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Research ArticleArticle

Improving the Translation of Organic Anion Transporting Polypeptide Substrates using HEK293 Cell Data in the Presence and Absence of Human Plasma via Physiologically Based Pharmacokinetic Modeling

Christine M. Bowman, Buyun Chen, Jonathan Cheong, Liling Liu, Yuan Chen and Jialin Mao
Drug Metabolism and Disposition July 2021, 49 (7) 530-539; DOI: https://doi.org/10.1124/dmd.120.000315

Abstract

Accurately predicting the pharmacokinetics of compounds that are transporter substrates has been notoriously challenging using traditional in vitro systems and physiologically based pharmacokinetic (PBPK) modeling. The objective of this study was to use PBPK modeling to understand the translational accuracy of data generated with human embryonic kidney 293 (HEK293) cells overexpressing the hepatic uptake transporters organic anion transporting polypeptide (OATP) 1B1/3 with and without plasma while accounting for transporter expression. Models of four OATP substrates, two with low protein binding (pravastatin and rosuvastatin) and two with high protein binding (repaglinide and pitavastatin) were explored, and the OATP in vitro data generated in plasma incubations were used for a plasma model, and in buffer incubations for a buffer model. The pharmacokinetic parameters and concentration-time profiles of pravastatin and rosuvastatin were similar and well predicted (within 2-fold of observed values) using the plasma and buffer models without needing an empirical scaling factor, whereas the dispositions of the highly protein bound repaglinide and pitavastatin were more accurately simulated with the plasma models than the buffer models. This work suggests that data from HEK293 overexpressing transporter cells corrected for transporter expression represent a valid approach to improve bottom-up PBPK modeling for highly protein bound OATP substrates with plasma incubations and low protein binding OATP substrates with or without plasma incubations.

SIGNIFICANCE STATEMENT This work demonstrates the bottom-up approach of using in vitro data directly without employing empirical scaling factors to predict the intravenous pharmacokinetic (PK) profiles reasonably well for four organic anion transporting polypeptide (OATP) substrates. Based on these results, using HEK293 overexpressing cells, examining the impact of plasma for highly bound compounds, and incorporating transporter quantitation for the lot in which the in vitro data were generated represents a valid approach to achieve more accurate prospective PK predictions for OATP substrates.

Introduction

Investigating the role of transporters during drug discovery and development is crucial as they can impact not only a drug’s pharmacokinetic (PK) profile, but also its target tissue exposure and pharmacological/toxicological effect (Giacomini et al., 2010). Two commonly examined transporters, organic anion transporting polypeptide (OATP)1B1 and OATP1B3, are hepatic basolateral uptake transporters whose clinical importance has been demonstrated in both genetic studies (Niemi et al., 2005; Zhang et al., 2006; Pasanen et al., 2007) and drug-drug interaction studies (Backman et al., 2002; Kyrklund et al., 2003; Simonson et al., 2004). As these interactions can lead to dose adjustments, and even drug withdrawals due to safety, regulatory agencies recommend evaluating drug candidates for their potential to be OATP1B1/3 inhibitors and substrates (if eliminated by the liver).

An increasingly used approach to mechanistically predict PK and transporter-mediated drug disposition is physiologically based pharmacokinetic (PBPK) modeling (Rostami-Hodjegan, 2012). In contrast to static methods where an in vitro parameter is used to predict a specific PK parameter, PBPK modeling is dynamic and can be used to predict the plasma concentration-time curve as well as time-varying changes in transporter uptake and inhibition (Sager et al., 2015). However, there have been challenges with the in vitro to in vivo extrapolation (IVIVE) of transporter kinetics to describe observed PK or drug-drug interaction data, leading to the inclusion of compound-dependent empirical scaling factors in PBPK models beyond physiologic scaling (Jones et al., 2012; Li et al., 2014). For instance, reported PBPK models of well known OATP substrates pravastatin, rosuvastatin, repaglinide, and pitavastatin needed to use empirical scaling factors when inputting in vitro hepatocyte data to capture the observed PK (Varma et al., 2012; Jones et al., 2012; Varma et al., 2013; Duan et al., 2017).

To improve transporter IVIVE, recommendations have included finding in vitro systems that are more relevant to in vivo, and accounting for transporter differences between in vitro systems and in vivo (Grimstein et al., 2019; Taskar et al., 2019). To more accurately capture in vivo kinetics, the addition of plasma to in vitro incubations has been explored, and a previous study using uptake data from plateable human hepatocytes in human plasma demonstrated that the concentration-time profiles of pravastatin could successfully be captured with PBPK modeling without an empirical scaling factor (Mao et al., 2018). A recent publication also found that including serum in human and monkey hepatocyte incubations decreased the empirical scaling factor values needed to capture in vivo uptake clearance (Liang et al., 2020). To bridge the difference in transporter expression levels between different in vitro systems (such as human embryonic kidney 293 (HEK293) cells and hepatocytes) and/or between in vitro and in vivo (such as hepatocytes and liver tissue), the use of a relative expression factor (REF) has been proposed with transporter abundance differences measured with liquid chromatography–tandem mass spectrometry/tandem mass spectrometry (Bosgra et al., 2014, Chan et al., 2019). Using this approach, Ishida et al. (2018) found that the uptake clearance of rosuvastatin in rats could be accurately predicted using Oatp-overexpressing cells and REF, whereas using sandwich cultured rat hepatocytes led to underprediction.

The objective of the current work is to understand the translational accuracy of using data generated in HEK293 cells overexpressing OATP1B1/3 with and without plasma, and using in-house transporter quantitation data for REF, as inputs for the PBPK models of pravastatin, rosuvastatin, repaglinide, and pitavastatin. The uptake clearance measured with this in vitro data are compared with the previously fitted uptake clearance values from PBPK models, and predictions of pharmacokinetic parameters and concentration-time profiles are examined. Although many have used hepatocytes for transporter IVIVE (Izumi et al., 2017), using transporter-overexpressing cells may be preferable due to information about specific transporter contributions, lack of lot variability, and cost (Kumar et al., 2021). Finding an appropriate in vitro system and incubation conditions is crucial for more accurate prospective PK predictions and may avoid the previously needed compound-specific empirical scaling factors.

Methods

Uptake in OATP1B1- and OATB1B3-Overexpressing Cells

Details on the in vitro data generation can be found in Bowman et al. (2020). Briefly, Corning TransportoCells Cryopreserved SLC Transporter Cells (human OATP1B1*1a OATP1B3 (lot 5278015), and control cells (lot 6075312) were used to measure the uptake of four OATP substrates (pravastatin, rosuvastatin, repaglinide, and pitavastatin) at various concentrations using protein-free buffer or 100% human plasma. Plasma protein binding of the compounds was measured with a Rapid Equilibrium Dialysis Plate (Thermo Fisher Scientific, Waltham, MA). The resulting unbound Km, maximum rate of active uptake (Jmax), and passive diffusion (CLPD) values can be found in Table 1. The CLPD values were later converted from the original units of μl/min per mg protein to ml/min per 106 HEK293 cells for the simulator required input of ml/min per 106 hepatocytes. The surface area, membrane composition etc. of HEK293 cells and hepatocytes were assumed to be similar to allow for the passive diffusion in HEK293 cells to be input here as a hepatocyte value.

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TABLE 1

The Jmax, Km,u, and CLPD values previously generated for pravastatin, rosuvastatin, repaglinide, and pitavastatin in OATP1B1- and OATP1B3-overexpressing cells in human plasma and buffer (Bowman et al., 2020)

Transporter Quantification in Overexpressing Cell Lines and Human Hepatocytes

ProteoExtract native membrane protein extraction kit (Millipore) was used to isolate membrane proteins from the Corning TransportoCells Cryopreserved SLC Transporter Cells mentioned above and suspended human hepatocytes (BioIVT Corp, Westbury, NY, 10-donor pooled) according to the protocol provided by the manufacturer. Twenty microliters of the extracted membrane fraction were mixed with 80 μl 5% deoxycholate in 25 mM Ammonium Bicarbonate. Deoxycholate was then removed by desalting spin column after dithiothreitol reduction and iodoacetamide alkylation. Trypsin was then added to each well in an enzyme to protein ratio of 1:20. Samples were digested at 37°C overnight. Heavy labeled peptides were spiked into the digestion mixture and the reaction was quenched with 0.5% of formic acid for liquid chromatography–mass spectrometry (LC-MS) analysis. The surrogate peptides measured were ITPTDSR, NVTGFFQSFK, YVEQQYGQPSSK, and SSSGNK for OATP1B1 and NQTANLTNQGK, NVTGFFQSLK, and IYNSVFFGR for OATP1B3. The LC-MS analysis was carried out on a Shimadzu Nexera (Columbia, MD) coupled to a Sciex QTRAP 6500 mass spectrometer (Foster City, CA). A Waters XBridge BEH C18 column (100 × 2.1 mm, 3.5 μm) (Milford, MA) was used with H2O (A) and methanol (B) both with 0.1% formic acid. Gradient elution profile at 300 μl/min and 40°C is as follows: 5% B increased to 50% B by 45.0 minutes, then to 90% B by 50 minutes, and returning to 5% B at 51 minutes with run time of 60 minutes. The calibration curve range was 0.12–30 ng/ml for each peptide.

Relative Expression Factor Scaling

The transporter quantitation results were used to account for abundance differences between the overexpressing HEK293 cells and hepatocytes in the form of REF. REF is traditionally a unitless scalar, for instance correcting for pmol per 106 cells in vivo versus in vitro; however here for the correction of abundance in HEK293 cells versus hepatocytes, the quantitation of HEK293 cells was measured as pmol per mg protein, leading to REF with units of mg per 106 cells. Since the mg protein per 106 cells is not necessarily the same for HEK293 cells and hepatocytes, this has been normalized in the REF equation (eq. 1). The amount of protein was determined using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA). The scaled uptake intrinsic clearance (CLint,T) is shown is eq. 2. Embedded Image Embedded Image

PBPK Models of Four OATP Substrates

The Simcyp simulator (version 19 release 1, Sheffield, UK) was used for this investigation along with the models of pravastatin, rosuvastatin, and repaglinide in the Simcyp compound library and the model of pitavastatin from Duan et al. (2017). These models are considered the base models. Although further development can be done, as recently described for rosuvastatin (Bowman et al., 2021), the purpose of this exercise was to compare the simulation results when using the currently investigated HEK293 cell data with in-house REF (and unchanged remaining model inputs) to the simulation results of the base models.

The models of pravastatin, rosuvastatin, and pitavastatin used the advanced dissolution, absorption, and metabolism model (Jamei et al., 2009) to describe intestinal absorption, whereas repaglinide used the first-order absorption model. For distribution, all four compounds used a full PBPK model, and the volume of distribution was predicted using the Rodgers and Rowland (2007) method. Permeability-limited models were used in the intestine for pravastatin and rosuvastatin; in the liver for all four compounds, and in the kidney for pravastatin. Details about the model inputs for these four compounds in the base models, HEK293 plasma models, and HEK293 buffer models are summarized in Table 2 and briefly described below.

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TABLE 2

Summary of the key input parameters of pravastatin, rosuvastatin, repaglinide, and pitavastatin used in the base model, HEK293 plasma model and HEK293 buffer model

Pravastatin

Base Model (Simcyp Simulator Version 19 Library File)

Pravastatin is a low protein binding statin [fraction unbound in plasma (fup) = 0.485)] that is minimally metabolized and undergoes biliary and renal clearance (Singhvi et al., 1990). To assign hepatic uptake contributions in the model of pravastatin, a global hepatic uptake CLint,T was back-calculated by fitting clinical intravenous data (Singhvi et al., 1990). The percentage of OATP1B1 and OATP1B3 contribution was then assigned based on data from HEK293-OATP1B1 and OATP1B3 cells along with relative expression data (from Simcyp pravastatin compound file summary 2020). The passive diffusion was measured in sandwich cultured human hepatocytes (SCHHs) (Jones et al., 2012). The hepatic efflux transporter multidrug resistance-associated protein 2 was assigned using a measured CLint,T from sandwich culture human hepatocytes (Jones et al., 2012) and a REF was included to account for abundance differences (Neuhoff and Tucker, 2013).

HEK293 Plasma Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

HEK293 Buffer Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

Rosuvastatin

Base Model (Simcyp Simulator Version 19 Library File)

Rosuvastatin is a relatively low protein binding statin (fup = 0.107) that is minimally metabolized and predominately undergoes biliary and renal excretion unchanged (Martin et al., 2003b; Bergman et al., 2006). Since inputting experimental transporter data in the rosuvastatin model could not capture the observed profile (Jamei et al., 2014), a global intrinsic clearance for active hepatic uptake was back-calculated using an intravenous clinical study (Martin et al., 2003a) and in vitro data were used to assign a percentage contribution to each hepatic uptake transporter included (OATP1B1, OATP1B3, OATP2B1, sodium/taurocholate cotransporting polypeptide) based on a meta-analysis of data (Harwood et al., manuscript in preparation). The passive diffusion input was based on a meta-analysis of five SCHH studies. For the hepatic efflux transporter breast cancer resistance protein, sandwich culture human hepatocyte data were input with activity corrections for absolute abundance (Li et al., 2009, Burt et al., 2016). Multidrug resistance-associated protein 4 was assigned using a relationship between rosuvastatin’s hepatocyte basolateral efflux and biliary clearance (Pfeifer et al., 2013) and correcting transporter expression differences (Harwood et al., manuscript in preparation).

HEK293 Plasma Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

HEK293 Buffer Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

Repaglinide

Base Model (Simcyp Simulator Version 19 Library File)

Repaglinide, a high protein binding antidiabetic drug (fup = 0.0188), is extensively metabolized by cytochrome P450 (CYP) 2C8 and CYP3A4 in the liver and gastrointestinal tract (Bidstrup et al., 2003). In the repaglinide model, hepatic uptake clearance was assigned to OATP1B1 after fitting clinical oral data (Kajosaari et al., 2005). The passive diffusion was measured in SCHHs (Jones et al., 2012).

HEK293 Plasma Model

The hepatic OATP1B1 inputs (Jmax, Km, REF) and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

HEK293 Buffer Model

The hepatic OATP1B1 inputs (Jmax, Km, REF) and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

Pitavastatin

Base Model (Duan et al., 2017)

Pitavastatin is a highly protein bound statin (fup = 0.005) that undergoes minimal metabolism and is eliminated unchanged in the bile (Hirano et al., 2005). The model developed by Duan et al. (2017) was used as a base model here, and for hepatic uptake, OATP1B1 and OATP1B3 CLint,T values generated in hepatocytes were input (Hirano et al., 2006). However, this led to underprediction of the systemic clearance, so empirical scaling factors of 18 for both transporters were then included based on optimization with intravenous and oral clinical data (US Food and Drug Administration, 2009). A passive diffusion value was input from the same source based on hepatocyte data (Hirano et al., 2006).

HEK293 Plasma Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

HEK293 Buffer Model

The hepatic OATP1B1 inputs (Jmax, Km, REF), OATP1B3 inputs (Jmax, Km, REF), and CLPD inputs were updated with the in vitro results of Table 1. The remaining parameters were kept the same as the base model.

PBPK Model Simulations

For each compound, an intravenous and oral PK simulation was conducted using the Simcyp simulator version 19 with the Sim-Healthy Volunteer population and compared with the observed clinical data. The OATP1B1/3 abundance in liver tissue was left as the simulator default values which are from a meta-analysis (Burt et al., 2016). Details of the clinical studies and the exact simulations run (number of subjects, age range, and proportion of females) can be found in Table 3. Ten trials were run for each simulation. The oral dosages for each compound were selected based on which had most clinical data available. For pravastatin, a 9.4 mg i.v. bolus dose and a 40 mg oral dose were examined; for rosuvastatin an 8 mg i.v. infusion and 80 mg oral dose were examined; for repaglinide a 2 mg i.v. infusion and 2 mg oral dose were examined; and for pitavastatin a 2 mg i.v. infusion and 2 mg oral dose were examined. Simulations were conducted for the base model, HEK293 buffer model, and HEK293 plasma model (Table 2).

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TABLE 3

Pharmacokinetic studies of pravastatin, rosuvastatin, repaglinide, and pitavastatin examined for this analysis and the simulations run using the Simcyp simulator

Results

Uptake in OATP1B1- and OATB1B3-Overexpressing Cells

Details about the in vitro results and interpretation can be found in Bowman et al. (2020), and the data are presented here in Table 1. Briefly, differences for each parameter (Km,u, Jmax, and CLPD) were found between the buffer and plasma incubations for both cells, with the largest differences noted for the highly protein bound repaglinide and pitavastatin. The Km,u values decreased in the plasma incubations as protein binding increased (with fold changes ranging from 1.91 to 619), whereas Jmax values also decreased in the plasma as protein binding increased but to a lesser extent than Km,u (the Jmax fold changes ranged from 1.22 to 97.4). In addition, the CLPD was higher in the human plasma incubations with the largest difference for pitavastatin (23.4-fold change) compared with the other three compounds (1.73–3.90-fold changes).

Transporter Quantitation

The full results of the transporter quantitation are presented in Supplemental Table 1. Ultimately the results from the ITPTDSR and NQTANLTNQGK peptides for OATP1B1 and OATP1B3 respectively were used for the calculation following eq. 1, leading to REF values of 5.63 for OATP1B1 and 1.87 for OATP1B3 as shown in Table 4.

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TABLE 4

Calculation of REF values used for OATP1B1 and OATP1B3

PBPK Model Simulations

The simulated PK results can be found in Table 5 and Figs. 1 and 2.

Fig. 1.
Fig. 1.

Simulated intravenous (IV) and oral (PO) concentration-time profiles of pravastatin, rosuvastatin, repaglinide, and pitavastatin using base models, HEK293 buffer models, and HEK293 plasma models. The simulated results are shown as a green line with the 5th and 95th percentiles shown as gray lines. The observed clinical data (references can be found in Table 3) are plotted as points.

Fig. 2.
Fig. 2.

Comparison of the AUC observed versus AUC predicted for the intravenous doses of pravastatin, rosuvastatin, repaglinide, and pitavastatin using the HEK293 plasma and HEK293 buffer models. The x-axis shows the observed AUC reported in the references of Table 3, and the y-axis shows the predicted AUC from the plasma and buffer models.

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TABLE 5

The geometric mean (AUC, Cmax) and median (tmax) of the observed and simulated PK parameters for pravastatin, rosuvastatin, repaglinide, and pitavastatin

For pravastatin, the simulated PK parameters [the area under the concentration-time curve (AUC), the maximal concentration, and the time to the maximal concentration] fell within 2-fold of the observed data for both the intravenous and oral doses using HEK293 data with REF in both plasma and buffer incubations (Table 5). The terminal phase of the intravenous profile was not fully captured with the HEK293 data, however given that it was not captured in the base model, this is expected (Fig. 1). The profiles of the oral dose were well predicted (using both plasma and buffer incubation data) with the Cmax and AUC predictions falling within the range of observed data. The sum of the OATP1B1 and OATP1B3 uptake CLint,T was relatively similar between the plasma (37.8 μl/min per 106 cells), buffer (24.0 μl/min per 106 cells), and base model (15.4 μl/min per 106 cells). The use of a passive diffusion value from the HEK293 incubations versus the SCHHs of the base model was explored, and the simulation results were not affected (Supplemental Table 3). This was expected given the percentage of pravastatin entering the liver by passive diffusion was 1% or lower with all pravastatin data.

For rosuvastatin, the simulated PK parameters were also within 2-fold of the observed data for both the intravenous and oral doses using the HEK293 data from both incubations (Table 5). The triphasic decline of the concentration-time profile of the intravenous dose appears to be better captured by the HEK293 plasma and buffer models (more of the observed data points fell within the 5th/95th percentile) than the base model (Fig. 1). For the oral profile, the simulated Cmax fell within the range of the observed, and the simulated AUC was slightly higher than the observed range using the HEK293 plasma and buffer models. In comparison, both the Cmax and AUC were slightly underpredicted using the base model. These differences in simulations using the HEK293 models versus the base model could be attributed to the difference in uptake CLint,T for OATP1B1 and OATP1B3—the sum of the uptake CLint,T for the two transporters in the HEK293 plasma incubations was 99.0 μl/min per 106 cells, and for the HEK293 buffer incubations was 49.7 μl/min per 106 cells, whereas it was much higher at 718 μl/min per 106 cells for the base model. The percentage of rosuvastatin entering the liver by passive diffusion was less than 1% in all cases and using a passive diffusion value from the HEK293 cells versus the SCHHs of the base model yielded similar results (Supplemental Table 3).

With repaglinide, although the simulated PK parameters were within 2-fold of the observed data for both the intravenous and oral doses using the HEK293 plasma and buffer models, there were differences in the predictions depending on the incubation data used. For the intravenous dose, the predicted AUC using the plasma model was 1.01-fold higher than the observed, whereas it was 1.85-fold higher with the buffer model. In addition, the concentration-time profile of the intravenous dose appeared to be better captured with the plasma model. Differences were also observed with the oral simulations. Using the plasma model the AUC, Cmax, and concentration-time profile were slightly underpredicted, whereas using the buffer model, the AUC was predicted to be on the higher end of the observed values and the concentration-time profile was slightly overpredicted. In comparison with the base model with an OATP1B1 CLint,T of 838.1 μl/min per 106 cells, the plasma model had a higher uptake of 1115 μl/min per 106 cells, whereas the buffer model had a lower uptake of 225.7 μl/min per 106 cells. The contribution of passive diffusion also varied and was higher than for the statins—the uptake percentage attributed to passive diffusion was 13.9% in the base model, was 2.8% with the plasma model, and was 7.4% with the buffer model. The lower contribution from the HEK293 cells provided more accurate predictions than the value from the SCHHs of the base model (Supplemental Table 3).

Pitavastatin, the highest protein bound compound, had the largest difference between the in vitro kinetic parameters determined in HEK293 plasma versus buffer incubations, and this held true for the PBPK simulation results as well. For the intravenous dose, the plasma model provided more accurate simulation results. The predicted AUC was within 2-fold of the observed using the plasma model (1.4-fold underpredicted), and the shape of the concentration-time profile was closer to the observed (Table 5, Fig. 1). The AUC was overpredicted using the buffer model by 4.4-fold and the concentration-time profile was not accurately captured. With the oral data, the plasma model also provided more accurate simulations. The AUC and Cmax were both underpredicted by 3-fold using the plasma model, and this was closer to the observed data than using the buffer model. The AUC and Cmax were overpredicted using the buffer model by 6.6-fold and 3.3-fold, respectively. In comparison, the base model (Duan et al., 2017) used in vitro hepatocyte data and a scaling factor of 18 was required to achieve accurate predictions. More specifically, the optimized OATP1B1/3 CLint,T of Duan et al. (2017) was 1143 μl/min per 106 cells, whereas the CLint,T of the plasma data were 3933 μl/min per 106 cells without an empirical scaling factor and the CLint,T of the buffer data were 150.4 without an empirical scaling factor. The percentage of uptake by passive diffusion was less than 2% in all cases and similar results were seen using either the HEK293 cell or hepatocyte data (Supplemental Table 3).

Discussion

Generating transporter kinetic data from appropriate in vitro systems is crucial for accurate IVIVE and prediction of human PK profiles using PBPK modeling. Although using hepatocytes has been explored, quantitatively using overexpressing cells and accounting for transporter expression is a more recent idea. This study explored the translational capability of data generated in transfected HEK293 cells with and without plasma and corrected for transporter expression, for human PK prediction using PBPK. To our knowledge, this is the first publication to demonstrate the bottom-up approach using in vitro OATP data directly without employing empirical scaling factors to predict the intravenous PK profiles reasonably well for multiple OATP substrates.

To understand the translation of OATP1B1/3 in vitro data, more emphasis was placed on capturing intravenous PK although both intravenous and oral simulations were conducted. Since alternative transporters may contribute to the absorption of oral doses, accurately accounting for them is critical and any uncertainty would be independent of the OATP1B1/3 inputs explored here.

The expression levels of OATP1B1 and OATP1B3 in HEK293 overexpressing cells and human hepatocytes were determined using liquid chromatography–tandem mass spectrometry quantitation. The ITPTDSR and NQTANLTNQGK peptides for OATP1B1 and OATP1B3 respectively were selected due to the low interfering signal compared with control cells. In addition, they demonstrated good linearity for relative quantitation purposes during method qualification. Due to the complex nature of cellular extracts, all unique peptides detectable by LC-MS were quantitated. For IVIVE, choosing a peptide with the best intrasystem selectivity and quantitative linearity is critical to accurate extrapolation.

Using theses REF values along with the previously reported Jmax and Km.values allowed exploration of bottom-up PBPK modeling of OATP contribution to compound PK. By investigating Jmax and Km values compared with the CLint,T values of base models, potential saturation could also be taken into account. After scaling the in vitro data with REF, OATP1B1 had a larger contribution to the uptake than OATP1B3 for pravastatin, rosuvastatin, and pitavastatin. This highlights the value of using overexpressing cell lines to understand specific transporter contributions, and the larger contribution of OATP1B1 for these compounds aligns with previous in vitro (Kunze et al., 2014; Izumi et al., 2018) and in vivo pharmacogenomic (Yoshida et al., 2013) studies.

For the two compounds with lower protein binding, pravastatin and rosuvastatin, comparable transporter kinetics were generated in plasma and buffer incubations. For pravastatin, the sum of the OATP1B1/3 uptake CLint,T was relatively similar between the plasma, buffer, and base models. For rosuvastatin, the OATP1B1/3 uptake CLint,T in the plasma and buffer models were lower than the fitted OATP1B1/3 uptake CLint of the base model, which agrees with the recent work of Kumar et al. (2021) who found that in vitro systems underpredicted rosuvastatin’s uptake clearance, and suggested endogenous factors were missing. Despite this, the observed systemic clearance of rosuvastatin was still captured here and hepatic uptake may not be the rate-determining step (Billington et al., 2019). For the two highly protein bound compounds, repaglinide and pitavastatin, where the kinetic data were substantially different between the incubations, the OATP uptake CLint,T from the plasma incubations were more aligned with the fitted/optimized uptake CLint,T of the base models.

For the PBPK simulations of pravastatin and rosuvastatin, the pharmacokinetic parameters and concentration-time profiles of the intravenous doses were similar and overall well predicted with the HEK293 plasma and buffer models. Larger differences in the repaglinide and pitavastatin simulations were noted, and the pharmacokinetic parameters and intravenous profiles were more accurately captured by the plasma models than the buffer models for both. Although pitavastatin’s concentration-time profile was not fully captured, this may be because the current base model does not include enterohepatic recirculation and/or because the volume is underpredicted (Kojima et al., 2001; Catapano, 2012).

It should be noted that the half-lives of pravastatin and pitavastatin were not accurately reflected in the base models (Supplemental Table 4). This manuscript shows promise for prospective predictions of clearance, which contribute to successful half-life predictions. On the other side, it is important to mention the gap that current volume predictions do not incorporate the impact of transporters, which may lead to higher observed values (Grover and Benet, 2009). Since the Rodgers and Rowland (2007) prediction method used does not account for transporters, it was not surprising to see half-life prediction error given the dependency on both clearance and volume. As an exercise, the volume of distribution was retrospectively raised to the observed range by increasing the Kp scalar, which improved half-life predictions, did not largely impact clearance predictions (Supplemental Table 4) and slightly improved PK profile predictions (Supplemental Fig. 1). Although it was beyond the scope of this work to improve the bottom-up approach for volume predictions, using allometry or tissue concentration data from rats (Chan et al., 2019) could be explored.

The results presented here align with previous work examining the hypothesis of protein-facilitated uptake (Baik and Huang, 2015; Fukuchi et al., 2017; Bowman et al., 2019; Bteich et al., 2019; Kim et al., 2019; Bi et al., 2021; Francis et al., 2021; Liang et al., 2020; Li et al., 2020). According to this idea, interactions between the drug-protein complex and the hepatocyte cell surface or transporters may lead to greater uptake and clearance for highly protein bound drugs (primarily acidic drugs examined to date) than would be predicted using traditional in vitro methods with protein-free buffer. The PBPK modeling results for repaglinide and pitavastatin emphasize that there is a difference when using in vitro data generated with and without plasma and suggest that plasma data may reflect the physiologically relevant condition, as the simulations for the highly bound OATP substrates captured the observed PK more closely than those using protein-free buffer data. Alternative explanations could be that plasma reduces the nonspecific incubation binding and/or improves the solubility of compounds, and more work is needed to mechanistically understand the differences seen between plasma versus protein-free buffer.

Ultimately the in vitro underprediction of clearance is likely multifactorial (Bowman and Benet, 2016; Wood et al., 2017), however, the inclusion of REF and the kinetic parameters presented here bring hope for the traditionally difficult bottom-up modeling of OATP substrates. The base models of the four compounds either used fitted clinical data to obtain a global CLint,T (and assigned fractions transported from in vitro data) (Jamei et al., 2014), or used SCHH data and required an 18-fold empirical scaling factor (Duan et al., 2017). Supplemental Table 5 describes the OATP1B1/3 inputs from additional models developed in the Simcyp simulator beyond those used here. For pravastatin, Varma et al. (2012) found that SCHH data overpredicted the intravenous concentration-time profile, leading to a scaling factor of 31, whereas Mao et al. (2018) found that plateable human hepatocytes in plasma could accurately capture pravastatin’s disposition. For rosuvastatin, Emami Riedmaier et al. (2016) and Wang et al. (2017) input a fitted global uptake CLint,T from clinical data, and assigned transporter contributions from in vitro data. For repaglinide, Varma et al. (2012) used SCHH data and needed a 17-fold empirical scaling factor. Additionally, Jones et al. (2012) input SCHH data for PBPK modeling using Berkeley Madonna and determined scaling factors of 21 for pravastatin, 12 for rosuvastatin, and 44 for repaglinide were needed to capture the active uptake in their models.

Based on the results presented here, selecting a physiologically relevant in vitro system such as HEK293 overexpressing cells (with or without plasma for low protein binding OATP substrates, and with plasma for high protein binding OATP substrates) and incorporating transporter quantitation may help achieve more accurate prospective PK predictions. This work demonstrates that the approach may avoid the compound-specific empirical scaling factors previously needed. For pravastatin and rosuvastatin, predictions may have been more accurate than previous work if there were functional activity differences with the transporters in the HEK293 cells and the previously used hepatocytes. Having accurate REF values may have also improved predictability: the transporter abundances were measured using the same experimental procedures for HEK293 cells and hepatocytes in-house; the same HEK293 lots used for kinetic data were used for abundance measurements; and a 10-donor mixed gender pooled hepatocyte lot was used to avoid interindividual variability. For repaglinide and pitavastatin, predictions may have been more accurate for the same reasons in addition to the use of plasma incubations. Although passive diffusion was a relatively small percentage of the uptake of these compounds, differences between plasma versus buffer incubations should be further explored and could impact predictions of compounds with larger passive contributions. Differences in passive diffusion between incubations have been noted with hepatocytes as well (Bowman et al., 2019; Liang et al., 2020), and possible explanations have been methodological or physiologic (Liang et al., 2020).

In conclusion, using HEK293 overexpressing transporter cells in plasma incubations and accounting for transporter expression demonstrates a promising approach for bottom-up PBPK modeling of OATP substrates. As additional hypotheses for the in vitro to in vivo discrepancy of transporter substrates are examined, such as differences with endogenous factors in vitro/in vivo, they could be built into this in vitro system and modeling approach.

Acknowledgments

The authors would like to thank Robert S. Jones for the hepatocyte protein measurement, Matthew Harwood and Sibylle Neuhoff from Certara for their discussions, and Eugene Chen and Kenta Yoshida for their review.

Authorship Contributions

Participated in research design: Bowman, B. Chen, Cheong, Liu, Y. Chen, Mao.

Conducted experiments: Bowman, B. Chen, Cheong, Liu.

Contributed new reagents or analytic tools: B. Chen, Cheong, Liu.

Performed data analysis: Bowman, B. Chen, Cheong, Liu, Y. Chen, Mao.

Wrote or contributed to the writing of the manuscript: Bowman, B. Chen, Cheong, Liu, Y. Chen, Mao.

Footnotes

Abbreviations

AUC
area under the concentration-time curve
CLint,T
intrinsic clearance
CLPD
passive diffusion
fup
fraction unbound in plasma
HEK293
human embryonic kidney 293
IVIVE
in vitro to in vivo extrapolation
Jmax
maximum rate of active uptake
LC-MS
liquid chromatography–mass spectrometry
OATP
organic anion transporting polypeptide
PBPK
physiologically based pharmacokinetic
PK
pharmacokinetic
REF
relative expression factor
SCHH
sandwich cultured human hepatocyte

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PBPK Modeling of OATP Substrates with HEK293 Data ± Plasma

Christine M. Bowman, Buyun Chen, Jonathan Cheong, Liling Liu, Yuan Chen and Jialin Mao
Drug Metabolism and Disposition July 1, 2021, 49 (7) 530-539; DOI: https://doi.org/10.1124/dmd.120.000315
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