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(83-11) Linear modelling and prediction of BioConcentration Factor (BCF) by theoretical molecular descriptors. Gramatica, Paola1, Papa, Ester*,1, 1 QSAR Research Unit-Dep. Structural and Functional Biology- University of Insubria- Via J.H. Dunant 3, Varese, Italy ABSTRACT- Bioconcentration by aquatic biota is an important factor in assessing the environmental behavior and potential hazard evaluation of a chemical, mainly for Persistent Bioaccumulative and Toxics (PBTs). Since the experimental determination of BCF values is expensive and time consuming, estimation methods have been widely used to supply missing data. Log P (LogKow) is, till now, the most widely used physicochemical descriptor for modelling bioconcentration, but for highly hydrophobic chemicals non-linear models must be applied. Analogous results have been obtained by modelling with connectivity indices and polarity correction factors. In this study the application of the Genetic Algorithms as Variable Subset Selection (GA-VSS) procedure to a wide set (more than 800) of molecular descriptors of different structural aspects, like 1D-constitutional, 2D-topological and 3D-descriptors ( i.e. WHIM descriptors and GETAWAY) produces highly predictive models of BCF in fish for 239 non-ionic organic compounds. The best linear regression model (by Ordinary Least Squares regression (OLS)), in which LogKow was never selected by GA-VSS among the best molecular descriptors, was always validated for its predictivity by leave-one-out, leave-more-out and external validation (the selection of the optimal and structurally most representative test set was derived by the experimental design technique). The approach shows that a satisfactory predictive model (Q2%>80%) can be obtained without using LogKow as descriptor or introducing polarity correction factors, simply by applying theoretical molecular descriptors calculable from the molecular structure. Key words: BCF , QSAR , Genetic Algoritms, PBTs |
