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METABOLISM, TRANSPORT, AND PHARMACOGENOMICS

A Global Drug Inhibition Pattern for the Human ATP-Binding Cassette Transporter Breast Cancer Resistance Protein (ABCG2)

Pharmaceutical Screening and Informatics, Department of Pharmacy, Uppsala University, Sweden (P.M., G.E., G.A., C.A.S.B., U.N., P.A.); and AstraZeneca R&D, Södertälje, Sweden (U.N.)

Received May 9, 2007; accepted July 5, 2007.

	Abstract

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In this article, we explore the entire structural space of registered drugs to obtain a global model for the inhibition of the drug efflux transporter breast cancer resistance protein (BCRP; ABCG2). For this purpose, the inhibitory effect of 123 structurally diverse drugs and drug-like compounds on mitoxantrone efflux was studied in Saos-2 cells transfected with human wild-type (Arg482) BCRP. The search for BCRP inhibitors throughout the drug-like chemical space resulted in the identification of 29 previously unknown inhibitors. The frequency of BCRP inhibition was 3 times higher for compounds reported to interact with other ATP-binding cassette (ABC) transporters than for compounds without reported ABC transporter affinity. An easily interpreted computational model capable of discriminating inhibitors from noninhibitors using only two molecular descriptors, octanol-water partition coefficient at pH 7.4 and molecular polarizability, was constructed. The discriminating power of this two-descriptor model was 93% for the training set and 79% for the test set, respectively. The results were supported by a global pharmacophore model and are in agreement with a two-step mechanism for the inhibition of BCRP, where both the drug's capacity to insert into the cell membrane and to interact with the inhibitory binding site of the transporter are important.

Most studies of BCRP-mediated drug transport have been performed in inside-out membrane vesicles, in which BCRP substrates added to the extravesicular medium are actively transported to the vesicle interior (Özvegy et al., 2001; Saito et al., 2006). Inside-out membrane vesicles can thereby provide direct information about the binding of drugs to the transporter from the intracellular compartment. However, in many BCRP-expressing tissues, such as the intestine and the blood-brain barrier, the efflux transporter is located in the externally facing membrane (Maliepaard et al., 2001; Fetsch et al., 2006). BCRP, therefore, most probably binds its substrates directly on their entry into the cell; consequently, it can be hypothesized that the plasma membrane plays a major role in the presentation of drugs to the transporter.

This hypothesis is supported by an increasing body of evidence indicating that the well studied ABC transporter P-gp binds its substrates and inhibitors from within the inner leaflet of the plasma membrane (Homolya et al., 1993; Shapiro and Ling, 1997; Gatlik-Landwojtowicz et al., 2006). In this model, the substrate first partitions into the plasma membrane and, after lateral diffusion within the membrane, binds to the transporter from the lipid phase in a second step. The two-step model for drug-P-gp interaction was recently corroborated by Omote and Al-Shawi (2006), who presented a model supported by molecular dynamics simulations, where surface active substrates partition into the lipid membrane and subsequently exchange their interactions with water or the polar lipid head groups in the membrane-cytosol interface for interactions with polar and charged amino acids in the lipid-water interface region of P-gp. This study clearly demonstrates the importance of the plasma membrane for determining drug binding to P-gp, but the mechanistic knowledge of drug transport mediated by other important ABC drug transporters such as BCRP is still lacking.

In this study, we use a global approach to determine the physicochemical properties necessary for drug binding to BCRP. A data set of 123 compounds spanning the entire structural space of registered drugs was studied for the inhibition of BCRP-mediated mitoxantrone efflux in Saos-2 cells transfected with human wild-type (Arg482) BCRP (Wierdl et al., 2003), resulting in the discovery of 29 previously unknown BCRP inhibitors. An easily interpreted computational model was developed that is capable of discriminating inhibitors from noninhibitors based on structural features related both to the drug's capacity to insert into the cell membrane and to interact with the inhibitory binding site of the transporter. The results were supported by a global pharmacophore model and are in agreement with a two-step mechanism for inhibition of BCRP.

	Materials and Methods

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Materials. Fumitremorgin C was kindly provided by Dr. Robert Robey (National Institutes of Health, Bethesda, MD). Ko143 was a kind gift from Dr. Gerrit-Jan Koomen (Van't Hoff Institute for Molecular Sciences, University of Amsterdam, The Netherlands). Celecoxib was a gift from Pfizer (Kalamazoo, MI). Famciclovir and imatinib were gifts from Novartis (Basel, Switzerland). Ganciclovir and valganciclovir were gifts from Roche (Palo Alto, CA), and erlotinib and saquinavir were from Roche (Basel, Switzerland). GF120918, valacyclovir, and zanamivir were gifts from GlaxoSmithKline (Stevenage, UK). Ritonavir and lopinavir were kindly provided by Abbott (Abbott Park, IL). Nevirapine and tipranavir were gifts from Boehringer-Ingelheim (Ingelheim, Germany). Astemizole was purchased from MP Biomedicals (Eschwege, Germany). All other compounds were purchased from Sigma-Aldrich (St. Louis, MO) and were of at least 95% purity. Structure representations and references for compounds lacking generic names are provided in Supplemental Fig. 1.

Cell Culture Procedure. Saos-2 cells transfected with wild-type (Arg482) human BCRP (Saos-2/wtABCG2) and control cells transfected with the parental transfection vector pcDNA3 (Saos-2/pcDNA3) were kindly provided by Dr. J. Schuetz (St. Jude Children's Research Hospital, Memphis, TN) (Wierdl et al., 2003). The cells were cultured in Dulbecco's modified Eagle's medium (Invitrogen, Carlsbad, CA) containing 10% fetal calf serum (Sigma-Aldrich) and 1 mg/ml Geneticin (G-418; Invitrogen) under an atmosphere of 5% CO₂ at 37°C.

Analysis of Transporter Expression. Total RNA was isolated with the RNAeasy minikit (QIAGEN, Hilden, Germany), using the protocol provided by the manufacturer with the addition of an extra on-column DNase step (QIAGEN). The RNA quality was measured using a Bioanalyser (Agilent, Palo Alto, CA), and RNA concentration was measured using a Nanodrop ND-1000 Spectrophotometer (Nanodrop, Wilmington, DE). cDNA was synthesized using the High Capacity cDNA Archive kit (Applied Biosystems, Foster City, CA) according to the manufacturer's protocol. Five hundred nanograms of the total RNA samples was added to a master mixture containing 10 µl of 10x reverse transcriptase buffer, 4 µl of 25x deoxynucleoside-5'-triphosphates, 10 µl of 10x random primers, 5 µl of Multiscribe RTase (50 U/µl), and 21 µl of nuclease-free water. The reverse transcriptase PCR mixture was incubated at 25°C for 10 min and at 37°C for 120 min, and the resulting cDNA was stored at –70°C pending quantitative PCR analysis.

The amount of cDNA in the samples was analyzed in an ABI Prism 7900 HT Sequence Detection System with custom-designed 384-well cards loaded with Assay-on-Demand gene expression assays (Applied Biosystems). The quantitative PCR analysis was performed using 1 µl of reaction mixture per gene, containing 1 ng of cDNA, TaqMan Universal PCR Master Mix (consisting of AmpliTaq Gold DNA polymerase, deoxynucleoside-5'-triphosphates with dUTP, passive reference, and optimized buffer), and the Assay-on-Demand gene expression product mixture containing the specific primers and the probe preloaded on the plate. The cycling conditions consisted of 2 min at 50°C, 10 min of polymerase activation at 95°C, and 40 cycles alternating between 95°C for 15 s and 60°C for 1 min. Amplification curves were analyzed using the SDS version 2.1 software (Applied Biosystems), extracting the threshold concentration (C_t) value as the cycle time when fluorescence is above a defined threshold level. The relative gene expression was determined as 2^–Ct, using cyclophilin A as the internal standard.

Mitoxantrone Efflux Kinetics. Saos-2/wtABCG2 or Saos-2/pcDNA3 cells were seeded in 96-well plates (Corning Life Sciences, Acton, MA) 48 h before the experiment. On the day of the experiment, the cells were washed twice with 37°C PBS (Invitrogen) and incubated for 60 min in Hanks' balanced salt solution (HBSS; SigmaAldrich), buffered with 25 mM HEPES to pH 7.4 and containing 0.5 to 50 µM mitoxantrone. After the uptake phase, the incubation solution was removed, and the cells were washed twice with 37°C PBS. Fresh PBS was added, and the mitoxantrone efflux was followed for 60 min at 37°C using continuous fluorescence measurement in a Tecan Saphire² plate reader (Tecan, Männedorf, Switzerland), with the excitation at 633 nm and detection at the 735-nm emission peak wavelength. Nonlinear regression (Prism version 4.02; GraphPad, San Diego, CA) was used to determine Michaelis-Menten kinetic parameters from the initial efflux rates, according to eq. 1:

(1)

where V is the efflux rate, V_max is the maximal efflux rate, [S] is the mitoxantrone concentration, and K_m is the mitoxantrone concentration at which V = 0.5 x V_max.

Efflux Inhibition Assay. A single-concentration method able to determine the affinity of both competitive and noncompetitive BCRP inhibitors was developed using Saos-2 cells transfected with human wild-type (Arg482) BCRP. Saos-2/wtABCG2 or Saos-2/pcDNA3 cells were seeded in 96-well plates 48 to 72h before the experiment. DMSO stock solutions of the compounds under study were diluted in HBSS to a final concentration of 50 µM, resulting in final DMSO levels of no more than 1% (v/v). As a comparison, DMSO levels of up to 10% were shown not to influence the mitoxantrone accumulation in control experiments. Cells were washed twice in 37°C PBS and incubated for 60 min in HBSS buffered with HEPES to pH 7.4, containing the studied compound and 1 µM mitoxantrone. After the uptake phase, the incubation solution was removed, and the cells were washed twice with ice-cold PBS. The cells were detached using 25 µl of trypsin solution (PBS containing 0.25% trypsin and 0.03% EDTA) and were resuspended in 175 µl of ice-cold PBS containing 2% fetal calf serum, 0.5% sodium azide, and 3.2 mM trisodium citrate. The cells were then placed on ice, and the intracellular mitoxantrone fluorescence was analyzed using a Beckman Coulter FC500 flow cytometer (Beckman Coulter, Fullerton, CA) with the excitation at 633 nm and detection using the FL4 channel (>675 nm). Flow cytometric detection was preferred over detection in fluorescence plate reader because this resulted in a higher signal/noise ratio (8x background compared with 2x background, Supplemental Fig. 2). The cells were gated based on forward and side scatter, and only viable cells (typically >80% of all analyzed events) were included in the analysis. This method of determining cell viability relies on the fact that nonviable cells differ from viable cells in size and shape. The procedure was validated in control experiments by costaining with the fluorescent viability marker propidium iodide.

To verify that intrinsic fluorescence of the studied compounds did not interfere with the mitoxantrone analysis, cells incubated with 50 µM compound solutions were analyzed using the same cytometer settings as in the mitoxantrone analysis. Doxorubicin was excluded from the data set because its intrinsic fluorescence significantly influenced the analysis. All other compounds in the data set had negligible intrinsic fluorescence at the selected wavelength (<10% fluorescence increase compared with the background levels detected in cells incubated with HBSS buffer only). To determine whether the observed increases in mitoxantrone accumulation were caused by specific inhibition of BCRP, a representative selection of the compounds (corresponding to 67% of the identified inhibitors) was also studied in mock vector-transfected cells. For all compounds, significantly lower effects on mitoxantrone accumulation were observed in control cells than in cells expressing BCRP (Supplemental Fig. 3). Therefore, we conclude that the observed increases in mitoxantrone accumulation are BCRP-specific.

The -fold increase in intracellular accumulation of mitoxantrone on coincubation with the compounds under study was used as a measure of the BCRP inhibition. The increase in mitoxantrone accumulation was normalized to that obtained using 0.5 µMofthe potent BCRP inhibitor Ko143 (100% inhibition). Compounds that increased the intracellular accumulation of mitoxantrone by more than a factor of 3 were classified as BCRP inhibitors. The statistical validity of the chosen cut-off value was shown by the fact that it resulted in the maximal statistical significance when the Student's t test was used to compare the experimental activity of the compounds above and below the cut-off.

The stability of the intracellular mitoxantrone levels throughout the analysis was demonstrated by incubating a full 96-well plate of Saos-2/wtABCG2 cells with 1 µM mitoxantrone solution. This resulted in a total coefficient of variation of 7% over the plate, and the mitoxantrone levels in the 12 last analyzed wells were statistically indistinguishable from those in the first 12 wells (p = 0.4; Student's t test). The interday variability of the intracellular fluorescence in Saos-2/wtABCG2 cells incubated with 1 µM mitoxantrone was 8% (n = 20). Likewise, the effect of the potent BCRP inhibitor Ko143 on intracellular mitoxantrone levels was determined on five separate occasions, giving an interday variability of 9%.

Computational Modeling. Molecular structures obtained from SciFinder Scholar 2006 (American Chemical Society, Washington DC) were used as the input for 3D structure generation using Corina version 3.0 (Molecular Networks, Erlangen, Germany). Octanol-water partition coefficients (S+logD_7.4) were calculated from the 3D structures using ADMETPredictor version 1.2.4 (SimulationsPlus, Lancaster, CA). Two-dimensional molecular descriptors were calculated using the software package SELMA (AstraZeneca R&D, Mölndal, Sweden). SELMA calculates a collection of commonly used molecular descriptors representing molecular size, flexibility, connectivity, polarity, charge, and hydrogen bonding potential.

The data set was divided into a training set used for model development and a test set used to validate the predictivity of the final model. The data set division was performed in two steps. First, the data set was divided into two groups based on their experimentally determined BCRP inhibitory effect. Eighteen representative compounds from the group of compounds inhibiting BCRP and 25 representative noninhibitors were then included in the test set, corresponding to approximately one third of the compounds in each activity group. The remaining 28 BCRP inhibitors and 52 noninhibitors were used as training set compounds. The test set selection procedure was based on ChemGPS descriptions of the molecules (Oprea and Gottfries, 2001). In ChemGPS, the position of a compound in the drug-like chemical space is determined using principal components calculated from descriptors of their chemical structure. By selecting the compounds with the largest distance to their nearest neighbors, structural diversity is maximized, resulting in a test set with structural features that are representative of the compounds in the training set (Fig. 1).

View larger version (41K): [in this window] [in a new window]   Fig. 1. The data set investigated is representative of registered oral drugs. The positions of the compounds in the drug space are determined by the first three ChemGPS principal components (t1, t2, and t3), which are summarized from a large number of molecular descriptors and describe mainly the size, polarity, and flexibility of the molecules, respectively. The large blue circles denote compounds in the training set, whereas the large yellow diamonds denote test set compounds. The small black circles denote a reference set of 150 registered drugs from the Physician's Desk Reference (2005). The test set was selected to be representative of the compounds in the training set.

View larger version (15K): [in this window] [in a new window]   Fig. 2. Characterization of the inhibition assay. A, mitoxantrone efflux kinetics in Saos-2 cells stably transfected with human wild-type (Arg482) BCRP (closed symbols) or in cells transfected with a control vector (open symbols). After loading the cells with mitoxantrone for 60 min, the efflux was monitored using continuous fluorescence detection. The data are presented as the mean of the initial efflux rates ± S.E. (n = 4). B, inhibition of mitoxantrone efflux after coincubation with increasing concentrations of Ko143. The intracellular mitoxantrone accumulation was detected using flow cytometry after an incubation time of 60 min. The closed symbols show the inhibition induced by Ko143 in BCRP-transfected cells (IC50 = 0.19 ± 0.09 µM), and the open symbols show the absence of inhibition in control vector transfected cells. The data are presented as the mean mitoxantrone accumulation ± S.E. (n = 4), normalized to the accumulation observed in control cells incubated with 1 µM mitoxantrone (100% inhibition). C, confocal microscopy visualization of intracellular mitoxantrone levels after incubation of BCRP-transfected cells with 1 µM mitoxantrone and 0, 0.1, 0.25, or 0.5 µM Ko143. The rightmost panels show control cells incubated with 1 µM mitoxantrone with and without the addition of 0.25 µM Ko143.

Low-energy conformations for the BCRP inhibitors in the training set were imported into Catalyst version 4.9 (Accelrys, San Diego, CA). Ten common feature pharmacophore hypotheses were developed using the common features algorithm (HipHop) with hydrogen bond acceptor, hydrogen bond donor, positively ionizable, negatively ionizable, hydrophobic, and ring aromatic features as possible pharmacophore features. The ability of the 10 hypotheses proposed by Catalyst to distinguish between BCRP inhibitors and noninhibitors in the training set was examined, and the hypothesis with the highest discriminatory power was selected for visualization of the preferential orientation of the BCRP inhibitors.

	Results

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Endogenous Transporter Expression in the BCRP Efflux Assay. Previous studies of active drug transport in transfected cell lines have shown that endogenous transporters and drug-metabolizing enzymes can have a significant influence on the results, obscuring the effects of the transfected transporter (e.g., Goh et al., 2002). We therefore used real-time PCR to determine the mRNA expression of 10 ABC transporters and 24 SLC transporters previously shown to transport drugs, as well as the expression of seven major drug-metabolizing CYP enzymes (see Supplemental Fig. 4).

In agreement with previous results on mRNA and protein expression (Wierdl et al., 2003), the Saos-2/wtABCG2 cells expressed significantly higher levels of BCRP/ABCG2 than of other ABC transporters (e.g., 17 times higher expression than of MRP1/ABCC1, which was the ABC transporter with the highest endogenous expression in Saos-2/wtABCG2 cells), whereas BCRP expression in cells transfected with the control pcDNA3 vector was undetectable. Both cell lines exhibited low or undetectable expression levels of major efflux transporter genes such as P-gp/ABCB1, MRP1/ABCC1, and MRP2/ABCC2, as well as of genes encoding major SLC drug transporters and drug-metabolizing CYP enzymes. The only SLC transporter with significant expression was SLC16A1 (monocarboxylate transporter 1), which was equally expressed in both Saos-2/pcDNA3 and Saos-2/wtABCG2 cells at levels corresponding to one fifth of BCRP/ABCG2. Only one compound in the data set, quercetin, has been reported to interact with monocarboxylate transporter 1 (Ozawa et al., 2004). The results indicate that active efflux in the BCRP-transfected Saos-2 cells is not confounded by other transporters or by oxidative metabolism, and we conclude that Saos-2/wtABCG2 cells constitute a close to ideal model for studying the interplay between active efflux and passive membrane permeability in isolation from confounding factors.

Characterization of the BCRP Efflux Assay. Mitoxantrone was chosen as a model substrate to follow BCRP-mediated efflux from Saos-2/wtABCG2 cells because mitoxantrone levels are readily measured using fluorescence detection (Doyle et al., 1998; Gupta et al., 2004). Figure 2A shows the concentration dependence of the initial efflux rate of mitoxantrone from Saos-2/wtABCG2 cells at nontoxic concentrations, using continuous detection in a fluorescence plate reader. The efflux kinetics were determined from this plot using nonlinear regression, resulting in an apparent K_m of 18 ± 3 µM. The K_m determination was in agreement with results obtained using flow cytometric detection (K_m = 17 ± 9 µM).

Coincubation of BCRP transfectants with 1 µM mitoxantrone and 0.5 µM of the specific BCRP inhibitor Ko143 resulted in complete inhibition of the mitoxantrone efflux (Fig. 2B), whereas inhibitor concentrations up to 1 µM did not significantly influence mitoxantrone efflux in cells transfected with the control vector. Confocal microscopy confirmed that intracellular mitoxantrone levels were completely restored in BCRP transfectants treated with 0.5 µM Ko143 (Fig. 2C).

Data Set Selection. At the onset of this study, the number of compounds reported to interact with BCRP was quite low compared with other major ABC transporters, amounting to around 30 compounds with an affinity for the wild-type BCRP. Furthermore, the published compounds were structurally homogenous, making predictions of structurally diverse drug-like molecules unreliable. We therefore started our data set selection by removing structural analogs from the data set obtained from the literature, including 18 of the 30 compounds with a published BCRP affinity in our data set.

Several of the compounds reported to have affinity for BCRP are known to also interact with other ABC transporters (Ozawa et al., 2004). We hypothesized that previously unknown BCRP-interacting compounds could be found among substrates and inhibitors of other major ABC transporters, so we added a set of structurally diverse compounds interacting with P-gp, MRP1, and MRP2 to the data set. For comparison, we also included a set of compounds that had not been previously indicated in ABC transporter interactions, selected from an in-house database of drugs from the Physician's Desk Reference (2005). Structural diversity in both the ABC-interacting and the noninteracting compound set was maintained by selecting compounds with the largest distance to their nearest neighbor in the ChemGPS drug-like chemical space (Oprea and Gottfries, 2001). Thereby, the coverage of the structural space of drug-like compounds was maximized (Fig. 1).

In summary, 123 endogenous or drug-like compounds were included in this study, divided into three groups: 1) compounds with a reported affinity for BCRP (n = 18), 2) compounds with an affinity for other major ABC transporters (n = 42), and 3) compounds with previously unknown ABC transporter affinity, selected to maximize the diversity of the data set (n = 63) (Table 1).

Inhibition of Mitoxantrone Efflux. Based on the kinetic parameters for mitoxantrone, we selected a standard concentration of 50 µM for the BCRP inhibition studies, corresponding to approximately 2.5 times the mitoxantrone K_m. At this concentration, 46 of the 123 compounds (37%) in this study inhibited BCRP-mediated mitoxantrone efflux, including all (100%) of the previously reported BCRP inhibitors. It is noteworthy that as many as 29 (63%) of the hits had not previously been reported to be BCRP inhibitors.¹ A closer examination of the results revealed a significant enrichment of hits in the group of compounds that were selected because of their reported affinity for other ABC transporters. Of the compounds in this group, 45% inhibited BCRP, compared with 16% in the compounds lacking published ABC affinity (Fig. 3). These results support the notion that a significant affinity overlap exists among the major ABC transporters (Bates et al., 2001).

View larger version (13K): [in this window] [in a new window]   Fig. 3. Inhibition of BCRP-mediated mitoxantrone efflux by the 123 compounds in this study. Mitoxantrone accumulation in Saos-2 cells stably transfected with human wild-type (Arg482) BCRP was measured after incubation with 1 µM mitoxantrone with or without 50 µM inhibitor. Of the 123 compounds, 46 (37%) inhibited BCRP-mediated mitoxantrone efflux at this concentration. The inhibition was normalized to that obtained using 0.5 µM of the potent BCRP inhibitor Ko143 (complete inhibition), and a greater than 3-fold increase in the intracellular mitoxantrone accumulation was used as the cut-off for significant BCRP inhibition (shown as a dashed line). The compounds are presented in the same order as in Table 1. Inhibition was confirmed for all previously reported BCRP inhibitors (A). For the hydrophilic BCRP substrate methotrexate (Km = 5700 µM), a concentration greater than 50 µM was required for inhibition, which reduced the hit frequency in this group to 94%. Almost 3 times as many BCRP inhibitors were found in the group of compounds selected because of their affinity for other ABC transporters (B; 45%) compared with the group of compounds that lacked reported ABC transporter affinity (C; 16%). The data are presented as means ± S.E. (n = 3–15).

It is generally accepted that drug binding to P-gp takes place from within the inner leaflet of the plasma membrane (Gottesman and Pastan, 1993; Gatlik-Landwojtowicz et al., 2006; Omote and Al-Shawi, 2006). In this model, the drug first needs to partition to the lipid bilayer, explaining the strong correlations often observed between P-gp affinity and measures related to membrane partitioning (Seelig and Landwojtowicz, 2000). To study whether BCRP functions by a similar mechanism, we included compounds with a wide range in lipophilicity in this study.

Although inhibitory effects were seen for all of the previously reported BCRP inhibitors, four of the BCRP substrates included (methotrexate, sulfasalazine, cimetidine, and nitrofurantoin) did not appear as hits in our cell-based experimental assay.¹ Because these four compounds also were the most hydrophilic ones, we reasoned that low passive membrane permeability was the most likely reason for the lack of activity of these compounds. Thus, higher extracellular concentrations of the compounds should lead to higher intramembraneous and intracellular levels because of an increased mass flux. Indeed, significantly increased intracellular mitoxantrone accumulation was observed at higher concentrations of cimetidine and nitrofurantoin, supporting the notion that passive membrane partitioning limits the access to the transporter for these compounds (Table 2). However, for methotrexate, the possible concentration range was limited by significant cell toxicity at concentrations above 500 µM. This very hydrophilic drug is a known BCRP substrate with reported K_m as high as 5700 µM (Mitomo et al., 2003), well exceeding the concentrations used in the present study. Thus, a low intrinsic affinity for the transporter in combination with poor membrane permeability probably explains the absence of an inhibitory effect for these compounds in this work.

To further investigate the importance of plasma membrane partitioning for drug binding to BCRP, we examined the correlation between BCRP inhibition and compound lipophilicity, expressed as the calculated octanol-water partition coefficient (logD_7.4) (Fig. 4A). We reasoned that it would be possible to define a lipophilicity cut-off, below which a compound would not reach the substrate binding site in adequate amounts to elicit an inhibitory effect. Indeed, no compounds with logD_7.4 below 0.5 appeared as hits in the cell assay. The median lipophilicity was 4.0 for the BCRP inhibitors and 0.7 for the noninhibitors, which indicates that the inhibitors would accumulate in the plasma membrane and is consistent with a need for membrane partitioning to reach the binding site (Fig. 4B).

View larger version (11K): [in this window] [in a new window]   Fig. 4. A, relationship between mitoxantrone efflux inhibition and logD7.4. The closed circles denote compounds that inhibited BCRP in this study, the open circles denote noninhibitors, and the closed squares denote BCRP substrates that did not inhibit BCRP in this study: 1) methotrexate, 2) nitrofurantoin, 3) cimetidine, and 4) sulfasalazine. The effect of increasing the concentration of these hydrophilic compounds above 50 µM is discussed in the text and in Table 2. The solid line shows the cut-off used to discriminate between BCRP-inhibitors and noninhibitors. B, frequency distribution of logD7.4 in BCRP inhibitors (shaded bars, solid line) and in noninhibitors (open bars, dashed line).

Computational Modeling of BCRP Inhibition. To determine which descriptors of molecular structure are important for the interaction between BCRP and its inhibitors, we developed a computational model for the discrimination between inhibitors and noninhibitors. In our search for the molecular requirements for BCRP inhibition, we primarily included 152 descriptors of molecular structure. It is surprising that the statistical analysis showed that the final model could be based on only two descriptors: logD_7.4 and the molecular polarizability. Both descriptors are highly correlated to the passive membrane permeability (Stenberg et al., 2001). Interestingly, this simple two descriptor model classified 93% of the BCRP active compounds and 92% of the inactive compounds in the training set correctly (Fig. 5), indicating that membrane partitioning is an important factor for drug interaction with BCRP. The OPLS-DA model was further evaluated using a structurally diverse test set, resulting in correct classifications for 83% of the BCRP active compounds and for 76% of the inactive ones, which confirmed the predictivity of the model. Inclusion of additional molecular descriptors only marginally increased the statistical quality of the model, whereas the interpretation of the model was unaffected because all descriptors were either related to the polarizability (molar refractivity) or to logD_7.4 (the number of nonpolar atoms, the number of carbon atoms, and the surface area of nonpolar atoms).

View larger version (10K): [in this window] [in a new window]   Fig. 5. Prediction of BCRP inhibition from two molecular descriptors. OPLS multivariate discriminant analysis was used to develop a model discriminating between BCRP inhibitors and noninhibitors. The final model was based on the two most influential molecular descriptors: logD7.4 and polarizability. A, dashed line shows the division between BCRP inhibitors and noninhibitors, as determined from the training set compounds. Compounds in the shaded area are predicted to be BCRP inhibitors. The closed symbols denote compounds experimentally determined to inhibit BCRP, and the open symbols denote noninhibitors. Compounds in the training set are shown as squares, and test set compounds (that were withheld from the model development, see Materials and Methods) are shown as circles. B, percentage of correct predictions of BCRP inhibition made by the OPLS-DA model. Predictions for the experimentally determined BCRP inhibitors are presented in the lower level graphs, and predictions for the noninhibitors are presented in the top-level graphs. White denotes true predictions, and black denotes false predictions. The models were evaluated both on the training set (left column) and on the test set (right column).

Using the same procedure, a polarizability-independent data set was constructed by selecting 10 pairs of active and inactive compounds with corresponding molecular polarizabilities. The most influential molecular descriptors in the polarizability-independent model were logD_7.4 and the surface area of nonpolar atoms, both of which are mainly related to compound lipophilicity, and the surface area of nitrogen atoms, which is related to the hydrogen bonding capacity and the possibility of acquiring a positive charge. The final model classified 100% of both the active and the inactive compounds in the polarizability-independent data set correctly. Polarizability is correlated to the surface activity and the size of the molecule; therefore, a correlation to membrane partitioning is possible. However, other descriptors highly related to membrane partitioning were required to discriminate between inhibitors and noninhibitors in the polarizability-independent data set. Thus, we deduce that polarizability is not primarily related to the membrane partitioning step but rather that this descriptor explains charge delocalizations that are important for - interactions and hydrogen bonds between the drugs and the transporter.

Pharmacophore Modeling. For the purpose of investigating the preferential three-dimensional orientation of the structurally heterogeneous BCRP inhibitors, we determined the molecular interaction points common to the 28 BCRP inhibitors in the training set. The modeling procedure resulted in a three-point pharmacophore consisting of two hydrophobic features and one hydrogen bond acceptor feature (Fig. 6). The requirement for at least one hydrogen bond acceptor function is consistent with the OPLS-DA models of the drug-transporter interaction and is also reminiscent of pharmacophore models previously presented for P-gp (e.g., Pajeva and Wiese, 2002; Chang et al., 2006).

View larger version (32K): [in this window] [in a new window]   Fig. 6. Three-dimensional orientation of the BCRP inhibitors. A, a three-point pharmacophore model was developed based on the BCRP inhibitors in the training set, using the common features algorithm (HipHop) in Catalyst (see Materials and Methods). The model consisted of two hydrophobic centers (shown in blue in B) and one hydrogen bond acceptor feature (shown in green in B). The arrow indicates the optimal direction of electron sharing in the hydrogen bond, and the hemisphere indicates the optimal placement of a hydrogen bond donor group in the BCRP binding site. B, potent BCRP inhibitor Ko143 mapped to the pharmacophore model with an excellent fit. The fit value describes how well the chemical features in the compound can be superimposed onto the pharmacophore interaction points, ranging from 0 (no fit) to 3 (perfect fit) for a three-point pharmacophore.

	Discussion

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The result that a logD_7.4 of at least 0.5 is needed for BCRP inhibition in the whole-cell assay used in this work indicates that membrane partitioning significantly influences inhibitor binding to BCRP. The limiting effect of the plasma membrane was supported by examining the hydrophilic BCRP substrates nitrofurantoin and cimetidine. Under the standard assay conditions, these two compounds were not able to inhibit BCRP, which is explained by their low membrane permeability and the absence of significant expression of uptake transporters in the Saos-2 cells. However, on increasing the concentration, and hence increasing the mass flux, both compounds inhibited BCRP. These results show that a high lipophilicity is not necessary for the binding of drugs to BCRP but is merely a prerequisite for reaching the binding site in sufficient amounts to elicit an inhibitory effect.

To investigate the molecular descriptors that determine the interaction between BCRP and its inhibitors, we used OPLS-DA multivariate analysis to develop a computational model for discrimination between inhibitors and noninhibitors. The two most influential molecular descriptors were logD_7.4 and the molecular polarizability, further demonstrating the significant influence of membrane partitioning on drug binding to BCRP. Interestingly, despite the fact that the final discriminant analysis model only contains two physicochemical parameters and was developed solely on the basis of qualitative data, it performs as well as, or better than, 3D pharmacophore models for the P-gp transporter developed from quantitative binding affinity data. For example, if the IC₅₀-based P-gp pharmacophores developed by Ekins et al. (2002) are used as classification models, with IC₅₀ = 50 µM as the cut-off for significant inhibition, on average, 60% of the classifications obtained for the test sets in these studies are correct. Furthermore, many more false hits than false misses are found using the previously published models. Penzotti et al. (2002) used a selection of four-point pharmacophores to classify compounds as either P-gp substrates or nonsubstrates, resulting in 53% correct classifications for the active compounds and 79% for the inactive compounds in the test set. In comparison, the two-descriptor model developed and presented here correctly classified 83% of the BCRP inhibitors and 76% of the noninhibitors in the test set. In addition to providing insight into the molecular mechanism of BCRP inhibition, the model will be useful in the development of new drugs, where it can be used to focus more extensive experimental efforts on compounds with a high likelihood of exhibiting BCRP interactions.

Because the importance of lipophilicity probably reflects a need for membrane partitioning to occur for a drug to reach the BCRP binding site, we reasoned that factors that are important for the binding of drugs to the transporter from the lipid bilayer could be revealed by studying a lipophilicity-independent subset of the compounds examined in this investigation. This analysis showed that, in addition to lipophilicity, descriptors related to -electron energies and the abundance of nitrogen atoms are important for discriminating inhibitors from noninhibitors. This suggests that hydrogen bonds and interactions involving -electron systems, such as - and -cation interactions, are involved in inhibitor binding to BCRP. The inhibitors in the training set could all be aligned to a pharmacophore consisting of hydrophobic features and a hydrogen bond acceptor feature, which is in accordance with the results from the OPLS-DA models (Fig. 6). It is noteworthy that the amino acids in the intracellular loops and the transmembrane domains of the BCRP protein contain a large number of aromatic and hydrogen bond donor side chains (Doyle et al., 1998), complementary to the molecular features in the models developed in this study. The same situation has been observed in the substrate binding regions of P-gp (Seelig, 1998; Omote and Al-Shawi, 2006; Shilling et al., 2006), and this similarity could well contribute to the overlapping substrate specificity of the two transporters.

View larger version (14K): [in this window] [in a new window]   Fig. 7. A proposed two-step model for drug binding to BCRP. The schematic illustration shows the efflux transporter BCRP inserted in the plasma membrane. An extracellularly applied compound needs to partition to the plasma membrane (1). This step is described mainly by the logD7.4 descriptor in the OPLS-DA model (Fig. 5). After flip-flop from the outer to the inner membrane leaflet (2) and lateral diffusion in the membrane, the compound can bind to the transporter (3). Hydrogen bonds and – interactions are probably involved in this step, which is described mainly by the polarizability descriptor in Fig. 5.

In conclusion, we investigated a data set representing the entire structural space of registered drugs to examine the inhibition of the drug efflux transporter BCRP. This resulted in the discovery of 29 previously unknown inhibitors. An easily interpretable computational model capable of discriminating inhibitors from noninhibitors was constructed based on structural features related both to the drug's capacity to insert into the cell membrane and to interact with the inhibitory binding site of the transporter. The discriminating power of this two-descriptor model was 93% for the training set and 79% for the test set, respectively. The results were supported by a global pharmacophore model and are in agreement with a two-step mechanism for the inhibition of BCRP (Fig. 7).

	Acknowledgements

 
We thank Pia Brokhøj, Nina Ginman, Aki Heikkinen, and Lucia Lazorova for skillful technical assistance and Constanze Hilgendorf, Johan Karlsson, and Anna-Lena Ungell for assistance with the gene expression analysis. We also thank Daisuke Nakai for valuable comments on the manuscript.

	Footnotes

 
This work was supported by the Swedish Research Council (Grant 9478), by the Knut and Alice Wallenberg Foundation, by the Swedish Fund for Research without Animal Experiments, and by the Swedish Animal Welfare Agency.

Article, publication date, and citation information can be found at http://jpet.aspetjournals.org.

doi:10.1124/jpet.107.124768.

ABBREVIATIONS: ABC, ATP-binding cassette; BCRP, breast cancer resistance protein; ABCG2, ATP-binding cassette transporter member G2; P-gp, P-glycoprotein; PCR, polymerase chain reaction; PBS, phosphate-buffered saline; HBSS, Hanks' balanced salt solution; DMSO, dimethyl sulfoxide; 3D, three-dimensional; OPLS-DA, orthogonal partial least-squares projection to latent structures discriminant analysis; SLC, solute carrier; MRP, multidrug resistance-associated protein; logD_7.4, octanol-water partition coefficient at pH 7.4; Ko143, 3-(6-isobutyl-9-methoxy-1,4-dioxo-1,2,3,4,6,7,12,12-octahydropyrazino[1',2':1,6]pryrido[3,4-b]indol-3-yl)-propionic acid tert-butyl ester.

The online version of this article (available at http://jpet.aspetjournals.org) contains supplemental material.

¹ During the course of this study, cimetidine, nitrofurantoin (Jonker et al., 2005), and sulfasalazine (Zaher et al., 2006) were reported to be BCRP substrates in independent studies. Because their affinity for BCRP had not been reported at the onset of this study, they were included in groups B and C in Table 1 and in Fig. 3.

Address correspondence to: Dr. Per Artursson, Pharmaceutical Screening and Informatics, Department of Pharmacy, Uppsala University, P.O. Box 580, SE-751 23 Uppsala, Sweden. E-mail: Per.Artursson{at}farmaci.uu.se

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