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Received: 30-Sep-2014; Revised: 16-Jan-2015; Accepted: 05-Feb-2015

This article has been accepted for publication and undergone full peer review but has not been through the copyediting,

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Record. Please cite this article as doi: 10.1002/prca.201400147.

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The Role of Quantitative ADME Proteomics to Support Construction of

Physiologically-Based Pharmacokinetic Models for Use in Small

Molecule Drug Development

Aki T. Heikkinen1,a, Floriane Lignet2, Paul Cutler2, Neil Parrott2,* 1 School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland 2 Pharmaceutical Sciences, Pharmaceutical Research & Early Development, Roche Innovation Center

Basel, Basel, Switzerland a Current affiliation: Admescope Ltd, Oulu, Finland

*CORRESPONDING AUTHOR

Neil Parrott

Pharmaceutical Sciences

Roche Pharmaceutical Research and Early Development

Roche Innovation Center Basel

Grenzacherstrasse 124, B70/R130

CH-4070 Basel

Switzerland

Tel: +41 61 68 80813

E-mail: neil_john.parrott@roche.com

List of abbreviations

ABC – ATP-binding cassette; ADME – absorption, distribution, metabolism, and excretion; BCRP –

breast cancer resistance protein; CYP – cytochrome P450; IVIVE – in vitro to in vivo extrapolation;

MDR1 – multidrug resistance protein 1, also known as P-glycoprotein (P-gp); mRNA – messenger

RNA; MRP2 – multidrug resistance associated protein 2; OATP – organic anion transporting

polypeptide; PD – pharmacodynamics; PK – pharmacokinetics; PBPK – physiologically-based

pharmacokinetics; SLC – solute carrier; UGT – uridine 5’-diphospho glucuronosyltransferase;

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Abstract

Pharmacokinetics (PK) refers to the time course of drug concentrations in the body and since

knowledge of PK aids understanding of drug efficacy and safety, numerous PK studies are performed

in animals and humans during the drug development process. In vitro to in vivo extrapolation (IVIVE)

and physiologically-based pharmacokinetic (PBPK) modeling are tools that integrate data from

various in silico, in vitro and in vivo sources to deliver mechanistic quantitative simulations of in vivo

PK. PBPK models are used to predict human PK and to evaluate the effects of intrinsic factors such as

organ dysfunction, age and genetics as well as extrinsic factors such as co-administered drugs. In

recent years the use of PBPK within the industry has greatly increased. However insufficient data on

how the abundance of metabolic enzymes and membrane transporters vary in different human

patient populations and in different species has been a limitation. A major advance is therefore

expected through reliable quantification of the abundance of these proteins in tissues. This review

describes the role of PBPK modelling in drug discovery and development, outlines the assumptions

involved in integrating protein abundance data, and describes the advances made and expected in

determining abundance of relevant proteins through mass spectrometric techniques.

Keywords Absolute quantification, in vitro to in vivo extrapolation, pharmacoproteomics, selected reaction

monitoring, targeted proteomics,

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Physiologically-based models of human pharmacokinetics Therapeutic and adverse effects of a drug are a result of both pharmacokinetics (PK), the fate of the

compound in the body and pharmacodynamics (PD), the biochemical and physiological effects of the

drug on the body. The aim of pharmaceutical development is to establish drugs that are both

effective and safe for their intended use in the patient populations. Thus, a requirement of

pharmaceutical development is to achieve sufficient understanding of the PK and PD behavior of

potential new drugs to allow reliable prediction. As part of that process, mathematical models are

increasingly used [1, 2].

PK is driven by drug absorption, distribution, metabolism and excretion (ADME). After oral

administration dissolution in intestinal fluids and permeation through the gut wall dictate the

absorption kinetics. Distribution is driven primarily by blood flows, permeation across endothelial

tissues and cell membranes and the drugs tendency to partition into tissues. Drug elimination occurs

via metabolism, primarily in the liver but also in other tissues, and via renal excretion into urine and

biliary excretion into the intestine and eventually to feces. Drug metabolism occurs principally

through 2 phases. Phase 1 oxidative metabolism, hydrolysis and reduction is primarily due to

cytochrome P450 enzymes (CYPs), alcohol and aldehyde dehydrogenases (ADH and ALDH) and

aldehyde oxidase (AO). Phase 2 conjugative bioconversion occurs via uridine 5’-diphospho

glucuronosyltransferases (UGT), sulfotransferases (SULT) and glutathione-S-transferase (GST) [1, 2].

In addition to metabolic enzymes, drug transporters are another group of ADME relevant proteins.

Transporters are important when passive diffusion of drugs across membranes is limited and are

expressed in tissues such as intestine, liver, kidney and brain. Transporters play an important role in

drug absorption, distribution, and excretion [3], and also in metabolism since they can regulate the

access of drug to metabolic enzymes within the cells, primarily the hepatocytes. Drug transporters

also play a major role at the blood brain barrier which is of importance for drugs whose

pharmacological site of action lies in the central nervous system and must also be considered for

compounds that cause centrally-mediated adverse effects. Key drug transporter groups include the

solute carrier (SLC) and ATP-binding cassette (ABC) families.

Physiologically-based pharmacokinetic (PBPK) modeling is a technique for simulation of the

concentration time course of synthetic or natural chemical substances in the body of human or

animal species [4]. PBPK models are built upon a structural architecture based on anatomy and

physiology and rely on an understanding of the mechanisms responsible for ADME. For many years,

PBPK modelling has been employed for health risk assessments to ascertain the exposure of the

body to environmental chemicals [5, 6]. More recently, with the emergence of in vitro to in vivo

extrapolation (IVIVE) approaches to predict drug metabolism in vivo, based on in vitro

measurements [7], together with models for predicting the partitioning of drugs between tissues

and plasma [8-11], PBPK has become an integral part of the drug discovery and development

process [12, 13]. The IVIVE approach is well established for CYPs [14] and for the other enzymes,

although less straight forward, it has been described in some studies [15]. IVIVE can also be applied

for active drug transport [16] and positive examples have started to appear in the literature [17-23].

Pharmaceutical PBPK models provide a quantitative mechanistic framework for predicting drug

concentrations in plasma, blood and tissues and, when combined with pharmacokinetic-

pharmacodynamic (PK-PD) models for the drug response, they can be used to link drug dosage to

clinical efficacy and safety. Thus, PBPK can guide the optimization of dosage regimens in various

patient populations [24, 25] and has recently been receiving attention from regulatory agencies as

pharmaceutical companies are including the approach in drug regulatory submissions [26]. Both the

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Food and Drug Administration (FDA) of the U.S. and the European Medicines Agency (EMA) advocate

the use of PBPK modelling [27-30] and have recently organized workshops to discuss best practices.

PBPK models start from a mathematical representation of the physiology of interest in the form of

ordinary differential equations. These equations describe the rates of substance movement between

different body compartments paralleling the flows of body fluids to various organs or tissues as well

as the distribution of compound within tissues and metabolic turnover (Figure 1). PBPK models

include a large number of parameters based on measured anatomical and physiological data to

represent a virtual individual or if parameter distributions are described, a population of individuals.

Numerous compound specific input data are also required to adapt the model to each drug being

simulated. While the physiological or “system specific” parts of the model may be re-used for

description of any drug in the population of interest, many of the compound specific inputs can be

re-used when a given compound is modelled in different systems (e.g. species or populations) [4].

Confidence in both, system and compound-specific parts of a model are increased via verification of

simulations against in vivo measurements of drug concentrations.

A whole body PBPK model is comprised of numerous sub-models, each containing a specific set of

algorithms, typically differential equations, to describe a process or set of processes. This enables

simulations to account for multiple mechanisms simultaneously, for example, the processes of drug-

tissue partitioning and drug metabolism. Sub-models that are particularly reliant on protein

abundance data are those describing the transport of drugs across cell membranes and the

metabolism of drugs within the cell. A well-established approach to predict in vivo metabolism

within a drug discovery setting is to measure the activity in vitro in tissue extracts. Then, assuming

that the activity is determined by the amounts of the protein in the tissue (system specific data) and

the rate at which a given quantity of protein metabolizes or transports the specific drug being

studied, (compound specific data), in vivo metabolism is calculated. Thus, measurements of the

rates of metabolism in vitro can serve as input to PBPK models where they are scaled up to the in

vivo situation based upon the relative amounts of the corresponding protein in vitro and in intact

tissue (IVIVE) [14, 31, 32] (Figure 2). Thereafter, when corresponding experimental in vivo is

available, the model parameters may need to be further optimized in order to improve the match

between simulations and experimental data.

While simulations of pharmacokinetics in an average individual are useful, they do not exploit the

full value of PBPK simulations whereby predictions of variability in a population can be used to

identify potential outliers in terms of drug response and safety. However, incorporation of

variability in the factors which determine ADME processes allows a “bottom-up” prediction of the

expected in vivo variability. Additionally, use of protein abundance as a surrogate for activity permits

use of inter-individual and intra-individual variability in ADME protein abundance to predict inter-

and intra-patient pharmacokinetic differences while a knowledge of the effect of genetic differences

on protein abundance introduces the potential for pharmacogenomic simulations. Another very

important utility of PBPK models is to capture the influence of intrinsic or extrinsic factors and to

predict differences in drug pharmacokinetics in different populations [33]. These populations might

be defined by intrinsic factors such as age, ethnicity or disease and by external factors such as co-

medication or lifestyle (e.g. smoking) [30, 34-37]. In order to build PBPK models for such

populations, detailed and reliable data on differences in ADME protein abundance in each sub-

population are required. The current PBPK models are limited by both the quantity and quality of the

basic protein abundance data available. However this is set to change with the increasing

application of quantitative ADME proteomics.

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Physiologically-based models of animal pharmacokinetics The ultimate goal for model-based drug development would be to perform reliable a priori simulation of expected clinical outcomes by integrating in vitro and in silico drug specific data into a human systems model. To date, such virtual clinical trials remain unachieved. One reason for this is the limitation in current PBPK models due to inadequacies of in vitro tools and incomplete understanding of IVIVE for ADME processes. Therefore, until reliable translation of in vitro data is completely achieved there remains a role for drug specific in vivo animal studies. Although species differences in metabolic enzyme and drug transporter function [38-40] can be viewed as an argument against the general validity of pre-clinical verification of IVIVE-PBPK, there are examples showing that confidence in clinical predictions is increased when IVIVE-PBPK predictions for a drug have been verified pre-clinically with animal in vivo data [41]. Furthermore, animal studies enable the probing of pharmacokinetic mechanisms inaccessible in human. For example, direct measurement of the impact of individual transporters on distribution of drugs through the blood brain barrier is not achieved in human, but has been reported for laboratory animals [42, 43]. Consequently, IVIVE-PBPK in animal species may be valuable as a proof-of-concept for novel technical and theoretical approaches. Additionally, animal testing is mandated by the health authorities as a part of the safety testing of new drugs and pharmaceutical companies still employ pre-clinical species in strategies to understand PK, PD and toxicology. Better characterization of ADME-related proteins in animal species supports a better quantitative understanding of species differences. Such differences affect the translation of the animal PK and safety data to the clinical setting and so better knowledge of ADME protein abundance in animals is valuable to drug development.

The limitations of relative gene expression and protein abundance data for IVIVE-PBPK High throughput measurement of the tissue-specific expression of messenger RNA (mRNA) is possible through the use of reverse transcription polymerase chain reaction (RT-PCR) and since the advent of microarrays, gene expression data can be obtained for thousands of genes simultaneously. In contrast, until recently, the availability of protein abundance data has been more limited. Consequently, use of gene expression (mRNA) as a surrogate of protein abundance has been reported, particularly for transporter proteins. Although some PBPK modelers have successfully used gene expression data to model molecules whose pharmacokinetics are strongly dependent on tissue-specific transporters [44] or to predict the impact of gut transporters on oral drug absorption [45], it must be recognized that mRNA expression can only deliver a relative quantitation between tissues and may not always correlate with protein abundance. Abundance of mRNA transcript is dependent on mRNA synthesis and degradation rates, whereas protein abundance is also determined by translation and protein degradation. Proteomic data now available on the abundances of thousands of proteins suggest that only one to two thirds of the variance in steady-state protein levels is explained by the corresponding mRNA abundance [46]. Specifically for ADME-related proteins, the correlation of protein and mRNA levels is mixed. Wantanabe and co-workers showed an excellent correlation between mRNA expression, CYP3A4 protein level and metabolic activity in 18 human livers [47]. In a more recent study [48], absolute protein abundance levels of drug-metabolizing enzymes and transporters in 17 human liver biopsies were compared with the mRNA expression levels. Protein levels of CYP3A4, CYP2B6, and CYP2C8 were highly correlated with the mRNA levels, whereas for other P450s, for UGTs and for the majority of drug transporters, the correlation was weak. Thus, despite some successful examples, gene expression data cannot serve as a generally reliable surrogate for enzyme and transporter activity and protein quantification data is needed to build reliable PBPK models.

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The majority of recently published ADME proteomics studies have aimed to deliver absolute protein levels. Yet, absolute protein quantification is not imperative for utility in IVIVE and PBPK. IVIVE is based on the relative abundance of ADME proteins in vitro and in vivo and similarly, predictions of intra- and inter-population variability are based on relative ADME protein abundances within or between populations. Nonetheless, availability of absolute protein levels allows bridging the results between studies and has therefore been advocated for the establishment of useful tools for virtual population simulations [49]. A pitfall in this is that accuracy of absolute protein quantification may vary between studies and, furthermore, evaluation of method accuracy has often been overlooked in the published ADME proteomics studies [50]. Thus, an alternative or supplementary approach to enable bridging of relative quantification data between studies would be to link the studies based on similar reference samples quantified in both studies. For example, data on enzyme levels in children relative to adults measured in 2 studies could be pooled if the adult levels were known (or assumed) to be equivalent.

Quantification of proteins in biological samples

Mass spectrometry based quantification of ADME proteins

In recent years the ability to precisely quantitate the expression of proteins, such as those mediating

drug metabolism and transport has advanced steadily. Whilst proteins can be quantified by a

number of methods, including immunoassays and label free proteomics, the advent of techniques

such as selected-reaction monitoring (SRM) has enabled relatively rapid development of highly

quantitative and specific assays in a multiplexed format [50-54].

A detailed critique of SRM is beyond the scope of this review and so for details on the potential and

challenges of SRM the reader is directed to recent publications on the technology in general [55-58]

and on more specific application for ADME proteins [50, 54, 59-62].

The key features of SRM, which make it suitable for quantitation of individual members of protein

families and sub-families, include specificity, which allows discrimination of closely homologous

proteins, the ability to multiplex in a single assay and the flexibility to work with various tissues and

subcellular fractions. For example, the peptide sequences of the members of human UGT1A

subfamily are up to 92% identical [63] and developing isoform specific immunoassays would be

extremely difficult. However, the nature of SRM means that single amino acid differences in

proteotypic peptides can be detected. This enables discrimination of isoforms of the same subfamily

[64, 65] as well as identification of specific polymorphisms in populations [66]. Thus, selection of

proteotypic peptides from sequences that span the polymorphism can be utilized for identification

of its frequency and effect on protein abundance [61]. Often, it is advantageous to determine

multiple proteotypic peptides per protein. However in proteins such as CYPs, the high level of

sequence homology between certain CYPs may mean that, whilst several peptides may be

measureable, most may not be unique to a specific CYP. Indeed, discrimination between close

homologues may need to be based on a single peptide. A related challenge is the reliance on highly

complete and well curated genomic information to enable unequivocal identification. This is a

recognized issue with animal species, such as the dog and minipig, which may have incomplete or

ambiguous genomes [64].

Another important consideration in the development and implementation of SRM assays is the

biological matrix. The primary use of the stable isotope standard may be as an internal single point

calibration for quantitation. However the stable isotope standard is also used to ensure that the

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endogenous peptide elutes with the correct retention time and fragments in the appropriate

manner. As such the standard is used for both quantitative and qualitative assessment. Along with

co-elution with the endogenous analyte the relative ratios of the transitions for a given peptide

should be equivalent between the standards and the analyte. Evidence suggests that interference

from the matrix is not eliminated by use of SRM and care must be taken to establish key assay

parameters in all relevant tissues [52]. Consequently, an assay which is valid in one tissue may not be

suitable for another. Also important for absolute quantification, is consideration of the loss of

material during sample preparation. For example, microsomal recovery has been reported to range

between 10-80% depending on the tissue and cell fractionation protocol used [64, 67, 68].

Approaches to account for this when CYP or UGT enzymes are quantified in microsomal samples are

well established [64, 67, 69], and enable estimation of the abundance of these proteins in intact

tissue. In contrast, both total membrane preparations and plasma membrane fractions have been

used for quantification of transporters [50], but the losses of material during sample preparation

have mostly not been controlled. However, a mathematical framework has been put forward

recently to obtain recovery factors from LC-MS/MS analysis to assess protein losses [70]. Some cell

membrane proteins, including transporters, can be internalized and this may occur during tissue

preparation potentially biasing the analysis [48]. Consequently, it is currently not clear which

biological matrix would be most relevant for transporter protein quantification and how to control

for losses during sample preparation.

One important application of PBPK modeling is to predict inter-individual variability in PK and

variability in the activity of ADME proteins is a key contributor. Various studies have shown high

apparent variability in ADME protein abundance in human tissues [48, 71-74]. However, such

observed variability is not purely biological variability but is also due to technical variation in all steps

downstream from tissue procurement. Furthermore, the study design applied often does not allow

thorough separation of biological and technical variability. Recent studies on metabolic enzyme

abundance in the human [75] and beagle dog [64] livers have included replicates of sample

preparation and protein quantification all the way downstream from intact tissue samples and the

analysis of variances suggested that 10–70% of the observed variability may be attributed to

technical, rather than biological causes, thus underscoring the importance of replicating sample

preparation in order to obtain more realistic estimates of biological variability. In addition to analysis

of variability in individual proteins, an analysis of the co-variation between proteins is important to

allow generation of realistic virtual populations [76]. Proteomic analysis of several ADME proteins

from the same donors enables such analysis of covariate relationships and apparent correlations

have been reported recently [71]. However, the data reported on metabolic enzyme abundance in

beagle dog liver [64] suggests that such apparent correlation may be in part the result of technical

co-variability (Figure 3). Thus these data may not represent biologically meaningful co-variation in

protein abundance. This once more stresses the importance of thorough experimental and statistical

separation of technical and biological sources of variability.

The use of standards in SRM approaches is critical for the quantitative performance of the assay and

the reproducibility both within lab and between labs [77, 78]. Most often ADME proteomics have

used stable isotope labeled (SIL) peptides, but synthetic ´concatamers´ (QconCAT) [71] and whole

proteins stable isotope labeled by amino acids in cell culture (SILAC) [79, 80], have also been

employed. Absolute SILAC quantification is not biased by incomplete protein digestion or peptide

degradation during digestion [81], provided that the behavior of native and labelled proteins is

equivalent. On the other hand QconCAT may be biased by differences in digestion of synthetic

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concatamer and native protein [82], while SIL may be biased by incomplete digestion of the protein

and degradation of peptides during digestion [83]. CYP and UGT enzymes contain only one or two

transmembrane helices [84-86] and the bulk of the protein is soluble so that digestion should tend

to be more complete and reproducible than in the case of transporter proteins which contain

multiple transmembrane helices. However, studies on transporters in the human liver suggest that

when denaturation and digestion methods are adequately optimized, quantification of surrogate

peptides can yield accurate protein quantification even in the case of transporters [79, 80].

Nonetheless, it must be emphasized that as long as the availability of whole protein standards of a

known concentration is lacking, LCMS/MS assays cannot be fully validated. Thus, although precise

relative quantification may be achieved, absolute quantification results must be treated with caution

[50].

The high cost of accurate peptide standards may make economic rather than technical issues the

limiting factor when a high degree of multiplexing is required. The advent qualitatively defined but

not purified or accurately quantified stable isotope peptide libraries has recently enabled highly

multiplexed SRM assays delivering precise relative quantitation [64]. Given the cost issues and the

uncertainty related to accuracy of absolute protein quantification, precise relative quantification in

combination with sample sharing to enable bridging between studies might be an alternative

approach.

Examples of the utility of protein abundance data for IVIVE and PBPK Hepatic elimination is a significant determinant of pharmacokinetics and it is not surprising that the

majority of LC-MS/MS-based proteomic studies characterizing enzyme and transporter abundances

have done so in the human liver [48, 53, 62, 71-74, 79, 87-97]. Furthermore, other human tissues

important for PK, namely intestine [52, 65, 98], kidneys [65], lungs [99] and the blood brain barrier

[100] have received attention as well as various tissues in animal species [51, 64, 101-104]. A

significant part of the existing data on CYP abundance is based on immunochemical protein

quantification methods and consequently applications for IVIVE and PBPK have employed these

data. In contrast, protein abundance data for other enzyme families, such as UGTs, and transporters

are primarily based on mass spectrometry quantification, although in many studies quantitative PK

prediction has not been directly evaluated.

Linear correlations demonstrated between metabolism or transport of specific probes and

corresponding protein abundance in vitro [48, 71, 101, 105] confirm that protein concentration is a

determinant of activity. However, it is well recognized that enzyme and transporter activity is also

affected by other factors such as accessory proteins, energy sources, co-factors and the specific

membrane and aqueous environments of the protein [106, 107]. This is of particular importance for

development and validation of in vitro models in which the protein function should mimic the

protein function in vivo and thus enable protein abundance based IVIVE. Furthermore, it has been

shown that additional scaling factors are sometimes required to correct for discrepancies between

enzyme activities in vitro and in vivo [108-110].

In spite of these potential complications several successful examples of protein abundance based in

vitro to in vivo extrapolations have been published, as covered in the following section.

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CYP metabolism CYP enzymes are responsible for the elimination of the majority of licensed drugs and CYP abundance data in human liver has been successfully used to predict hepatic elimination and drug-drug interactions due to inhibition and induction of these enzymes [111-113]. In addition, CYP3A4 abundance data in the liver and gut wall has been successfully exploited for the extrapolation of gut wall metabolism of CYP3A substrates from in vitro data generated in human liver microsomes [114-116]. This illustrates that enzyme abundance accompanied by activity measured in a single in vitro system can be used for prediction of turnover in various tissues. Similarly, abundances of CYP3A12 and CYP2B11 in the dog intestine and liver were successful in predicting intestinal metabolism and hepatic elimination in vivo [32, 101]. The possibility to predict drug pharmacokinetics in populations rather than simply an ‘average’ individual rests on the accurate characterization of the variability and co-variance of model parameters, including those for enzyme and transporter protein abundance. Therefore, it is critical to establish not only wild-type protein abundance but also the frequency and abundance of proteins possessing variant alleles. Furthermore, not all polymorphisms affect protein abundance but may affect function [117] which needs to be considered when generating those individuals in a PBPK population [76]. Understanding the variability within populations and between sub-populations is of major importance as lower than expected exposure may result in loss of drug efficacy whereas higher than expected exposure increases the risk for adverse effects. CYP2D6 is responsible for the metabolism and elimination of approximately 25% of clinically used drugs and shows the largest phenotypic variability among the CYPs, largely due to genetic polymorphism [118]. Vieira et al [119] simulated the effects of genetic polymorphism on the pharmacokinetics for four drugs that are eliminated by both CYP3A4 and CYP2D6. Another study by Dickinson et al [120] used protein abundances for the different genotypes of CYP2C9 for IVIVE of the metabolism of (S)-warfarin and applied PBPK modelling to understand differences in the pharmacokinetics and pharmacodynamics. Yet, another study by Rowland Yeo and co-workers [117], incorporated data on the frequency of the different allelic forms of CYP2C8 into a PBPK model and then predicted differences in the relative exposure of rosiglitazone according to CYP2C8*3 genotype. In this case it is interesting to note that the polymorphism affected the metabolic capacity of the enzyme rather than protein abundance. Therefore, in vitro data on the metabolic turnover of rosiglitazone in microsomes produced from the different phenotypes were necessary for reliable simulations. The differences in pharmacokinetics between populations can be attributed in-part to differences in CYP abundance. For example, predictions of clearance in Japanese and Caucasians [121] were made based on enzyme abundances. More recently, Barter et al. [34] integrated specific abundances and phenotypic frequencies associated with several CYPs in Caucasian and Chinese population models and were able to simulate observed population differences in the clearance of several reference drugs. In a further study, focusing on pediatric PBPK modelling, Johnson et al [36], compiled information on growth and maturation of organ size and function, including abundance of various CYP enzymes, in neonates, infants and children and showed that PBPK modeling resulted in better predictions than simple allometry (scaling of PK parameters based on body size). This was especially true in children <2 years old where enzyme levels change more rapidly and opens the opportunity to use simulations to make clinical studies in this patient population ´confirmatory´ rather than ´exploratory´. Examples of population PBPK models leveraging data on CYP protein abundance are expected to increase as more and better CYP abundance data become available and with the recent application of multiplexed targeted proteomics to the UGT enzymes [71, 122] we can expect similar examples to emerge for drugs metabolized by UGTs.

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Active transport Absorption, distribution and excretion of compounds with poor passive membrane permeability is often significantly affected by active transport. PBPK modeling of such compounds has been limited by insufficient information on protein abundance and the lack of verified methods to translate in vitro measurements of transport to the in vivo situation [115, 116, 123]. This limits the predictive utility of the approach since in vivo data is needed to allow optimization of empirical scaling factors [17]. However, with increasing data available on transporter abundance in tissues, some examples of quantitative transporter IVIVE and PBPK are emerging. Li et al [94] demonstrated that drug transporter abundance is altered in cultured rat hepatocytes compared to native liver tissue and provided evidence that incorporation of transporter protein abundance data improves correlation between predicted and observed biliary excretion in vivo. Others have established transporter expressing in vitro tools and a PBPK model, incorporating protein abundance data, to predict the contribution of active transporters to hepatobiliary disposition and thus to assess potential for transporter-based drug-drug interactions due to inhibition [23, 124]. Furthermore, PBPK modeling studies on the transporter mediated drug-drug interactions of pravastatin by Varma et al [22], and the following discussion in the literature [125, 126] have reached the conclusion that while transporter abundance data has utility for quantitative prediction, the relationship between abundance and activity is not always straight forward. Thus, some transporters still requiring empirically fitted scaling factors. In this case, scaling factors for two transporters had to be simultaneously estimated by fitting the model to the plasma concentration-time profile of a single dose of pravastatin, all other model parameters being fixed. Organic anion transporting polypeptide 1B1 (OATP1B1), an SLC family member, is involved in hepatic transport of a large range of drugs. A study by Prasad et al [73] demonstrated that abundance in human liver is genotype dependent and showed by PBPK modelling that differences in protein abundance between wildtype and 388GG genotypes are predictive of the in vivo genotypic differences in repaglinide and rosuvastatin PK. Furthermore, other genotype dependent differences in OATP1B1 protein abundance were reported and applied to predict the expected PK. Again the power of such predictions is that they allow clinical studies to be ´confirmatory´ rather than ´exploratory´ in nature. The group of Tetsuya Terasaki (Tohoku University, Sendai, Japan) [42, 43, 100, 102, 103] have established the abundance of various drug transporters in the blood brain barrier of mouse, cynomolgus monkey, common marmoset and human. The utility of such data has been established and validated in mouse and cynomolgus monkey, with a model to predict the steady state brain to plasma drug concentration ratio for P-glycoprotein (P-gp/MDR1) substrates. Their model incorporates transporter abundance and drug specific in vitro measurements of transporter activity and tissue binding. However, there is room for further development as this model is limited to steady state concentrations and is not able to predict kinetics of drug uptake to and elimination from the brain.

The future. What is needed and where can we expect to see progress within the next 5 years? One of the strengths of PBPK modeling is that it facilitates separation of drug and system-related parameters including the levels of ADME-related proteins. Thus, PBPK facilitates consistent sharing of system knowledge across multiple drug development projects. To fully leverage this capability for drug development, including replacement of clinical studies, stringently verified systems libraries need to be established to capture the impact of variability in protein abundance in different patient populations. Thus, there is a need to strengthen the models that are currently available to address ethnic variations [34], hepatic impairment [35], renal impairment [127], cancer [128] and obesity [129] and to expand these models to cover other diseases and populations. Furthermore, prediction of the variability in PK based on variability in ADME protein abundance is still very limited [36, 75]

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and needs to be better established. Better systems models for prediction of PK variability in different patient populations will require incorporation of proteomics data and verification via top down approaches based on clinical datasets [130]. A large amount of consistent, high quality proteomics data is needed to build such models and this needs to be collected from numerous patients. Thus, access to patient samples is critical and it is essential that meta-analysis can be applied to build up a consistent and verified systems platform. This can only be facilitated through a reliable means to compare data between studies. Recent achievements in MS based ADME proteomics and successful applications of the data in PBPK clearly show the possibilities but there is much room for improvement. Accepted and established standards are needed to allow comparison of absolute protein abundance data across studies. This is of major importance as there is considerable variability between laboratories in the phenotypes of some of the in vitro systems used, which makes it essential to establish distinct IVIVE scaling factors for each laboratory. As quality proteomics data are generated and published to characterize patient populations in terms of differences in ADME proteins and their variability systems models will improve. However if this is to be achieved then certain recommendations need to be followed to enable pooling of data from various sources:

1. Losses of material during sample preparation should be quantified and reported thoroughly. This has been a common practice in quantification of microsomal metabolic enzymes [64, 67, 69, 101], but generally has not been done in transporter quantification studies [70].

2. Methodology applied must be reported transparently in every detail 3. Quantification results in individual samples should be published as data supplements 4. Sample processing should be replicated in order to enable separation of technical and

biological variability. 5. A set of tissue samples should be made available to allow for direct comparison and analysis

of methodological differences between laboratories and enabling normalization of results between studies.

In addition to advances in ADME proteomics, parallel development of relevant in vitro assays and continuous verification of IVIVE approaches and PBPK models utilizing the proteomics data is required.

Acknowledgement Preparation of this review has been supported by the Roche Postdoc Fellowship (RPF) program (ATH

and FL).

The authors have declared no conflict of interest.

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Figure 1. Schematic of a physiologically-based pharmacokinetic (PBPK) model.

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Figure 2. General protocol for in vitro to in vivo extrapolation (IVIVE). Vmax and Km,u are the maximum transport/metabolism rate and the unbound substrate concentration required for 50% of Vmax, respectively. kcat and [E] are turnover number and enzyme/transporter abundance, respectively.

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Figure 3. Apparent correlation of CYP3A12 and CYP3A26 abundance in beagle dog liver microsomes. Three batches of microsomes per donor were prepared from 14 liver donors and CYP protein abundance in each batch was quantified using SRM. Panel A shows the abundance of CYP3A12 and CYP3A26 in individual samples and suggests correlation between the two. In the panel B the samples from the same donor are connected with lines to illustrate that apparent correlation is substantially contributed by positive technical covariance. Data from [64].

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