine.

The levels of drug-metabolizing enzymes (DMEs) and transporter proteins in the human intestine are pertinent to determine oral drug bioavailability. Despite the paucity of reports on such measurements, it is well recognized that these values are essential for translating in vitro data on drug metabolism and transport to predict drug disposition in gut wall. In the current study, clinically relevant DMEs [cytochrome P450 (P450) and uridine 5'-diphospho-glucuronosyltransferase (UGT)] and drug transporters were quantified in total mucosal protein preparations from the human jejunum (n = 4) and ileum (n = 12) using quantification concatemer-based targeted proteomics. In contrast to previous reports, UGT2B15 and organic anion-transporting polypeptide 1 (OATP1A2) were quantifiable in all our samples. Overall, no significant disparities in protein expression were observed between jejunum and ileum. Relative mRNA expression for drug transporters did not correlate with the abundance of their cognate protein, except for P-glycoprotein 1 (P-gp) and organic solute transporter subunit alpha (OST-α), highlighting the limitations of RNA as a surrogate for protein expression in dynamic tissues with high turnover. Intercorrelations were found within P450 [2C9-2C19 (P = 0.002, R 2 = 0.63), 2C9-2J2 (P = 0.004, R 2 = 0.40), 2D6-2J2 (P = 0.002, R 2 = 0.50)] and UGT [1A1-2B7 (P = 0.02, R 2 = 0.87)] family of enzymes. There were also correlations between P-gp and several other proteins [OST-α (P < 0.0001, R 2 = 0.77), UGT1A6 (P = 0.009, R 2 = 0.38), and CYP3A4 (P = 0.007, R 2 = 0.30)]. Incorporating such correlations into building virtual populations is crucial for obtaining plausible characteristics of simulated individuals. SIGNIFICANCE STATEMENT: A number of drug transporters were quantified for the first time in this study. Several intercorrelations of protein abundance were reported. mRNA expression levels proved to be a poor reflection of differences between individuals regarding the level of protein expression in gut. The reported abundance of drug-metabolizing enzymes and transporters and their intercorrelations will contribute to better predictions of oral drug bioavailability and drug-drug interactions by linking in vitro observations to potential outcomes through physiologically based pharmacokinetic models.

Proteomic characterisation of drug metabolising enzymes and drug transporters in pig liver.

Liver enzymes and transporters play an essential role in xenobiotic metabolism, distribution and elimination. Pre-clinical safety assessment relies on studies on animal models, including the (mini)pig. The pig shares many anatomical and physiological characteristics with humans, and there is currently a gap in information about porcine metabolism and disposition pathways and their similarities and differences from human ones.Three different sample preparation methods (filter-aided sample preparation (FASP), enhanced FASP (eFASP) and in-solution sample preparation) were used to prepare porcine liver tissue (two samples) for proteomic analysis. The analysis relied on rapid-separation liquid chromatography coupled to Orbitrap mass spectrometry in data-dependent acquisition mode. MASCOT was used for identification and relative label-free quantification was based on spectral counting.The three sample preparation methods provided complementary results, allowing characterisation of approximately 70 pharmacologically relevant proteins. The main quantified proteins included 16 cytochrome P450 (CYP) enzymes, 5 UGT enzymes, and 11 transporters. In addition, 20 Phase I and 14 Phase II enzymes were also characterised. Inter-operator differences were negligible and the pig liver pies for CYP, UGT and efflux transporter proteins were established. Human homologues of the quantified CYP, UGT and transporter proteins were identified.

Response to "Determining Allele-Specific Protein Expression (ASPE) Using a Novel Quantitative Concatamer Based Proteomics Method".

A recent publication in this journal reported the application of a targeted proteomic strategy using a quantitative concatemer (QconCAT) standard to the assessment of allele-specific expression of UGT2B15 claiming this methodology to be a "novel" approach ( J. Proteome Res. 2018, 17 (10), 3606-3612, DOI: 10.1021/acs.jproteome.8b00620). While the application is not common, the method was previously described and reported by our group in relation to the quantification of CYP2B6 alleles ( J. Proteome Res. 2013, 12 (12), 5934-5942, DOI: 10.1021/pr400279u) to assess the expression of a prevalent polymorphism in a Caucasian population.

Proteomic Quantification of Human Blood-Brain Barrier SLC and ABC Transporters in Healthy Individuals and Dementia Patients.

The blood-brain barrier (BBB) maintains brain homeostasis by controlling traffic of molecules from the circulation into the brain. This function is predominantly dependent on proteins expressed at the BBB, especially transporters and tight junction proteins. Alterations to the level and function of BBB proteins can impact the susceptibility of the central nervous system to exposure to xenobiotics in the systemic circulation with potential consequent effects on brain function. In this study, expression profiles of drug transporters and solute carriers in the BBB were assessed in tissues from healthy individuals ( n = 12), Alzheimer's patients ( n = 5), and dementia with Lewy bodies patients ( n = 5), using targeted, accurate mass retention time (AMRT) and global proteomic methods. A total of 53 transporters were quantified, 19 for the first time in the BBB. A further 20 novel transporters were identified but not quantified. The global proteomic method identified another 3333 BBB proteins. Transporter abundances, taken together with the scaling factor, microvessel protein content per unit tissue (BMvPGB also measured here), can be used in quantitative systems pharmacology models predicting drug disposition in the brain and permitting dose adjustment (precision dosing) in special populations of patients, such as those with dementia. Even in this small study, we see differences in transporter profile between healthy and diseased brain tissue.

Quantification of Proteins Involved in Drug Metabolism and Disposition in the Human Liver Using Label-Free Global Proteomics.

There is an urgent need (recognized in FDA guidance, 2018) to optimize the dose of medicines given to patients for maximal drug efficacy and limited toxicity (precision dosing), which can be facilitated by quantitative systems pharmacology (QSP) models. Accurate quantification of proteins involved in drug clearance is essential to build and improve QSP models for any target population. Here we describe application of label-free proteomics in microsomes from 23 human livers to simultaneously quantify 188 enzymes and 66 transporters involved in xenobiotic disposition, including 17 cytochrome P450s (CYPs), 10 UDP-glucuronosyltransferases (UGTs), 7 ATP-binding cassette (ABC) transporters, and 11 solute carrier (SLC) transporters; six of these proteins are quantified for the first time. The methodology allowed quantification of thousands of proteins, allowing estimation of sample purity and understanding of global patterns of protein expression. There was overall good agreement with targeted quantification and enzyme activity data, where this was available. The effects of sex, age, genotype, and BMI on enzyme and transporter expression were assessed. Decreased expression of enzymes and transporters with increasing BMI was observed, but a tendency for older donors to have higher BMIs may have confounded this result. The effect of genotype on enzymes expression was, however, clear-cut, with CYP3A5*1/*3 genotype expressed 16-fold higher compared with its mostly inactive *3/*3 counterpart. Despite the complex, time-consuming data analysis required for label-free methodology, the advantages of the label-free method make it a valuable approach to populate a broad range of system parameters simultaneously for target patients within pharmacology and toxicology models.

GASP and FASP are Complementary for LC-MS/MS Proteomic Analysis of Drug-Metabolizing Enzymes and Transporters in Pig Liver.

Sample preparation is a critical step in the proteomic workflow. Numerous different approaches are used, tailored to the type of sample, the aims of the experiment, analytical method, and to an extent, user preference. This has resulted in large variation in reported protein abundances. In this study, the complementarity of two different sample preparation techniques is demonstrated for the study of absorption, distribution, metabolism, and excretion (ADME) related proteins from pig liver tissue. Filter-aided sample preparation (FASP) is a well-established and widely used method, while gel-aided sample preparation (GASP) is a relatively new method optimized and simplified from previous gel-associated digestion techniques. To investigate each method, the number of peptides and proteins characterized, reproducibility of results, and their real-time application are examined. While both methods have their merits and limitations, for example, FASP is the less technical of the two methods, while GASP is time efficient, ultimately the two methods show significant differences in the peptides identified and therefore, the use of both methods should be considered when examining and quantifying ADME related proteins. Data are available via ProteomeXchange with identifier PXD011324.

Identification and quantification of blood-brain barrier transporters in isolated rat brain microvessels.

The blood-brain barrier (BBB) maintains brain homeostasis by tightly regulating the exchange of molecules with systemic circulation. It consists primarily of microvascular endothelial cells surrounded by astrocytic endfeet, pericytes, and microglia. Understanding the make-up of transporters in rat BBB is essential to the translation of pharmacological and toxicological observations into humans. In this study, experimental workflows are presented in which the optimization of (a) isolation of rat brain microvessels (b) enrichment of endothelial cells, and (c) extraction and digestion of proteins were evaluated, followed by identification and quantification of BBB proteins. Optimization of microvessel isolation was indicated by 15-fold enrichment of endothelial cell marker Glut1 mRNA, whereas markers for other cell types were not enriched. Filter-aided sample preparation was shown to be superior to in-solution sample preparation (10251 peptides vs. 7533 peptides). Label-free proteomics was used to identify nearly 2000 proteins and quantify 1276 proteins in isolated microvessels. A combination of targeted and global proteomics was adopted to measure protein abundance of 6 ATP-binding cassette and 27 solute carrier transporters. Data analysis using proprietary Progenesis and open access MaxQuant software showed overall agreement; however, Abcb9 and Slc22a8 were quantified only by MaxQuant, whereas Abcc9 and Abcd3 were quantified only by Progenesis. Agreement between targeted and untargeted quantification was demonstrated for Abcb1 (19.7 ± 1.4 vs. 17.8 ± 2.3) and Abcc4 (2.2 ± 0.7 vs. 2.1 ± 0.4), respectively. Rigorous quantification of BBB proteins, as reported in this study, should assist with translational modeling efforts involving brain disposition of xenobiotics.

Data Generated by Quantitative Liquid Chromatography-Mass Spectrometry Proteomics Are Only the Start and Not the Endpoint: Optimization of Quantitative Concatemer-Based Measurement of Hepatic Uridine-5'-Diphosphate-Glucuronosyltransferase Enzymes with Reference to Catalytic Activity.

Quantitative proteomic methods require optimization at several stages, including sample preparation, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and data analysis, with the final analysis stage being less widely appreciated by end-users. Previously reported measurement of eight uridine-5'-diphospho-glucuronosyltransferases (UGT) generated by two laboratories [using stable isotope-labeled (SIL) peptides or quantitative concatemer (QconCAT)] reflected significant disparity between proteomic methods. Initial analysis of QconCAT data showed lack of correlation with catalytic activity for several UGTs (1A4, 1A6, 1A9, 2B15) and moderate correlations for UGTs 1A1, 1A3, and 2B7 (Rs = 0.40-0.79, P < 0.05; R2 = 0.30); good correlations were demonstrated between cytochrome P450 activities and abundances measured in the same experiments. Consequently, a systematic review of data analysis, starting from unprocessed LC-MS/MS data, was undertaken, with the aim of improving accuracy, defined by correlation against activity. Three main criteria were found to be important: choice of monitored peptides and fragments, correction for isotope-label incorporation, and abundance normalization using fractional protein mass. Upon optimization, abundance-activity correlations improved significantly for six UGTs (Rs = 0.53-0.87, P < 0.01; R2 = 0.48-0.73); UGT1A9 showed moderate correlation (Rs = 0.47, P = 0.02; R2 = 0.34). No spurious abundance-activity relationships were identified. However, methods remained suboptimal for UGT1A3 and UGT1A9; here hydrophobicity of standard peptides is believed to be limiting. This commentary provides a detailed data analysis strategy and indicates, using examples, the significance of systematic data processing following acquisition. The proposed strategy offers significant improvement on existing guidelines applicable to clinically relevant proteins quantified using QconCAT.