A Pharmacophore Hypothesis for P-Glycoprotein ... - ACS Publications

J. Med. Chem. 2005, 48, 2927-2935

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A Pharmacophore Hypothesis for P-Glycoprotein Substrate Recognition Using GRIND-Based 3D-QSAR Giovanni Cianchetta,†,‡ Robert W. Singleton,† Meng Zhang,† Marianne Wildgoose,† Dennis Giesing,† Arnaldo Fravolini,‡ Gabriele Cruciani,§ and Roy J. Vaz*,† Sanofi-Aventis Pharmaceuticals, 1041 Rt 202-206N, Bridgewater, New Jersey 08807; Dipartimento di Chimica e Tecnologia del Farmaco, Universita` di Perugia, Via del Liceo 1, I-06123 Perugia, Italy; Dipartimento di Chimica, Laboratorio di Chemiometria, Universita` di Perugia, Via Elce di Sotto, 10, I-06123 Perugia, Italy; and Molecular Discovery Ltd., 4, Chandos Street, W1A 3BQ, London, United Kingdom Received October 12, 2004

Trying to understand the complex interactions that substrates and inhibitors have with the efflux transporter P-glycoprotein has been the subject of various publications. In this work, we have confined our study to substrates by picking a diverse set of 129 compounds based on the efflux ratios from Caco-2 permeability measurements. These compounds were then evaluated for P-glycoprotein inhibition using a calcein-AM assay. The subsequent data was used in a 3D-QSAR analysis using GRIND pharmacophore-based and physicochemical descriptors. Pharmacophore-based descriptors produced a much more robust model than the one obtained from physicochemical-based descriptors. This supports the process proposed by Seelig and co-workers previously published whereby the substrate enters the membrane as the first step and is then recognized by P-glycoprotein in a second step. The strong correlation, highlighted by PLS statistical analysis, between pharmacophoric descriptors and inhibition values suggests that substrate interaction, with perhaps the mouth of the protein or another binding site, plays a key role in the efflux process, yielding a model in which diffusion across the membrane is less important than substrate-protein interaction. One pharmacophore emerged from the analysis of the model. We pose that the recognition elements, at least determined by the molecules used in this study, are two hydrophobic groups 16.5 Å apart and two hydrogen-bond-acceptor groups 11.5 Å apart and that the dimensions of the molecule also plays a role in its recognition as a substrate. Multidrug resistance protein 1 (MDR1) gene in human cells encodes a broad specificity efflux pump, P-glycoprotein (Pgp). This protein belongs to the family of proteins labeled ATP-binding cassette (ABC) transporters, which are found in all cells in every species.1 The most recent annotation2 of the human genome sequence revealed 49 genes for ABC proteins, which are grouped into seven subfamilies labeled ABCA to ABCG. MDR1 falls under class B of ABC proteins (ABCB1), and this family of MDR proteins in mammals includes two other groups of sequences besides the MDR1-like proteins, the MDR3-like (ABCB4) proteins and the BSEPlike (ABCB11) proteins involved in the export of phosphatidyl choline and bile salts, respectively. All three members of the MDR family, localized in the plasma membrane and in polarized cells in the apical membrane, are “full transporters”. A full transporter is shown to consist of two sets of six transmembrane helices (TMDs), each followed by a nucleotide binding domain (NBD), and can form a fully functional transporter by itself. Multidrug resistance-associated transporters (MRP) (ABCC1) belong to the C class of ABC proteins. MRP1, the second identified ABC drug trans* Corresponding author. Phone:+1-908-231-4816. E-mail: roy.vaz@ aventis.com. † Sanofi-Aventis Pharmaceuticals. ‡ Dipartimento di Chimica e Tecnologia del Farmaco, Universita ` di Perugia. § Dipartimento di Chimica, Universita ` di Perugia, and Molecular Discovery Ltd.

porter,3 has been shown to transport both modified and unmodified xenobiotics. Other MRP proteins, MRP2, MRP3, and MRP6, are very homologous, and these include an additional five TM regions in the N-terminal segment. The NBD domain structure of several ABC proteins have been determined by X-ray crystallography to high resolution and seem to be highly conserved.4 A complete ABC transporter, from Escherichia coli as well as from Vibrio cholera, a lipid flippase, MsbA, has been solved using X-ray crystallography.5,6 In addition, the electron crystallography structure of Pgp at 2 nm,7 as well as the structure of the cystic fibrosis transmembrane conductance regulator (CFTR) also at 2 nm,8 has been determined. Both of these structures show a conformational change in the presence of nucleotide binding. Homology models have been built for both forms of Pgp: the nucleotide free form9 and the nucleotide-bound form10,11 based on the MsbA structure. Models built on the structures of other solved transporters such as BtuCD, Rad50, and MJ0796 failed to satisfy the known biochemical as well as the electron microscopy data available.11 The model built by Pajeva et al.10 based on published cross-linking constraints and mutant inhibition data possibly identify the two- or three-site model proposed by Shapiro and co-workers.12-14 In addition, a twopharmacophore model proposed by Garrigues et al.15 as well as cooperativity between the sites16 could point to

10.1021/jm0491851 CCC: $30.25 © 2005 American Chemical Society Published on Web 03/16/2005

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Journal of Medicinal Chemistry, 2005, Vol. 48, No. 8

a large substrate binding pocket which could have some regulatory function as well or sites that have different functions within the transporter. Entry to the site seems to take place within the membrane bilayer itself with access from the cytoplasmic leaflet. Compounds might be released either to the aqueous phase or to the extracellular leaflet. The exact nature of the mechanism of binding and release of the compound is yet to be determined. Most evidence now seems to point toward substrates gaining access to Pgp from the membrane. It also seems likely that Pgp intercepts compounds at the plasma membrane before they enter the cytosol.17 The most convincing evidence comes from the acetoxymethyl (AM) esters of several fluorescent indicator dyes, such as calcein-AM.18 If the dye permeates past the plasma membrane, then cystolic esterases hydrolyze the ester to a fluorescent product, calcein, which is the basis of the assay utilizing this dye. Several different in vitro assays have been utilized to study the binding of substrates and inhibitors to Pgp. The usual Pgp assays consist of efflux across a monolayer of cells, an ATPase assay, or the calcein-AM fluorescence assays.18,19,20 Recent papers have suggested that efflux ratios themselves are not useful as a determinant of Pgp activity.21,22 In examining data from similar assays on the same compounds from different research groups, compounds can be misclassified even using data from two different types of assays,19,20 e.g. itraconazole. Hence using any publicly available data for the purposes of creating a model is difficult.23 This work, therefore, concentrates on modeling based on data from a calcein-AM assay on a nonpolarized cell line. Calcein-AM is a nonfluorescent, lipophilic, and therefore highly cell-permeable ester. Once inside the cell the ester bonds are rapidly cleaved by nonspecific esterases, generating highly fluorescent calcein, which is trapped inside the cell because of its hydrophilic nature and charge. Cells expressing high levels of Pgp rapidly expel nonfluorescent calcein-AM from the plasma membrane, reducing accumulation of fluorescent calcein in the cytosol.24-27 Inhibition of the transporter by a substrate or an inhibitor decreases efflux, which results in a higher intracellular calcein fluorescence. The degree of inhibition of Pgp activity can be calculated from the fluorescence increase and is normalized so the value from untreated cells is 0% and 100% is the value for cyclosporin A, which is a competitive inhibitor of Pgp-drug binding.28 Since the calcein-AM assay is not able to distinguish whether a compound is a substrate or inhibitor of Pgp, selection of the molecules included in the data set was done according to the ratio of the basolateral to apical transport (b2a) to the apical to basolateral transport (a2b) in a Caco-229 cell monolayer. All the chosen compounds show values of the b2a/a2b ratio larger than 1, which implies that the molecules are substrates, since Pgp is expressed only on the apical surface of the Caco-2 cells. Besides the homology-based models of Pgp, there have been several other ligand-based models published. In addition to identifying physical properties30 that seem to be shared by modulators of Pgp, pharmacophore models have been proposed comprising of two electrondonor groups being 2.5 or 4.6 Å apart.31,32 Multiple

Cianchetta et al.

pharmacophores for distinguishing between substrates and nonsubstrates have also been identified from a large data set available from published data.33 A support vector machine approach has also been used to classify between substrates and nonsubstrates together with other statistical classification methods.34 Another more general quantitative structure-activity relationship (QSAR) based on a monolayer efflux classification as substrates or nonsubstrates has been established. A linear discriminant analysis between the substrates and nonsubstrates could be achieved by using 27 descriptors of 95 structures.35 In addition, several specific threedimensional QSAR (3D-QSAR) models have been developed for propafenone-type molecules36 as well as phenothiazines.37 Pharmacophore models have also been proposed for inhibitors.38 Seelig31 has suggested that in a two-step process involving partitioning into the membrane and further interaction with the transporter, partitioning into the membrane has a more negative free energy than that required for interaction with Pgp. The work described in this paper centers around first selecting several compounds that have an efflux ratio in a Caco-2 assay greater than 1 and then subjecting those compounds to a calcein-AM assay. Pgp is the only efflux transporter for which calcein-AM is a substrate or inhibitor that is expressed in the cell line used. There is no detectable MRP1 in the cell line. The data from that assay is then analyzed using an alignmentindependent 3D-QSAR39 methodology. In this work we describe the methodology as well as the experimental data used to assemble the 3D-QSAR. Materials and Methods Cell Culture Conditions. The P-glycoprotein assay utilized the cell line MES-SA/DX5.40-42 The cells were cultured in T-flasks and maintained in log phase growth in a humidified 37 °C/5% CO2 atmosphere. The medium consisted of McCoy’s 5A supplemented with 10% fetal bovine serum (FBS), 1% antibiotic/antimycotic, and 0.2 µg/mL doxorubicin. Cells were harvested with 0.25% (w/v) trypsin-1 mM EDTA solution, washed with McCoy’s 5A medium, and quantified with a hemocytometer. Assay Conditions. Sixteen hours prior to test compound addition, the cells were harvested, counted, and resuspended in McCoy’s 5A medium containing 10% FBS. Assay plates were black walled/clear bottomed, 384-well, poly-D-lysine-coated plates supplied by Becton Dickinson. The cells were seeded in the assay plate at a density of 20 000 cells/well in a total volume of 50 µL. Immediately before dosing the cells, they were washed twice with Hanks balanced salt solution (HBSS). Once the wash solution was removed, 50 µL of HBSS containing the test compound was pipeted onto the cells. Immediately after dosing with the test compound, 30 µL of a calcein-AM43-HBSS mixture was added to the assay solution. Final concentrations were at a maximum of 20 µM for the test compound, 1.6 µM for calcein-AM, and 0.2% for the vehicle DMSO. Cells were incubated for 2 h at 37 °C in a humidified 5% CO2 incubator. Plates were read by utilizing an Analyst HT Molecular Devices fluorescent plate reader. Signal was measured using a λex ) 485 nm, λem ) 525 nm filter combination, and a λ ) 505 nm dichroic mirror. Data Calculations. The P-glycoprotein assay was performed in triplicate with a serial dilution of 10 concentrations. Data were calculated with Activity Base (ID Business Solutions) with values normalized to the positive control, cyclosporin A. Z′ 29 values of 0.6 were routinely achieved for the assay.

P-Glycoprotein Substrate Recognition


Table 1. log of the Normalized Inhibition Values of the Publicly Available Compounds

hydrogen bond (Ehb), electrostatic interactions (Eel), and an entropic term:

name

act.

name

act.

nefazodone clofazimine ebastine azelastine trimebutine ritanserin sufentanil terfenadine thioridazine verapamil loperamide bepridil amiodarone fluphenazine chlorprothixene

1.83 1.8 1.76 1.75 1.75 1.69 1.68 1.65 1.65 1.63 1.58 1.56 1.46 1.4 1.27

quininone thiethylperazine thiothixene diltiazem sertraline oxybutynin dipyridamole terguride chlorphenoxamine emetine 16β-methylprednisone benoxinate salmeterole acepromazine

1.26 1.26 1.23 1.18 1.16 1.12 1.01 1 0.91 0.88 0.86 0.84 0.83 0.8

Dataset. A data set comprised of 129 molecules, 100 SanofiAventis proprietary compounds and 29 publicly available compounds (Table 1), was studied in order to obtain a 3D quantitative structure property relationship (3D-QSPR) model able to identify the structural features that a molecule should possess in order to be recognized as a substrate of Pgp. All the chosen compounds show values of the b2a/a2b ratio larger than 1, which implies that the molecules are substrates. The degree of inhibition of Pgp activity, calculated from the slope of the fluorescence increase of the calcein-AM assay, was used as activity data. The inhibition values are transformed so the value from untreated cells is 0% and 100% is the value for cyclosporin A, which is a competitive inhibitor of Pgp-drug binding.28 The inhibition value of each substrate was then transformed in the common logarithm in order to reduce the residuals for the larger values. The activity range spans from 2.32 to 0.37, covering 1.95 log units. The molecules were divided into a training set (109 compounds) and a test set (20 molecules). Activity values of the test set molecules range between 1.86 and 0.59 log units. The test set was chosen in such a way as to fully cover the activity range; the data set was divided in four classes according to the activity value (2.32-1.63, 1.62-1.23, 1.22-0.84, 0.830.37). Then five molecules for each class were randomly chosen to ensure that the chosen molecule was similar to the training set. Computational Methods. Molecular models and subsequent geometry optimization were calculated using the molecular modeling software package SYBYL 6.9.244 on a Linux workstation with Redhat enterprise WS version 3. 3D structures were obtained from smiles notation by means of the Unity45 program included in the Sybyl package. CONCORD46 was used to generate a single conformation that was used for the model development and to analyze the test set compounds. Then energy minimization was performed with the standard TRIPOS force field using the Powell method with initial Simplex optimization. Gradient termination was set to 0.05 kcal/(mol Å). When needed, conformational analysis was performed with software MOE47 2004.03 release (2004.03 release), limiting the number of conformers to 50 and used as described. Besides compounds with quaternary nitrogen atoms, structures were considered to be uncharged. The high structural diversity of the molecules that form the data set made it very difficult to find rules for superimposition of the structures; hence, an alignment-independent method was required to analyze the data set. Modeling Tools. GRID.48 The program GRID is a computational procedure for determining energetically favorable binding sites on molecules of known structure. The program calculates the interactions between the molecule and a probe group which is moved through a regular grid of points in a region of interest around the target molecule, and at each point, the interaction energy between the probe and the target molecule is calculated as the sum of Lennard-Jones (Elj),

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Exyz ) ΣElj + ΣEel + ΣEhb + S GRID contains a table of parameters to describe each type of atom occurring in each of the ligand molecules. The GRIND Approach.49 GRID-alignment-independent descriptors (GRIND) were chosen due to their ability to represent pharmacodynamic properties in such a way that they are no longer dependent upon their positions in 3D space. The GRIND calculation starts by computing several molecular interaction fields (MIFs) using the GRID program. The MIFs characterize the potential of interaction between the molecule of interest and a particular chemical probe (e.g., water, amine nitrogen, etc.). The GRIND approach aims to extract the information enclosed in the MIFs and compress it into new types of variables whose values are independent of the spatial position of the molecule studied. Most relevant regions are extracted from the MIFs using an optimization algorithm that uses the intensity of the field at a node and the mutual node-node distances between the chosen nodes as a scoring function. A discrete number of categories, each one representing a small rank of distances, are considered. In classical autocorrelation, all correlations are summed, making it impossible to trace each value back to the descriptor space. In the case of the GRIND autocorrelograms, the mathematical transformations have been simplified: for each distance bin, only the highest product is kept, thus allowing its representation in the original 3D space as a line linking two specific MIF nodes. GRIND variables are then grouped into blocks representing interactions between couples of nodes generated by the same probe (autocorrelograms) or combination of probes (crosscorrelograms). Such variables constitute a matrix of descriptors that can be analyzed using multivariate techniques, such as principal component analysis (PCA) and partial least squares (PLS) regression analysis. All the calculations were done by means of the program Almond 3.2.0. In this version a new kind of descriptor has been added that is able to describe the shape of the molecule using the same GRIND formalism. Shape descriptors are represented in a correlogram-like form, where the autocorrelograms describe the distance between certain regions defining the spatial extent of the molecule and the cross-correlograms describe the distance between these regions and other regions representing relevant interactions of the compounds Almond Descriptors. Some 940 Almond descriptors have been obtained using four GRID probes: DRY (which represent hydrophobic interactions), O (sp2 carbonyl oxygen, representing H-bond acceptor), N1 (neutral flat NH, like in amide, H-bond donor), and the TIP probe (molecular shape descriptor). The grid spacing was set to 0.5 Å and the smoothing window to 0.8. The number of filtered nodes was set to 100 with 50% relative weights. Ten groups of variables were produced by Almond: four autocorrelograms and six cross-correlograms. The VolSurf Approach.50 The VolSurf program is a computational procedure for producing and exploring the physicochemical properties space of a molecule, starting from 3D maps of interaction energies between the target molecule and different chemical probes. The basic concept of VolSurf is to compress the information present in 3D GRID maps into a few quantitative numerical descriptors. The principal advantage of these descriptors is that they are related to global physicochemical molecular properties and do not require structural superimposition for a 3D-QSAR analysis. VolSurf descriptors can be adequately employed to describe complex biological data involving a significant impact of kinetic properties as commonly present in cell systems.51 Volsurf Descriptors. Ninety-four Volsurf descriptors have been obtained using three standard GRID probes: OH2 (water probe), DRY (hydrophobic probe), and O (sp2 carbonyl oxygen, H-bond acceptor.

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Cianchetta et al. Table 2. Experimental and Predicted log of the Normalized Inhibition Values of the Test Set Compounds

Figure 1. PCA score plot derived from the analysis of the GRIND descriptors calculated for the entire training set of 129 compounds. The gray objects represent the compounds that form the training set and the black dots represent the molecules of the test set.

Results Chemometric Models. To gain better knowledge of the substrate-transporter interaction mechanism, both physicochemical and pharmacophoric descriptors were employed. The relative importance of the two aspects was studied by producing a mixed pharmacophoric and physicochemical model and analyzing the weight of the descriptors in the resulting model. Structural variance of the data set was analyzed with principal component analysis52 (PCA) performed on the complete set of Almond descriptors calculated for the compounds that comprised the training and test sets. The first two components explained 35% of the structural variance of the data set. Figure 1 shows that no structural outliers are present in the data set and that the training and test sets share similar chemical space. Physicochemical and Pharmacophoric Model. The PLS53 multivariate data analysis of the activity vs the complete set of VolSurf descriptors (94) was carried out using the algorithm implemented in the program. The PLS analysis resulted in a five-latent-variables (LV) model with an r2 ) 0.7166; the cross-validation of the model using the leave-one-out (LOO) method yielded q2 values of 0.5179, leave-two-out (LTO) yielded q2 values of 0.5170, and random grouping (RG) yielding q2 values of 0.4937. No variable selection was possible using FFD factorial selection implemented in the VolSurf program. A weak correlation was found between VolSurf descriptors and the activity values. Table 2 shows activity values of the test set previously defined predicted by means of the physicochemical model. The same descriptors were then merged with 940 pharmacophoric descriptors produced by the Almond program, giving a total of 1034 variables, which after filtering descriptors with no variability, left 749 active descriptors (94 physicochemical descriptors and 655 pharmacophoric descriptors). The two blocks of variables were scaled using the block unscaled weights (BUW) procedure implemented in the Golpe54 program using weighting factors suggested by the program. The operation scales each variable block separately, whereas the relative scales of single variables within

name

expl activity

test•compound•1 test•compound•2 azelastine ritanserin verapamil test•compound•3 test•compound•4 test•compound•5 chlorprothixene thiethylperazine test•compound•6 test•compound•7 test•compound•8 test•compound•9 16β-methylprednisone salmeterole test•compound•10 test•compound•11 test•compound•12 test•compound•13

1.86 1.80 1.75 1.69 1.63 1.44 1.38 1.33 1.27 1.26 1.19 1.15 1.02 0.94 0.86 0.83 0.80 0.75 0.67 0.59

predicted valuesa 1 2 3 1.29 1.83 1.62 1.58 1.34 1.53 1.68 1.40 1.21 1.27 0.97 1.50 0.98 1.10 0.35 0.32 0.64 0.88 0.38 0.46

1.41 0.67 2.49 0.98 1.08 0.68 0.53 0.31 1.81 -0.69 -1.95 -0.92 -0.66 1.88 1.69 0.04 -1.15 -1.88 0.86 -3.29

1.30 1.73 1.51 1.61 1.33 1.4 1.77 1.49 1.18 1.29 0.88 1.63 1.07 1.16 0.29 0.23 0.63 0.87 0.38 0.38

a Predicted value 1 ) pharmacophoric model. Predicted value 2 ) physicochemical model. Predicted value 3 ) pharmacophoric plus physicochemical model. The pharmacophoric model and the pharmacophoric plus physicochemical model yielded similar predicted values, while the physicochemical model gave lower quality predictions.

each block remain unchanged. The weighting coefficients are obtained through an equalization of the block variances, which results in giving each block the same importance within the model.55 The PLS analysis carried out on the resulting matrix yielded a three-latent-variables model with an r2 ) 0.8041. The cross-validation of the model using the LOO method gave q2 values of 0.7178. A variable selection was applied to reduce the variable number using FFD factorial selection implemented in the Golpe program using all the default values suggested by the program. The resulting number of active variable decreased from 749 to 664 (91 physicochemical and 573 pharmacophoric descriptors). A new PLS multivariate data analysis was performed yielding a three-latent-variables model with an r2 ) 0.8176. The cross-validation of the model yielded q2 values of 0.7306 with LOO, 0.7291 with LTO, and 0.7277 with RG. Quality and robustness of the obtained model were tested by predicting the activity of the test set previously defined as shown in Table 2. Physicochemical and Pharmacophoric Model Interpretation. Figure 2 shows the PLS pseudocoefficients profile of the third component of the PLS model. Variables contributing positively toward activity are depicted with positive bars and those contributing negatively with negative bars. Some of the pharmacophoric descriptors significantly contribute to the description of the variance of the pharmacological activity data while the physicochemical descriptors are relatively less important in the PLS model. A more detailed analysis reveals that the VolSurf descriptors that correlate with the pharmacological data are molecular weight (MW), rugosity (ROH2), polarizability (POL), molecular volume (VOH2), and hydrogen bonding (HB1O); calculated log P is only the 94th descriptor in the list of all descriptors ordered by impact on the model. The ratio volume/surface (R) is a measure of molecular wrinkled surface (rugosity). The smaller the



Figure 2. PLS coefficients profile of the pharmacophoric and physicochemical descriptors model. The PLS coefficients plot of the pharmacophoric plus physicochemical descriptors model. The second class of descriptors (in gray) seems to be less correlated to the variance of the biological data than the first.

Figure 3. The overall quality of the obtained model is demonstrated in the plot of the experimental activities (experimental Y) versus the data calculated with our model (calculated Y). The plot shows that the entire training set could be modeled without any significant outlier behavior. The color of the objects in the chart is based upon the Caco-2 efflux ratio value for the molecule.

ratio, the larger is the rugosity. Polarizability (POL) is an estimation of the average molecular polarizability, calculated following an additive method. Molecular volume (V) is defined when a water probe is interacting with a target solute molecule. It represents the water solvent excluded volume (in Å3), i.e., the volume contained within the water accessible surface computed at 0.20 kcal/mol. Hydrogen-bonding variables (HB1-HB8) represent details about the hydrogen-bonding capabilities of the targets. log P is computed by mean of a linear equation derived by fitting VolSurf descriptor to experimental data on water/octanol partition coefficient. Pharmacophoric Model. The PLS analysis of the physicochemical and pharmacophoric descriptors attributed a significant contribution to the pharmacophoric descriptors toward the variance of the activity data. To analyze in further detail the pharmacophoric aspect of the interaction, the PLS multivariate data analysis correlating the activity with the complete set of variables (940) was carried out using the algorithm implemented in the Almond program. In the first instance, five of the Sanofi-Aventis compounds were strong outliers (data not shown); conformational analysis was performed on these molecules using the software MOE (2004.03 release). The final chosen conformation was that which gave the best correlation value in the model. The PLS analysis resulted in a three-latentvariables model with an r2 ) 0.8176. The cross-validation of the model using LOO yielded q2 values of 0.7227, with the LTO method yielding q2 values of 0.7224 and RG yielding q2 values of 0.7142. A variable selection was applied to reduce the variable number using FFD factorial selection implemented in

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the Almond program using all the default values suggested by the program. The resulting number of active variable decreased from 653 to 576. A new PLS multivariate data analysis was performed, yielding a threelatent-variables model with an r2 ) 0.8285. The crossvalidation of the model using LOO yielded q2 values of 0.7459, with the LTO method yielding q2 values of 0.7456 and RG yielding q2 values of 0.7400. The quality and robustness of the obtained model were tested by predicting the activity of the test set previously defined as shown in Table 2. These values confirm the good overall quality and robustness of the obtained model. Figure 3 shows the plot of the experimental versus calculated biological activities. The entire set could be modeled without any significant outlier behavior, although structurally different classes of compounds are present in the data set. This fact confirms a common mechanism of action and consequently common structural features required. Table 3 summarizes the statistical features of the models. Pharmacophoric Model Interpretation. From a visual analysis of the PLS pseudo-coefficients profile of the third component of the PLS model, it is possible to detect the descriptors that have a greater importance in the chemometric model (Figure 4). The most important 3D-pharmacophoric descriptors in the PLS model suggest a common pharmacophore for all the substrates. Activity increases strongly in molecules with high value of the descriptors 33-23, 11-33, 13-8, 14-41, and 44-43. Descriptors are explained in detail in Table 4. The

Table 3. Statistical Data of the Models

Volsurf after FFD Volsurf + Almond after FFD Almond after FFD

no. of variables

active variables

no. of LV

r2

LOO

q2 LTO

RG

94 94 1034 1034 940 940

94 94 749 664 653 576

5 5 3 3 3 3

0.7166 0.7166 0.8041 0.8176 0.8176 0.8285

0.5179 0.5179 0.7178 0.7306 0.7227 0.7459

0.5170 0.5170 0.7171 0.7291 0.7224 0.7456

0.4937 0.4937 0.7034 0.7277 0.7142 0.7400

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Cianchetta et al. Table 4. Meaning of the Almond Descriptors That Are Highly Correlated to the Variance of the Experimental Data 33-23 11-33 13-8 14-41 44-43

related to the presence of two H-bond-acceptor atoms placed 11.5 Å apart the distances between two hydrophobic regions: it has to be 16.5 Å apart a distance of 4 Å between the hydrophobic region previously described and an H-bond-acceptor group a distance of 20.5 Å between a hydrophobic region of the molecule and one of the edges of the same molecule related to the size, being the distance of 21.5 Å that is required between two edges of the molecule

Discussion Figure 4. The PLS coefficients plot highlights the descriptors, which are directly (positive values) or inversely (negative values) correlated to the activity (consensus Y). Activity particularly increases with high values of the 33-23, 11-33, 13-8, 14-41, and 44-43 descriptors.

importance of these descriptors can be assessed by looking at the correlogram profiles of different active compounds in Figure 5. The most important descriptors in the PLS model can be arranged to obtain an approximate pharmacophore valid for molecules actively transported by Pgp. The pharmacophore consists of two H-bond-acceptor groups and two hydrophobic areas, and the size of the molecule plays a major role in the interaction (Figure 6).

To gain better understanding of the substratetransporter interaction mechanism, both physicochemical and pharmacophoric descriptors were employed. Usually QSAR descriptors, such as those used in the program Volsurf, will play a larger role in explaining variance in data if the variance is dependent on a physical phenomena. For example, Volsurf has been shown to be very effective in explaining the permeability of molecules through membranes such as the bloodbrain barrier as well as intestinal cell monolayers.51 However, pharmacophoric descriptors, such as those available in the program Almond, are more effective in explaining variance in data that is dependent on a shape-based interaction.

Figure 5. Correlogram profiles of two molecules, the antihistaminic azelastine (high affinity for Pgp, upper profile) and the anticholinergic chlorphenoxamine (low affinity for Pgp, middle profile), compared with the pseudo-coefficient of the second latent variable of the model (lower profile). The two profiles are quite similar, except for the lack of 33 descriptors (in black in the figure) in the region of the correlogram (distance between two H bond acceptor groups in the molecule) around 11.5 Å shown by the low-affinity molecule. This difference includes the two H-bond-acceptor atoms, placed 11.5 Å apart, which is a requirement for a molecule to be a Pgp substrate.


Figure 6. Resulting pharmacophore for P-glycoprotein actively transported molecules. The depicted molecule is the analgesic (narcotic) sufentanyl. The colored areas around the molecules are the GRID fields produced by the molecule: yellow for DRY probe, green for TIP probe, and blue for N1 probe.

In this particular case, Almond descriptors play a larger role in explaining Pgp efflux inhibition by substrates. The physicochemical descriptors that highly correlate to the Pgp affinity are size and shape (MW, R, and V) and the hydrogen-bonding capabilities of the molecule (HB). Size and shape of the molecule have been correlated to Pgp affinity by several authors in the past (Litman,56 Osterberg,57 Didziapetris,58 and Gombar59). The capability of a molecule to establish hydrogen bonds has also been correlated with Pgp affinity by several authors.23 The importance of this descriptor has been confirmed by the pharmacophoric analysis that assigns the highest correlation with affinity for Pgp to the distance between two H-bond acceptors. Many studies have suggested that increased lipophilicity could enhance activity.59 More recently it has been stated that substrates can, in fact, be relatively hydrophilic. The work of Litman et al.56 was one of the few studies suggesting that affinity does not correlate with log P. They suggested that affinity could be better correlated with surface area. Ecker et al.61 showed, both with linear regression analysis using log P as the independent variable and with multiple linear regression analysis, that log P did not significantly contribute to the description of the variance of the pharmacological activity data. In our model, calculated log P shows only a weak correlation with the affinity for Pgp. Analysis of the 129 molecules reveals that 50 compounds have calculated log P greater that 5, 70 have calculated log P lower than 5 and greater than 2, and 9 have calculated log P between 2 and 1. Among these three classes of com-


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pounds, high, medium, and low affinity molecules are equally distributed. The complete pharmacophoric model that we obtained for molecules actively transported by Pgp is not in complete agreement with previously reported models. While many authors in the past have demonstrated that H-bond-acceptor groups and hydrophobic areas are important features to interact with Pgp, their models differ in the spatial arrangements of such features. This could be due to differences in data, use of different methods, or use of 2D versus 3D structures to obtain the models. Elements of the pharmacophore are common with previous work, even though GRIND descriptors are related to distances between MIFs, while pharmacophoric descriptors, produced by other methods, are often related to the position of atoms. For example, the most predominant pharmacophore in the ensemble of pharmacophores developed by Penzotti et al.27 seems to have two hydrophobic sites separated by a similar distance. This also seems to be the case in both pharmacophores developed by Garrigues et al.15 Also three of the elementssthe two hydrophobes and the hydrogen-bond acceptorsas well as the approximate distances between these elements developed by Ekins et al.38,62 are in common with the pharmacophore developed here for substrates. Several papers proposing multiple recognition sites for Pgp have been presented in the past. In this work, the pharmacophoric analysis of the data set shows that the requirements to interact with Pgp are the same for all 129 compounds. We have not included known R-site binders, anthracyclines, in our data set, and hence, even though we have included a diverse data set, we cannot say definitively that the pharmacophore found represents one or more of the binding sites that have been described in the literature. Two of the molecules present in the database, verapamil and dipyridamole, are known to bind in the H-site described by Shapiro and Ling.14 We also cannot definitely state that the pharmacophore defines any functional site within the transporter. Further work trying to define the location of the corresponding amino acids in a protein homology model is in progress. This work supports the two-step process proposed by Seelig.31 However, the strong correlation, highlighted by PLS statistical analysis, between pharmacophoric descriptors and the inhibition values suggests that substrate interaction with the protein plays a key role in the efflux process, yielding a model in which diffusion across the membrane (first step) is less important than substrate-protein interaction (second step). In our hypothesis, Pgp substrates, being prevalently lipophilic, can easily cross the membrane and tend to accumulate in the bilayer. Here they will interact with the protein by means of pharmacophoric recognition. Interaction will trigger a sequence of transformations in the protein (conformational changes, ATP hydrolysis, etc.) that have a great impact on the rate of the efflux process. The high concentration that substrates reach in the bilayer can help to explain the broad specificity showed by Pgp. A recent review63 suggests that binding of substrates to the TMDs initiates the transport cycle by facilitating ATP-dependent closed dimer formation,

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as being the first step in the ATP switch model for transport by ABC transporters. Our hypothesis would be in agreement with the work of Dye et al.,64 since the mouth of the protein may be seen as the ON-site that first interacts with the substrate. The model would be in accordance even with the suggestion of Homolya et al.,65 the pioneers in the use of calcein-AM as a Pgp substrate, namely, that calceinAM and other fluorescent methyl esters are expelled directly from the cell membrane, before reaching the cytoplasmic phase. Preemptive pumping of calcein-AM is also in accordance with the theoretical analysis of Stein,66 who showed that the initial rate of substrate accumulation is reduced by pumping only if such pumping is preemptive. Conclusions In this work, we have shown that, for a diverse set of substrates, a pharmacophore could be identified. This pharmacophore played a larger role in explaining the variance in PGP inhibition data from a calcein-AM fluorescent assay than physicochemical descriptors, suggesting that interacting with the protein is more important to inhibition than diffusion to the efflux transporter. Further work is underway to locate the corresponding site within the PGP transporter itself. References (1) Dassa, E. Pylogenetic and Functional Classification of ABC (ATP•Binding Cassette) Systems. In ABC Proteins: From Bacteria to Man; Holland, I. B., Cole, S. P. C., Kuchler, K., Higgins, C. F., Eds; Academic Press: New York, 2003; pp 3-37. (2) http://nutrigene.4t.com/humanabc.htm (3) Bates, S. E. Solving the Problem of Multidrug Resistance: ABC Transporters in Clinical Oncology. In ABC Proteins: From Bacteria to Man; Holland, I. B., Cole, S. P. C., Kuchler, K., Higgins, C. F., Eds; Academic Press: New York, 2003; pp 359391. (4) Linton, K. J.; Rosenber, M. F.; Kerr, I. D.; Higgins, C. F. Structure of ABC Transporters. In ABC Proteins: From Bacteria to Man; Holland, I. B., Cole, S. P. C., Kuchler, K., Higgins, C. F., Eds; Academic Press: New York, 2003; pp 65-80. (5) Chang, C.; Roth, C. B. Structure of MsbA from E. coli: A homology of the multi drug resistance ATP binding cassette (ABC) transporters. Science 2001, 293, 1793-1800. (6) Chang, G. Structure of MsbA from Vibrio cholera: A Multidrug Resistance ABC Transporter Homlog in a Closed Conformation. J. Mol. Biol. 2003, 330, 419-430. (7) Rosenberg, M. F.; Kamis, A. B.; Callaghan, R.; Higgins, C. F.; Ford, R. C. Three-dimensional Structures of the Mammalian Multidrug Resistance P-glycoprotein Demonstrate Major Conformational Changes in the Transmembrane Domains upon Nucleotide Binding. J. Biol. Chem. 2003, 278, 8294-8299. (8) Rosenberg, M. F.; Kamis, A. B.; Aleksandrov, L. A.; Ford, R. C.; Riordan, J. R. Purification and Crystallization of the cystic fibrosis transmembrane conductance regulator (CFTR). J. Biol Chem., to be published. (9) Seigneuret, M.; Garnier-Suillerot, A. A Structural Model for the Open conformation of the mdr1 P-glycoprotein based on the Msba Crystal Structure. J. Biol. Chem. 2003, 278, 30115-30124. (10) Pajeva, I. K.; Globisch, C.; Wiese, M. Structure-Function Relationships of Multidrug Resistance P-Glycoprotein. J. Med. Chem. 2004, 47, 2523-2533. (11) Stenham, D. R.; Campbell, J. D.; Sanson, M. S. P.; Higgins, C. F.; Kerr, I. D.; Linton, K. J. An Atomic detail model for the human ATP binding cassette transporter P-glycoprotein derived from disulfide cross-linking and homology modeling. FASEB 2003, 17, 2287-2289. (12) Shapiro, A. B.; Ling, V. Positively cooperative sites for drug transport by P-glycoprotein with distinct drug specificities. Eur. J. Biochem. 1997, 250, 130-137. (13) Shapiro, A. B.; Fox, K.; Lam, P.; Ling, V. Stimulation of P-glycoprotein-mediated drug transport by prazosin and progesterone. Evidence for a third drug-binding site. Eur. J. Biochem. 1999, 259, 841-850. (14) Shapiro, A. B.; Ling, V. The Mechanism of ATP-Dependent Multidrug Transport by P-Glycoprotein. Acta Physiol. Scand. 1993, 163, Suppl 643, 227-234.

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