Interaction of the estrogen receptor͞ligand complex with a DNA estrogen response element is known to regulate gene transcription. In turn, specific conformations of the receptor-ligand complex have been postulated to influence unique subsets of estrogen-responsive genes resulting in differential modulation and, ultimately, tissue-selective outcomes. The estrogen receptor ligands raloxifene and tamoxifen have demonstrated such tissue-specific estrogen agonist͞antagonist effects. Both agents antagonize the effects of estrogen on mammary tissue while mimicking the actions of estrogen on bone. However, tamoxifen induces significant stimulation of uterine tissue whereas raloxifene does not. We postulate that structural differences between raloxifene and tamoxifen may influence the conformations of their respective receptor͞ligand complexes, thereby affecting which estrogen-responsive genes are modulated in various tissues. These structural differences are 4-fold: (A) the presence of phenolic hydroxyls, (B) different substituents on the basic amine, (C) incorporation of the stilbene moiety into a cyclic benzothiophene framework, and (D) the imposition of a carbonyl ''hinge'' between the basic amine-containing side chain and the olefin. A series of raloxifene analogs that separately exemplify each of these differences have been prepared and evaluated in a series of in vitro and in vivo assays. This strategy has resulted in the development of a pharmacophore model that attributes the differences in effects on the uterus between raloxifene and tamoxifen to a low-energy conformational preference imparting an orthogonal orientation of the basic side chain with respect to the stilbene plane. This three-dimensional array is dictated by a single carbon atom in the hinge region of raloxifene. These data indicate that differences in tissue selective actions among benzothiophene and triarylethylene estrogen receptor modulators can be ascribed to discrete ligand conformations.
Using in vitro data, we previously built Catalyst 3-dimensional quantitative structure activity relationship (3D-QSAR) models that qualitatively rank and predict IC 50 values for P-glycoprotein (P-gp) inhibitors. These models were derived and tested with data for inhibition of digoxin transport, calcein accumulation, vinblastine accumulation, and vinblastine binding. In the present study, 16 inhibitors of verapamil binding to P-gp were predicted using these models. These inhibition results were then used to generate a new pharmacophore that consisted of one hydrogen bond acceptor, one ring aromatic feature, and two hydrophobes. This model predicted the rank order of the four data sets described previously and correctly ranked the inhibitory potency of a further four verapamil metabolites identified in the literature. The degree of similarity in rank ordering prediction by these inhibitor pharmacophore models generated to date confirms a likely overlap in the sites to which the three P-gp substrates used in these studies (verapamil, vinblastine, and digoxin) bind. Alignment of the three substrate probes indicated that they are likely to bind the same or overlapping sites within P-gp. Important features on these substrates include multiple hydrophobic and hydrogen bond acceptor features, which are widely dispersed and in agreement among most of the five inhibitor pharmacophores we have described so far. These 3D-QSAR models will be useful for future prediction of likely substrates and inhibitors of P-gp.
P-glycoprotein (P-gp) is an efflux transporter involved in limiting the oral bioavailability and tissue penetration of a variety of structurally divergent molecules. A better understanding of the structural requirements of modulators of P-gp function will aid in the design of therapeutic agents. Toward this goal, threedimensional quantitative structure-activity relationship (3D-QSAR) models were generated using in vitro data associated with inhibition of P-gp function. Several approaches were undertaken with multiple iterations, yielding Catalyst 3D-QSAR models being able to qualitatively rank-order and predict IC 50 values for P-gp inhibitors excluded from the model in question. The success of these validations suggests that a P-gp pharmacophore for 27 inhibitors of digoxin transport in Caco-2 cells consisted of four hydrophobes and one hydrogen bond acceptor. A second pharmacophore generated with 21 inhibitors of vinblastine binding to plasma membrane vesicles derived from CEM/VLB 100 cells contained three ring aromatic features and one hydrophobic feature. A third pharmacophore generated with 17 inhibitors of vinblastine accumulation in P-gp expressing LLC-PK1 cells contained four hydrophobes and one hydrogen bond acceptor. A final pharmacophore was generated for inhibition of calcein accumulation in P-gp expressing LLC-PK1 cells and found to contain two hydrophobes, a ring aromatic feature, and a hydrogen bond donor. The similarity of features for the pharmacophores of P-gp inhibitors of digoxin transport and vinblastine binding suggest some commonality in their binding sites. Utilization of such models may prove to be of value for prediction of molecules that may modulate one or more P-gp binding sites.
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