| Abstract
| - Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors bindingto aromatase enzyme have been developed based on simulated 13C nuclear magnetic resonance (NMR)data. Three of the models were based on comparative spectral analysis (CoSA), and the two other modelswere based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model basedon five principal components had an explained variance (r2) of 0.78 and a leave-one-out (LOO) cross-validated variance (q2) of 0.71. A CoSASA model that used the assigned 13C NMR chemical shifts from asteroidal backbone at five selected positions gave an r2 of 0.75 and a q2 of 0.66. The 13C NMR chemicalshifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 withthe relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structuralparameter quantitative structure−activity relationship (QSAR) model of the same compounds. QSAR modelingsuffers from errors introduced by the assumptions and approximations used in partial charges, dielectricconstants, and the molecular alignment process of one structural conformation. One postulated reason thatthe variances of QSDAR models are better than the QSAR models is that 13C NMR spectral data, based onquantum mechanical principles, are more reflective of binding than the QSAR model's calculated electrostaticpotentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model thesteroid inhibitor activity in relation to the aromatase enzyme.
|
