A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV).

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.