Alternative QSAR models for selected estradiol and cytochrome P450 ligands: comparison between classical, spectroscopic, CoMFA and GRID/GOLPE methods

Abstract

The performance of the spectroscopic EVA (eigenvalue) and EEVA (electronic eigenvalue) methods was tested with data sets applying coumarin 7-hydroxylation inhibitors (28 compounds) for cytochrome P450 mouse CYP2A5 and human CYP2A6 enzymes and 11β-, 16α-, and 17α-substituted estradiol derivatives (30 compounds) for the lamb uterine estrogen receptor, and compared with the performance of the classical Hansch-type, CoMFA and GRID/GOLPE methods. Besides the internal predictability, the external predictability of the models was tested with several randomized training and test sets to ensure the validity and reliability of the models. Partial least squares (PLS) regression was employed as a general statistical tool with the EVA and EEVA methods. Some supplementary models were also built using only one PLS component with McGowan's volumes (MgVol and MgVol2) as additional descriptors and employing multiple linear regression (MLR) as the modelling tool. In general, both the internal and external performance of the EVA model, and more especially the EEVA model, with one PLS component and MgVol parameters was satisfactory, being either as good as or clearly better than that of the Hansch-type, CoMFA and GRID/GOLPE models.