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

Transporter expressions as part of required scaling factor to support in vitro in vivo extrapolation for blood-brain barrier drug permeability

Access of drugs to the central nervous system is limited by the blood-brain barrier, and this in turn affects drug efficacy/toxicity. To date, most drug discovery optimization paradigms have relied heavily on in vitro transporter assays and preclinical species pharmacokinetic evaluation to provide a qualitative assessment of human brain penetration. Because of the lack of human brain pharmacokinetic data, mechanistic models for preclinical species, combined with in vitro and in silico data, are useful for translation to human. These models require transporter expression data to be measured in both in vitro and in vivo systems. The purpose of this work was to quantify transporter expression and generate scaling factors (SFs) to enable in vitro in vivo extrapolation (IVIVE) of transporter-mediated processes and to support the development of PBPK model of the brain in rats. SF represents the ratio of abundance of the relevant transporter in the tissue relative to transporter expressing cells. Using quantitative proteomics with QconCAT technology, the expression of human and rat P-gp (ABCB1/Abcb1) and BCRP/Bcrp (ABCG2/Abcg2) was measured in rat brain microvessels, mock and transfected cell lines including, Madin-Darby Canine Kidney I (MDCK I), Madin-Darby Canine Kidney II (MDCK II) and Porcine Kidney epithelial cells (LLC-PK1). P-gp expression ranged from 32 to 71 pmol/mg in rat brain microvessels, exceeding literature values of 14.1-25.2 pmol/mg protein. Conversely, Bcrp expression ranged between 0.02-0.27 pmol/mg protein lower than the literature range (2-6.2 pmol/mg protein). P-gp expression in MDCK I and LLC-PK1 cells transfected with rat Mdr1a was similar (within 1.5-fold) as was human P-gp expression in MDR1 transfected LLC-PK1 and MDCK II cells. The generated SFs were 34.4 and 50.4 for brain P-gp (depending on the cell line used) and 0.53 for brain Bcrp. Endogenous P-gp transporter was detected in MDCK II cell lines when protein expression was measured using a surrogate peptide that was shared across species. The current work provides a framework for proteomics-informed translation of in vitro P-gp and BCRP-related kinetics of drugs and supports the development of PBPK models to predict drug disposition in the brain.

Development of a PBPK Model for Lamotrigine which Incorporates Metabolism by UGT2B10: Impact of UGT2B10 Poor Metabolizer Phenotype and Pregnancy

An updated physiologically based pharmacokinetic (PBPK) model was developed for lamotrigine by incorporating a component of metabolism due to a UDP-glucuronyltransferase (UGT) 2B isozyme. This was assigned to UGT2B10 based on recent in vitro data in our laboratory demonstrating metabolism of lamotrigine by this isozyme (Tang et al. AAPS J 26:107, 2024). The PBPK model developed in this work was able to reasonably recapitulate the exposure of lamotrigine after single (IV and Oral) and multiple (Oral) doses. The predicted/observed maximal plasma concentration (Cmax) ratio ranged from 0.8 to 1.4 across all simulated studies and for 16 out of 18 simulated studies was between 0.8 and 1.25. Similarly, the predicted/observed area under the curve (AUC) ratio ranged from 0.6 to 1.44 across all simulated studies and for 18 out of 26 of the simulated studies the ratio was between 0.8 and 1.25. There was a slight tendency to overpredict the lamotrigine AUC on multiple dosing. The median predicted fraction metabolised (fm) by UGT2B10 in the model was 60%. With this fm value, the in vivo clinical DDI between lamotrigine and valproate was reasonably recapitulated considering only UGT2B10 inhibition (Predicted/Observed AUC ratios ranged from 0.65 - 1.2). Information on the prevalence of UGT2B10 poor metabolizer phenotypes and longitudinal changes in UGT1A4 and UGT2B10 expression during pregnancy were incorporated into the PBPK model and the plasma concentrations in subjects with different UGT2B10 phenotypes and in different trimesters of pregnancy were simulated. The simulated concentrations in pregnant subjects were in line with those reported during pregnancy.

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 International Society for the Study of Xenobiotics 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 outlook that was outlined for future meetings. SIGNIFICANCE STATEMENT: This perspective explores current and emerging applications of quantitative proteomics in translational pharmacology and precision medicine and outlines the outlook for improved integration into model-informed drug development.

PBPK Modelling for Drugs Cleared by Non-CYP Enzymes: State-of-the-Art and Future Perspectives

Physiologically-based pharmacokinetic (PBPK) modeling has become the established method for predicting human pharmacokinetics (PK) and drug-drug interactions (DDI). The number of drugs cleared by non-CYP enzyme metabolism has increased steadily and to date, there is no consolidated overview of PBPK modeling for drugs cleared by non-CYP enzymes. This review aims to describe the state-of-the-art for PBPK modeling for drugs cleared via non-CYP enzymes, to identify successful strategies, to describe gaps and to provide suggestion to overcome them. To this end, we conducted a detailed literature search and found 58 articles published before the 1st of January 2023 containing 95 examples of clinical PBPK models for 62 non-CYP enzyme substrates. Reviewed articles covered the drug clearance by uridine 5'-diphospho-glucuronosyltransferases (UGTs), aldehyde oxidase (AO), flavin-containing monooxygenases (FMOs), sulfotransferases (SULTs) and carboxylesterases (CES), with UGT2B7, UGT1A9, CES1, FMO3 and AO being the enzymes most frequently involved. In vitro-in vivo extrapolation (IVIVE) of intrinsic clearance and the bottom-up PBPK modeling involving non-CYP enzymes remains challenging. We observed that the middle-out modeling approach was applied in 80% of the cases, with metabolism parameters optimized in 73% of the models. Our review could not identify a standardized approach used for model optimization based on clinical data, with manual optimization employed most frequently. Successful development of models for UGT2B7, UGT1A9, CES1, and FMO3 substrates provides a foundation for other drugs metabolized by these enzymes and guides the way forward in creating PBPK models for other enzymes in these families. Significance Statement Our review charts the rise of PBPK modeling for drugs cleared by non-CYP enzymes. Analyzing 58 articles and 62 non-CYP enzyme substrates, we found that UGTs, AO, FMOs, SULTs, and CES were the main enzyme families involved and that UGT2B7, UGT1A9, CES1, FMO3 and AO are the individual enzymes with the strongest PBPK modeling precedents. Approaches established for these enzymes can now be extended to additional substrates and to drugs metabolized by enzymes that are similarly well characterized.

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.

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.

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.

Characterization of Hepatic UDP-Glucuronosyltransferase Enzyme Abundance-Activity Correlations and Population Variability Using a Proteomics Approach and Comparison with Cytochrome P450 Enzymes

The expression of ten major drug-metabolizing UDP-glucuronosyltransferase (UGT) enzymes in a panel of 130 human hepatic microsomal samples was measured using a liquid chromatography-tandem mass spectrometry-based approach. Simultaneously, ten cytochromes P450 and P450 reductase were also measured, and activity-expression relationships were assessed for comparison. The resulting data sets demonstrated that, with the exception of UGT2B17, 10th to 90th percentiles of UGT expression spanned 3- to 8-fold ranges. These ranges were small relative to ranges of reported mean UGT enzyme expression across different laboratories. We tested correlation of UGT expression with enzymatic activities using selective probe substrates. A high degree of abundance-activity correlation (Spearman's rank correlation coefficient > 0.6) was observed for UGT1As (1A1, 3, 4, 6) and cytochromes P450. In contrast, protein abundance and activity did not correlate strongly for UGT1A9 and UGT2B enzymes (2B4, 7, 10, 15, and 17). Protein abundance was strongly correlated for UGTs 2B7, 2B10, and 2B15. We suggest a number of factors may contribute to these differences including incomplete selectivity of probe substrates, correlated expression of these UGT2B isoforms, and the impact of splice and polymorphic variants on the peptides used in proteomics analysis, and exemplify this in the case of UGT2B10. Extensive correlation analyses identified important criteria for validating the fidelity of proteomics and enzymatic activity approaches for assessing UGT variability, population differences, and ontogenetic changes. SIGNIFICANCE STATEMENT: Protein expression data allow detailed assessment of interindividual variability and enzyme ontogeny. This study has observed that expression and enzyme activity are well correlated for hepatic UGT1A enzymes and cytochromes P450. However, for the UGT2B family, caution is advised when assuming correlation of expression and activity as is often done in physiologically based pharmacokinetic modeling. This can be due to incomplete probe substrate specificities, but may also be related to presence of inactive UGT protein materials and the effect of splicing variations.

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.

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.

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.

Incorporating Ontogeny in Physiologically Based Pharmacokinetic Modeling to Improve Pediatric Drug Development: What We Know About Developmental Changes in Membrane Transporters

Developmental changes in the biological processes involved in the disposition of drugs, such as membrane transporter expression and activity, may alter the drug exposure and clearance in pediatric patients. Physiologically based pharmacokinetic (PBPK) models take these age-dependent changes into account and may be used to predict drug exposure in children. As a result, this mechanistic-based tool has increasingly been applied to improve pediatric drug development. Under the Prescription Drug User Fee Act VI, the US Food and Drug Administration has committed to facilitate the advancement of PBPK modeling in the drug application review process. Yet, significant knowledge gaps on developmental biology still exist, which must be addressed to increase the confidence of prediction. Recently, more data on ontogeny of transporters have emerged and supplied a missing piece of the puzzle. This article highlights the recent findings on the ontogeny of transporters specifically in the intestine, liver, and kidney. It also provides a case study that illustrates the utility of incorporating this information in predicting drug exposure in children using a PBPK approach. Collaborative work has greatly improved the understanding of the interplay between developmental physiology and drug disposition. Such efforts will continue to be needed to address the remaining knowledge gaps to enhance the application of PBPK modeling in drug development for children.

Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.

What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows

The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.

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.

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.