F.Consolaro 1, P.Gramatica 1, H.Walter 2 and R.Altenburger 2 1 QSAR Research Unit - DBSF - University of Insubria - VARESE - ITALY 2 UFZ Centre for Environmental Research - LEIPZIG - GERMANY Web: INTRODUCTION Environmental exposure situations are often characterized by a multitude of heterogeneous chemicals with different mechanisms of action and type of effect. The EEC priority List 1 (Council Directive 76/464/EEC) consists of heterogeneous environmental chemicals with mostly unknown or unspecific modes of action, so it was used to select components for mixture experiments in the EEC PREDICT (Prediction and Assessment of the Aquatic Toxicity of Mixtures of Chemicals) project. A list of 202 compounds was studied for structural similarity to identify the most representative and dissimilar chemicals and to find an objective method to group them on the basis of their structural aspects. These chemicals have been then tested for their algal toxicity and the experimental results have been modelled by the already cited molecular descriptors. The comparison with analogous models obtained on congeneric environmental chemicals will be discussed. STRUCTURAL DESCRIPTION OF COMPOUNDS Molecular descriptors represent the way chemical information contained in the molecular structure is transformed and coded. Among the theoretical descriptors, the best known, obtained simply from the knowledge of the formula, are: molecular weight and count descriptors (1D-descriptors, i. e. counting of bonds, atoms of different kind, presence or counting of functional groups and fragments, etc.). Graph-invariant descriptors (2D- descriptors, including both topological and information indices), are obtained from the knowledge of the molecular topology. WHIM molecular descriptors [1] contain information about the whole 3D-molecular structure in terms of size, symmetry and atom distribution. All these indices are calculated from the (x,y,z)-coordinates of a three-dimensional structure of a molecule, usually from a spatial conformation of minimum energy: 37 non- directional (or global) and 66 directional WHIM descriptors are obtained. A complete set of about two hundred molecular descriptors has been obtained [2]. [1] Todeschini R. and Gramatica P.; Quant.Struct.-Act.Relat. 1997, 16, [2] Todeschini R. and Consonni V. - DRAGON - Software for the calculation of the molecular descriptors., Talete srl, Milan (Italy) Download: CHEMOMETRIC METHODS Several chemometric analyses have been applied to the compounds (represented by molecular descriptors) to group the more similar ones, in accordance with a multivariate structural approach, and with the final aim to highlight the structurally most dissimilar compounds. The analyses performed are: Hierarchical Cluster Analysis: Hierarchical Cluster Analysis: hierarchical clustering was performed with the aim of finding clusters of the studied compounds in high dimensional space, using molecular descriptors as variables. Different distance metrics (Euclidean, Manhattan, Pearson) and different linkages (Complete, average, single, etc.) were used and compared to find the best way to cluster these compounds. Principal Component Analysis (PCA): Principal Component Analysis (PCA): this analysis was used to calculate just a few components from a large number of variables. These components allow the highlighting of the distribution of the compounds according to structure, and find the similarity between compounds assigned to the same cluster. Kohonen Maps: Kohonen Maps: this is an additional way of mapping similar compounds by using the so-called “self-organized topological feature maps”, which are maps that preserve the topology of a multidimensional representation within a toroidal two- dimensional representation. The position of the compounds in this map shows the similarity level of the structure of the EEC List 1 compounds. 100 Similarity Dendrogram of hierarchical cluster analysis. Euclidean distance - complete linkage. Variables = first 10 structural principal components Benzene derivatives (2) Chloroaliphatic compounds (7) DDT - PCBs (11) Organo-phosphates (12) Phen.-Triaz. (10) PAH (15) Chlorinated aliphatics (9) 0 These different chemometric approaches have shown that the structurally most dissimilar compounds are: N. Substance Chemical Class 1atrazine Triazine 2biphenyl Aromate 3chloralhydrat Chlorinated aliphatics 42,4,5-trichlorophenol Benzene derivative 5fluoranthene PAH 6lindane HCH 7naphthalene PAH 8parathion Organophosphate 9phoxime Organophosphate 10tributyltin chloride Organotin 11triphenyltin chloride Organotin REGRESSION MODELS QSAR models were developed by Ordinary Least Square regression (OLS) method. The selection of the best subset variables for modelling the algal toxicity of the studied compounds was done by a Genetic Algorithm (GA-VSS) approach and all the calculations have been performed by using the leave-one-out (LOO) and leave-more- out (LMO) procedures and the scrambling of the responses for the validation of the models. R 2 = 78 Q 2 LOO = 62.1 Q 2 LMO = 61.7 SDEP = SDEC = nO is the number of O atoms and IDE is the mean information content on the distance equality. A QSAR model has been obtained, with acceptable fitting properties but without an adequate predictive capability. This is probably due to the presence of structurally dissimilar and with unknown mechanism of action chemicals. HETEROGENEOUS COMPOUNDS CONGENERIC COMPOUNDS (NITROBENZENES) HETEROGENEOUS + CONGENERIC COMPOUNDS R 2 = 93.9 Q 2 LOO = 91.8 Q 2 LMO = 87.5 SDEP = SDEC = CONCLUSIONS The chemometric analyses here applied have been turned up to be very useful in ranking the studied chemicals in according to their structural similarity or dissimilarity. In modelling of structural heterogeneous compounds with unknown mode of action, not very satisfactory QSAR models have been obtained. The role of specific parameters, such as directional WHIMs, capable to describe particular molecular features relevant for explaining the specific mode of action, is always important in QSAR models for congeneric chemicals. Increasing heterogeneity increases the role of structural and topological descriptors, accounting for general molecular features, not related to specific mode of action. nOH is the number of OH groups, Sp is the sum of polarizabilities and Ds is the 3D-WHIM considering the global electrotopological distribution. The information explained by these descriptors are related to the electronic distribution of the molecular atoms and are more specific in respect to the mode of action than the selected descriptors in the heterogeneous set models. The quality of this model is very satisfactory both in fitting and in prediction. nO is the number of O atoms, IDDM is the mean information content on the distance degree magnitude while E1e is a directional 3D-WHIM descriptor of atomic distribution weighted on the electronegativity. Here are selected a topological descriptor (IDDM) that probably represents the heterogeneous compounds and a 3D- WHIM descriptor (E1e) that probably represents the homogeneous compounds. The performances of this model are satisfactory, considering that the data set is composed by structurally different compounds and that for many of them the mechanism of action is unknown. R 2 = 77 Q 2 LOO = 69.7 Q 2 LMO = 69.7 SDEP = SDEC = RANKING OF “EEC PRIORITY LIST 1” FOR STRUCTURAL SIMILARITY AND MODELLING OF ALGAL TOXICITY