Predicting toxic equivalence factors from 13C nuclear magnetic resonance spectra for dioxins, furans, and polychlorinated biphenyls using linear and nonlinear pattern recognition methods.
Two quantitative spectrometric data-activity relationships (QSDAR) models have been developed relating 29 dioxin or dioxin-like molecules to their toxic equivalence factors (TEFs). These models were based on patterns in simulated 13C nuclear magnetic resonance (NMR) data with the patterns defined by comparative spectral analysis (CoSA). Two versions of CoSA multiple linear regression (MLR) models using 7 or 10 spectral bins had, respectively, explained variances (r2) of 0.88 and 0.95, and leave-one-out (LOO) cross-validated variances (q2) of 0.78 and 0.88. A third, artificial neural network model--using a feed forward, back propagating, three-layer neural network--produced an r2 of 0.99, a LOO q2 of 0.82, and a leave-three-out q2 of 0.81. A postulated reason that the results of these QSDAR models are better than traditional quantitative structure-activity relationship (QSAR) models is based on the difference in descriptors rather than on any differences in pattern recognition approach. Results suggest that the 13C NMR spectral data contain molecular quantum mechanical information more reflective of each molecule's biochemical properties than do the calculated electrostatic potentials and molecular alignment assumptions used in developing QSAR models. The QSDAR models provide a rapid, simple way to model the toxicity of dioxin and dioxin-like compounds.
Buzatu
Dan A
DA
Division of Chemistry, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, USA. dbuzatu@nctr.fda.gov
Beger
Richard D
RD
Wilkes
Jon G
JG
Lay
Jackson O
JO
D000818:Animals; D002247:Carbon Isotopes; D004147:Dioxins / Q000633:toxicity*; D004785:Environmental Pollutants / Q000633:toxicity*; D005544:Forecasting; D005663:Furans / Q000633:toxicity*; D016014:Linear Models; D009682:Magnetic Resonance Spectroscopy; D010363:Pattern Recognition, Automated; D011078:Polychlorinated Biphenyls / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship
D002247:Carbon Isotopes; D004147:Dioxins; D004785:Environmental Pollutants; D005663:Furans; D011078:Polychlorinated Biphenyls