Materials.

[³H]Tetraethylammonium ([³H]TEA; 13.2 Ci/mmol) was a custom synthesis by Amersham Biosciences (Piscataway, NJ). [³H]1-Methyl-4-phenylpyridinium (80 Ci/mmol) was purchased from ARC, Inc. (St. Louis, MO). NBD-TMA was synthesized as described previously (Bednarczyk et al., 2000) (and is commercially available from Macrocyclics, Inc., TX). The phenylpyridinium and quinolinium analogs were synthesized as described previously (Wright et al., 1995). Other chemicals were purchased from Sigma Chemical Corp. (St. Louis, MO) or Aldrich Chemical Co. (Milwaukee, WI). Octanol/water partition coefficients (Log P) were calculated using CLOGP software (Daylight Chemical Information Systems; www.daylight.com).

Generation, Transfection, and Culture of HeLa Cells Expressing hOCT1 and hOCT1-V5His cDNAs.

The hOCT1 cDNA in the pEXO vector was kindly provided by Dr. Kathleen M. Giacomini (University of California, San Francisco, CA). hOCT1 was removed from pEXO using KpnI and XhoI and ligated into pIZ-V5His (Invitrogen, Carlsbad, CA). hOCT1 was removed from pIZ-V5His at HindIII andXhoI and ligated into pcDNA3.1 to generate a plasmid for production of wild-type hOCT1. Because of a future interest in isolating hOCT1 protein from cells, we generated a histidine-tagged hOCT1 sequence by muting the stop codon and introducing a frame shift mutation to interpose an alanine in frame with the V5His tag of the vector, pIZ-V5His. This was accomplished using the following primers: the OpIE2 promoter site on the plasmid was used as the forward primer (5′-CGCACCGATCTGGTAAACAC-3′) and 5′-GCCCTCTAGACTCGAGGCGGTGCCCGAGGGTTC-3′ as the reverse primer containing the mutated sequence. The mutated PCR fragment was cut atHindIII and XhoI to generate a full-length cDNA sequence that was ligated back into the parent vector. Another mutation was performed on the pIZ-hOCT1-V5His sequence to insert anEcoRI cut site after the stop codon of the His-tag using the reverse primer 5′-GGATTTAGTCAGATGAATTCAATGGTGATG-3′, in conjunction with the forward primer for the OpIE2 promoter priming site on the plasmid. The resulting PCR fragment was digested withHindIII and EcoRI and ligated into pUC18, from which it was digested with BamHI and EcoRI and ligated into pcDNA3, creating pcDNA3-hOCT1-V5His. All DNA sequencing was performed by the Arizona Research Laboratory–Laboratory of Molecular Systematics and Evolution (University of Arizona, Tucson, AZ).

The pcDNA3-hOCT1-V5His was linearized with PvuI and transfected into HeLa cells using Effectine (QIAGEN, Valencia, CA) according to the manufacturer's instructions (with the exception that we used a cDNA-to-enhancer ratio of 1:8, mass/volume). Transfected cells were plated at ∼50% confluence and the medium was changed 18 to 24 h later. At 72 h after transfection, the HeLa cells were exposed to, and thereafter continuously grown in, culture media containing 400 μg/ml G418 (Inivtrogen) to select for cells that had incorporated the cDNA construct. To select cells stably expressing OCT1 transport activity, the cells were exposed to 20 μM NBD-TMA, a fluorescent organic cation (or a pH-insensitive derivative of NBD-TMA (Bednarczyk et al., 2000)), for 20 min, then trypsinized and resuspended in phosphate-buffered saline plus 250 μM TPrA. The subpopulation of fluorescent cells was sorted using a fluorescence-activated cell sorter (Arizona Research Laboratories, Tucson AZ). From the reclaimed cells, individual clones were selected by dilution cloning. HeLa cells were also stably transfected with the wild-type hOCT1 in pcDNA3.1 (i.e., without addition of the C-terminal V5His sequence) using the same general method.

HeLa cells stably transfected with pcDNA3-hOCT1-V5His or pcDNA3.1-hOCT1 were cultured at 37° in a humidified 95% air/5% CO₂ environment. The growth media used to maintain the cells was RPMI 1640 supplemented with 2 mM glutamine, 50 U/ml penicillin, 50 μg/ml streptomycin, and 400 μg/ml G418. Cells were trypsinized (trypsin + 0.25 mM EDTA; Invitrogen) and seeded at ∼600,000 or ∼300,000 cells/well of a 12-well culture plate (Falcon or Cell Star) and transport experiments were performed 2 or 3 days later, respectively.

Fluorescence of NBD-TMA⁺ Loaded Cells.

HeLa cells stably transfected with pcDNA3-hOCT1-V5His were loaded with 10 to 20 μM NBD-TMA in Waymouth buffer (135 mM NaCl, 13 mM HEPES, 2.5 mM CaCl₂, 1.2 mM MgCl₂, 0.8 mM MgSO₄, 5 mM KCl, and 28 mM glucose) for 5 to 30 min. The solution was then aspirated and the cells were rinsed with Waymouth buffer plus 100 to 250 μM TPrA and maintained in this solution during fluorescence microscopy.

Measurement of Transport.

After aspiration of the culture medium, the cells in each well of a 12-well plate were twice exposed, each time for 15 min, to 1 ml of Waymouth buffer at room temperature. After the second 15-min incubation, the buffer was aspirated and Waymouth buffer containing 1 μCi/ml [³H]TEA, with or without inhibitor, was added. After a prescribed interval, the buffer containing radiolabeled substrate was aspirated and the cells were washed twice with 1 ml of ice-cold Waymouth buffer. The cells were then solubilized with 1 ml of 0.2 N NaOH and 1% SDS. This solution was pipetted repeatedly until homogenous, then neutralized with 200 μl of 1 N HCl. Ten milliliters of scintillation cocktail were then added to this homogenate, and radioactivity in the resulting solution was counted in a scintillation counter. The cells in each of three additional wells were counted using a cell counter (Beckman Coulter, Fullerton, CA) and the counts averaged to determine the number of cells per well (routinely 1.0–1.2 million cells/well). Cells in three additional wells were solubilized with 1 ml of 0.2 N NaOH later neutralized with 200 μl of 1 N HCl and used to determine the average protein content of each well (Bio-Rad, Hercules, CA). The concentration of inhibitor that reduced uptake by 50% (IC₅₀) was generated using Sigma Plot by fitting the data to the following equation (Groves et al., 1994): J = [(J _app[T])/(IC₅₀ + [I])] + C, whereJ is the uptake of [³H]TEA;J _app is a constant related to the product of the maximal rate of TEA uptake and the ratio of the inhibitor IC₅₀ and theK _t for TEA transport; [T] is the concentration of [³H]TEA; [I] is the inhibitor concentration, and C is a constant representing the nonsaturable component of [³H]TEA uptake.

Modeling with Catalyst and Cerius².

The computational molecular modeling studies were carried out using Silicon Graphics Octane workstations (SGI, Mountain View, CA). The 3D structures of substrates were built interactively using either Catalyst version 3.1 or 3.4. (Accelrys, San Diego, CA) and used to generate a 3D-QSAR model. The number of conformers generated for each substrate was limited to a maximum of 255 with an energy range of 20 kcal/mol. Ten hypotheses were generated using conformers for the 22 molecules in the training set and the IC₅₀ values, after manual selection of the hydrogen bond donor, hydrogen bond acceptor, hydrophobic, and negative ionizable pharmacophoric features. After assessing all 10 hypotheses generated, the lowest energy cost hypothesis was considered the best. The goodness of the structure-activity correlation was estimated from calculatedr values. Statistical significance of the retrieved hypothesis was verified by permuting the response variable; i.e., the activities of the training set compounds were mixed a number of times (so that each value was no longer assigned to the original molecule) and the Catalyst hypothesis generation procedure was repeated. Multiple conformations of the eight molecules in the test set were generated using the same method as the training set before fitting the conformers to the pharmacophore to generate a prediction.

Cerius² (Accelrys) was used to generate more than 200 molecular descriptors for the 30 molecules used in the Catalyst studies. The forward stepwise regression method incorporated within Cerius² was then used to relate the log IC₅₀ to a selection of these descriptors, and hence result in a QSAR model. The model was validated for numerical stability and internal consistency using both the leave-one-out cross-validation method and by permuting, or randomizing, the response variable.

hOCT1 Sequence Variation and Stable Cell Line Characteristics.

The sequence of the hOCT1 cDNA provided by K. Giacomini differed by one base from the sequence reported by Zhang et al. (1997) (Genbank accession number U77086). That difference was a guanine, rather than an adenine, at nucleotide 1275. The resulting codon encoded a valine rather than a methionine at amino acid 408. The sequence of an hOCT1 fragment (from position 1197 to 1588), amplified using RT-PCR from human renal mRNA, confirmed the presence of G at position 1275 (i.e., valine at amino acid 408) in a sequence that otherwise matched that ofZhang et al. (1997). The full-length cDNA coding for hOCT1, as provided by Dr. Giacomini, was therefore used for the preparation of the hOCT1-V5His construct employed in the studies described below.

Figure 1A shows cells stably transfected with pcDNA3-hOCT1-V5His after a 10-min exposure to 20 μM NBD-TMA. Figure 1B is a fluorescence image of the same cells, showing that only a fraction of the transfected cells that were resistant to G418 actually transported NBD-TMA. Cells transfected with hOCT2, rbOCT1, and rbOCT2 also showed increased fluorescence after exposure to NBD-TMA (data not shown). In fact, only 5 to 10% of the ‘parent’ population of cells expressed transport activity. Cells positive for hOCT1-V5His-mediated transport of NBD-TMA were selected using 1) flow cytometry, 2) dilution-cloning, and 3) cloning rings and discs. Figure1C shows [³H]TEA uptake data from wild-type HeLa cells, the parent cell population, and cells selected from the parent cell line. The parent cell population showed about twice as much radiolabeled TEA accumulation as the wild-type HeLa cells. After selection of NBD-TMA–containing cells using flow cytometry, there was a 6-fold increase in accumulation of radiolabeled TEA. The remaining studies reported here used a clonal cell line selected from this population.

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Figure 1

A, differential interference contrast image (400×) of HeLa cells stably transfected with pcDNA3.1-hOCT1 after a 10 min exposure to 20 μM NBD-TMA⁺ (note the elongated cell for reference). B, fluorescence image of the same field of view showing NBD-TMA⁺ accumulation in a subpopulation of these cells. C, Accumulation of 0.04 μM [³H]TEA⁺ by untransfected wild-type HeLa cells (HeLa_wt), HeLa cells stably transfected with pcDNA3-hOCT1-V5His (Parent), and cells sorted from the parent cell population by flow cytometry (Sorted). The height of each bar indicates the mean (± S.E.) of 60-min uptakes measured in three confluent wells (∼1 × 10⁶ cells).

Figure 2A shows the time course of [³H]TEA uptake into HeLa cells stably expressing hOCT1-V5His. TEA uptake was approximately linear for 5 to 7 min, and a 5-min uptake provided a reasonable estimate of the initial rate of transport. Figure 2B shows the effect of increasing concentrations of unlabeled substrate on the rate of hOCT1-mediated transport of TEA from which the kinetics of transport of this substrate was estimated. In three separate experiments, the apparentK _t for TEA transport was 164 ± 17.9 μM, with a J _max of 3.5 ± 0.33 nmol mg⁻¹ 5 min⁻¹. These values are comparable with those reported by Zhang et al. (1998)in their study of hOCT1-mediated transport in transiently transfected HeLa cells. Consequently, we elected to use the transport of TEA, and the inhibition of TEA transport, in the remaining studies. We also measured the kinetics of TEA transport in cells stably expressing the wild-type hOCT1 (i.e., without the V5His tagged sequence; data not shown). In these experiments, the K _tfor TEA transport in the wild-type clones was similar to that of the tagged protein, suggesting that the presence of the V5His sequence at the C terminus of hOCT1 had no significant effect on the interaction of substrate with the transport binding site. Hereafter, hOCT1-V5His is referred to simply as hOCT1.

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Figure 2

Kinetic data acquired from HeLa cells stably transfected with pcDNA3-hOCT1-V5His. In each plot, the solid points represent the mean (± S.E.) of uptakes measured in three confluent wells of cells in a single, representative experiment. A, time-dependent accumulation of 0.04 μM [³H]TEA⁺. B, kinetics of hOCT1-mediated TEA transport as shown by the profile in inhibition of 0.05 μM [³H]TEA⁺ uptake produced by increasing concentrations of unlabeled TEA⁺ (Malo and Berteloot, 1991). The curve was fit to the data using a nonlinear regression algorithm (SigmaPlot, v3.0). C, relationship between calculated hydrophobicity (Log P) of nTAAs and the measured IC₅₀ each displayed against hOCT1-mediated TEA uptake. Each point represents the mean (± S.E.) of IC₅₀ values calculated in three separate experiments.

To further assess the kinetic characteristics of our hOCT1 cell line, we examined the inhibition of hOCT1-mediated TEA transport produced by a series of tetraalkylammonium compounds (nTAAs: TEA, TPrA, TBA, and TPeA). The IC₅₀ for each compound was determined by measuring the 5-min accumulation of [³H]TEA in the absence or presence of five incremental concentrations of inhibitor (Table 1). Figure 2C plots the relationship between the log of the measured IC₅₀values for the test agents and their calculated Log P values (CLog P), confirming the existence of a strong correlation between hydrophobicity and IC₅₀ for the nTAAs commonly used in organic cation studies. Additionally, the IC₅₀ values for these compounds compared favorably with those reported previously for nTAA inhibition of hOCT1 expressed in HeLa cells (Zhang et al., 1999) and Xenopus laevis oocytes (Dresser et al., 2000).

Pharmacophore 3D-QSAR Generation.

We next used a computational approach to develop QSARs and a predictive model of inhibitor/hOCT1 interaction. The commercial software program Catalyst was used to model the structural features that facilitate interaction of inhibitors at the hOCT1 binding site. A set of chemically diverse molecules with IC₅₀ values for hOCT1 spanning more than 3 orders of magnitude (Table 2) was used for pharmacophore construction. These 22 molecules identified a pharmacophore with four features of interaction with the transporter. The four features included a positive ionizable feature and three hydrophobes at distances of 5.12, 4.19, and 5.32 Å from the center of the positive ionizable feature (Fig. 3A). Regression of the logs of experimentally determined and the estimated IC₅₀ values for the compounds contributing to the model resulted in a correlation coefficient of 0.86 (Fig.4, ●; Table 2). Compounds with the highest apparent affinity for the transporter (i.e., those with the lowest IC₅₀ values) generally possessed the greatest number of pharmacophore features, whereas compounds with structures that included fewer of these features generally showed a lower apparent affinity for the transporter. This can be seen in Fig.3B, where the most potent inhibitor, clonidine, (IC₅₀ = 0.71 μM) is characterized by all four pharmacophore features, but the least potent inhibitor, choline (IC₅₀ = 3.5 mM), is described by only one of the features, (i.e., the positive ionizable feature) (Fig. 3C). Additionally, choline has a hydrogen bond donor near two of the hydrophobes, perhaps contributing to its failure to inhibit TEA transport to a significant degree. Although many of the chemical features of the diverse group of molecules in the training set were in spatial agreement with the model pharmacophore, some molecular structures clearly lay outside the area described by the model. This can be readily seen for ranitidine (IC₅₀ = 21.7 μM; Fig. 3D). Furthermore, there was generally poor correlation (r = 0.54) between the Catalyst-produced estimates of IC₅₀ and the experimentally determined values for the pyridinium- and quinolinium-based set of substrates described in the following group of experiments (Fig. 4, ○; Table 2). Nevertheless, the Catalyst-produced model was more accurate than the often-cited correlation between the hydrophobicity of substrates or inhibitors and the apparent affinity of OC transporters for these compounds. Figure 5 shows the relationship between the log of the IC₅₀ for each of the members of the training set and their CLog P values. The correlation (r = 0.73) was less than that of the Catalyst model (r = 0.86), which may reflect the substantial variation in the three dimensional structure of several inhibitors with similar CLog P values. Indeed, when a structurally homologous group of molecules is examined (e.g., the nTAAs), the correlation between hydrophobicity and apparent affinity for hOCT1 can be quite striking (Fig. 5, ○).

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Figure 3

A, the structure of the Catalyst-produced pharmacophore illustrating three hydrophobic areas (cyan) and a positive ionizable feature (red). B, clonidine mapped to the pharmacophore model. C, choline mapped to the pharmacophore model. D, ranitidine mapped to the pharmacophore model.

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Figure 4

The relationship between the Catalyst-predicted IC₅₀ values and the measured IC₅₀ values. The solid line describes the linear regression of the data representing the 22 molecules comprising the training set (●; r = 0.86); the dotted lines illustrate the 95% confidence interval of the regression. ○, the eight molecules from the test set (r = 0.54).

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Figure 5

Relationship between observed IC₅₀ and calculated hydrophobicity (Log P) for the training set of compounds used to generate the Catalyst-produced pharmacophore. The solid line depicts the linear regression of these data (r = 0.73), and the dotted lines show the 95% confidence interval for the regression. The dashed line depicts the linear regression for a subset of these data (i.e., the nTAAs) (r = 0.99). Each point is the mean of measurements made in three or four separate experiments.

Descriptor-Based QSAR Generation.

Previous work has shown that a descriptor-based QSAR model generated using Cerius² can be superior in its ability to predict activity parameters than a Catalyst-derived model (Ekins and Obach, 2000). Consequently, a descriptor-based QSAR model for hOCT1 was built using the molecular descriptors generated by the Cerius² program. The following equation identifying molecular descriptors found to be correlated with inhibition of hOCT1 activity was produced using forward stepwise regression:Log IC50=3.94081−0.212694×S_ssNH+0.458416×ADME_solubility−0.0725515×Jurs­RNCS−0.363722×Shadow­ν−0.0899946×Atype_H_52 in which S_ssNH defines an E-state key for sum of nitrogens connected to a hydrogen and to two single bonds (Ghose et al., 1998); ADME solubility is a descriptor for prediction of aqueous solubility; Jurs-RNCS is related to the negative charge surface area mapped over the solvent-accessible surface area of individual atoms; Shadow-ν is a member of the shadow indices descriptors and is the ratio of largest to smallest dimension; and Atype_H_52 is the number of times that each AlogP atom appears in the molecule and is part of the Thermodynamic family (AlogP_atypes) of descriptors (Viswanadhan et al., 1989). The correlation of the experimentally determined (Log) IC₅₀ values and those predicted for the descriptor-based model described in eq. 1 generated a correlation coefficient of 0.95 (Fig. 6). After permuting the log data with the descriptors and attempting to generate eqs. 9 times (to give 90% confidence in the models produced), the mean r value was 0.65 ± 0.14, which represented 2.1 S.D. from ther value described for the model in eq. 1

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Figure 6

The relationship between the Cerius 2 predicted IC₅₀ values and the measured IC₅₀ values. The solid line describes the linear regression of the data representing all 30 molecules used in the present study (r = 0.95); the dotted lines illustrate the 95% confidence interval of the regression. ○, the eight molecules from the test set.

Interaction of N-1-Substitued Pyridiniums and Quinoliniums with hOCT1.

Of the five molecular descriptors presented in eq. 1, the relative importance of the Shadow-ν term (i.e., the ratio of the largest to the smallest dimension of the inhibitor) was particularly intriguing. In a previous study, we used a series of phenylpyridinium and quinolinium compounds to examine the influence of the spatial distribution of hydrophobic mass on the interaction of inhibitors with the OC/H⁺exchanger of rabbit renal brush border membrane vesicles (Wright and Wunz, 1999). Of relevance to the present observations is the fact that the ratio of the largest to the smallest dimension (Shadow-ν values) of these compounds varies systematically. The QSAR model presented in eq. 1 suggests that systematic variation of molecular dimension should have a predictable influence on the interaction of test agents with hOCT1. Consequently, we elected to investigate the inhibitory efficacy of a group of phenylpyridinium and quinolinium compounds with ethyl, hydroxyethyl, or methylphenyl side groups located at the N1 position (Fig. 7).

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Figure 7

Structural formulae of the molecules used to assess the influence of the Shadow-ν dimensional descriptor on hOCT1-mediated TEA transport.

The present work, and results from previous studies (e.g., Ullrich et al., 1992; Wright et al., 1995) led to the hypothesis that the IC₅₀ values of the test agents would vary inversely with hydrophobicity. This was generally the case for each ‘genus’ of chemical (i.e., 4-phenylpyridiniums, 3-phenylpyridiniums, and quinoliniums): for each ‘species’ within a given genus, increasingly hydrophobic residues at the N1 position were associated with a decrease in IC₅₀ for hOCT1-mediated TEA transport (Fig. 8). However, there was systematic variation in the apparent affinity of hOCT1 for the different structural groups. The more elongated 4-phenylpyridiniums consistently had lower IC₅₀ values for hOCT1 than did the 3-phenylpyridiniums, which, in turn, had lower IC₅₀ values than the least linear compounds, the quinoliniums (Fig. 8; Table 2). This observation suggests that, although hydrophobicity is clearly an important determinant of molecular interaction with hOCT1, changes in molecular dimension can also predictably influence apparent affinity of the transporter for a molecule.

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Figure 8

The relationship between the IC₅₀ for inhibition of hOCT1-mediated [³H]TEA transport and the calculated hydrophobicity (Log P) of the members of the three sets of test compounds: 4-phenylpyridiniums (4P, ●); 3-phenylpyridiniums (3P, ░); and quinoliniums (Q, ▵). Each point is the mean (± S.E.) of IC₅₀ values measured in three or four separate experiments. Inset, within each set of test agents, Log P was systematically increased by altering the N ¹-substituent (-2-hydroxyethyl to -ethyl to -(phenyl)methyl; see Fig. 7). The relationship between the IC₅₀ for inhibition of hOCT1-mediated [³H]TEA transport and the calculated hydrophobicity (Log P) of the three sets of test compounds, and the nTAA compounds (⋄).

In a previous study of the influence of planar hydrophobic mass on the binding of OCs to the luminal OC/H exchanger of rabbit renal BBMs (Wright and Wunz, 1999) we noted that, in general, planar substrates bind with greater affinity to the exchanger than nTAA compounds of comparable hydrophobicity. Indeed, there is a marked discrepancy between hydrophobicity and binding efficacy between nTAAs and the selected pyridinium/quinolinium compounds at the luminal OC/H⁺ exchanger. This led to the hypothesis that the globular (rather than planar) nature of nTAAs places a substantial fraction of their hydrophobic mass away from the postulated planar binding surface of the OC/H⁺ exchanger, in contrast to the situation afforded by planar substrates. No such discrepancy was noted between the interactions of these two groups of compounds with hOCT1 (inset, Fig. 8). To extend the examination of the influence of globular, rather than planar, hydrophobic mass on interaction with hOCT1, the inhibitory efficacies of two compounds used in the development of the computational model were compared. Cyclohexylamine and amantadine are unsaturated hydrocarbons with a primary amine-containing side group. Importantly, the structure of cyclohexylamine is fully contained within amantadine, with the latter containing additional hydrophobic mass that is supraplanar to a postulated binding surface (refer to Fig. 7). Amantadine is known to be a substrate of basolateral OC transport in intact rat tubules (Wong et al., 1990), and it is likely that cyclohexylamine, which is a substructure of amantadine, is also a substrate. Interestingly, the presence of additional, nonplanar hydrophobic mass increased the inhibitory effectiveness of amantadine 25-fold over that observed for cyclohexylamine (IC₅₀ values of 19 and 540 μM, respectively; see Table 2).