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Resolving the Distribution−Metabolism Interplay of Eight OATP Substrates in the Standard Clearance Assay with Suspended Human Cryopreserved Hepatocytes Par̈ Nordell, Susanne Winiwarter, and Constanze Hilgendorf* Drug Safety and Metabolism, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE-431 83 Mölndal, Sweden S Supporting Information *

ABSTRACT: Uptake transporters may act to elevate the intrahepatic exposure of drugs, impacting the route and rate of elimination, as well as the drug−drug interaction potential. We have here extended the assessment of metabolic drug stability in a standard human hepatocyte incubation to allow for elucidation of the distribution−metabolism interplay established for substrates of drug transporters. Cellular concentration−time profiles were obtained from incubations of eight known OATP substrates at 1 μM, each for two different 10-donor batches of suspended cryopreserved human hepatocytes. The kinetic data sets were analyzed using a mechanistic mathematical model that allowed for separate estimation of active uptake, bidirectional diffusion, metabolism and nonspecific extracellular and intracellular binding. The range of intrinsic clearances attributed to active uptake, diffusion and metabolism of the test set spanned more than 2 orders of magnitude each, with median values of 18, 5.3, and 0.5 μL/min/106 cells, respectively. This is to be compared with the values for the apparent clearance from the incubations, which only spanned 1 order of magnitude with a median of 2.6 μL/min/106 cells. The parameter estimates of the two pooled 10-donor hepatocyte batches investigated displayed only small differences in contrast to the variability associated with use of cells from individual donors reported in the literature. The active contribution to the total cellular uptake ranged from 55% (glyburide) to 96% (rosuvastatin), with an unbound intra-to-extracellular concentration ratio at steady state of 2.1 and 17, respectively. Principal component analysis showed that the parameter estimates of the investigated compounds were largely influenced by lipophilicity. Active cellular uptake in hepatocytes was furthermore correlated to pure OATP1B1-mediated uptake as measured in a transfected cell system. The presented approach enables the assessment of the key pathways regulating hepatic disposition of transporter and enzyme substrates from one single, reproducible and generally accessible human in vitro system. KEYWORDS: drug transporters, modeling, statins, kinetics, uptake, hepatocytes

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amphiphilic xenobiotics.11,12 The OATP-mediated transport contributes for instance to the hepatoselectivity of HMG-CoA reductase inhibitors (“statins”).13−16 A dependency on active uptake is, however, also associated with a certain susceptibility to drug−drug interactions and variability caused by genetic polymorphisms, with the particular attention of regulatory authorities.17 With optimization strategies to improve metabolic stability becoming more successful, distribution mediated by transport processes will continue to have an increasing impact on elimination, emphasizing the need to assess transport characteristics early on in drug discovery.18 Ideally, such characterization would comprise the drug clearance processes attributed to transporters and metabolic enzymes in the same in vitro study, and additionally allow for a reproducible use, thus facilitating optimization during the discovery phase.

INTRODUCTION Isolated human hepatocytes represent a physiologically relevant experimental platform for the evaluation of liver-related metabolism, toxicity and drug−drug interactions, of wide use in the preclinical phase of drug discovery programs.1 In nearly all new chemical entities’ characterization the in vitro measure of the metabolic stability obtained from hepatocyte incubations is a key component in the prediction of the pharmacokinetic profile.2−6 For the majority of oral drugs, passive membrane permeability is sufficient to quickly equilibrate the unbound extra- and intracellular concentrations of drug. As a result the apparent clearance from an in vitro incubation will approximate the intrinsic metabolic clearance driven by the unbound concentration of the substrate at the site of the enzymatic process.7 However, it is now widely recognized that drug uptake mediated by membrane-associated transporter proteins can have clinically relevant effects on drug disposition in vivo as well as at the cellular level in vitro.8−10 Members of the organic anion transporter polypeptide (OATP) family have been identified as important uptake transporters expressed in the liver, with a substrate profile that covers a broad range of © 2013 American Chemical Society

Received: Revised: Accepted: Published: 4443

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uptake clearance varied more than 6-fold between the donors investigated. In the present study, pooled cryopreserved hepatocytes were selected, as to gain a general understanding independent from interindividual differences. This paradigm made pooled hepatocytes the in vitro system most widely applied throughout drug discovery programs that allow for reproducibility in the assessment of metabolic properties of new chemical entities. We analyzed the apparent depletion of eight OATP substrates with different degrees of metabolism in two batches of pooled human cryopreserved hepatocytes in suspension. A mechanistic kinetic model, similar to that applied by Paine and coworkers,29 enabled the extraction of the intrinsic processes passive diffusion, active uptake and metabolism. This model allowed for detailed understanding of underlying mechanisms critical to the identification of the key pathways regulating hepatic disposition of transporter and enzyme substrates from one single, generally accessible human in vitro system.

The principal manner in which active transport affects the clearance from a conventional hepatocyte incubation is well illustrated by decomposing the apparent clearance viewed from the unbound medium concentration CLmed into intrinsic processes at steady-state conditions.19−21 CLmed = CL int ,met ×

CL int ,up + CL int ,diff CL int ,diff + CL int ,eff + CL int ,met (1)

where CLint,up, CLint,diff, CLint,eff and CLint,met are the intrinsic clearances of active uptake, passive diffusion, active efflux and metabolism, respectively. While biliary excretion is one of the primary mechanisms of elimination in vivo, it is not clear to what extent the function of efflux transporters is retained after isolation.22−24 In Figure 1 the apparent clearance from the

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EXPERIMENTAL SECTION Chemicals. Valsartan, pravastatin, bosentan, atorvastatin and glyburide were obtained as 10 mM DMSO stock solutions from the AstraZeneca Compound Management Team in Mö lndal, Sweden, while rosuvastatin, fexofenadine and pitavastatin were obtained in solid form from Chemtronica, Sweden. The hepatocyte suspension medium (HSM) was prepared by supplementing Williams Medium E (Sigma-Aldrich Research, St. Louis, MO, USA) with 25 mM HEPES and 2 mM L-glutamine (pH 7.40). Mineral and silicon oil (Sigma-Aldrich Research, St. Louis, MO, USA) were mixed to give a final density of 1.015 g/mL for use in the oil-spin experiments. Assessment of Hepatocyte Concentration−Time Profiles Using an Oil-Spin Procedure. The two batches (UMJ and IRK) of pooled human cryopreserved hepatocytes were obtained from Celsis In Vitro Technologies (Brussels, Belgium). Beckman 0.5 mL microtubes (Fisher Scientific GTF AB, Sweden) were prepared ahead of incubation start by addition of 15 μL of 4% cesium chloride (CsCl) and 140 μL of the oil mixture followed by spinning at 4000g for 2 min. Compound DMSO stocks were on the day of each experiment diluted in HSM to 2 μM (0.1% DMSO). Cryopreserved hepatocytes were thawed and resuspended in HSM according to the manufacturer’s recommended procedures. Cell density and viability (consistently >82%) were determined using a CASY cell counter (Innovatis AG, Germany). The cell suspension was diluted to 3.2 million cells/mL, and experiments were started by mixing equal volumes of prewarmed compound and cell solutions in a glass vial to yield 1 μM drug and 1.6 × 106 cells/mL in the final incubation. The temperature was controlled using a water bath set to 50 rpm (linear shaking) at 37 °C. At selected time points (typically 15, 30, 45 s, 1, 2, 3, 5, 15, 30, 60, 90, and 120 min) a 100 μL aliquot was removed from the incubation, dispensed into a microtube prepared with oil and immediately centrifuged at 7000g for 15 s to separate cells from medium using a benchtop Eppendorf MiniSpin centrifuge equipped with the appropriate rotor/adapters. Control incubations at 4 °C were performed for each compound to account for the passive processes, diffusion and nonspecific binding. Medium samples were immediately taken from the supernatant above the oil layer, whereafter the tubes containing the cell pellets were directly put on dry ice. Cell samples were prepared for analysis by cutting frozen tube tips containing the cell pellets below the oil/CsCl interface into a

Figure 1. CLmed surfaces calculated from eq 1, varying CLint,up and CLint,diff from 0.1 to 100 μL/min/106 cells (CLint,eff = 0) at three CLint,met levels (sheets from top to bottom represent CLint,met = 10, 1, and 0.1 μL/min/106 cells).

medium is calculated from eq 1 (neglecting active efflux). From such an illustration it is obvious how the same observed clearance may have a diverse mechanistic origin, resulting from the interplay of active uptake, diffusion and metabolism. To be able to define an in vitro kinetic profile of actively transported drugs according to eq 1 the approach typically comprises (1) experiments in which the cellular and media fractions are separated and (2) analysis based on physiologically inspired mechanistic models that decompose flux between the extra- and intracellular compartment into a unidirectional transporter-driven and a bidirectional passive part.25−28 The concepts were effectively applied by Paine et al.,29 who studied the overall kinetics of atorvastatin, cerivastatin and indomethacin in rat hepatocyte suspensions and described the extrapolation of data to the in vivo pharmacokinetics in the rat. With a larger set of 16 known transporter substrates Yabe et al. developed a saturable uptake model to investigate the active uptake phase in suspended rat hepatocytes.30 Reported from the same lab, a comprehensive evaluation of the uptake kinetics for 7 OATP substrates with plated rat31 and plated human cryopreserved hepatocytes32 gave a mechanistic background to interspecies discrepancy observed on the transporter level. Importantly, the latter study highlighted how human interdonor variability in uptake transporter activity may compromise the predictive value of the in vitro estimates: for rosuvastatin, relying extensively on carrier mediation, active 4444

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96-well plate. 200 μL of methanol containing warfarin as a volume marker was added to each well, and the plate was mixed at room temperature using a plate shaker for 1 h. After 100 μL of water had been added to each well, the plate was centrifuged at 4000g at 4 °C for 20 min. The supernatants were, together with samples from the medium fraction, diluted to attain the same methanol concentration, and finally analyzed by a LC/ MS/MS system consisting of an HTS PAL injector (CTC Analytics, Zwingen, Switzerland), HP 1100 LC binary pump (Agilent Technology, Germany) equipped with a reversedphase C18 Atlantis T3 column (Waters Corp., USA) and triple quadrupole mass spectrometer API4000 (Applied Biosystems/ MDS Sciex, Canada). For each combination of drug and hepatocyte batch (UMJ and IRK), data was collected from duplicate incubations at 37 °C and 4 °C run in parallel at the same test occasion. Mechanistic Mathematical Model To Elucidate the Underlying Processes of Drug Distribution, Binding and Metabolism. The sets of kinetic data from the two batches of cryopreserved hepatocytes were evaluated in terms of the unsaturable mechanistic model as outlined in Figure 2. In the

[D]cell,u = [D]cell,tot × fucell

Equations 4 and 5 define the change of the unbound drug concentration in the cell (of volume Vcell) and the medium (of volume Vmed) compartments, respectively, with time: d[D]cell,u dt

d[D]med,u dt

[D]med,u Vcell

− (CLout + CL int ,met) ×

[D]cell,u Vcell

= −CL in ×

[D]med,u Vmed

+ CLout ×

[D]cell,u Vmed

(5)

where CLin and CLout represent the sum of clearances associated with drug transport from the medium into the cells and from the cells to the medium, respectively, and CLint,met is the intrinsic clearance of drug due to metabolism in the cellular compartment (Figure 2). At steady-state conditions, when [D]cell,u/dt = 0 (eq 4), the unbound cell-to-medium concentration ratio [D]cell,u/[D]med,u, described by the partition coefficient Kp,uu, can be calculated from the clearance estimates:19,20 [D]cell,u [D]med,u

=

CLout

CL in + CL int ,met

(6)

In a general description of transport in the system, CLin and CLout would both comprise an active and a passive component. However, since the localization of canalicular drug transport proteins may not be retained in the suspended hepatocyte model, active efflux from the cellular compartment to the medium is assumed to be limited.24 If CLint,up and CLint,diff describe intrinsic clearance due to active uptake and bidirectional passive diffusion, respectively, CLin and CLout are then given by

Figure 2. Schematic representation of the proposed mechanistic model comprising a medium, a cellular compartment and an outer cell membrane compartment. Transport of drug from the medium into the cell and out from the cell to the medium is described by CLin and CLout, respectively. Kmem and fucell describe binding to the outer cell membrane and intracellular binding, respectively. The cellular fractions collected experimentally include the cellular and the membrane model compartments (shaded area).

CL in = CL int ,diff + CL int ,up

(7)

CLout = CL int ,diff

(8)

Taking into account extracellular (eq 2) and intracellular (eq 3) binding, numerical integration of eqs 4 and 5 gives the concentration profiles of the cellular and the medium compartments. Using the nonlinear least-squares solver of the commercial software package Matlab 7.12 (MathWorks Inc., Natick, MA, 2011), profiles were simultaneously fitted to data measured at 37 and 4 °C data from both the UMJ and the IRK batches of cells. At 4 °C processes associated with transporter or enzymatic activity, CLint,up and CLint,met, were considered inactivated. The fitting procedure allowed the values of these two adjustable parameters, CLint,up and CLint,met, to be varied individually with batch and temperature, while parameters describing passive diffusion and binding (CLint,diff, Kmem and fucell) were restricted to one common estimate throughout all four sets of data. It was observed that medium-loss data showed larger variability than data from the cellular fraction, in particular for the low turnover drugs. Medium data was therefore excluded from the model fitting procedure and only used as an additional control of the mass balance. Also, only the 4 °C data obtained at approximate steady-state conditions (here generally regarded at >60 min after incubation start) were used in the parameter fit. The structural identifiability of model parameters was confirmed using the Matlab-based toolbox GenSSI (Generating Series Approach for Testing Structural Identifiability).33

model, the incubation volume is divided into three compartments, a medium, an intracellular compartment and an outer membrane compartment, similar to the model used by Paine and co-workers.29 The drug quantified from the cellular fraction was considered to include drug in the intracellular and the membrane compartments. Extracellular binding was assumed to be dominated by drug associating with the outer surface of the cellular membrane, described as a rapid equilibrium established between unbound drug in the medium (Dmed,u) and drug in the membrane compartment (Dmem): [D]mem [D]med,u × [M]

= CL in ×

(4)

K p,uu =

K mem =

(3)

(2)

where [M] denotes the concentration of membrane with respect to the medium volume of unit million cells/mL and Kmem the membrane association constant. If fucell is the fraction of unbound drug inside the hepatocytes, the unbound intracellular concentration [D]cell,u is related to the total intracellular concentration [D]cell,tot by eq 3: 4445

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Figure 3. (Top) Cellular amount (intracellular + membrane bound) of transporter substrates rosuvastatin, pitavastatin and glyburide in the 100 μL samples collected from 1 μM incubations at 1.6 million cells/mL. Black symbols indicate experimentally observed values at 37 °C for the UMJ (△) and the IRK (▽) batches, for which the model best-fit simulated profiles are represented by the solid and the dashed line, respectively. Gray symbols represent observed values at 4 °C, with the fitted steady-state amount given by the dotted line. Data for each batch and temperature were obtained from two parallel incubations. (Bottom) Simulated total (solid) and unbound (dashed) intracellular concentration at 1 million cells/mL plotted together with the medium concentration profile (dotted) for the UMJ batch.

Table 1. Best-Fit Parameter Estimates for the Set of Transporter Substrates, Ordered from Low to High Lipophilicity (SE in Parentheses)a

drug

LogD7.4b

valsartan

−2.1

pravastatin

−0.77

rosuvastatin

−0.44

fexofenadine

0.47

pitavastatin

0.91

bosentan

1.0

atorvastatin

1.1

glyburide

2.2

CLint,up (μL/ min/106 cells)

CLint,diff (μL/ min/106 cells)

CLint,met (μL/ min/106 cells)

4.6 (0.16) 4.9 (0.17) 3.1 (0.21) 4.0 (0.26) 12 (0.49) 7.8 (0.42) 0.75 (0.067) 0.65 (0.086) 150 (11) 120 (9.2) 32 (3.9) 24 (3.9) 61 (6.8) 72 (7.8) 130 (17) 120 (17)

0.42 (0.055)

0.15 (0.031) 0.10 (0.022) 0.37 (0.056) 0.78 (0.074) 0.11 (0.022) 0.086 (0.023) 0d 0d 1.1 (0.16) 1.2 (0.17) 1.0 (0.15) 0.85 (0.20) 0.62 (0.15) 0.29 (0.12) 8.9 (0.69) 5.6 (0.43)

0.70 (0.13) 0.53 (0.10) 0.24 (0.029) 13 (2.1) 9.9 (2.1) 13 (2.5) 100 (12)

fucell

Kmem (mL/106 cells)

Kp/Kp,uu

CLinc (μL/ min/ 106 cells)

0.45 (0.039) 0.87 (0.057) 0.53 (0.098) 0.46 (0.046) 0.058 (0.0074) 0.041 (0.0038) 0.037 (0.0045) 0.024 (0.0011)

7.0 × 10−3 (0.43 × 10−3) 5.1 × 10−3 (0.3 × 10−3) 4.8 × 10−3 (0.95 × 10−3) 7.0 × 10−3 (0.40 × 10−3) 0.056 (0.0098) 0.096 (0.0075) 0.063 (0.011) 0.084 (0.012)

19/9.0 23/10 4.1/3.5 3.7/3.2 38/20 26/14 8.8/4.2 7.8/3.7 190/11 150/8.9 93/3.8 77/3.2 140/5.4 170/6.3 86/2.1 87/2.1

1.9 1.4 2.0 3.9 2.8 1.6 0 0 8.8 8.5 3.7 2.6 2.7 1.3 18 11

f up.active (%) 92 92 81 85 96 94 76 73 92 90 76 71 82 84 56 55

OATP1B1c (pmol/min/ mg protein) 12 (1.4) 2.9 (0.55) 4.7 (0.40) 0.20 (0.092) 64 (2.0) 14 (1.0) 30 (1.6) 15 (1.0)

a For parameters allowed to be individually varied (CLint,up and CLint,met) obtained values are given for both the UMJ (top) and the IRK (bottom) batches of cryopreserved hepatocytes. bExperimental values for logD7.4 were taken from the AstraZeneca internal database. cRate of OATP1B1mediated uptake determined 2 min after incubation start. dCLint,diff set to 0 during fitting procedure.

Assessment of Drug Uptake into OATP1B1-Expressing HEK-293 Cells. Stably transfected OATP1B1-expressing and empty vector (Mock) transfected HEK-293 cells were obtained from the AZ-cell bank. The cell line has been generated in-house and validated as published previously.36 Cells were cultured in Dulbecco’s modified Eagle medium (DMEM, Sigma-Aldrich Research, St. Louis, MO, USA), supplemented with 10% fetal bovine serum, 4 mM L-glutamine, and 1 mg/mL Geneticin. Transport studies were performed on cell monolayers in Hank’s balanced salt solution (HBSS, Gibco, Life Technologies Europe) set to pH = 7.40 in poly-D-lysine

Concentration profiles were calculated based on the assumption of a cellular volume Vcell of 4.0 μL/106 cells.34 The variances of parameter estimates were obtained from the Jacobian evaluated at the point estimates of the parameters.35 The sum of simulated concentration−time profiles in the medium, cell and membrane compartments at 1 million cells/ mL represents a total depletion curve typically obtained in conventional hepatocyte incubations, from which an estimate of the clearance from the incubation (CLinc) can be calculated by linear regression. 4446

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coated 24-well plates (Becton Dickinson labware, U.K.) 72 h after seeding. In brief, after the culture medium was removed, cells were washed twice followed by 10 min preincubation in HBSS. Experiments were started by addition of substrate solutions diluted from 10 mM DMSO stocks in HBSS to give a final concentration of 1 μM. At selected time points (0.5, 1, 2, 5, and 10 min) the solution was removed completely and the cells were lysed using a 50% acetonitrile solution. After mixing and centrifugation sample supernatants were analyzed by the LC/MS/MS system described above and quantified from a standard curve. Each sample was run in triplicate. Concentrations were normalized against total protein determined using Pierce BCA Protein Assay Kit (Thermo Fisher Scientific Inc., USA). The OATP1B1-mediated uptake rate was at each time point determined from (amount (OATP1B1) − amount (Mock))/time. Analysis of Known Molecular Properties, Model Parameters and OATP1B1-Mediated Uptake of Investigated Drugs by Principal Component Analysis (PCA). A principal component analysis of the resulting parameters was performed using SIMCA-P+ 12.0.1 (Umetrics AB, Umeå, Sweden). All CL and OATP1B1 transporter values were logarithmized. Values for logD7.4 and conventional CLint in human liver microsomes (HLM) were taken from the AstraZeneca internal database. Regression analysis was performed to assess the linear relation between logD7.4 and parameters of interest.

Figure 4. Distribution of fitted model clearance parameters CLint,up, CLint,diff and CLint,met as well as the estimated apparent clearance parameter CLinc. Dotted lines indicate the median value for each parameter set. Coding (increasing logD7.4): 1 = valsartan, 2 = pravastatin, 3 = rosuvastatin, 4 = fexofenadine, 5 = pitavastatin, 6 = bosentan, 7 = atorvastatin, 8 = glyburide for the UMJ (open symbols) and the IRK batch (filled symbols). For CLint,diff, the two batches share a common estimate (represented by open symbols).

individually set parameters describing transporter and metabolic activity: CLint,up estimates for the test compounds are within a factor of 1.3, whereas CLint,met estimates are slightly more variable (within a factor of 2.1). Accordingly, the uptake and metabolic descriptors agree in general well between the two batches, and a batch-related difference was not indicated by a paired Student’s t test at significance level 0.05. The fraction of the total uptake that is attributed to CLint,up is >90% for valsartan, rosuvastatin and pitavastatin (Table 1), illustrating the importance of this route for the internalization. For the more lipophilic glyburide, associated with higher passive permeability, just about 50% of the total uptake is due to an active process, even though the active uptake CLint,up of glyburide is one of the highest in the data set. Assessment of Cell-to-Medium Concentration Ratios and the Apparent Metabolic Clearance from the Incubation. Log-transformed medium and intracellular concentration simulated for rosuvastatin, pitavastatin and glyburide over time at 1 million cells/mL are displayed in Figure 3 (bottom). The cell-to-medium concentration ratios at steady state, described by the distribution parameters Kp and Kp,uu in Table 1, can be calculated from eq 6. Kp given by the mean total (bound and unbound) concentration ratio for the UMJ and the IRK batch, varies between approximately 4 (pravastatin) and 170 (pitavastatin) (Table 1). The mean unbound concentration ratio Kp,uu, which in contrast to Kp is independent of the binding properties, varies instead between approximately 2 (glyburide) and 17 (rosuvastatin). In conventional assessment of metabolic clearance the cells and medium are homogeneously sampled over time at a typical hepatocyte concentration of 1 million cells/mL. An estimate of the apparent clearance of drug obtained from such a standard assay (CLinc) can be obtained by analyzing the simulated depletion of drug from the whole incubation (cells, medium and membrane compartments) at 1 million cells/mL. Since all drugs in the set exhibit Kp,uu ratios above 1, estimated CLinc values are generally larger than CLint,met values: simulated CLinc displayed a median value of 2.6 μL/min/106 cells, compared to the CLint,met median value of 0.5 μL/min/106 cells (Figure 4

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RESULTS Assessment of Hepatocyte Drug Concentration Profiles Using an “Oil-Spin” Procedure. Eight transporter substrates were incubated with two separate batches of cryopreserved human hepatocytes UMJ and IRK at 1 μM, and the intracellular and medium concentration−time profiles from two incubations were collected. Cellular fraction data obtained for three representative drugs, rosuvastatin, pitavastatin and glyburide, are shown in the top panel of Figure 3 (data for all eight compounds are given as Supporting Information, Figure S1). Mechanistic Mathematical Model To Elucidate the Underlying Processes of Drug Distribution, Binding and Metabolism. The concentration data from the UMJ and IRK batches overlapped in general well which allowed both data sets to be fitted simultaneously. The procedure restricted estimates of bidirectional diffusion (CLint,diff), membrane binding (Kmem) and intracellular binding (fucell) to be shared between the two batches and gave individual (batch-specific) estimates of the model descriptors of active uptake (CLint,up) and metabolism (CLint,met). Model best-fit estimates are summarized in Table 1, with model simulations for the three representative drugs included in Figure 3 (simulations for all eight compounds are given as Supporting Information Figure S1, with residuals in Supporting Information Figure S2). The ranges of CLint,up, CLint,diff, and CLint,met for the investigated drugs span more than 2 orders of magnitude each as shown in Figure 4. Median values for CLint,up, CLint,diff and CLint,met are 18, 5.3, and 0.5 μL/ min/106 cells, respectively, indicating the trend that CLint,up values were in general higher than both CLint,met and CLint,diff for these compounds. Fexofenadine, a rather polar compound, was considered metabolically stable (CLint,met fixed to 0 during fitting procedure), since no compound depletion was measurable during the time of the incubation. Figure 4 also shows the batch-specific differences for the estimates of 4447

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Analysis of Known Molecular Properties, Model Parameters and OATP1B1-Mediated Uptake of Investigated Drugs by Principal Component Analysis (PCA). The obtained model estimates, experimental data for logD7.4 and human liver microsome (HLM) CL int from the AstraZeneca database were subjected to a principal component analysis (PCA). Figure 7 shows the resulting score (A) and

and 5). The CLinc values span roughly 1 order of magnitude, which is considerably smaller than the range of the CLint,met values.

Figure 5. Comparison of estimated intrinsic metabolic clearance from the mechanistic model to the estimated apparent clearance from the incubation (given values represent a mean of the estimates obtained for the UMJ and IRK data). CLinc = CLint,met, expected for highly permeable drugs, indicated by dashed line. Coding (increasing logD7.4): 1 = valsartan, 2 = pravastatin, 3 = rosuvastatin, 5 = pitavastatin, 6 = bosentan, 7 = atorvastatin, 8 = glyburide. Metabolically stable fexofenadine not included.

Assessment of Drug Uptake into OATP1B1-Expressing HEK-293 Cells. The specific uptake transport via OATP1B1 was evaluated from time-course data collected using HEK 293-cells, stably transfected with OATP1B1 or empty vector (Mock). Rates were measured 2 min after incubation start within the approximately linear phase of uptake (1 μM of substrate). The rate of OATP1B1-specific uptake was calculated as difference in uptake between OATP1B1 and empty vector transfected cells. In Figure 6, the rate of OATP1B1-specific uptake is compared to the mean CLint,up estimate from incubations with the two hepatocyte batches. The active uptake observed in the two systems correlates for the presented data set, with an R2 of 0.79.

Figure 7. Score (A) and loadings (B) plots describing the first two components obtained from PCA. Score plot coding (increasing logD7.4): 1 = valsartan, 2 = pravastatin, 3 = rosuvastatin, 4 = fexofenadine, 5 = pitavastatin, 6 = bosentan, 7 = atorvastatin, 8 = glyburide.

loadings plots (B) for the first two components, which explain about 80% of the variability of the data. There is a high degree of colinearity of the data, as more than 60% of the variability is explained by the first component alone. The first component shows mainly the influence of lipophilicity, where the more lipophilic compounds like glyburide and atorvastatin are found on the right-hand side of the score plot, whereas hydrophilic compounds like fexofenadine and valsartan are situated more to the left. In the loadings plot it can be seen that all CL values as well as the binding parameter Kmem are positively correlated to logD7.4. fucell, on the other hand, is found on the opposite side and thus negatively influenced by lipophilicity. Pairwise linear regression strengthens these findings; CLint,diff, CLint,met and Kmem values show a positive correlation with logD 7.4, demonstrating R2 values between 0.67 and 0.9, whereas fucell shows a negative correlation with R2 = 0.79 (plots and equations shown in Supporting Information, Figure S3). The PCA detects furthermore colinearity of transporter related parameters in the second component: both uptake parameters (CLint,up and OATP1B1 uptake) and Kp,uu are found in the upper part of the loadings plot.

Figure 6. Comparison of active hepatocellular to OATP1B1-specific uptake. Initial rate of OATP1B1-mediated uptake calculated from the difference in amount internalized with OATP1B1 and empty vector transfected HEK-293 cells after 2 min of incubation. Data points represent a mean obtained from three 1 μM incubations. CLint,up is the mean of the estimates obtained for the UMJ and IRK batch. Dashed line taken from linear regression (log(HEK-OATP1B1 rate) = 0.81 × log(CLint,up) − 0.067, R2 = 0.79). Coding (increasing logD7.4): 1 = valsartan, 2 = pravastatin, 3 = rosuvastatin, 4 = fexofenadine, 5 = pitavastatin, 6 = bosentan, 7 = atorvastatin, 8 = glyburide. 4448

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Use of 4 °C incubations for this purpose is debated, since the decrease of membrane fluidity at low temperature also may affect the passive permeability.27 However, by limiting the use of control data to steady-state conditions, collected between 60 and 120 min after incubation start, the kinetics of the 4 °C experiment has no influence on CLint,diff in our model. The steady-state conditions, equivalent to unbound intra- and extracellular drug concentrations being equal, allows instead for estimation of fucell when a limited difference in intracellular binding between 4 and 37 °C is assumed. The in vitro unbound cell-to-medium concentration ratio (Kp,uu) provides a key measure for evaluating organ exposure of drugs distributing into the liver cells. Rosuvastatin and glyburide show clear differences, as illustrated by simulations of their unbound and total cell and medium concentrations over time (Figure 3, bottom). The relatively lipophilic glyburide is effectively internalized by uptake transporters. However, only a moderate Kp,uu ratio is attained (approximately 2), primarily as a consequence of the high passive permeability effectively equilibrating large unbound cell-to-medium concentration ratios. The mean (UMJ and IRK batch) active uptake clearance of the less permeable rosuvastatin is about 8% of that estimated for glyburide, but results, attributed to the larger contribution to the total uptake, in a mean Kp,uu ratio of 17. Figure 5 illustrates the impact of differences in cellular unbound exposure (Kp,uu) on the rate of metabolic clearance obtained directly from the incubation. The intrinsic metabolic clearance (CLint,met) for bosentan and rosuvastatin differs by a factor of 10, showing the greater susceptibility for bosentan to undergo metabolic transformation. However, due to the higher Kp,uu ratio for rosuvastatin, the apparent clearance (CLinc) of bosentan is actually only a factor of 1.4 higher than that of rosuvastatin. In a conventional clearance assay the two compounds would thus appear as displaying comparable metabolic stability. Likewise, the higher apparent clearance predicted for pitavastatin compared to bosentan is not primarily an effect of a lower stability, but rather is caused by the effective raise of the pitavastatin intracellular concentration due to transporter-mediated uptake. The considerably smaller variation of the CLinc values, compared to the separate intrinsic clearances, within the data set (Figure 4) illustrates in analogy with Figure 1 the limited mechanistic information such measures contain. The interbatch difference of active uptake clearance was in general small compared to the difference observed in between tested compounds, indicative of a similar overall transporter activity profile. Menochet et al. reported on average 2.8-fold difference in uptake clearance between two individual donors.32 For comparison, the UMJ batch gave on average 11% higher uptake clearance than the IRK batch. A review of compound substrate profiles did not indicate that the differences still identified with the pooled batches are related to the direct function of specific uptake transporters. Comparison to uptake data from monolayer cultures showed good agreement with our data despite the differences in experimental setting and the analytical approach: the mean (UMJ and IRK batch) uptake clearance estimates are 1.6 (valsartan), 1.3 (pravastatin), 1.1 (rosuvastatin), 3.2 (pitavastatin), 1.6 (bosentan) times higher than those reported for the donor exhibiting the highest transporter activity by Menochet and co-workers.32 Higher rates with isolated hepatocytes may to some extent be directly attributed to the culture format, such as the larger medium exposed membrane area. However, the comparison shows that

DISCUSSION The search for new chemical entities with high metabolic stability has led to compounds with a higher dependency on active transport mechanisms for both uptake and elimination. For such compounds the conventional hepatocyte clearance assays, where homogeneous samples of cells and medium are collected, may be less appropriate for determining the intrinsic metabolic clearance since the unbound intracellular concentration then can deviate strongly from that of the medium (Figure 1). While an in vitro assay that utilizes the loss of parent drug from the incubation medium into hepatocytes (“mediumloss” assay) can allow for more effective prediction of the in vivo situation,37 a further definition of the underlying mechanisms is likely to rely on the use of physiologically inspired mathematical models. Recent reports have presented hepatocellular models for description of kinetics in different in vitro settings.26,27,29−32 In the current study we aimed at elucidating processes of drug distribution, binding and metabolism from incubations replicating the conditions at which hepatocyte metabolic clearance routinely is assessed in drug discovery: incubations at one single drug concentration using pooled batches of human cryopreserved hepatocytes, with samples typically being collected over a longer period of time during one to three hours. The obtained kinetic data sets were analyzed using a mechanistic mathematical model including five adjustable parameters: active uptake clearance (CLint,up), bidirectional passive diffusion (CLint,diff), intracellular metabolism (CLint,met), intracellular unbound fraction of the drug (fucell) and the unspecific binding term Kmem describing the rapid equilibrium binding of the drug to the outer cell membrane (Figure 2). The observed and simulated intracellular drug concentrations match in general well over the time of the incubations (Figure 3 and Supporting Information Figures S1 and S2). The model fit parameters (Table 1) span a wide range of values (Figure 4), illustrating the applicability of the approach for drugs with diverse properties. In the current parametrization, efflux transporter functionality, or any bidirectionality of uptake transporters, is considered limited (CLout = CLint,diff). Even if this assumption finds support in the literature,24 there are reports of efflux activity also in short-term hepatocyte cultures.22,23 While the current experimental approach is not appropriate for identifying active efflux back into the medium (CLint,eff), it is of interest to comment on the implications of falsely ignoring such activity in the suspended hepatocyte model. First, since a general parametrization would attribute CLout to CLint,eff + CLint,diff, active efflux is inevitably smaller than or equal to CLint,diff in Table 1. Knowing that the studied drugs at least to some extent do permeate the cellular membranes passively, CLint,eff is however likely to be considerably smaller. Furthermore, since CLin = CLint,diff + CLint,up, CLint,eff > 0 would result in a higher CLint,up. However, considering the trend of CLint,up being larger than CLint,diff (Figure 4), the effect on CLint,up would in general be relatively small. Therefore, the CLint,diff and CLint,up estimates presented in Table 1 can be considered as an upper limit to the passive permeation and a lower limit to the active uptake, respectively. Estimates of binding (Kmem and fucell) and distribution (Kp,uu) are independent of the specific interpretation of CLin and CLout. Critical to this strategy outlined is the control measurement at which the activity of active transport and metabolism is low. 4449

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preparation of the pooled batches, involving one additional freezing cycle, does not necessarily compromise the drug transporter function extensively. The finding agrees with recent studies specifically addressing the utility of cryopreserved human hepatocyte suspensions for uptake studies.38,39 Various transporter proteins may be involved in the hepatocellular uptake of the drugs investigated. However, OATP1B1 is generally considered a main contributor, with particular impact to the ADME properties of, e.g., statins. OATP1B1 specific kinetics were obtained from time-course data with OATP1B1-transfected HEK-293 cells, using vectortransfected cells as baseline. OATP1B1 uptake was well correlated to CLint,up as obtained from the hepatocytes, especially for the statin subset (Figure 6). Two of the compounds, fexofenadine and glyburide, showed somewhat higher uptake in hepatocytes than in the OATP1B1 experiment. This could fit with their uptake being largely influenced by OATP1B340 and OATP2B141 and not solely dependent on OATP1B1. However, also atorvastatin is likely influenced by other transporters42,43 and does not diverge from the general correlation. PCA was used to further investigate intercorrelations within the data set including OATP1B1-uptake data and internal AstraZeneca data on logD7.4 and metabolic stability in human liver microsomes (HLM). Lipophilicity is known to be an important molecular property that influences passive permeation, membrane association and substrate binding to, e.g., metabolizing enzymes and thereby metabolic stability.44−46 Parameter estimates are indeed found to be highly correlated to logD7.4, the main denominator of the first component of the loadings plot (Figure 7, bottom). The CL parameters are primarily distinguished in the second component. It can be noted that the closeness of CLint,met and HLM CLint in the loadings plot indicates that it was possible to extract the pure metabolism descriptor in the present analysis of the hepatocyte data. The detoxification machinery of the hepatocyte relies on the interplay of uptake, biotransformation and efflux transport. While the apparent clearance obtained from the conventional hepatocyte incubation directly reflects metabolic stability when distribution mainly is governed by passive permeation, interpretation becomes less straightforward when active transport contributes to the uptake. We have found the proposed three-compartmental model applicable for resolving single concentration, time-course data obtained from incubations with eight drugs of diverse pharmacokinetic profiles. We have furthermore found pooled batches of cryopreserved human hepatocytes, allowing for an extended, reproducible use, a relevant alternative to preparations from individual donors for screening purposes. This rationalized procedure is foreseen to have good potential for application in the drug discovery setting allowing larger numbers of compounds to be explored with a reduced experimental effort compared to full kinetic characterization with multiple test concentrations.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +46 (0) 317065349. Fax: +46 (0)317763743. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge Drs. Ken Grime and Patrik Lundquist for careful review and suggestions on the manuscript.

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ABBREVIATIONS USED CLinc, apparent metabolic clearance with respect to the total incubation concentration; CLmed, metabolic clearance with respect to the medium concentration; CLin, sum of clearances associated with transport from the medium into the cells; CLint,diff, intrinsic diffusion clearance; CLint,eff, intrinsic active efflux clearance; CLint,met, intrinsic metabolic clearance; CLint,up, intrinsic active uptake clearance; CLout, sum of clearances associated with transport from the cells to the medium; fucell, intracellular fraction unbound; HLM, human liver microsomes; Kp/Kp,uu, total/unbound cell-to-media concentration ratio at steady-state; Kmem, outer membrane binding constant; OATP, organic anion transporter polypeptide

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Cellular fraction experimental and simulated profiles for transporter for all eight substrates (with corresponding residual plots) and linear relation of model parameters to logD7.4. This material is available free of charge via the Internet at http:// pubs.acs.org. 4450

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