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Research ArticleArticle

Quantitative Prediction of Human Renal Clearance and Drug-Drug Interactions of Organic Anion Transporter Substrates Using In Vitro Transport Data: A Relative Activity Factor Approach

Sumathy Mathialagan, Mary A. Piotrowski, David A. Tess, Bo Feng, John Litchfield and Manthena V. Varma
Drug Metabolism and Disposition April 2017, 45 (4) 409-417; DOI: https://doi.org/10.1124/dmd.116.074294

Abstract

Organic anion transporters (OATs) are important in the renal secretion, and thus, the clearance, of many drugs; and their functional change can result in pharmacokinetic variability. In this study, we applied transport rates measured in vitro using OAT-transfected human embryonic kidney cells to predict human renal secretory and total renal clearance of 31 diverse drugs. Selective substrates to OAT1 (tenofovir), OAT2 (acyclovir and ganciclovir), and OAT3 (benzylpenicillin, oseltamivir acid) were used to obtain relative activity factors (RAFs) for these individual transporters by relating in vitro transport clearance (after physiologic scaling) to in vivo secretory clearance. Using the estimated RAFs (0.64, 7.3, and 4.1, respectively, for OAT1, OAT2, and OAT3, respectively) and the in vitro active clearances, renal secretory clearance and total renal clearance were predicted with average fold errors (AFEs) of 1.89 and 1.40, respectively. The results show that OAT3-mediated transport play a predominant role in renal secretion for 22 of the 31 drugs evaluated. This mechanistic static approach was further applied to quantitatively predict renal drug-drug interactions (AFE ∼1.6) of the substrate drugs with probenecid, a clinical probe OAT inhibitor. In conclusion, the proposed in vitro-in vivo extrapolation approach is the first comprehensive attempt toward mechanistic modeling of renal secretory clearance based on routinely employed in vitro cell models.

Introduction

Accurate prediction of human pharmacokinetic properties during the drug design stage is very important to identify and progress candidate molecules that can be successful in the clinic (Hosea et al., 2009; Di et al., 2013). With the improved understanding in metabolic biotransformation pathways, medicinal design teams are able to achieve good metabolic stability; however, such chemical space may likely possess transporter-mediated disposition as the major clearance mechanism (hepatic uptake and renal clearance) (Varma et al., 2015). The kidney plays a key role in the detoxification of xenobiotics and endogenous products (Russel et al., 2002; Lee and Kim, 2004; Feng et al., 2010). In an analysis of 391 drugs with human clearance data available, ∼31% (123 compounds) showed predominant renal elimination contribution (i.e., renal clearance more than 50% of total body clearance), implying the need for renal clearance projections in drug discovery (Varma et al., 2009). Renal elimination is the net result of several processes, involving glomerular filtration, active secretion, and tubular reabsorption, and possibly renal metabolism (Russel et al., 2002; Lee and Kim, 2004; Feng et al., 2010; Morrissey et al., 2013).

Glomerular filtration is a unidirectional passive process that occurs for most small molecules (mol. wt. <5000 Da) regardless of their ionization state. Renal secretion is a process where transporters actively secrete compounds into the renal tubule. The Solute Carrier Family 22A transporter system, which includes organic anion transporters (OATs) and organic cation transporters (OCTs), predominantly govern this process (Lee and Kim, 2004; Feng et al., 2010; Morrissey et al., 2013). Localized on the basolateral membrane of the proximal tubules, OAT1/2/3 and OCT2 are involved in the uptake of drugs and are associated with clinical DDIs (Morrissey et al., 2013). On other hand, tubular reabsorption often depends on the passive permeability of compounds (Varma et al., 2009; Scotcher et al., 2016).

Allometric scaling using animal data are widely applied for extrapolating the pharmacokinetic parameters, including renal clearance, to predict clinical pharmacokinetics (Paine et al., 2011). Such a scaling methodology may be useful for drugs that are eliminated in the urine by glomerular filtration process. However, allometry is not a reliable methodology for drug cleared predominantly by a transporter-mediated active process because of possible species difference in transporter expression and function (Chu et al., 2013). The prediction of active secretion is limited by a lack of established in vitro-in vivo extrapolation (IVIVE) methodologies. Secretion of ionic drugs involves basolateral uptake and subsequent efflux across the apical membrane into the urine. However, the renal uptake clearance of anionic drugs is typically similar to renal secretory clearance with uptake often being the rate-determining process (Sirianni and Pang, 1997; Watanabe et al., 2011; Varma et al., 2015). Therefore, establishing IVIVE of basolateral uptake clearance alone could provide quantitative prediction of renal secretory clearance.

Our goal is to establish an IVIVE method based on relative activity factor (RAF) to predict human renal clearance of OAT substrates using in vitro transport data. Transporter-specific uptake clearance was measured in human embryonic kidney (HEK) 293 cells singly transfected with human OAT1, OAT2, and OAT3, and scaled to in vivo secretory clearance via RAFs, which were established using selective substrates. This bottom-up approach was further extended to predict OAT-mediated drug interactions with probenecid, a recommended probe inhibitor.

Materials and Methods

Materials.

HEK293 cells transfected with OAT1 and OAT3 were obtained from Dr. Kathleen Giacomini (University of California, San Francisco, San Francisco, CA). HEK293 cells transfected with OAT2-variant 1 were obtained from Dr. Ryan Pelis (Dalhousie University, Halifax, Canada). All the compounds used in the assay were obtained from Pfizer chemical inventory system or procured from Sigma-Aldrich (St. Louis, MO). Biocoat poly-d-lysine 48-well plates were obtained from Corning Inc (Corning, NY). Fetal bovine serum was purchased from Sigma-Aldrich. Dulbecco’s Modified Eagle’s Medium, Hygromycin B, Gentamicin, and sodium pyruvate were obtained from Gibco Life Technologies (Waltham, MA). Hank’s balanced salt solution, HEPES, and 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid) were obtained from Lonza (Allendale, NJ). 3H-Para aminohippuric acid, 3H-estrone sulfate, and 3H-cGMP were purchased from PerkinElmer (Waltham, MA).

Clinical Data Collection.

Human renal clearance and the plasma fraction unbound of the drugs were primarily taken from our previous compilations (Obach et al., 2008; Varma et al., 2009; Lombardo et al., 2014) and additional literature publications. Where available, human blood-to-plasma (BP) ratio was extracted from the literature or from the internal database. The BP ratio was assumed to be 0.55 for anionic drugs and 1 for nonacidic drugs when the experimental data were not available. However, no significant impact of BP ratio was noted on the overall predictions. DDI (percentage change in renal clearance) data were collected primarily from the University of Washington drug interaction database (https://www.druginteractioninfo.org) and an additional PubMed search.

Transport Studies Using OAT-Transfected HEK293 Cells.

Large batches of cryopreserved OAT-transfected and wild-type HEK293 cells were prepared and validated for their transport activity before and after batch preparation. All of the uptake studies were carried out using the same batch of cells per cell type according to the procedures reported earlier (Feng et al., 2008; Cheng et al., 2012). Cryopreserved HEK293 cells were directly seeded in 48-well poly-d-lysine–coated plates 48 hours before each experiment, at densities of 1.2 × 105 cells/well in a volume of 0.2 ml/well. Cell monolayer confluence was verified visually under the microscope. Transport buffer was prepared at pH 7.4 using Hank’s balanced salt solution supplemented with 20 mM HEPES and 5.55 mM dextrose. All experiments were carried out at 1 or 3 µM substrate concentration, which is typically below the reported Lineweaver Burk constant values of some known OAT substrates. Immediately before the experiment, the cells were washed twice with 0.2 ml of transport buffer and then incubated with 200 µl of DPBS buffer containing the test compound at 37°C. At different time points, the cellular uptake was terminated by washing the cells three times with 0.2 ml each of ice-cold transport buffer DPBS and lysed directly on the plate with 100% methanol containing internal standard, and samples were quantified by liquid chromatography tandem mass spectrometry methodology. For each compound, cellular uptake was measured at six different time point in duplicates per cell type. Controls (3H-para aminohippuric acid (2 µM), 3H-cGMP (2 µM), or 3H-estrone sulfate (0.2 µM) for OAT1, OAT2, and OAT3, respectively) were run in each study to assess the assay variability and qualify the utility of rate data for secretory clearance predictions. No correction was applied, whereas the controls yielded rates within 30% of the mean across different days (data not shown).

Liquid chromatography tandem mass spectrometry analysis was conducted using a Sciex (Framingham, MA) Triple Quad 5500 tandem mass spectrometry system in electrospray ionization mode. Other instrumentation consisted of Shimadzu (Columbia, MD) LC-20AD pumps and ADDA autosampler (Apricot Designs, Covina, CA). Liquid chromatography was performed using either a Kinetex C18 or a Synergi Polar (30 × 2 mm) analytical column Phenomenex (Torrance, CA). Analytes were eluted with a gradient profile starting with 0.1% formic acid in water and an increasing concentration of 0.1% formic acid in acetonitrile.

Inhibition Studies Using OAT-Transfected HEK293 cells.

Inhibition studies were carried out in poly-d-lysine–coated 96-well plates with HEK293 cells seeded at densities of 0.60 × 105 cells/well, in a volume of 0.1 ml/well. The required final assay concentrations of probenecid were prepared using transport buffer spiked with probe substrates, 3H-para aminohippuric acid (2 µM), 3H-cGMP (2 µM), or 3H-estrone sulfate (0.2 µM) in OAT1, OAT2, and OAT3 cells, respectively. All solutions contained a final concentration of dimethylsulfoxide below 1% (v/v). Uptake was started by the addition of 0.1 ml of probe-spiked transport buffer without or with probenecid. The plates were then incubated for 4 minutes at 37°C with shaking at 150 rpm. The experiment was stopped by washing cells three washes with 0.15 ml/well ice-cold transport buffer. Samples were retrieved by lysing the cells with 0.1 ml of 10 mM Tris-HCL, pH 7.5, 75 mM NaCl, 125 mM NaF, 2.5 mM EDTA, and 0.5% Tergitol-type NP40. Radioactivity in each sample was quantified by measurement on a PerkinElmer MicroBeta TriLux Liquid Scintillation Counter.

Mechanistic Model to Estimate In Vitro Transport Clearances.

A two-compartment model (compartments representing the media and cell) was developed to estimate the intrinsic passive clearance (CLpass) and intrinsic active uptake clearance via individual transporters (CLint,OAT1, CLint,OAT2, CLint,OAT3) by simultaneously fitting the cell accumulation (Acell) data from all four cell lines. This model is analogous to the method described previously to analyze transport data in other cell systems (Poirier et al., 2008). Equations 1–6 are used in this modeling process:Embedded Image(1)Embedded Image(2)Embedded Image(3)Embedded Image(4)Embedded Image(5) andEmbedded Image(6)Where, Cew, Ciw, Aew, Aiw, Vew (0.2 ml), and Viw repesent concentration, amount, and volume of the extracellular and intracellular compartments. fu,ew and fu,c are extracellular and intracellular unbound fractions. Ka,mem represents nonspecific binding affinity to cell membranes, and Ka,trans is nonspecific binding affinity to transfected transporter. PR is the measured protein concentration per well. CpPR is the number of cells per measured protein (4 million cells/mg), and VpC is cell volume (1.7µl/million cells) measured assuming a spherical structure (14.8 µm diameter).

Prediction of Renal Clearance and Drug Interactions.

Renal blood clearance (CLrenal,b) is determined by glomerular filtration, tubular secretion, and reabsorption processes, and can be mathematically described by (Russel et al., 2002; Feng et al., 2010):Embedded Image(7)where, fu,b is the unbound fraction in blood, GFR is the glomerular filtration rate, CLsec is renal secretory clearance, and Freabs is the fraction of filtered and secreted drug that is reabsorbed. CLrenal,b is equal to renal plasma clearance (CLrenal,p) divided by BP ratio. For the majority of compounds tested in this study, passive permeability or passive transport clearance is very low, and so the Freabs was assumed to be negligible (see Results and Discussion). Assuming a well-stirred model, CLsec is expressed as follows:Embedded Image(8)where Qr is the renal blood flow (15.7 ml/min per kg) (Davies and Morris, 1993) and CLint,sec is the intrinsic secretory clearance obtained from in vitro uptake studies. In vitro active uptake clearance (in microliters per minute per milligram of protein) mediated by each OAT was corrected using physiologic scalars to obtain in vitro scaled CLint,OATx in units of milliliters per minute per kilogram: 0.25 mg of protein per million HEK293 cells (measured), 1 million HEK293 cells per million proximal tubule cells (assumed), 60 million proximal tubule cells per gram kidney, and 4.3 g of kidney per kilogram of body weight (Davies and Morris, 1993; Imamura et al., 2011; Watanabe et al., 2011). In vitro CLint,sec was estimated from the in vitro active uptake clearance of individual OATs and the RAFs of the corresponding transporter.Embedded Image(9)RAF for a given OAT was estimated from the in vivo CLint,sec and the in vitro uptake clearance of one or multiple selective substrates for that transporter, as follows:Embedded Image(10)The prediction of change in renal clearance in the presence of probenecid, a recommended probe inhibitor for OAT-based interactions, was calculated using the following set of equations:Embedded Image(11)Embedded Image(12)Embedded Image(13)CL′int,sec and CL′sec are intrinsic secretory and overall secretory clearance in the presence of the inhibitor drug, respectively. IC50 is the inhibition constant, and Cmax,u is the maximum unbound plasma concentration of inhibitor drug.

Model Predictability.

Prediction bias and precision were assessed with average fold error (AFE) in eq. 14:Embedded Image(14)N is the number of observations.

Results

In Vitro Transport of OAT Substrates.

Uptake of 31 drugs was evaluated in wild-type and transporter-transfected HEK293 cell expressing OAT1, OAT2, and OAT3. Tenofovir showed selective uptake by OAT1-transfected cells, whereas its uptake by OAT2- and OAT3-transfected cells was not significantly (F test with α-value of 0.05) different from the uptake by wild-type cells. Similarly, acyclovir and ganciclovir showed selective uptake by OAT2. Benzylpenicillin (penicillin G) and oseltamivir acid were selectively transported by OAT3 (Fig. 1). Data were fitted to a two-compartment model to estimate the transporter-mediated active uptake clearance (CLint,OAT) and CLpass. Table 1 shows the estimated uptake clearance of the 5 selective substrates as well as other 26 compounds evaluated in this study. The in vitro active uptake clearance was scaled to obtain the in vivo value per body weight using the physiologic scaling factors. Using the selective substrates, RAFs were derived to bridge the difference between the scaled in vitro uptake clearance and the in vivo intrinsic secretory clearance for each transporter. The obtained RAFs are 0.64, 7.3, and 4.1, respectively, for OAT1, OAT2, and OAT3.

Fig. 1.
Fig. 1.

Time course of uptake by OAT-transfected and wild-type HEK293 cells of selective substrates. Curves represent data fitting to two-compartment model to estimate active and passive intrinsic clearances.

View this table:
TABLE 1

Summary of in vitro mean intrinsic transport clearances obtained using HEK293 cells and predicted secretory and total renal clearance of 31 drugs investigated

Prediction of Active Secretion and Renal Clearance.

Secretory intrinsic clearance (CLint,sec) and the renal plasma clearance predicted using the transport data and the obtained RAFs showed good agreement with the observed values (Fig. 2). The predicted CLint,sec and renal clearance are within 2-fold for 68% and 84% of the observed values, respectively, for the 31 compounds evaluated. Acetazolamide, adefovir, bumetanide, pravastatin, rosuvastatin, and zalcitabine are notable outliers, whereas the predicted intrinsic secretory clearance for the other 25 compounds were within 3-fold of the observed values (Fig. 2A). Acetazolamide is the only compound showing >3-fold underprediction of renal clearance (Fig. 2B).

Fig. 2.
Fig. 2.

IVIVE to predict intrinsic secretory clearance (A) and total plasma renal clearance (B) of 31 drugs. Red, blue, and green data points represent OAT1-, OAT2-, and OAT3-selective substrates, respectively. Diagonal solid, dashed, and dotted lines represent unity, 2-fold error, and 3-fold error, respectively.

Figure 3 depicts the predicted percentage contribution of the individual OATs and glomerular filtration to the human renal clearance. Interestingly, none of the compounds tested exhibited a measurable contribution of all three OATs for their renal clearance. Finally, OAT3-mediated transport emerged as the predominant driver to active secretion of 22 compounds.

Fig. 3.
Fig. 3.

Predicted contribution of OAT1, OAT2, OAT3, and glomerular filtration to the renal clearance of the drugs evaluated. Gemfibrozil was not shown because the predicted active secretion is negligible.

Permeability-Tubular Reabsorption Relationship.

Using a separate dataset of 47 compounds with human renal plasma clearance lower than fu,p · GFR (Supplemental Table 1), an empirical relationship between the fraction reabsorbed [Freabs = 1 − (CLrenal,p/fu,p.GFR)] and apparent permeability (Papp) across MDCK-low efflux cells (pH 6.5) was derived. Permeability values were obtained from our previous work (Varma et al., 2012). We applied sigmoidal model [Freabs = Pappa/(ba + Pappa), where a represents the slope factor and b is the value of Papp at which Freabs equals 0.5] that was previously reported (Scotcher et al., 2016). Similar to the finding of Scotcher et al. (2016), no correlation was seen for basic drugs. However, a significant correlation (r2 = 0.9) was noted for the combined set of acids, neutral, and zwitterions (n = 31), with the estimated values of 2.9 ± 0.6 and 6.0 ± 0.5 × 10−6 cm/s for a and b, respectively (Fig. 4A). On the basis of this relationship, we note that the tubular reabsorption of the majority of compounds (28 of 31) in the OATs (renal secretory) dataset is <0.15 (Fig. 4B).

Fig. 4.
Fig. 4.

Correlation between apparent permeability and tubular fraction reabsorbed. (A) Correlation was established using a separate dataset of nonbases (closed points, n = 31) employing the Hill model. Curve represents the data fit (shaded area − 95% confidence interval) of nonbases to sigmoidal model, Freabs = Pappa/(ba + Pappa). Bases (open points, n = 16) did not show a distinct trend and were not considered for model fitting. The vertical dotted line represents the ECCS permeability cutoff. (B) Predicted renal fraction of the compounds reabsorbed in the current OAT dataset on the basis of the established correlation.

Prediction of Renal DDIs with Probenecid.

In vitro inhibition studies showed concentration-dependent inhibition of OAT1-mediated p-aminohippurate uptake and OAT3-mediated E3S uptake by probenecid with the estimated IC50 values of 9.6 and 4.5 µM, respectively (Fig. 5). Probenecid also inhibited OAT2-mediated cGMP uptake in the HEK293 cells, however, with a low inhibition potency (IC50 ∼853 µM). The change in renal clearance when coadministered with probenecid was estimated using these IC50 values for each OAT isoform and the clinically observed free Cmax for the corresponding dose of probenecid. A free drug hypothesis was assumed, and the DDI predictions are based on the clinically observed free maximum plasma concentration of probenecid. Free Cmax was estimated based on the observed pharmacokinetics at different doses of probenecid (Selen et al., 1982) adjusted for the concentration-dependent plasma unbound fraction (Emanuelsson et al., 1987). Hence, free Cmax values of 6.2, 15, 24.4, and 51 µM were used for the corresponding perpetrator (probenecid) dose of 500, 750, 1000, and 1500 mg, respectively. Clinical data of change in renal clearance when dosed in combination with probenecid were extracted from the published literature, for the OATs substrates evaluated in the in vitro rate study. A total of 18 clinical interactions with probenecid as an inhibitor drug were evaluated with the mechanistic DDI model (eqs. 11–13) (Table 2), which captured the filtration and secretory components of the victim drug and interaction at the level of three OAT isoforms. The predicted change in renal clearance was within ±40% of the observed mean value for 16 of 18 cases (89%), with an AFE of 1.66 when using this mechanistic static model (Fig. 6). Acyclovir, which is predominantly transported by OAT2, is the only false-negative prediction (predicted <25% and observed ≥25%).

Fig. 5.
Fig. 5.

Probenecid concentration-dependent inhibition of OAT1-mediated p-aminohippurate uptake, OAT2-mediated cGMP uptake, and OAT3-mediated E3S uptake in transfected cells. Mean (and 95% confidence interval) of IC50 against each OAT is provided. Each data point represents the mean ± S.D. of n = 3.

View this table:
TABLE 2

Summary of mechanistic static model based predictions of OAT-mediated DDIs with probenecid

Fig. 6.
Fig. 6.

Observed versus predicted change in human renal clearance of the OAT substrate drugs when coadministered with probenecid. Mechanistic static model predicted and observed values are within 40% for 16 of 18 cases (89%). Diagonal solid and dashed lines represent unity and ±25% and ±40% error. Dotted horizontal and vertical lines represent bioequivalence limits.

Discussion

Renal clearance determines the pharmacokinetics of several drugs, particularly extended clearance classification system (ECCS) class 3A, 3B, and 4 drugs (Varma et al., 2009, 2015). However, methodologies to predict renal clearance are somewhat limited to allometric scaling of dog or rat renal clearance, which has been suggested to be a potential option (Paine et al., 2011). Our goal is to establish an IVIVE methodology to quantitatively predict renal clearance for drugs undergoing active secretion, primarily driven by OAT-mediated transport. In this process, we: 1) obtained transport rates employing HEK293 cells stably transfected with individual human OATs; 2) identified selective substrates for each of the three OATs expressed on the basolateral membrane of proximal tubule cells; and 3) established RAFs for the three OATs using the in vitro active transport and in vivo secretory clearance of the selective substrates. The obtained RAFs and the in vitro transport data were then used to predict renal clearance and to assess the contribution of individual OATs to the secretion for 31 drugs. Finally, this approach was extended to predict renal DDIs between these substrate drugs and probenecid, a recommended clinical OAT probe inhibitor.

Of the drugs evaluated in OAT1, OAT2, and OAT3 substrate assays, tenofovir was identified as a selective substrate to OAT1, whereas acyclovir and ganciclovir behaved as OAT2-selective substrates. Benzylpenicillin and oseltamivir acid were selective to OAT3, with no measurable active uptake in the OAT1- and OAT2-transfected cells (Fig. 1). These observations are generally consistent with those of earlier reports (Cheng et al., 2012; Maeda et al., 2014). RAFs, bridging the in vitro and in vivo human intrinsic secretory clearance after correcting for the physiologic scalars, were obtained using these selective substrates. The RAF values represent a collated correction for the: 1) differences in the transporter expression levels between the in vitro transfected cells and proximal tubule cells; 2) in vitro-in vivo differences in transport activity per protein expressed; and 3) uncertainty in the physiologic parameters (e.g., cells per gram of kidney) needed for scaling in vitro data. The prediction accuracy in the active secretion clearance and the total renal clearance in terms of AFE were 1.89 and 1.41, respectively.

Early predictions of renal clearance are important for optimizing the total body clearance in a chemical series and facilitate dose projections, as well as, evaluate DDI risk. The ECCS framework suggests that drugs with low passive permeability are likely cleared by urinary route (>70% of the systemic clearance), with the exception of high–molecular-weight (>400 Da) acids or zwitterions (class 3B), in which case, hepatic uptake may be the rate-determining process to the systemic clearance (Varma et al., 2015). Therefore, ECCS provides an early indication of the potential contribution of the renal clearance to the total body clearance and the renal DDI liability, which needs to be followed up with quantitative predictions. The availability of IVIVE methodologies for quantitative predictions is very limited. Watanabe et al. (2011) applied the IVIVE approach based on in vitro uptake clearance by human kidney slices to predict secretory renal clearance for a set of 10 anionic drugs and showed good predictability. However, such a methodology is limited by the availability of human kidney tissue. Kunze et al. (2014) used in vitro bidirectional permeability across LLC-PK1 cells to establish a model incorporating both active secretion and tubular reabsorption to predict renal clearance of about 20 drugs. Although simple and amenable to high-throughput screening platforms, the latter methodology, which assumes that LLC-PK1 cells (derived from pig kidney epithelium) quantitatively express all relevant uptake and efflux transporters, as in human proximal tubule cells, may potentially mispredict active secretion. For example, LLC-PK1 cells show limited OAT activity and may underestimate active transport for acids and zwitterions (Hori et al., 1993). Additionally, differential expression of apical ATP binding cassette and solute carrier transporters influence the transcellular permeability in vitro, although uptake into the cell compartment is often the rate-determining step in secretory clearance (Watanabe et al., 2011; Posada et al., 2015). Use of established scaling factors for predicting in vivo clearance from the recombinant systems is a well-proven practice for metabolic pathways (Obach et al., 1997). However, such IVIVE approaches are very limited for transporter-mediated clearance. Posada et al. (2015) applied the initial rate data obtained from OAT3-transfected cells to develop a middle-out physiologically based pharmacokinetic model to recover renal clearance of a single substrate drug, pemetrexed. To the best of our knowledge, the current study represents the first comprehensive attempt to establish the IVIVE methodology for prospective translation of transport data based on stably transfected cell systems to quantitatively predict transporter-mediated renal clearance and DDIs.

The proposed IVIVE approach relies on certain assumptions, which need careful consideration on its application. Primarily, the renal clearance of the drugs assessed here was assumed to be driven by glomerular filtration and a tubular secretion process with no tubular reabsorption. It is generally believed that reabsorption is associated largely with passive transport along the length of the nephron. Therefore, we used an apparent relationship between transcellular permeability and the fraction reabsorbed (Papp − Freabs correlation) (Scotcher et al., 2016) to demonstrate that tubular reabsorption has a minimal role in the renal clearance of drugs in our dataset (Fig. 4). A separate dataset of compounds with renal clearance lower than glomerular filtration (i.e., net reabsorption) was developed, and the Papp − Freabs correlation was established using transcellular permeability measured in low-efflux MDCK cells (Varma et al., 2012). Based on this relationship, we note that most of the compounds in the OAT IVIVE dataset are predicted to have Freab values of less than 0.15 (Fig. 4B), and thus reabsorption was not considered in the overall renal clearance predictions. However, static or dynamic reabsorption models may be applied in conjunction with the active secretion translation to predict renal clearance of compounds with high permeability (Scotcher et al., 2016). It should be emphasized that renal clearance contribution to the total body clearance is generally <30% for moderately and highly permeable drugs (Papp >5 × 10−6 cm/s), as defined by the biopharmaceutics drug disposition classification system and the ECCS (Wu and Benet, 2005; Varma et al., 2012, 2015); therefore, the need for early prediction of renal clearance in this space is generally less. Second, we assumed that unidirectional transport across the basolateral membrane, but not the apical membrane, of proximal tubule cells is the rate-determining step for the secretory clearance (i.e., loss from the blood compartment). Although some of the evaluated compounds are known substrates to efflux transporters on the apical membrane of proximal tubule cells (Supplemental Table 2), this assumption is generally accepted, particularly for OAT substrates (Sirianni and Pang, 1997; Watanabe et al., 2011; Varma et al., 2015). Finally, it was also assumed that the tubular secretion is determined by OAT-mediated transporter only, whereas other solute carriers on the basolateral membrane of the proximal tubule have a minimal role. Based on the available literature and our internal data, these drugs are not substrates to the other clinically relevant transporter (OCT2), with the exception of cimetidine and famotidine (Supplemental Table 2). Nevertheless, RAF for OCT2 can also be established adopting the approach described here for OATs. Other basolateral transporters, such as OATP4C1, may also contribute to the renal secretion of certain drugs (Mikkaichi et al., 2004); however, limited data are available on the OATP4C1 transport activity for the 31 compounds evaluated here. However challenging, a quantitative understanding of the contribution of other basolateral uptake transporters and apical efflux and reabsorption transport to clearance from the blood compartment may further improve the predictions.

Recent reports suggest a role of OAT2 in the renal secretion of creatinine (Lepist et al., 2014) and some antiviral drugs (Cheng et al., 2012). We assessed the contribution of OAT2 to renal clearance of the compounds in our dataset (Fig. 3). Interestingly, only acyclovir, ganciclovir, penciclovir, and ketorolac showed significant OAT2-mediated transport, with OAT2 contributing almost entirely to the active secretion for all, except ketorolac. Similarly, only five compounds showed significant contribution of OAT1 (i.e., adefovir, cilostazol, ketoprofen, tenofovir, and ketorolac). Interestingly, no single compound had a notable contribution from all three transporters. ECCS class 1A and 3A compounds (low–molecular-weight acids/zwitterions) show major involvement of OAT1 or OAT3, whereas class 4 compounds (low-permeability bases/neutrals) are secreted by either OAT2 or OAT3. However, all class 3B compounds (high–molecular-weight, low-permeability acids/zwitterions) are predominantly secreted by OAT3 alone. Overall, OAT3 emerged as a major contributor to renal secretion for the majority of the compounds evaluated, implying its clinical significance for a wide variety of drugs.

Regulatory guidelines suggest in vitro and follow-up clinical assessment for the OAT1- and OAT3-mediated clearance of investigational drugs (European Medicines Agency, 2012; Food and Drug Administration, 2012). Probenecid can potently inhibit OAT1 and OAT3 in vivo and can serve as a clinical probe inhibitor for these two transporters. The in vivo inhibition potency (1 + Cmax,u/IC50) of probenecid (1.5-g dose) can reach up to about 6- and 12-fold against OAT1 and OAT3, respectively, implying that an estimated ∼84% (OAT1) and >90% (OAT3) of the transporter-mediated secretory clearance can be inhibited by probenecid at a clinically relevant dose. On the other hand, probenecid provided only a minimal inhibition of OAT2-mediated renal secretion (<5% inhibition). Therefore, an alternative clinical probe inhibitor should be considered when the investigational drug is selectively or predominantly transported by OAT2 (e.g., acyclovir, ganciclovir). A review of the literature suggested indomethacin as the only plausible clinical probe inhibitor. However, based on the in vitro IC50 (2.1 µM) (Shen et al., 2015) and free Cmax (0.7 µM), it may only cause ∼30% inhibition of OAT2 at its therapeutic dose.

In conclusion, to the best of our knowledge, this is the first report providing a comprehensively validated IVIVE method that allows for the quantitative prediction of human renal clearance and DDIs on the basis of in vitro transport data obtained using transporter-transfected cell systems. Additionally, this mechanistic static approach provides a basis for dynamic physiologically based modeling of renal secretory clearance and DDIs.

Acknowledgments

The authors thank David Rodrigues, Larry Tremaine, and Tristan Maurer for valuable input during this work.

Authorship Contributions

Participated in research design: Mathialagan, Tess, Litchfield, Varma.

Conducted experiments: Mathialagan, Piotrowski.

Contributed new reagents or analytic tools: Mathialagan, Tess, Varma.

Performed data analysis: Mathialagan, Feng, Varma.

Wrote or contributed to the writing of the manuscript: Mathialagan, Piotrowski, Tess, Feng, Litchfield, Varma.

Footnotes

    • Received February 3, 2017.
    • Accepted February 6, 2017.

  • All authors are full-time employees of Pfizer Inc. No other potential conflicts of interest relevant to this article are reported.

  • dx.doi.org/10.1124/dmd.116.074294.

  • ↵Embedded ImageThis article has supplemental material available at dmd.aspetjournals.org.

Abbreviations

Acell
cell accumulation
AFE
average fold error
BP
blood-to-plasma
CLint,OATx
intrinsic active uptake clearance
CLint,sec
intrinsic secretory clearance
CLpass
intrinsic passive clearance
CLrenal,b
renal blood clearance
CLrenal,p
renal plasma clearance
CLsec
renal secretory clearance
Cmax,u
maximum unbound plasma concentration
CpPR
number of cells per measured protein
DDI
drug-drug interaction
DPBS
Dulbecco’s phosphate buffer saline
ECCS
extended clearance classification system
fu,b
unbound fraction in blood
Freabs
fraction reabsorbed
GFR
glomerular filtration rate
HEK
human embryonic kidney
IVIVE
in vitro-in vivo extrapolation
Ka,mem
nonspecific binding affinity to cell membranes
Ka,trans
nonspecific binding affinity to transfected transporter
OAT
organic anion transporter
OCT
organic cation transporter
Papp
apparent permeability
PR
measured protein concentration per well
RAF
relative activity factor
VpC
cell volume measured assuming a spherical structure

References

    1. Aherne GW,
    2. Piall E,
    3. Marks V,
    4. Mould G, and
    5. White WF
    (1978) Prolongation and enhancement of serum methotrexate concentrations by probenecid. BMJ 1:1097–1099.
    OpenUrlAbstract/FREE Full Text
    1. Brown G,
    2. Zemcov SJ, and
    3. Clarke AM
    (1993) Effect of probenecid on cefazolin serum concentrations. J Antimicrob Chemother 31:1009–1011.
    OpenUrlFREE Full Text
  3. ↵
    1. Cheng Y,
    2. Vapurcuyan A,
    3. Shahidullah M,
    4. Aleksunes LM, and
    5. Pelis RM
    (2012) Expression of organic anion transporter 2 in the human kidney and its potential role in the tubular secretion of guanine-containing antiviral drugs. Drug Metab Dispos 40:617–624.
    OpenUrlAbstract/FREE Full Text
    1. Chennavasin P,
    2. Seiwell R,
    3. Brater DC, and
    4. Liang WM
    (1979) Pharmacodynamic analysis of the furosemide-probenecid interaction in man. Kidney Int 16:187–195.
    OpenUrlCrossRefPubMed
  5. ↵
    1. Chu X,
    2. Bleasby K, and
    3. Evers R
    (2013) Species differences in drug transporters and implications for translating preclinical findings to humans. Expert Opin Drug Metab Toxicol 9:237–252.
    OpenUrlCrossRefPubMed
    1. Cimoch PJ,
    2. Lavelle J,
    3. Pollard R,
    4. Griffy KG,
    5. Wong R,
    6. Tarnowski TL,
    7. Casserella S, and
    8. Jung D
    (1998) Pharmacokinetics of oral ganciclovir alone and in combination with zidovudine, didanosine, and probenecid in HIV-infected subjects. J Acquir Immune Defic Syndr Hum Retrovirol 17:227–234.
    OpenUrlPubMed
  7. ↵
    1. Davies B and
    2. Morris T
    (1993) Physiological parameters in laboratory animals and humans. Pharm Res 10:1093–1095.
    OpenUrlCrossRefPubMed
  8. ↵
    1. Di L,
    2. Feng B,
    3. Goosen TC,
    4. Lai Y,
    5. Steyn SJ,
    6. Varma MV, and
    7. Obach RS
    (2013) A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab Dispos 41:1975–1993.
    OpenUrlAbstract/FREE Full Text
  9. ↵
    1. European Medicines Agency
    (2012) Guideline on the Investigation of Drug Interactions. Committee for Human Medicinal Products, European Medicines Agency, London.
  10. ↵
    1. Emanuelsson BM,
    2. Beermann B, and
    3. Paalzow LK
    (1987) Non-linear elimination and protein binding of probenecid. Eur J Clin Pharmacol 32:395–401.
    OpenUrlCrossRefPubMed
  11. ↵
    1. Feng B,
    2. LaPerle JL,
    3. Chang G, and
    4. Varma MV
    (2010) Renal clearance in drug discovery and development: molecular descriptors, drug transporters and disease state. Expert Opin Drug Metab Toxicol 6:939–952.
    OpenUrlCrossRefPubMed
  12. ↵
    1. Feng B,
    2. Obach RS,
    3. Burstein AH,
    4. Clark DJ,
    5. de Morais SM, and
    6. Faessel HM
    (2008) Effect of human renal cationic transporter inhibition on the pharmacokinetics of varenicline, a new therapy for smoking cessation: an in vitro-in vivo study. Clin Pharmacol Ther 83:567–576.
    OpenUrlCrossRefPubMed
  13. ↵
    1. Food and Drug Administration
    (2012) Guidance for Industry: Drug Interaction Studies: Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations. Center for Drug Evaluation and Research, Food and Drug Administration, Rockville, MD.
    1. Gisclon LG,
    2. Boyd RA,
    3. Williams RL, and
    4. Giacomini KM
    (1989) The effect of probenecid on the renal elimination of cimetidine. Clin Pharmacol Ther 45:444–452.
    OpenUrlCrossRefPubMed
    1. Hill G,
    2. Cihlar T,
    3. Oo C,
    4. Ho ES,
    5. Prior K,
    6. Wiltshire H,
    7. Barrett J,
    8. Liu B, and
    9. Ward P
    (2002) The anti-influenza drug oseltamivir exhibits low potential to induce pharmacokinetic drug interactions via renal secretion-correlation of in vivo and in vitro studies. Drug Metab Dispos 30:13–19.
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Hori R,
    2. Okamura M,
    3. Takayama A,
    4. Hirozane K, and
    5. Takano M
    (1993) Transport of organic anion in the OK kidney epithelial cell line. Am J Physiol 264:F975–F980.
    OpenUrl
  17. ↵
    1. Hosea NA,
    2. Collard WT,
    3. Cole S,
    4. Maurer TS,
    5. Fang RX,
    6. Jones H,
    7. Kakar SM,
    8. Nakai Y,
    9. Smith BJ,
    10. Webster R, et al.
    (2009) Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches. J Clin Pharmacol 49:513–533.
    OpenUrlCrossRefPubMed
  18. ↵
    1. Imamura Y,
    2. Murayama N,
    3. Okudaira N,
    4. Kurihara A,
    5. Okazaki O,
    6. Izumi T,
    7. Inoue K,
    8. Yuasa H,
    9. Kusuhara H, and
    10. Sugiyama Y
    (2011) Prediction of fluoroquinolone-induced elevation in serum creatinine levels: a case of drug-endogenous substance interaction involving the inhibition of renal secretion. Clin Pharmacol Ther 89:81–88.
    OpenUrlCrossRefPubMed
    1. Inotsume N,
    2. Nishimura M,
    3. Nakano M,
    4. Fujiyama S, and
    5. Sato T
    (1990) The inhibitory effect of probenecid on renal excretion of famotidine in young, healthy volunteers. J Clin Pharmacol 30:50–56.
    OpenUrlCrossRefPubMed
  20. ↵
    1. Kunze A,
    2. Huwyler J,
    3. Poller B,
    4. Gutmann H, and
    5. Camenisch G
    (2014) In vitro-in vivo extrapolation method to predict human renal clearance of drugs. J Pharm Sci 103:994–1001.
    OpenUrl
    1. Laskin OL,
    2. de Miranda P,
    3. King DH,
    4. Page DA,
    5. Longstreth JA,
    6. Rocco L, and
    7. Lietman PS
    (1982) Effects of probenecid on the pharmacokinetics and elimination of acyclovir in humans. Antimicrob Agents Chemother 21:804–807.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Lee W and
    2. Kim RB
    (2004) Transporters and renal drug elimination. Annu Rev Pharmacol Toxicol 44:137–166.
    OpenUrlCrossRefPubMed
  23. ↵
    1. Lepist E-I,
    2. Zhang X,
    3. Hao J,
    4. Huang J,
    5. Kosaka A,
    6. Birkus G,
    7. Murray BP,
    8. Bannister R,
    9. Cihlar T,
    10. Huang Y, et al.
    (2014) Contribution of the organic anion transporter OAT2 to the renal active tubular secretion of creatinine and mechanism for serum creatinine elevations caused by cobicistat. Kidney Int 86:350–357.
    OpenUrlCrossRefPubMed
    1. Li KY,
    2. Qiu Y,
    3. Jiang Y,
    4. Luo CH,
    5. Lin XP,
    6. Wang J, and
    7. Yang N
    (2014) Effect of probenecid on pharmacokinetics and tolerability of olmesartan in healthy chinese volunteers. Curr Ther Res Clin Exp 76:7–10.
    OpenUrl
    1. Liu S,
    2. Beringer PM,
    3. Hidayat L,
    4. Rao AP,
    5. Louie S,
    6. Burckart GJ, and
    7. Shapiro B
    (2008) Probenecid, but not cystic fibrosis, alters the total and renal clearance of fexofenadine. J Clin Pharmacol 48:957–965.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Lombardo F,
    2. Obach RS,
    3. Varma MV,
    4. Stringer R, and
    5. Berellini G
    (2014) Clearance mechanism assignment and total clearance prediction in human based upon in silico models. J Med Chem 57:4397–4405.
    OpenUrl
  27. ↵
    1. Maeda K,
    2. Tian Y,
    3. Fujita T,
    4. Ikeda Y,
    5. Kumagai Y,
    6. Kondo T,
    7. Tanabe K,
    8. Nakayama H,
    9. Horita S,
    10. Kusuhara H, et al.
    (2014) Inhibitory effects of p-aminohippurate and probenecid on the renal clearance of adefovir and benzylpenicillin as probe drugs for organic anion transporter (OAT) 1 and OAT3 in humans. Eur J Pharm Sci 59:94–103.
    OpenUrlCrossRefPubMed
    1. Massarella JW,
    2. Nazareno LA,
    3. Passe S, and
    4. Min B
    (1996) The effect of probenecid on the pharmacokinetics of zalcitabine in HIV-positive patients. Pharm Res 13:449–452.
    OpenUrlPubMed
  29. ↵
    1. Mikkaichi T,
    2. Suzuki T,
    3. Onogawa T,
    4. Tanemoto M,
    5. Mizutamari H,
    6. Okada M,
    7. Chaki T,
    8. Masuda S,
    9. Tokui T,
    10. Eto N, et al.
    (2004) Isolation and characterization of a digoxin transporter and its rat homologue expressed in the kidney. Proc Natl Acad Sci USA 101:3569–3574.
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Morrissey KM,
    2. Stocker SL,
    3. Wittwer MB,
    4. Xu L, and
    5. Giacomini KM
    (2013) Renal transporters in drug development. Annu Rev Pharmacol Toxicol 53:503–529.
    OpenUrlCrossRefPubMed
  31. ↵
    1. Obach RS,
    2. Baxter JG,
    3. Liston TE,
    4. Silber BM,
    5. Jones BC,
    6. MacIntyre F,
    7. Rance DJ, and
    8. Wastall P
    (1997) The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 283:46–58.
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Obach RS,
    2. Lombardo F, and
    3. Waters NJ
    (2008) Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos 36:1385–1405.
    OpenUrlAbstract/FREE Full Text
    1. Odlind B,
    2. Beermann B, and
    3. Lindström B
    (1983) Coupling between renal tubular secretion and effect of bumetanide. Clin Pharmacol Ther 34:805–809.
    OpenUrlPubMed
  34. ↵
    1. Paine SW,
    2. Ménochet K,
    3. Denton R,
    4. McGinnity DF, and
    5. Riley RJ
    (2011) Prediction of human renal clearance from preclinical species for a diverse set of drugs that exhibit both active secretion and net reabsorption. Drug Metab Dispos 39:1008–1013.
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Poirier A,
    2. Lavé T,
    3. Portmann R,
    4. Brun M-E,
    5. Senner F,
    6. Kansy M,
    7. Grimm H-P, and
    8. Funk C
    (2008) Design, data analysis, and simulation of in vitro drug transport kinetic experiments using a mechanistic in vitro model. Drug Metab Dispos 36:2434–2444.
    OpenUrlAbstract/FREE Full Text
  36. ↵
    1. Posada MM,
    2. Bacon JA,
    3. Schneck KB,
    4. Tirona RG,
    5. Kim RB,
    6. Higgins JW,
    7. Pak YA,
    8. Hall SD, and
    9. Hillgren KM
    (2015) Prediction of renal transporter mediated drug-drug interactions for pemetrexed using physiologically based pharmacokinetic modeling. Drug Metab Dispos 43:325–334.
    OpenUrlAbstract/FREE Full Text
  37. ↵
    1. Russel FG,
    2. Masereeuw R, and
    3. van Aubel RA
    (2002) Molecular aspects of renal anionic drug transport. Annu Rev Physiol 64:563–594.
    OpenUrlCrossRefPubMed
  38. ↵
    1. Scotcher D,
    2. Jones C,
    3. Rostami-Hodjegan A, and
    4. Galetin A
    (2016) Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance. Eur J Pharm Sci 94:59–71.
    OpenUrl
  39. ↵
    1. Selen A,
    2. Amidon GL, and
    3. Welling PG
    (1982) Pharmacokinetics of probenecid following oral doses to human volunteers. J Pharm Sci 71:1238–1242.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Shen H,
    2. Liu T,
    3. Morse BL,
    4. Zhao Y,
    5. Zhang Y,
    6. Qiu X,
    7. Chen C,
    8. Lewin AC,
    9. Wang XT,
    10. Liu G, et al.
    (2015) Characterization of organic anion transporter 2 (SLC22A7): a highly efficient transporter for creatinine and species-dependent renal tubular expression. Drug Metab Dispos 43:984–993.
    OpenUrlAbstract/FREE Full Text
  41. ↵
    1. Sirianni GL and
    2. Pang KS
    (1997) Organ clearance concepts: new perspectives on old principles. J Pharmacokinet Biopharm 25:449–470.
    OpenUrlCrossRefPubMed
  42. ↵
    1. Varma MV,
    2. Feng B,
    3. Obach RS,
    4. Troutman MD,
    5. Chupka J,
    6. Miller HR, and
    7. El-Kattan A
    (2009) Physicochemical determinants of human renal clearance. J Med Chem 52:4844–4852.
    OpenUrlCrossRefPubMed
  43. ↵
    1. Varma MV,
    2. Gardner I,
    3. Steyn SJ,
    4. Nkansah P,
    5. Rotter CJ,
    6. Whitney-Pickett C,
    7. Zhang H,
    8. Di L,
    9. Cram M,
    10. Fenner KS, et al.
    (2012) pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery. Mol Pharm 9:1199–1212.
    OpenUrlPubMed
  44. ↵
    1. Varma MV,
    2. Steyn SJ,
    3. Allerton C, and
    4. El-Kattan AF
    (2015) Predicting clearance mechanism in drug discovery: extended clearance classification system (ECCS). Pharm Res 32:3785–3802.
    OpenUrlCrossRefPubMed
  45. ↵
    1. Watanabe T,
    2. Kusuhara H,
    3. Watanabe T,
    4. Debori Y,
    5. Maeda K,
    6. Kondo T,
    7. Nakayama H,
    8. Horita S,
    9. Ogilvie BW,
    10. Parkinson A, et al.
    (2011) Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments. Drug Metab Dispos 39:1031–1038.
    OpenUrlAbstract/FREE Full Text
  46. ↵
    1. Wu C-Y and
    2. Benet LZ
    (2005) Predicting drug disposition via application of BCS: transport/absorption/ elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res 22:11–23.
    OpenUrlCrossRefPubMed
    1. Yasui-Furukori N,
    2. Uno T,
    3. Sugawara K, and
    4. Tateishi T
    (2005) Different effects of three transporting inhibitors, verapamil, cimetidine, and probenecid, on fexofenadine pharmacokinetics. Clin Pharmacol Ther 77:17–23.
    OpenUrlCrossRefPubMed
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Predicting Renal Clearance of OAT Substrates

Sumathy Mathialagan, Mary A. Piotrowski, David A. Tess, Bo Feng, John Litchfield and Manthena V. Varma
Drug Metabolism and Disposition April 1, 2017, 45 (4) 409-417; DOI: https://doi.org/10.1124/dmd.116.074294
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