Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding to the AhR

Abstract

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) modeling technique which uses NMR spectral and structural information that is combined in a 3D-connectivity matrix has been developed. A 3D-connectivity matrix was built by displaying all possible assigned carbon NMR chemical shifts, carbon-to-carbon connections, and distances between the carbons. Two-dimensional ¹³C-¹³C COSY and 2D slices from the distance dimension of the 3D-connectivity matrix were used to produce a relationship among the 2D spectral patterns for polychlorinated dibenzofurans, dibenzodioxins, and biphenyls (PCDFs, PCDDs, and PCBs respectively) binding to the aryl hydrocarbon receptor (AhR). We refer to this technique as comparative structural connectivity spectral analysis (CoSCoSA) modeling. All CoSCoSA models were developed using forward multiple linear regression analysis of the predicted ¹³C NMR structure-connectivity spectral bins. A CoSCoSA model for 26 PCDFs had an explained variance (r²) of 0.93 and an average leave-four-out cross-validated variance (q₄ ²) of 0.89. A CoSCoSA model for 14 PCDDs produced an r² of 0.90 and an average leave-two-out cross-validated variance (q₂ ²) of 0.79. One CoSCoSA model for 12 PCBs gave an r² of 0.91 and an average q₂ ² of 0.80. Another CoSCoSA model for all 52 compounds had an r² of 0.85 and an average q₄ ² of 0.52. Major benefits of CoSCoSA modeling include ease of development since the technique does not use molecular docking routines.