The protein product of the human ether-a-go-go gene (hERG) is a potassium channel that when inhibited by some drugs may lead to cardiac arrhythmia. Previously, a three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore model was constructed using Catalyst with in vitro inhibition data for antipsychotic agents. The rationale of the current study was to use a combination of in vitro and in silico technologies to further test the pharmacophore model and qualitatively predict whether molecules are likely to inhibit this potassium channel. These predictions were assessed with the experimental data using the Spearman's rho rank correlation. The antipsychotic-based hERG inhibitor model produced a statistically significant Spearman's rho of 0.71 for 11 molecules. In addition, 15 molecules from the literature were used as a further test set and were also well ranked by the same model with a statistically significant Spearman's rho value of 0.76. A Catalyst General hERG pharmacophore model was generated with these literature molecules, which contained four hydrophobic features and one positive ionizable feature. Linear regression of log-transformed observed versus predicted IC 50 values for this training set resulted in an r 2 value of 0.90. The model based on literature data was evaluated with the in vitro data generated for the original 22 molecules (including the antipsychotics) and illustrated a significant Spearman's rho of 0.77. Thus, the Catalyst 3D-QSAR approach provides useful qualitative predictions for test set molecules. The model based on literature data therefore provides a potentially valuable tool for discovery chemistry as future molecules may be synthesized that are less likely to inhibit hERG based on information provided by a pharmacophore for the inhibition of this potassium channel.

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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.