Models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding affinity to the aryl hydrocarbon receptor developed using C-13 NMR data

Quantitative spectroscopic data-activity relationship (QSDAR) models for po lychlorinated dibenzofurans (PCDFs), dibenzodioxins (PCDDs), and biphenyls (PCBs) binding to the aryl hydrocarbon receptor (AhR) have been developed b ased on simulated C-13 nuclear magnetic resonance (NMR) data. All the model s were based on multiple linear regression of comparative spectral analysis (CoSA) between compounds. A 1.0 ppm resolution CoSA model for 26 PCDF comp ounds based on chemical shifts in five bins had an explained variance (r(2) ) of 0.93 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.90 . A 2.0 ppm resolution CoSA model for 14 PCDD compounds based on chemical s hifts in five bins had an r(2) of 0.91 and a q(2) of 0.81. The 1.0 ppm reso lution CoSA model for 12 PCB compounds based on chemical shifts in five bin s had an r(2) of 0.87 and a q(2) of 0.45. The models with more compounds ha d a better q(2) because there are more multiple chemical shift populated bi ns available on which to base the linear regression. A 1.0 ppm, resolution CoSA model for all 52 compounds that was based on chemical shifts in 12 bin s had an r(2) of 0.85 and q(2) of 0.71. A canonical variance analysis of th e 1.0 ppm CoSA model for all 52 compounds when they were separated into 27 strong binding and 25 weak binding compounds was 98% correct. Conventional quantitative structure-activity relationship (QSAR) modeling suffer from er rors introduced by the assumptions and approximations involved in calculate d electrostatic potentials and the molecular alignment process. QSDAR model ing is not limited by such errors since electrostatic potential calculation s and molecular alignment are not done. The QSDAR models provide a rapid, s imple and valid way to model the PCDF, PCDD, and PCB binding activity in re lation to the aryl hydrocarbon receptor (AhR).