Development and validation of an average mammalian estrogen receptor-based QSAR model

Development and evaluation of quantitative structure activity relationships (QSARs) for predicting estrogen receptor binding from chemical structure requires reliable algorithms for three-dimensional (3D) QSAR analysis and establishment of structurally diverse training sets of chemicals whose modes of action and measures of potency are well defined. One approach to selecting an appropriate training set is to minimize the biological variability in the model development, by using structurally restricted data sets. A second approach is to extend the structural diversity of chemicals at the cost of increased variability of biological assays. In this study, the second approach was used by organizing a training set of 151 chemicals with measured human alpha Estrogen Receptor (ERalpha), mouse uterine, rat uterine, and MCF7 cell Relative Binding Affinities (RBAs). The structurally augmented training set was submitted to a 3D pattern recognition analysis to derive a model for average mammalian ER binding affinity by employing the COmmon REactivity PAttern (COREPA) approach. Elucidation of this pattern required examination of the conformational flexibility of the compounds in an attempt to reveal areas in the multidimensional descriptor space, which are most populated by the conformers of the biologically active molecules and least populated by the inactive ones. The approach is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. Reactivity patterns associated with mammalian ER binding affinity were obtained in terms of global nucleophilicity (E(HOMO)), interatomic distances between nucleophilic sites, and local nucleophilicity (charges or delocalizabilities) of those sites. Based on derived patterns, descriptor profiles were established for identifying and ranking compounds with RBA of > 150, 150-10, 10-1 and 1-0.1% relative to 17beta-estradiol. Specificity of reactivity profiles was found to increase gradually with increasing affinities associated with RBAs ranges under study. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative mammalian ER binding affinity potential for large chemical data sets. The validity of the RBA predictions were confirmed by independent development and comparison with measured RBA values.