Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models
Introduction
Polychlorinated biphenyls (PCBs) are one of the most widespread and persistent groups of persistent organic pollutants (POPs) in the environment, including 209 congeners characterized by the number and position of the chlorine atoms on the biphenyl core. PCBs can harm human health and the ecological environment and are different from common organic pollutants due to their high stability, toxicity, environmental persistence, bioaccumulation, long-distance migration ability and other characteristics (Zhang et al., 2007, Alkhatib and Weigand, 2002). Therefore, studies on PCBs have received substantially more global attention.
Approximately 1.5 million tons of PCBs were produced globally in the form of complex mixtures serving as dielectric fluids in transformers and capacitors and as a plasticizer agent in paint and rubber sealants since the 1930s (Bidleman et al., 2010). In the 1970s, PCBs were banned globally because of their adverse effects on immunity, nerves and endocrine systems and, especially, because they are capable of being passed down to the next generations, resulting in further adverse effects (Zhang et al., 2011).
The octanol-air partition coefficient (KOA), which is defined as the ratio of solute concentration in air versus octanol when the octanol-air system is at equilibrium, is a key physicochemical parameter for describing the partition of organic pollutants between the atmosphere and the environmental organic phase. Therefore, KOA has important significance for the evaluation of organic pollutants in the atmosphere (Liu et al., 2013), soil (Harner et al., 2001), plant (Platts and Abraham, 2000), and humans (Betterman et al., 2002), as well as in aerosol (Dachs and Eisenreich, 2000, Wang et al., 2011) migration and allocation behaviours.
Because of PCBs' long-range transport potential, they have been detected in remote areas that were devoid of human activities, such as the Arctic (Zhang et al., 2014), Antarctic (Marco et al., 2015), Tibetan Plateau (Zheng et al., 2012), Alps (Tato et al., 2011), and some high latitude areas, especially in the polar areas. Moreover, PCBs have been detected in a wide range of biological samples, including fish (Su et al., 2012), chicken egg yolks (Rawn et al., 2012) and in human samples, such as breast milk (Hassine et al., 2012), blood (Jotaki et al., 2011) and adipose tissue (Arrebola et al., 2010). Therefore, it is of critical importance to evaluate the properties and global mobility of PCBs among various compartments of the natural environment.
QSAR model has a long history of development. For the method of 1D-QSAR, the affinity is correlated with global molecular properties of ligands, which is one value per property and ligand (pKa, log P, etc.) (Hopfinger, 1980). For the method of 2D-QSAR, the affinity is correlated with structural patterns (connectivity, 2D pharmacophore, etc.) without consideration of an explicit 3D representation of these properties (Hansch and Fljita, 1964, Fujita et al., 1964, Free and Wilson, 1964). And for the method of 3D-QSAR, the affinity is correlated with the three-dimensional structure of the ligands (Crippen, 1979, Cramer et al., 1988, Cramer Iii et al., 1988, Klebe et al., 1994).
Harner and Mackay (1995), as well as Harner and Bidleman (1996) have developed a generator column method to measure KOA values of 19 PCBs. Because the generator column method is time-consuming, Zhang et al. (1999) proposed a multicolumn method to estimate KOA for semi-volatile organic compounds. These experimental methods have many drawbacks, including the need for special equipment and samples, as well as large investments of money, time, and labour. Chen et al. (2002) utilized nine quantum chemical descriptors to construct the classical Hansch-type modeling and to predict the KOA values of PCBs. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) are two powerful methods in 3D-QSAR approach, which take the 3D-conformation property of compounds into consideration, can be helpful in exploring and visualizing useful structural information that influences the activity of compounds.
Note that geometric and electronic descriptors depend on the 3D coordinates of the atoms. 3D-QSAR, which refers to the use of force field calculations to compute spatial properties of the three-dimensional structure of compounds, provides valuable information on the forces and interactions of molecules (Cruciani, 2003, Langer and Bryant, 2008). 3D-QSAR models can aid the design of new beneficial compounds and may be useful in the screening of a large number of chemicals for migratory effects, as well as for gaining a deeper understanding of the migration mechanism (Salahinejad and Ghasemi, 2014). CoMFA and CoMSIA have been widely used to construct 3D models, which use 3D structure as descriptors. They overcome the limitations of the conventional 2D model in characterizing the relationship between property and structure and have a clearer physical meaning and more abundant information of the molecular field energy. The CoMFA method involves the generation of a common three-dimensional lattice around a set of molecules and calculation of the steric and electrostatic interaction energies at the lattice points (Cramer et al., 1988), while the CoMSIA method uses the similarity functions represented by Gaussian (Klebe et al., 1994).
In this study, QSAR models were constructed with 3D descriptors according to the experimental values of log KOA for 19 PCBs congeners. Two types of QSAR methods, CoMFA and CoMSIA, are used to predict the log KOA values of the remaining 190 PCBs congeners and to investigate the relationship between the structures of PCBs and their persistent migration ability. These results are expected to be beneficial in predicting the log KOA values of homologues and derivatives of PCBs and providing the theoretical basis for further elucidation of the global migration behaviour of PCBs.
Section snippets
Data set
For 19 PCBs, log KOA values were determined directly byHarner and Mackay (1995) and Harner and Bidleman (1996) at 293 K using a generator column method. To facilitate the QSAR analysis, the logarithm of KOA (log KOA) was taken as the index of PCB migration ability. The whole data set (containing 19 compounds) was divided in the ratio of 4:1 into a training set (containing 15 compounds) for 3D-QSAR model generation and a test set (containing 4 compounds) for model validation. The selection of the
The prediction of log KOA
CoMFA and CoMSIA models were used to forecast 209 types of PCBs, and the experimental and predicted log KOA values and residual values for PCBs are given in Table A1 (see Appendix A).
CoMFA model analysis based on the predicted KOA value of PCBs
The results of the CoMFA model are summarized in Table 1. CoMFA models are considered reliable and acceptable if q2 was greater than 0.50 and r2 is greater than 0.90 (Golbraikh and Tropsha, 2002). Using 15 components, this model yields the optimum number of components n of 12, a cross-validated q2 of 0.91 (>0.5), a
Conclusions
QSAR models were constructed with 3D descriptors according to the experimental values of log KOA for 19 PCB congeners. Two types of QSAR methods, CoMFA and CoMSIA, are used to predict the remaining 190 PCB congeners and to investigate the relationship between the structures of PCBs and their persistent migration properties. The main research conclusions are as follows:
- (1)
CoMFA and CoMSIA models show satisfactory fitting ability and acceptable predictive ability of the 190 PCBs. The two models
Acknowledgements
The research was supported by the Fundamental Research Funds for the Central Universities in 2013 (JB2013146) and the Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-Year Plan Period (2008BAC43B01).
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