MODELLING OF PHYSICO-CHEMICAL PROPERTIES FOR ORGANIC POLLUTANTS F. Consolaro, P. Gramatica and S. Pozzi QSAR Research Unit, Dept. of Structural and Functional Biology, University of Insubria, Varese, Italy web-site: INTRODUCTION Many organic pollutants are causes of contamination of air, water, soil and food, with possible adverse effects on human and animal health. The proper use of chemicals must be based on an understanding of their behaviour in the environment. This behaviour is strongly influenced by the chemical’s physico-chemical properties. Unfortunately, for a large number of these compounds, the experimental data of several physico-chemical properties are not known or, when known, not all data are homogeneous; this hinder an accurate and comparable evaluation of the future environmental fate of the considered compounds. EEC Priority List 1 compounds and Persistent Organic Pollutants (POPs) are the classes of compounds considered here. APPLICATION OF QSARs FOR THE PREDICTION OF PHYSICO-CHEMICAL PROPERTIES Due to the difficulties in obtaining reliable experimental data, QSAR studies provide a complementary tool that can profitably be used to provide data from accurately validated models. Quantitative structure-activity relationship approaches are based on the assumption that the structure of a molecule must contain features responsible for its physical and chemical properties, and on the possibility of representing a molecule by numerical descriptors. Thus, molecular descriptors represent the way by which chemical information is transformed and coded, to deal with chemical, pharmacological and toxicological problems. Many chemical descriptors have been used in this work: 1D-descriptors, that are structural descriptors obtained from a simple knowledge of the molecular formula, 2D or topological descriptors, that are obtained from the knowledge of the molecular topology, and 3D-descriptors, recently developed (1), that contain information about the whole 3D-molecular structure in terms of size, shape, symmetry and atom distribution. Before the calculation of the descriptors, the minimum energy conformations of all the compounds were obtained by the molecular mechanics method of Allinger (MM+). Then, the selected properties were modelled by the Selection of the best Subset Variables (VSS) method, through the Genetic Algorithm (GA-VSS) approach, where the response is obtained by Ordinary Least Squares regression (OLS). All calculations were performed using the leave-one- out and leave-more-out procedures of cross-validation, maximising the cross-validated R squared (Q 2 ). Standard Deviation Error in Prediction (SDEP) and Standard Deviation Error in Calculation (SDEC) are also evaluated. In spite of the great variability of the molecular structures of the studied compounds, models with good predictive power have been obtained. The reliability of predicted data was subsequently checked by the leverage approach; only reliable predicted data were then used. (1) R. Todeschini, WHIM-3D/QSAR- Software for the calculation of the WHIM descriptors. Rel. 4.1 for Windows, Talete s.r.l., Milano (Italy) Download: EEC Priority List 1 compounds are chemicals of high diffusion that are dangerous for the environment. The hazard relates to both their toxicity for different organisms and their physico-chemical properties that determine their environmental fate, persistence and bioaccumulation. This List includes a large number of heterogeneous compounds, for which the following physico-chemical properties were considered: melting point, boiling point, density, refraction index, vapour pressure, water solubility and log Kow. Log vapour pressure = nBO E10 -4 IDMT HY Log water sol. = Ss L3v Ku Log K ow = MW UI L3m Log vapour pressure = P1s Tv Log water sol. = nC nCl nOH nNH2 MW: molecular weight Se: total electronegativity HY: hydrophilicity factor E3m: emptiness along the third direction with atomic masses weights nBO: number of bonds IDMT: total information content on the distance magnitude HY: hydrophilicity factor nC: number of C atoms nCl: number of Cl atoms nOH: number of OH groups nNH2: number of NH2 groups The dimension of the molecules (MW, Se) appears the most important factor. The selection of E3m descriptor confirms the importance of the emptiness factor and the atomic masses in the determination of this property. The hydrophilicity factor selected by this model seems to be the most relevant variable in predicting vapour pressure (by standardised regression coefficients). In this model all the selected variables are count descriptors. CONCLUSIONS The good predictive power of models, here presented, indicates that they will accurately predict environmental fate and effects of organic chemicals for which experimental data are not available. Reliable predictive models will enable fast and accurate assessments to be made of the environmental profiles of proven or potentially hazardous chemicals. The ability to accurately estimate the environmental fate and effect of commercial chemicals, and to obtain a better insight into the structural features important for such behaviour, will be very helpful in formulating improved environmental policy. For this reason we have available, for anyone interested in it, a table with all the data predicted by the above reported regression models. Density = E10 -3 MW E10 -2 Se HY E3m Persistent Organic Pollutants (POPs) EEC Priority List 1 compounds Persistent Organic Pollutants are long-lived and fat-soluble compounds that have toxic effects on animal reproduction, development and immunological function. Some POPs are also probably carcinogenic. These compounds are of 12 chemical classes, including polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated dibenzo-p-dioxins, pesticides and chlorinated benzene. The POPs environmental fate depends upon a variety of physico-chemical properties such as vapour pressure, Henry’s law constant, boiling point, melting point, log K ow, log K oc, water solubility and also total surface area and molar volume. Ss: total electrotopological charge L3v:dimension along the third direction with van der Waals volume weight Ku: shape with unitary weights This model points out the importance of size variables (Ss) and the presence of out-of-plane atoms (L3v) in predicting water solubility. The descriptor Ku underlines the relevance of molecular shape (more linear molecules (K=1) are less soluble). MW: molecular weight UI: insaturation index L3m:dimension along the third direction with atomic mass weight The descriptors MW and UI confirm the importance of molecular size and insaturation in order to predict POPs K ow. L3m points out the relevance along the third dimension of chlorine atoms. P1s: shape along principal axis with electrotopological charge weight Tv: dimension linear term with van der Waals weights The selected descriptors point out the importance of the molecular size (Tv) and of the linearity (P1s) in determining POP vapour pressure. 2r/P002