ERB-196

A QSAR study is reported to predict the binding affinity of a set of 81 modulators for both of human estrogen receptor α and β (ERα and ERβ).In this study, the derived QSAR models were built by forward stepwise multilinear regression (MLR) and nonlinear radial basis function neural networks (RBFNN), resp.The statistical characteristics of the external test set provided by multiple linear model (R2=0.814, F=61.277, RMS=0.5461 for ERα; R2=0.600, F=21.039, RMS=0.6707 for ERβ) indicated satisfactory stability and predictive ability of the model built.The predictive ability for ERβ of RBFNN model is somewhat superior: R2=0.7691, F=32.012, RMS=0.5764, and the similar result was obtained for ERα of the test set: R2=0.7950, F=54.131 RMS=0.3120.Overall, the appropriate results proved the models to be meaningful and useful to predict and virtual screen of the derivatives with high binding activity.

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.