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In this study, the depuration half-lives of 62 polychlorinated biphenyl (PCB) congeners in juvenile rainbow trout (Oncorhynchus mykiss) were estimated from their structural features based on QSPR methodology. A genetic algorithm (GA) was applied as a variable subset selection strategy and QSPR models established upon multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and support vector regression (SVR) procedures. Robustness and predictive stability of the constructed models were evaluated through internal and external validation methods. The high numerical values of [Formula: see text] and [Formula: see text], and low RMSE in the case of the MLP NN model, confirm the supremacy of this model as well as nonlinear dependency of molecular structural features to the PCB congeners half-lives. In the best developed QSPR model the following four descriptors are used; lopping centric index (Lop), mean topological charge index of order 1 (JGI1), Geary autocorrelation lag-8/weighted by atomic Sanderson electronegativities (GATS8e) and highest eigenvalue of Burden matrix/weighted by atomic masses (BEHm3). Analysis of the descriptors involved in these models revealed that 2D molecular structural features, compactness and electronegativities are the main factors contributing to the half-lives of PCBs. The structural information presented in this work can be used for further evaluation of half-lives of PCBs and other similar structural compounds in the environment.
a Chemometrics Laboratory, Faculty of Chemistry , University of Mazandaran , Babolsar , Iran.
This article was published in the following journal.
Name: SAR and QSAR in environmental research
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Industrial products consisting of a mixture of chlorinated biphenyl congeners and isomers. These compounds are highly lipophilic and tend to accumulate in fat stores of animals. Many of these compounds are considered toxic and potential environmental pollutants.
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Steroidal compounds related to PROGESTERONE, the major mammalian progestational hormone. Progesterone congeners include important progesterone precursors in the biosynthetic pathways, metabolites, derivatives, and synthetic steroids with progestational activities.