Quantitative structure–activity relationship modeling of bioconcentration factors of polychlorinated biphenyls
ALAN ROY KATRITZKY, Maksim Radzvilovits, Svetoslav H. Slavov, Kalev Kasemets, Kaido Tämm, Mati M. Karelson · Toxicological & Environmental Chemistry Reviews · 2010
The bioconcentration factors (BCFs) of 57 polychlorinated biphenyl (PCB) congeners were modeled by quantitative structure–activity relationship (QSAR) based on 486 constitutional, topological, geometrical, electrostatic, quantum chemical, and thermodynamic descriptors derived solely from molecular structure and calculated using CODESSA Pro (comprehensive descriptors for structural and statistical analysis) software. Descriptors utilized for the general model were selected by various statistical validation techniques. Multilinear models were developed using the best multilinear regression algorithm to relate experimental BCF to a set of molecular descriptors. The proposed two-parameter model satisfactorily describes the relationship between observed and calculated values in terms of statistical parameters. Polarity, structural flexibility, and spatial mass distribution of a molecule were demonstrated to be the main factors influencing the ability of PCB to (1) penetrate through lipophilic cell membranes and (2) bind specifically and nonspecifically to biological targets, hence affecting BCF. Comparison to other models indicated advantages of the proposed model over previously reported ones. Derived two-parameter regression equation has improved statistics and is based on theoretical descriptors with a definite physicochemical meaning; it is easier to use and interpret due to the mathematical simplicity of the linear QSAR approach. Internal validation and scrambling procedure confirmed the stability and reliable predictive ability of the general model and indicated the absence of chance correlations. External validation demonstrated that the presented model can be applied to structurally similar sets of compounds, thus extending the domain of applicability of the model.