PHYSICO-CHEMICAL PROPERTIES MODELLING FOR ENVIRONMENTAL POLLUTANTS

1. PHYSICO-CHEMICAL PROPERTIES MODELLING FOR ENVIRONMENTAL POLLUTANTS F. Consolaro, P. Gramatica and M. Pavan QSAR Research Unit - Department of Structural and Functional Biology - University of Insubria VARESE (ITALY) E-mail: fedec@mailserver.unimi.it Web-site: http://fisio.dipbsf.uninsubria.it/dbsf/qsar/QSAR.html INTRODUCTION The hazards for the environment of many organic pollutants relate to both their toxicity for different organisms and their physico-chemical properties that determine their environmental fate, persistence, bioaccumulation, etc. Unfortunately, for a large number of these compounds, the experimental data of several physico-chemical properties are not known or, where known, not all data are homogeneous; this hinders an accurate and comparable evaluation of the environmental fate of new compounds. In this work we propose new QSAR models of many physico-chemical properties for well known environmental pollutants as: PAHs, PCBs and variousnon ionic organic pesticides. APPLICATION OF QSARs FOR THE PREDICTION OF PHYSICO-CHEMICAL PROPERTIES Many different kinds of 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-WHIM descriptors(1), that contain information about the whole 3D-molecular structure in terms of size, shape, symmetry and atom distribution. The descriptors have been calculated from the minimum energy conformations of the compounds, obtained by the molecular mechanics method of Allinger (MM+) using the package HYPERCHEM. Then, the physico-chemical properties were modelled by Ordinary Least Squares regression (OLS) by the selection of the best subset of variables method, through the Genetic Algorithm (GA-VSS) approach. All calculations were performed using the leave-one-out and leave-more-out procedures of cross-validation, maximising the cross-validated R squared (Q2). 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 proposed. (1) R. Todeschini and V. Consonni, DRAGON- Software for the calculation of the molecular descriptors. Rel. 1.0 for Windows, Talete s.r.l., Milano (Italy) 2000. Download: http://www.disat.unimib.it/chm. Persistent Organic Pollutants (POPs) Non ionic organic pesticides Log Koc = 1.35 + 0.008 MW + 0.28 nNO - 0.19 nHA + 0.33 CIC - 0.27 MAXDP + 0.05 Ts MW: molecular weight nNO: n. of NO groups nHA: n. of acceptor atoms for H-bonds CIC: complementary information content (neighbourhood symmetry) MAXDP: maximal electrotopological positive variation Ts: WHIM total size index weighted by atomic electritopological states Log Koa = 11.46 + 0.052 Ss – 19.78 P1u + 1.21L1e Ss: sum of Kier-Hall electrotopological states P1u: 1st component shape directional WHIM index L1e:: 1st component size directional WHIM index, weighted by atomic Sanderson electronegativities QSPR PREDICTION OF PARTITION PROPERTIES and ENVIRONMENTAL PARTITIONING OF ORGANIC PESTICIDES The experimental and predicted data of partition properties for a total of 173 non ionic organic pesticides of different chemical classes are then combined in Principal Component Analysis, as shown in the graph below: CLASSIFICATION OF POPs MOBILITY POTENTIAL The experimental and predicted data of physico-chemical properties for a total of 87 POP are combined in Principal Component Analysis, as shown in the graph below: VOLAIILITY V.p. logKow Classification from Wania and Mackay (2) The distribution of the studied compounds shows that is possible to use the PC1 to classify all the 87 POPs in one of the four class of mobility (high, relatively high, relatively low and low mobility). Thus, these 4 here proposed groups have been used as “a priori” classes in classification logKoc Solub. SOLUBILITY SORPTION m.p. b.p. The distribution of the 173 chemicals in the PC1-PC2 space shows that the PC1 scores separate sorbed from soluble pesticides, while the PC2 scores separate volatile and not-volatile pesticides. Finally, regression models by the OLS method are then obtained on PC1 and PC2 scores with the aim of predicting the partitioning behaviour of each pesticide starting only from the knowledge of a few theoretical descriptors of molecular structure, selected by Genetic Algorithm (count descriptors: nC, nS, nX, nCO, nCIC; unsaturation index UI: hydrophobicity index HYF and WHIM descriptors of global dimension Tm). (2) Frank Wania and Donald Mackay, Environmental Science & Technology, Vol. 30, NO. 9, 1996 All used classification methods give models with satisfactory prediction power. The simplest model and consequently the most interesting one is developed with CART (here reported): the selected descriptors are above all related to the molecular size. CART Classification Tree  2 6 . 6 5 PC1 MODEL:Q2LOO: 84.9% R2: 85.8% Q2LMO: 84.7% SDEP: 0.69 SDEC: 0.67 Selected descriptors: nC - nS - nX - nCO PC2 MODEL:Q2LOO: 78.3% R2: 80.3% Q2LMO: 77.7% SDEP: 0.52 SDEC: 0.47 Selected descriptors: nCIC - UI - HYF - Tm i i MW 1 6 1 . 2 4 Classification CART model properties NoModelError MR% MR% MRcv% 59.76 3.66 7.32 nH 2 . 5 0  2: Valence connectivity index MW: molecular weight nH: n. of Hydrogen atoms 1 2 4 3 assigned classes CONCLUSIONS The good predictive power of models, here presented, indicates that they will accurately predict physico-chemical properties 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 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.