Skip to main content
Advertisement

Main menu

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET

User menu

  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Drug Metabolism & Disposition
  • Other Publications
    • Drug Metabolism and Disposition
    • Journal of Pharmacology and Experimental Therapeutics
    • Molecular Pharmacology
    • Pharmacological Reviews
    • Pharmacology Research & Perspectives
    • ASPET
  • My alerts
  • Log in
  • My Cart
Drug Metabolism & Disposition

Advanced Search

  • Home
  • Articles
    • Current Issue
    • Fast Forward
    • Latest Articles
    • Archive
  • Information
    • Instructions to Authors
    • Submit a Manuscript
    • FAQs
    • For Subscribers
    • Terms & Conditions of Use
    • Permissions
  • Editorial Board
  • Alerts
    • Alerts
    • RSS Feeds
  • Virtual Issues
  • Feedback
  • Visit dmd on Facebook
  • Follow dmd on Twitter
  • Follow ASPET on LinkedIn
Research ArticleArticle

Impact of Experimental Conditions on the Evaluation of Interactions between Multidrug and Toxin Extrusion Proteins and Candidate Drugs

Christian Lechner, Naoki Ishiguro, Ayano Fukuhara, Hidetada Shimizu, Naoko Ohtsu, Masahito Takatani, Kotaro Nishiyama, Ikumi Washio, Norio Yamamura and Hiroyuki Kusuhara
Drug Metabolism and Disposition August 2016, 44 (8) 1381-1389; DOI: https://doi.org/10.1124/dmd.115.068163
Christian Lechner
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Naoki Ishiguro
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ayano Fukuhara
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hidetada Shimizu
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Naoko Ohtsu
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Masahito Takatani
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kotaro Nishiyama
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ikumi Washio
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Norio Yamamura
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hiroyuki Kusuhara
Pharmacokinetics and Non-Clinical Safety Department, Nippon Boehringer Ingelheim Co., Ltd., Kobe, Japan (C.L., N.I., A.F., H.S., N.O., M.T., K.N., I.W., N.Y.); and Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan (H.K.)
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF
Loading

Abstract

Multidrug and toxin extrusion transporters (MATEs) have a determining influence on the pharmacokinetic profiles of many drugs and are involved in several clinical drug-drug interactions (DDIs). Cellular uptake assays with recombinant cells expressing human MATE1 or MATE2-K are widely used to investigate MATE-mediated transport for DDI assessment; however, the experimental conditions and used test substrates vary among laboratories. We therefore initially examined the impact of three assay conditions that have been applied for MATE substrate and inhibitor profiling in the literature. One of the tested conditions resulted in significantly higher uptake rates of the three test substrates, [14C]metformin, [3H]thiamine, and [3H]1-methyl-4-phenylpyridinium (MPP+), but IC50 values of four tested MATE inhibitors varied only slightly among the three conditions (<2.5-fold difference). Subsequently, we investigated the uptake characteristics of the five MATE substrates: [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]estrone-3-sulfate (E3S), and rhodamine 123, as well as the impact of the used test substrate on the inhibition profiles of 10 MATE inhibitors at one selected assay condition. [3H]E3S showed atypical uptake characteristics compared with those observed with the other four substrates. IC50 values of the tested inhibitors were in a similar range (<4-fold difference) when [14C]metformin, [3H]thiamine, [3H]MPP+, or [3H]E3S were used as substrates but were considerably higher with rhodamine 123 (9.8-fold and 4.1-fold differences compared with [14C]metformin with MATE1 and MATE2-K, respectively). This study demonstrated for the first time that the impact of assay conditions on IC50 determination is negligible, that kinetic characteristics differ among used test substrates, and that substrate-dependent inhibition exists for MATE1 and MATE2-K, giving valuable insight into the assessment of clinically relevant MATE-mediated DDIs in vitro.

Introduction

The renal tubular secretion of cationic drugs is mediated by specific sets of transporters in the basolateral and apical membranes of the proximal tubule cells. The first step of the renal secretion process is the basolateral uptake of organic cations from the circulation into the proximal tubule cells. The main responsible transporter for this process is organic cation transporter 2 (OCT2) (SLC22A2) (Inui et al., 2000; Fujita et al., 2006). In contrast to the comprehensive knowledge about this OCT2-mediated basolateral uptake of organic compounds, understanding of the molecular mechanisms underlying the subsequent apical secretion into the tubular lumen and identification of the involved transporters have only recently begun. Growing evidence has been found that this process is presumably mediated by the two human multidrug and toxin extrusion (MATE) isoforms MATE1 (SLC47A1) and its paralog MATE2-K (SLC47A2), which are abundantly expressed in the apical membrane of proximal tubule cells and work as H+/organic ion antiporters driven by an inwardly directed H+ gradient (Yonezawa and Inui, 2011; Motohashi and Inui, 2013). Both MATE isoforms share a partially overlapping substrate specificity, and it has been shown that they transport a wide range of cationic, zwitterionic, and anionic compounds in vitro, including several renally secreted drugs such as metformin, cimetidine, and others (Masuda et al., 2006; Tanihara et al., 2007; Chen et al., 2009).

A number of subsequent in vitro and in vivo studies have demonstrated the clinical importance of MATE transporters, including their role as determinants of the pharmacokinetic profiles of various drugs and their direct involvement in several clinical drug-drug interactions (DDIs) (Tsuda et al., 2009; Kusuhara et al., 2011; Ito et al., 2012). As a consequence, MATEs are now perceived as transporters of emerging importance by the International Transporter Consortium (Hillgren et al., 2013), and regulatory authorities such as the U.S. Food and Drug Administration and the European Medicines Agency (EMA) have included MATE in vitro evaluation into their guidelines for drug-interaction studies (EMA (2012) Guideline on the Investigation of Drug Interactions (EMA/CHMP/EWP/125211/2010; http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf) (U.S. Food and Drug Administration, 2012).

The most commonly used in vitro tool to investigate MATE-mediated transport activities are cell-based transport assays with recombinant epithelial cell lines expressing the human MATE1 or MATE2-K isoform. Numerous studies using such cell systems have helped in understanding the molecular function, driving force, and substrate specificity of MATE transporters and allowed for the identification of a wide range of compounds as substrates or inhibitors of MATE1 and/or MATE2-K (Tanihara et al., 2007; Tsuda et al., 2007; Yonezawa and Inui, 2011); however, the applied experimental conditions such as buffer compositions and extracellular and intracellular pH vary considerably among different laboratories, and to the best of our knowledge, it has not yet been investigated whether these varying conditions can affect in vitro assay outputs. In addition, varying test substrates such as metformin, tetraethylammonium (TEA), 1-methyl-4-phenylpyridinium (MPP+), or 4-(4-(dimethylamino)styryl)-N-methylpyridinium (ASP+) are being used in different laboratories and it has been demonstrated for a number of SLC drug transporter isoforms such as OCT2 and the organic anion transporting polypeptides (OATPs) 2B1 and 1B1 that the used substrate can severely affect the inhibitory effects of tested compounds (Shirasaka et al., 2012; Belzer et al., 2013; Izumi et al., 2013; Hacker et al., 2015). First evidence of a such like substrate-dependent inhibition has also been reported for MATE1 (Martínez-Guerrero and Wright, 2013).

To investigate the possible effect of varying assay conditions, we initially examined the impact of three different conditions on substrate and inhibitor profiling of compounds using MATE1- and MATE2-K-expressing cells. Based on the results, a suitable condition was selected under which MATE-mediated uptake characteristics of five different test substrates and the impact of the used test substrate on the inhibition profiles of 10 different MATE inhibitors were investigated.

Materials and Methods

Chemicals and Reagents.

[3H]Thiamine (20 Ci/mmol) and [3H]1-methyl-4-phenylpyridinium ([3H]MPP+; 80 Ci/mmol) were purchased from American Radiolabeled Chemicals (Saint Louis, MO), [3H]estrone-3-sulfate ([3H] estron-3-sulfate [E3S]; 45 Ci/mmol) was purchased from PerkinElmer (Waltham, MA), and [14C]metformin (90 mCi/mmol) was purchased from Moravek Biochemicals (Brea, CA). Unlabeled thiamine, MPP+, E3S, and rhodamine 123 were purchased from Sigma-Aldrich (St. Louis, MO), and unlabeled metformin was purchased from Wako Pure Chemical Industries (Osaka, Japan). All other chemicals and reagents were of analytical grade and are commercially available.

Cell Culture and Transfection.

Human embryonic kidney (HEK) 293 cells (Health Science Research Resources Bank, Osaka, Japan) were cultured in low glucose Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum, 100 U/ml penicillin, 100 μg/ml streptomycin, and 0.25 μg/ml amphotericin B (all from Life Technologies, Carlsbad, CA) at 37°C in an atmosphere of 5% CO2 and 95% relative humidity. For the generation of MATE1- and MATE2-K-expressing cells, parental HEK293 cells were initially seeded onto poly-d-lysine-coated 24-well plates at a density of 0.75 × 105 cells/well. On the next day, the cells were transfected with pcDNA3.1(-)/MATE1 (accession number NM_018242.2), pcDNA3.1(-)/MATE2-K (accession number AB250364.1) or control vector using FuGENE 6 transfection reagent (Promega, Madison, WI) according to the manufacturer’s instructions. Approximately 24 hours after transfection, the medium was changed to culture medium supplemented with 5 mM sodium butyrate, and the cells were incubated for additional 24 hours to induce transporter gene expression.

Uptake Experiments Using Transiently Transfected HEK293 Cells.

Uptake experiments with transiently transfected HEK293 cells were conducted approximately 48 hours after transfection using three different experimental conditions (conditions A–C, Table 1). For intracellular acidification used in condition A, cells were initially washed twice and preincubated with K+-based transport buffer (pH 7.4) supplemented with 20 mM NH4Cl for 10 minutes at 37°C and subsequently incubated for additional 5 minutes with NH4Cl-free K+-based transport buffer. In uptake experiments performed under condition B and C, cells were washed twice and preincubated with respective transport buffer (pH 7.4) for 10 minutes at 37°C. For the investigation of pH-dependent uptake at an extracellular pH between 6.0 and 7.0, 20 mM HEPES in the transport buffer was replaced with 20 mM 4-morpholine-ethanesulfonic acid (MES).

View this table:
  • View inline
  • View popup
TABLE 1

Experimental conditions

Uptake assays were initiated by aspiration of the preincubation buffer and addition of transport buffer (pH 7.4) containing radiolabeled [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]E3S, or unlabeled rhodamine 123 and test inhibitors where applicable. Uptake was terminated at the designated incubation times by washing the cells three times with ice-cold transport buffer. To determine the uptake of radiolabeled substrates, cells were solubilized with NaOH for 1 hour at 37°C, followed by the addition of an equal amount of HCl to neutralize the cell lysates. Aliquots of the lysates were transferred to scintillation vials containing scintillation cocktail (Hionic Fluor; PerkinElmer) and radioactivity was measured in a liquid scintillation counter (TRI-CARB 3110 TR, PerkinElmer). The remaining cell lysates were used to determine the protein concentration using the Lowry method with bovine serum albumin as standard.

To ensure reproducibility of the inhibition studies, more than 10% of all IC50 values were redetermined in a separate experiment, and the observed differences between repetitions were within a 2-fold range.

Rhodamine 123 Measurement by Fluorescence Detection.

Rhodamine 123 concentration was analyzed by transferring aliquots of the lysates to black 96-well plates (PerkinElmer) and determining the fluorescence intensity (485 nm excitation, 535 nm emission) with a fluorescence plate reader (EnVision 2102, PerkinElmer). The calibration curve in a range from 0.1–100 μM was linear (R2 > 0.9994) and 1 μM rhodamine 123 solution was used as control. The obtained inter-assay coefficient of variation (plate-to-plate variation) from the control samples was 17% (n = 11).

Data Analysis.

Cellular uptake was normalized to the amount of radioactivity in the buffer and protein concentration in each well and was calculated as given in eq. 1:Embedded Image(1)where Uptake is the mean uptake (μl/designated time per milligram of protein), Ccell is the radioactivity associated with the cell specimens (dpm/designated time per milligram of protein), and Cbuffer is the radioactivity concentration in the buffer (dpm/μl). For uptake experiments with rhodamine 123, mean uptake was calculated using fluorescence intensity instead of radioactivity. Transporter-mediated uptake was calculated by subtracting the uptake in empty-vector transfected cells from that in transporter expressing cells.

Kinetic parameters were obtained using eq. 2:Embedded Image(2)where v is the uptake rate of the substrate (pmol/min per milligram of protein), S is the substrate concentration in the buffer (μM), Km is the Michaelis-Menten constant (μM), and Vmax is the maximum uptake rate (pmol/min per milligram of protein). Fitting was performed by the nonlinear least-squares method using the GraphPad PRISM software (Version 6.04, GraphPad Software, La Jolla, CA).

If statistical significant inhibition was observed, we determined the half-inhibitory concentrations (IC50) of test inhibitors using the GraphPad PRISM software based on the four-parameter logistic equation (eq. 3):Embedded Image(3)where CL represents the uptake clearance, I is the inhibitor concentration, and Hill slope is the slope factor.

Statistical Analysis.

Statistically significant differences in this study were determined using Student’s two-tailed unpaired t tests. P < 0.05 and P < 0.01 were considered significant.

Results

Impact of Different Assay Conditions on Substrate Profiling and Inhibitor Profiling.

Various experimental conditions are available for in vitro MATE evaluation such as the use of an intracellular acidification technique to generate an outwardly directed H+ gradient or the use of different buffer systems to change the extracellular concentration of H+ and other ions. Among these various experimental conditions, three conditions (A, B, and C; see Table 1) were tested to check their impact on the substrate and inhibition profiles of known MATE substrates and inhibitors. To do this, we conducted uptake studies with three in vitro probe substrates ([14C]metformin, [3H]thiamine and [3H]MPP+) and examined the inhibitory effects of four inhibitors (pyrimethamine, quinidine, ondansetron, and N-butylpyridinium chloride (NBuPy-Cl)) on the MATE1- and MATE2-K-mediated uptake of the three in vitro probe substrates at three different assay conditions.

Experiments conducted at condition A generally resulted in significantly higher uptake rates of all test substrates than experiments conducted at conditions B and C (Fig. 1). In HEK293-MATE1 cells, the average substrate uptake after 1 minute under condition A was 2.3-fold ([14C]metformin), 2.3-fold ([3H]thiamine), and 2.6-fold ([3H]MPP+) higher than under condition B and 2.4-fold ([14C]metformin), 2.3-fold ([3H]thiamine), and 2.7-fold ([3H]MPP+) higher than under condition C. Similar observations were made in HEK293-MATE2-K cells, where the average uptake after 1 minute under condition A was 2.1-fold ([14C]metformin), 2.6-fold ([3H]thiamine), and 4.0-fold ([3H]MPP+) greater than under condition B and 2.8-fold ([14C]metformin), 3.6-fold ([3H]thiamine), and 5.9-fold ([3H]MPP+) greater than under condition C.

Fig. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 1.

Uptake of [14C]metformin (A), [3H]thiamine (B), and [3H]MPP+ (C) at three different assay conditions. Uptake of [14C]metformin (10 μM), [3H]thiamine (1 μM), and [3H]MPP+ (1 μM) was determined for 1 minute in HEK293-MATE1, HEK293-MATE2-K, and HEK293-mock cells at three different assay conditions (conditions A–C; see Table 1). Each bar represents the mean value ± S.E of triplicate measures from at least four separate experiments. Asterisks (*) represent significantly different uptake compared with condition A (P < 0.01).

Under all three conditions, uptake of [14C]metformin, [3H]thiamine, and [3H]MPP+ was decreased along with an increasing concentration of each of the four tested inhibitors except for NBuPy-Cl (Supplemental Figs. 1–4). As shown in Table 2 and Fig. 2, IC50 values of all inhibitors for MATE1- and MATE2-K-mediated uptake of the three in vitro probe substrates varied only slightly among the three conditions. In HEK293-MATE1 cells, all observed differences were within a 2.5-fold range, with an average variation of 0.9-fold between condition A and condition B, 1.0-fold between condition A and condition C, and 1.1-fold between condition B and condition C, respectively. The correlation coefficients were 0.979, 0.986, and 0.997. In HEK293-MATE2-K cells, the average variations were 1.1-fold between condition A and condition B, 1.3-fold between condition A and condition C, and 1.3-fold between condition B and condition C and correlation coefficients of 0.996, 0.993, and 0.997, respectively.

View this table:
  • View inline
  • View popup
TABLE 2

IC50 values for multidrug and toxin extrusion (MATE)1- and MATE2-K-mediated uptake of [3H]MPP+, [3H]thiamine and [14C]metformin at different assay conditions

Uptake of [14C]metformin (10 μM, 1 min), [3H]thiamine (1 μM, 1 min), and [3H]MPP+ (1 μM, 1 min) was determined in the absence and presence of various concentrations of inhibitors at three different assay conditions (conditions A–C; see Table 1) as shown in Supplemental Figs. 1–4. IC50 values were estimated by nonlinear regression analysis and are given as mean ± S.D. from one experiment.

Fig. 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 2.

Comparison of IC50 values between different assay conditions. Uptake of [14C]metformin (10 μM, 1 minute), [3H]thiamine (1 μM, 1 minute), and [3H]MPP+ (1 μM, 1 minute) was determined in the absence and presence of various concentrations of inhibitors as shown in Supplemental Figs. 1–4, and IC50 values were estimated by nonlinear regression analysis (see Table 2). All experiments were conducted with HEK293-MATE1 (closed squares) and HEK293-MATE2-K cells (open squares) and at three different assay conditions (conditions A–C; see Table 1). (A) Condition A versus condition B, (B) condition A versus condition C, (C) condition B versus condition C. Each point represents the mean value ± relative error from one experiment.

Uptake Characteristics of Five Substrates.

MATE transporter isoforms accept a large variety of substrates from organic cations to organic anions and from compounds with low to high molecular weights. To know whether uptake profiles differ among MATE substrates, [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]E3S, and rhodamine 123 were nominated as test substrates based on selection criteria such as clinical relevance, frequency of use in the literature, and physicochemical properties. All substrate profiling studies using these compounds (i.e., time-, concentration-, and pH-dependent studies) were conducted at condition A, which was previously selected as experimental condition for further characterization.

Uptake of all substrates into HEK293-MATE1- and HEK293-MATE2-K cells increased with time and was significantly higher than in the vector-transfected control cells (Supplemental Fig. 5). [14C]metformin, [3H]thiamine, and [3H]MPP+ uptake was linear over the first 2 minutes while uptake of [3H]E3S and rhodamine 123 was linear over the first 5 minutes. Based on these findings, we selected an incubation time for subsequent studies at which MATE1- and MATE2-K-mediated transport activities of each substrate were within the initial linear phase (1 minute for [14C]metformin, [3H]thiamine, and [3H]MPP+ and 2 minutes for [3H]E3S and rhodamine 123). Subsequently, transport activities in HEK293-MATE1 and HEK293-MATE2-K cells were assessed at increasing concentrations of the five test substrates to determine their kinetic profiles. The results are shown as Eadie-Hofstee plots in Fig. 3 and in Supplemental Fig. 6, and kinetic parameters (Km and Vmax) obtained from the concentration-dependency studies are given in Table 3. MATE1- and MATE2-K-mediated uptake of [14C]metformin, [3H]thiamine, [3H]MPP+, and rhodamine 123 was saturable, whereas atypical kinetic characteristics were observed with [3H]E3S. Km values of [14C]metformin, [3H]MPP+, and rhodamine 123 were lower in HEK293-MATE1 than in HEK293-MATE2-K cells, indicating higher affinities of all tested compounds for MATE1 than for MATE2-K. Rhodamine 123 had the lowest Km values of all compounds in both HEK293-MATE1 (Km = 0.793 μM) and HEK293-MATE2-K cells (Km = 10.2 μM), whereas the highest Km values were found for [14C]metformin transport (Km = 208 μM and Km = 2.28 mM in HEK293-MATE1 and HEK293-MATE2-K cells, respectively).

Fig. 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 3.

Concentration-dependent uptake of [14C]metformin (A), [3H]thiamine (B), [3H]MPP+ (C), [3H]E3S (D) and rhodamine 123 (E) by HEK293 cells expressing MATE1 or MATE2-K. Uptake of [14C]metformin (10–10,000 μM, 1 minute), [3H]thiamine (0.1–1000 μM, 1 minute), [3H]MPP+ (0.1–1000 μM, 1 minute), [3H]E3S (0.1–300 μM, 2 minutes) and rhodamine 123 (0.1–100 μM, 2 minutes) was determined in HEK293-MATE1 (closed circles) and HEK293-MATE2-K (closed squares) at condition A (see Table 1). Data are shown as Eadie-Hofstee plots. Transporter-mediated uptake was calculated by subtracting the uptake in HEK293-mock cells from that in transporter expressing cells. Each point represents the mean value ± S.E. of triplicate measures from one experiment in case of [14C]metformin and [3H]MPP+ and from one representative experiment of at least two separate experiments in case of the other substrates.

View this table:
  • View inline
  • View popup
TABLE 3

Saturation kinetics of multidrug and toxin extrusion (MATE)1- and MATE2-K-mediated uptake of [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]E3S, and rhodamine 123

Uptake of [14C]metformin (10–10,000 μM, 1 min), [3H]thiamine (0.1–1000 μM, 1 min), [3H]MPP+ (0.1–1000 μM, 1 min), [3H]E3S (0.1–300 μM, 2 min),and rhodamine 123 (0.1–100 μM, 2 min) was determined at condition A (see Table 1). Kinetic parameters were estimated by nonlinear regression analysis and are given as mean ± S.D. from one experiment in case of [14C]metformin and [3H]MPP+ and from one representative experiment of at least two separate experiments in case of the other substrates.

Figure 4 shows the results of the investigation of uptake of the test substrates at different pH conditions in a range from pH 6.0 to pH 8.0. The pH-dependent uptake of [14C]metformin, [3H]thiamine, and [3H]MPP+ showed a peak at an extracellular pH of 7.5 for MATE1 and increased until pH 8.0 for MATE2-K. The pH-dependent uptake properties of rhodamine 123 were comparable to those observed by the above-mentioned three substrates, although the peak of MATE1-mediated transport was observed at an extracellular pH of 7.0. In contrast, a distinct pH dependency was found with [3H]E3S for MATE1 and MATE2-K, which both showed a continuous decrease of uptake activities along with an increasing extracellular pH.

Fig. 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 4.

pH-dependent uptake of [14C]metformin (A), [3H]thiamine (B), [3H]MPP+ (C), [3H]E3S (D), and rhodamine 123 (E) by HEK293 cells expressing MATE1 or MATE2-K and control cells. Uptake of [14C]metformin (10 μM, 1 minute), [3H]thiamine (1 μM, 1 minute), [3H]MPP+ (1 μM, 1 minute), [3H]E3S (10 μM, 2 minutes), and rhodamine 123 (1 μM, 2 minutes) was determined in HEK293-MATE1 (closed circles), HEK293-MATE2-K (closed squares), and HEK293 mock cells (open circles) at condition A (see Table 1). Each point represents the mean value ± S.E. of triplicate measures from one experiment in case of [14C]metformin and from one representative experiment of at least two separate experiments in case of the other substrates.

Impact of the Used Test Substrate on IC50 Determination.

For clarification of the question whether IC50 determination is affected by the used substrate, the inhibitory effects of 10 compounds (pyrimethamine, cimetidine, trimethoprim, zosuquidar, valspodar, quinidine, ondansetron, famotidine, topotecan, and NBuPy-Cl) on MATE1- and MATE2-K–mediated uptake of [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]E3S, and rhodamine 123 were examined. Uptake of all test substrates was decreased in the presence of an increasing concentration of each of the tested inhibitors except zosuquidar, for which no inhibition was observed. The calculated IC50 values are summarized in Table 4 and the comparisons of the calculated IC50 values between [14C]metformin and the remaining four substrates—[3H]thiamine, [3H]MPP+, [3H]E3S, and rhodamine 123—are shown in Fig. 5.

View this table:
  • View inline
  • View popup
TABLE 4

IC50 values for multidrug and toxin extrusion (MATE)1- and MATE2-K-mediated uptake of [14C]metformin, [3H]thiamine, [3H]MPP+, [3H]E3S, and rhodamine 123

Uptake of [14C]metformin (10 μM, 1 min), [3H]thiamine (1 μM, 1 min), [3H]MPP+ (1 μM, 1 min), [3H]E3S (10 μM, 2 min), and rhodamine 123 (1 μM, 2 min) was determined in the absence and presence of various concentrations of inhibitors. IC50 values were estimated by nonlinear regression analysis and are given as mean ± S.D. from one experiment.

Fig. 5.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 5.

Comparison of IC50 values between different test substrates. Uptake of [14C]metformin (10 μM, 1 minute), [3H]thiamine (1 μM, 1 minute), [3H]MPP+ (1 μM, 1 minute), [3H]E3S (10 μM, 2 minutes), and rhodamine 123 (1 μM, 2 minutes) was determined in the absence and presence of various concentrations of inhibitors, and IC50 values were estimated by nonlinear regression analysis (see Table 4). All experiments were conducted with HEK293-MATE1 (closed squares) and HEK293-MATE2-K cells (open squares) at condition A (see Table 1). (A) [14C]metformin versus [3H]thiamine, (B) [14C]metformin versus MPP+, (C) [14C]metformin versus [3H]E3S, and (D) [14C]metformin versus rhodamine 123. Each point represents the mean value ± relative error from one experiment.

Compared with [14C]metformin, the IC50 values for the uptake of [3H]thiamine, [3H]E3S, and [3H]MPP+ were all within a 4-fold range (Fig. 5A-C). Low differences and a good correlation (correlation coefficients of 0.974 for MATE1 and 0.998 for MATE2-K) were found between [14C]metformin and [3H]thiamine. All observed differences were within a 2-fold (MATE1) and 3-fold (MATE2-K) range, and the average variation was 1.2-fold and 1.7-fold, respectively. The observed differences between [3H]E3S and [14C]metformin were all within a 3-fold (MATE1 and MATE2-K) range, with an average variation of 0.7-fold and 1.6-fold and correlation coefficients of 0.998 and 0.995 for MATE1 and MATE2-K, respectively. The observed differences between [3H]MPP+ and [14C]metformin were within a 3-fold (MATE1) and 4-fold (MATE2-K) range, with an average variation of 1.7-fold and 2.6-fold and a correlation coefficient of 0.981 and 0.985, respectively. IC50 values of all inhibitors against rhodamine 123 uptake were considerably higher than those against the other four test substrates (Fig. 5D). The average variation of IC50 values compared with [14C]metformin was 9.8-fold for MATE1. Inhibitors showing >4-fold variation for MATE1 are trimethoprim (IC50 > 100 μM, >24-fold difference), quinidine (IC50 > 100 μM, > 17-fold difference), cimetidine (IC50 = 36.8 μM, 14-fold difference), ondansetron (IC50 = 3.96 μM, 9-fold difference), famotidine (IC50 = 6.72 μM, 7-fold difference), valspodar (IC50 > 15 μM, 6-fold difference), and topotecan (IC50 = 26.9 μM, 5-fold difference). The corresponding correlation coefficient for MATE1 was only 0.468. A similar observation was made for the inhibition of MATE2-K–mediated uptake, with and average variation of 4.1-fold and >4-fold differences observed with famotidine (IC50 = 24.1 μM, 8-fold difference), ondansetron (IC50 = 1.74 μM, 7-fold difference), cimetidine (IC50 = 30.5 μM, 6-fold difference), and pyrimethamine (IC50 = 0.833 μM, 5-fold difference).

Discussion

The IC50 values of investigational drugs for drug transporters are determinant for the magnitude of DDIs at clinical settings. Thus, there are great concerns on the standardization of in vitro experimental conditions to determine robust and reliable IC50 values in the pharmaceutical industry. The present study focused on MATE transporters, the importance of which is recently perceived by the regulatory authorities and the International Transporter Consortium. The latter has included an overview of available methods for the in vitro evaluation of MATEs in their latest white paper on emerging transporters of clinical importance (Hillgren et al., 2013); however, the possibility of different results with different experimental conditions was not discussed, which is why the present study is an important contribution to future white papers.

To obtain higher activities in MATE uptake studies, an artificial pH gradient is often generated by using an alkaline buffer system in the extracellular compartment or by preacidification of the intracellular compartment using an NH4Cl prepulse (Hillgren et al., 2013). Currently, there is little information on whether these varying experimental conditions affect the assessment of the interactions of test compounds with MATEs. We therefore compared the uptake of three typical in vitro probe substrates ([14C]metformin, [3H]thiamine, and [3H]MPP+) and the inhibitory profiles of four inhibitors with particular characteristics (pyrimethamine as potent, quinidine as moderate, ondansetron as MATE1-preferring, NBuPy-Cl as MATE2-K-preferring) at three different assay conditions.

Condition A (with NH4Cl prepulse) resulted in significantly higher uptake rates of all three test compounds than those of conditions B and C (both without NH4Cl prepulse). It was therefore assumed that the increased MATE activity was caused by the intracellular preacidification through the NH4Cl prepulse used in condition A. In contrast, no significant difference was detected between condition B (K+-based buffer) and condition C (Na+-based buffer), representing a depolarized or a polarized state of the cell membrane, respectively. This finding implicates that the membrane potential was not a determining factor for the MATE-mediated uptake of the tested substrates and is well in line with previous reports that have shown that a valinomycin-induced depolarization of the membrane had no effect on MATE1- and MATE2-K-mediated uptake of the prototypical MATE substrate TEA (Otsuka et al., 2005; Tsuda et al., 2009).

As shown in Fig. 2 and Table 2, only slight differences in the IC50 values among the three tested conditions were found for each combination of substrate and inhibitor (<2.5-fold difference). These results suggest that the selected condition does not substantially affect the IC50 determination of test compounds. Because DDI risks of drug candidates are directly extrapolated from the IC50 values, a robust and reliable IC50 determination is essential during drug development. From this perspective, the use of condition A is preferable because it results in higher uptake ratios, is therefore less susceptible to small changes of experimental conditions, and will deliver more robust results.

It is well known that MATEs accept a large variety of substrates, but to date, no study has directly compared the transport profiles of test substrates with diverse chemical and functional characteristics (such as their frequency of use, clinical relevance, or physicochemical properties). Based on this, we chose five literature-reported MATE substrates with a range of different attributes and assessed their transport characteristics using the previously selected experimental condition A. The selected compounds were MPP+ as prototypical, metformin as clinically relevant, thiamine as physiologic, and E3S as atypical (i.e., anionic) substrate. In addition, rhodamine 123 was selected since it has a relatively high molecular weight and the ability to interact with P-glycoprotein (P-gp), both unusual features among MATE substrates.

MATE1- and MATE2-K–mediated uptake of all test compounds was pH-dependent (Fig. 4). The observed pH dependencies of [14C]metformin, [3H]thiamine, [3H]MPP+, and rhodamine 123 were similar to those reported previously for MPP+ and TEA (Tanihara et al., 2007; Tsuda et al., 2007; Dangprapai and Wright, 2011; Astorga et al., 2012). As opposed to this, uptake of [3H]E3S continuously decreased with increased pH and showed atypical saturation kinetics in both HEK293-MATE1 and HEK293-MATE2-K cells (Fig. 3). Such pH dependency has also been observed for norfloxacine (Ohta et al., 2009) and cephalexin (Watanabe et al., 2010). Since these compounds are weak acids (pKa of 4.5 and 5.7, respectively), the extracellular pH affects the percentage of the neutral and zwitterionic forms at the examined range; however, E3S is a fairly strong acid, with a pKa value of −3, and it can be assumed that it predominantly exists in its anionic form over the complete extracellular pH range from pH 6.0 to 8.0. Consequently, the reasons for the unusual uptake characteristics of [3H]E3S remain unknown but might be a general feature of MATE-mediated transport of anionic drugs.

Recently, it was gradually recognized that substrate-dependent differences in IC50 values exist in transporters such as OATP1B1, OATP2B1, and OCT2 (Shirasaka et al., 2012; Belzer et al., 2013; Izumi et al., 2013; Hacker et al., 2015), as well as in the cytochrome P450 enzyme CYP3A4 (Kenworthy et al., 1999; Obach et al., 2006). As for MATEs, Martínez-Guerrero and Wright (2013) showed a substrate-dependent inhibition of MATE1 with a set of ionic liquids. We therefore determined the IC50 values of a set of 10 selected inhibitors using the five aforementioned test substrates to further investigate the substrate dependency of IC50 values for MATE1 and MATE2-K. The inhibitors were selected based on clinical relevance (drugs with known DDI), high inhibition potency (low IC50), and selectivity (for either MATE1 or MATE2-K) as reported in the literature. Additionally, three known P-gp inhibitors (zosuquidar, valspodar, and quinidine) were included to take account of the reported overlapping substrate specificity between P-gp and MATEs (Tanihara et al., 2007). No pronounced substrate dependency was found in IC50 values among [14C]metformin, [3H]MPP+, [3H]thiamine, and [3H]E3S. In contrast, markedly higher values (>4-fold) were determined with rhodamine 123. These substantial substrate-dependent changes of IC50 values with rhodamine 123 have to be taken into account when considering it as a potential test substrate in a fluorescent assay system. The use of rhodamine 123 as the only test substrate will likely lead to an underestimation of the DDI risks of candidate drugs, possibly resulting in severe clinical safety issues, and is therefore not advised.

When selecting an appropriate test substrate, one of the determining factors is good transferability of the results to in vivo results from clinical studies. A clinically relevant MATE substrate (i.e., a therapeutic drug or a test substrate with transport characteristics similar to a typical test drug) can contribute to a good in vitro-in vivo correlation. In this regard, the clinically used antidiabetic metformin would be well suited. Several studies have reported clinical DDIs with known MATE inhibitors (cimetidine, cephalexin, or pyrimethamine) that significantly changed the pharmacokinetic parameters of concomitantly administered metformin (Somogyi et al., 1987; Jayasagar et al., 2002; Kusuhara et al., 2011). IC50 determination in our study identified [14C]metformin as the most conservative test substrate for the detection of interactions with MATE2-K and, in most cases, with MATE1. [14C]metformin is hence unlikely to underestimate DDI risks and therefore is rated as an appropriate substrate for MATE in vitro studies.

Kato et al. (2014) recently identified thiamine as an endogenous MATE substrate that could be a useful biomarker for the detection of DDIs involving MATEs). By monitoring the urinary excretion of thiamine in clinical studies, MATE-mediated DDIs of drug candidates could be evaluated without the need to administer exogenous probe drugs; however, this requires that the inhibition profiles of MATE inhibitors are similar when thiamine and other typical MATE probe substrates, such as MPP+ and metformin, are used as substrates. Here, we have shown that [3H]thiamine has comparable in vitro substrate characteristics to [3H]MPP+ and that the IC50 values of 10 tested inhibitors are similar to those obtained with [3H]MPP+ and [14C]metformin. Therefore, [3H]thiamine could be a valuable probe substrate for the in vitro prediction of MATE-mediated DDIs, particularly if it is also used as biomarker in clinical studies. In this case, the in vitro data reflect an effectively used combination of test compounds during clinical phase 1 studies. MPP+ and E3S are commonly used as a prototypical in vitro test substrate for several other transporters, but they have a low clinical relevance since they are either exogenous and neurotoxic (MPP+) or there is no information about the possible use as biomarker for the assessment of MATE-mediated DDIs in the literature (E3S).

In conclusion, our study has investigated the impact of the experimental conditions and the choice of the test substrate on the determination of IC50 values of known inhibitors against MATE1 and MATE2-K. We demonstrated that the IC50 values of four selected inhibitors did not significantly change with the used assay condition and therefore conclude that all of the three tested in vitro assay conditions are applicable. Furthermore, we recommend to use [14C]metformin, [3H]thiamine, or [3H]MPP+ as test substrates based on the comparable IC50 values of 10 test inhibitors. Taken together, we believe our findings will contribute to the establishment of a robust and reliable standard assay system for the in vitro assessment of MATE-mediated DDIs.

Authorship Contributions

Participated in research design: Lechner, Ishiguro, Fukuhara, Washio, Yamamura, Kusuhara.

Conducted experiments: Lechner, Shimizu, Ohtsu, Takatani, Nishiyama.

Performed data analysis: Lechner, Ishiguro, Fukuhara, Shimizu, Ohtsu, Takatani, Nishiyama, Washio, Kusuhara.

Wrote or contributed to the writing of the manuscript: Lechner, Ishiguro, Kusuhara.

Footnotes

    • Received November 3, 2015.
    • Accepted June 3, 2016.
  • This study was supported by Boehringer Ingelheim.

  • dx.doi.org/10.1124/dmd.115.068163.

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

Abbreviations

ASP+
4-(4-(dimethylamino)styryl)-N-methylpyridinium
DDI
drug-drug interaction
E3S
estron-3-sulfate
HEK293
human embryonic kidney 293, MATE, multidrug and toxin extrusion
MES
4-morpholineethanesulfonic acid
MPP+
1-methyl-4-phenylpyridinium
NBuPy-Cl
N-butylpyridinium chloride
OATP
organic anion transporting polypeptide
OCT2
organic cation transporter 2
P-gp
P-glycoprotein
TEA
tetraethylammonium
  • Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics

References

  1. ↵
    1. Astorga B,
    2. Ekins S,
    3. Morales M, and
    4. Wright SH
    (2012) Molecular determinants of ligand selectivity for the human multidrug and toxin extruder proteins MATE1 and MATE2-K. J Pharmacol Exp Ther 341:743–755.
    OpenUrlAbstract/FREE Full Text
  2. ↵
    1. Belzer M,
    2. Morales M,
    3. Jagadish B,
    4. Mash EA, and
    5. Wright SH
    (2013) Substrate-dependent ligand inhibition of the human organic cation transporter OCT2. J Pharmacol Exp Ther 346:300–310.
    OpenUrlAbstract/FREE Full Text
  3. ↵
    1. Chen Y,
    2. Teranishi K,
    3. Li S,
    4. Yee SW,
    5. Hesselson S,
    6. Stryke D,
    7. Johns SJ,
    8. Ferrin TE,
    9. Kwok P, and
    10. Giacomini KM
    (2009) Genetic variants in multidrug and toxic compound extrusion-1, hMATE1, alter transport function. Pharmacogenomics J 9:127–136.
    OpenUrlCrossRefPubMed
  4. ↵
    1. Dangprapai Y and
    2. Wright SH
    (2011) Interaction of H+ with the extracellular and intracellular aspects of hMATE1. Am J Physiol Renal Physiol 301:F520–528.
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Fujita T,
    2. Urban TJ,
    3. Leabman MK,
    4. Fujita K, and
    5. Giacomini KM
    (2006) Transport of drugs in the kidney by the human organic cation transporter, OCT2 and its genetic variants. J Pharm Sci 95:25–36.
    OpenUrlCrossRefPubMed
  6. ↵
    1. Hacker K,
    2. Maas R,
    3. Kornhuber J,
    4. Fromm MF, and
    5. Zolk O
    (2015) Substrate-dependent inhibition of the human organic cation transporter OCT2: a comparison of metformin with experimental substrates. PLoS One 10:e0136451.
    OpenUrlCrossRefPubMed
  7. ↵
    1. Hillgren KM,
    2. Keppler D,
    3. Zur AA,
    4. Giacomini KM,
    5. Stieger B,
    6. Cass CE,
    7. Zhang L, and
    8. International Transporter Consortium
    (2013) Emerging transporters of clinical importance: an update from the International Transporter Consortium. Clin Pharmacol Ther 94:52–63.
    OpenUrlCrossRefPubMed
  8. ↵
    1. Inui K-I,
    2. Masuda S, and
    3. Saito H
    (2000) Cellular and molecular aspects of drug transport in the kidney. Kidney Int 58:944–958.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Ito S,
    2. Kusuhara H,
    3. Yokochi M,
    4. Toyoshima J,
    5. Inoue K,
    6. Yuasa H, and
    7. Sugiyama Y
    (2012) Competitive inhibition of the luminal efflux by multidrug and toxin extrusions, but not basolateral uptake by organic cation transporter 2, is the likely mechanism underlying the pharmacokinetic drug-drug interactions caused by cimetidine in the kidney. J Pharmacol Exp Ther 340:393–403.
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Izumi S,
    2. Nozaki Y,
    3. Komori T,
    4. Maeda K,
    5. Takenaka O,
    6. Kusano K,
    7. Yoshimura T,
    8. Kusuhara H, and
    9. Sugiyama Y
    (2013) Substrate-dependent inhibition of organic anion transporting polypeptide 1B1: comparative analysis with prototypical probe substrates estradiol-17β-glucuronide, estrone-3-sulfate, and sulfobromophthalein. Drug Metab Dispos 41:1859–1866.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Jayasagar G,
    2. Krishna Kumar M,
    3. Chandrasekhar K,
    4. Madhusudan Rao C, and
    5. Madhusudan Rao Y
    (2002) Effect of cephalexin on the pharmacokinetics of metformin in healthy human volunteers. Drug Metabol Drug Interact 19:41–48.
    OpenUrlPubMed
  12. ↵
    1. Kato K,
    2. Mori H,
    3. Kito T,
    4. Yokochi M,
    5. Ito S,
    6. Inoue K,
    7. Yonezawa A,
    8. Katsura T,
    9. Kumagai Y,
    10. Yuasa H,
    11. et al.
    (2014) Investigation of endogenous compounds for assessing the drug interactions in the urinary excretion involving multidrug and toxin extrusion proteins. Pharm Res 31:136–147.
    OpenUrlCrossRefPubMed
  13. ↵
    1. Kenworthy KE,
    2. Bloomer JC,
    3. Clarke SE, and
    4. Houston JB
    (1999) CYP3A4 drug interactions: correlation of 10 in vitro probe substrates. Br J Clin Pharmacol 48:716–727.
    OpenUrlCrossRefPubMed
  14. ↵
    1. Kusuhara H,
    2. Ito S,
    3. Kumagai Y,
    4. Jiang M,
    5. Shiroshita T,
    6. Moriyama Y,
    7. Inoue K,
    8. Yuasa H, and
    9. Sugiyama Y
    (2011) Effects of a MATE protein inhibitor, pyrimethamine, on the renal elimination of metformin at oral microdose and at therapeutic dose in healthy subjects. Clin Pharmacol Ther 89:837–844.
    OpenUrlCrossRefPubMed
  15. ↵
    1. Martínez-Guerrero LJ and
    2. Wright SH
    (2013) Substrate-dependent inhibition of human MATE1 by cationic ionic liquids. J Pharmacol Exp Ther 346:495–503.
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Masuda S,
    2. Terada T,
    3. Yonezawa A,
    4. Tanihara Y,
    5. Kishimoto K,
    6. Katsura T,
    7. Ogawa O, and
    8. Inui K
    (2006) Identification and functional characterization of a new human kidney-specific H+/organic cation antiporter, kidney-specific multidrug and toxin extrusion 2. J Am Soc Nephrol 17(8):2127–35.
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Motohashi H and
    2. Inui K
    (2013) Multidrug and toxin extrusion family SLC47: physiological, pharmacokinetic and toxicokinetic importance of MATE1 and MATE2-K. Mol Aspects Med 34:661–668.
    OpenUrlCrossRefPubMed
  18. ↵
    1. Obach RS,
    2. Walsky RL,
    3. Venkatakrishnan K,
    4. Gaman EA,
    5. Houston JB, and
    6. Tremaine LM
    (2006) The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions. J Pharmacol Exp Ther 316:336–348.
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Ohta KY,
    2. Imamura Y,
    3. Okudaira N,
    4. Atsumi R,
    5. Inoue K, and
    6. Yuasa H
    (2009) Functional characterization of multidrug and toxin extrusion protein 1 as a facilitative transporter for fluoroquinolones. J Pharmacol Exp Ther 328:628–634.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Otsuka M,
    2. Matsumoto T,
    3. Morimoto R,
    4. Arioka S,
    5. Omote H, and
    6. Moriyama Y
    (2005) A human transporter protein that mediates the final excretion step for toxic organic cations. Proc Natl Acad Sci USA 102:17923–17928.
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Shirasaka Y,
    2. Mori T,
    3. Shichiri M,
    4. Nakanishi T, and
    5. Tamai I
    (2012) Functional pleiotropy of organic anion transporting polypeptide OATP2B1 due to multiple binding sites. Drug Metab Pharmacokinet 27:360–364.
    OpenUrlCrossRefPubMed
  22. ↵
    1. Somogyi A,
    2. Stockley C,
    3. Keal J,
    4. Rolan P, and
    5. Bochner F
    (1987) Reduction of metformin renal tubular secretion by cimetidine in man. Br J Clin Pharmacol 23:545–551.
    OpenUrlCrossRefPubMed
  23. ↵
    1. Tanihara Y,
    2. Masuda S,
    3. Sato T,
    4. Katsura T,
    5. Ogawa O, and
    6. Inui K
    (2007) Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H(+)-organic cation antiporters. Biochem Pharmacol 74:359–371.
    OpenUrlCrossRefPubMed
  24. ↵
    1. Tsuda M,
    2. Terada T,
    3. Asaka J,
    4. Ueba M,
    5. Katsura T, and
    6. Inui K
    (2007) Oppositely directed H+ gradient functions as a driving force of rat H+/organic cation antiporter MATE1. Am J Physiol Renal Physiol 292:F593–F598.
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Tsuda M,
    2. Terada T,
    3. Ueba M,
    4. Sato T,
    5. Masuda S,
    6. Katsura T, and
    7. Inui K
    (2009) Involvement of human multidrug and toxin extrusion 1 in the drug interaction between cimetidine and metformin in renal epithelial cells. J Pharmacol Exp Ther 329:185–191.
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. U.S. Food and Drug Administration
    (2012) Guidance for Industry: Drug Interaction Studies - Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendation, Draft Guidance, Silver Spring, Maryland.
  27. ↵
    1. Watanabe S,
    2. Tsuda M,
    3. Terada T,
    4. Katsura T, and
    5. Inui K
    (2010) Reduced renal clearance of a zwitterionic substrate cephalexin in MATE1-deficient mice. J Pharmacol Exp Ther 334:651–656.
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Yonezawa A and
    2. Inui K
    (2011) Importance of the multidrug and toxin extrusion MATE/SLC47A family to pharmacokinetics, pharmacodynamics/toxicodynamics and pharmacogenomics. Br J Pharmacol 164:1817–1825.
    OpenUrlCrossRefPubMed
PreviousNext
Back to top

In this issue

Drug Metabolism and Disposition: 44 (8)
Drug Metabolism and Disposition
Vol. 44, Issue 8
1 Aug 2016
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Editorial Board (PDF)
  • Front Matter (PDF)
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Drug Metabolism & Disposition article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Impact of Experimental Conditions on the Evaluation of Interactions between Multidrug and Toxin Extrusion Proteins and Candidate Drugs
(Your Name) has forwarded a page to you from Drug Metabolism & Disposition
(Your Name) thought you would be interested in this article in Drug Metabolism & Disposition.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Research ArticleArticle

Impact of Experimental Conditions on MATE Assessment

Christian Lechner, Naoki Ishiguro, Ayano Fukuhara, Hidetada Shimizu, Naoko Ohtsu, Masahito Takatani, Kotaro Nishiyama, Ikumi Washio, Norio Yamamura and Hiroyuki Kusuhara
Drug Metabolism and Disposition August 1, 2016, 44 (8) 1381-1389; DOI: https://doi.org/10.1124/dmd.115.068163

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Research ArticleArticle

Impact of Experimental Conditions on MATE Assessment

Christian Lechner, Naoki Ishiguro, Ayano Fukuhara, Hidetada Shimizu, Naoko Ohtsu, Masahito Takatani, Kotaro Nishiyama, Ikumi Washio, Norio Yamamura and Hiroyuki Kusuhara
Drug Metabolism and Disposition August 1, 2016, 44 (8) 1381-1389; DOI: https://doi.org/10.1124/dmd.115.068163
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Authorship Contributions
    • Footnotes
    • Abbreviations
    • References
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF + SI
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • In Vivo Functional Effects of CYP2C9 M1L
  • Clearance pathways: fevipiprant with probenecid perpetrator
  • Predicting Volume of Distribution from In Vitro Parameters
Show more Articles

Similar Articles

  • Home
  • Alerts
Facebook   Twitter   LinkedIn   RSS

Navigate

  • Current Issue
  • Fast Forward by date
  • Fast Forward by section
  • Latest Articles
  • Archive
  • Search for Articles
  • Feedback
  • ASPET

More Information

  • About DMD
  • Editorial Board
  • Instructions to Authors
  • Submit a Manuscript
  • Customized Alerts
  • RSS Feeds
  • Subscriptions
  • Permissions
  • Terms & Conditions of Use

ASPET's Other Journals

  • Journal of Pharmacology and Experimental Therapeutics
  • Molecular Pharmacology
  • Pharmacological Reviews
  • Pharmacology Research & Perspectives
ISSN 1521-009X (Online)

Copyright © 2021 by the American Society for Pharmacology and Experimental Therapeutics