QSAR and chemometric approaches for setting water quality objectives for dangerous chemicals.

In order to evaluate environmentally safe levels of dangerous chemicals, there is the need for a set of toxicological data on organisms representative of the ecosystems, which is often unavailable or inadequate. In this article, a predictive approach was applied to a set of 125 chemicals (derived from the European priority list in compliance with Directive 76/464/EEC), for which water quality objectives were available. Toxicological data on organisms representative of the aquatic environment (algae, Daphnia, and fish) were taken from the literature or predicted by means of quantitative structure--activity relationships. This provided toxicological data on all three organisms for 97 of 125 chemicals and on at least two organisms (Daphnia and fish) for the whole data set. Principal Component Analysis was applied in order to perform an a priori classification of chemicals based on toxicity data. Then several classification models, based on traditional and nontraditional molecular descriptors, were applied. Classification models gave results in agreement with the a priori classification as well as with the original water quality objectives classification. The behavior of some outliers was explained. The approach described appears to be a useful tool for the preliminary classification of chemicals that are dangerous to the aquatic environment for which toxicological data are inadequate.

[1]  K. Verschueren.  Handbook of environmental data on organic chemicals , 1977 .

[2]  Paola Gramatica,et al.  3D‐modelling and Prediction by WHIM Descriptors. Part 6. Application of WHIM Descriptors in QSAR Studies , 1997 .

[3]  S. Kooijman,et al.  A safety factor for LC50 values allowing for differences in sensitivity among species , 1987 .

[4]  Marina Lasagni,et al.  New molecular descriptors for 2D and 3D structures. Theory , 1994 .

[5]  George W. Ware.  EEC Water Quality Objectives for Chemicals Dangerous to Aquatic Environments (List 1) , 1994 .

[6]  Paola Gramatica,et al.  Classification of organic solvents and modelling of their physico-chemical properties by chemometric methods using different sets of molecular descriptors , 1999 .

[7]  Danail Bonchev,et al.  Information theoretic indices for characterization of chemical structures , 1983 .

[8]  N. V. van Straalen,et al.  Ecotoxicological evaluation of soil quality criteria. , 1989, Ecotoxicology and environmental safety.

[9]  C. D. S. Tomlin,et al.  The Pesticide Manual , 2003 .

[10]  P. G. Wells,et al.  Acute lethal toxicity of hydrocarbons and chlorinated hydrocarbons to two planktonic crustaceans: The key role of organism-water partitioning , 1986 .

[11]  Lemont B. Kier,et al.  An index of electrotopological state for atoms in molecules , 1991 .

[12]  M. Randic.  Characterization of molecular branching , 1975 .

[13]  Roberto Todeschini,et al.  The K correlation index: theory development and its application in chemometrics , 1999 .

[14]  Jerome H. Friedman,et al.  Classification: Oldtimers and newcomers , 1989 .

[15]  K. Leeuwen.  Ecotoxicological effects assessment in the Netherlands: Recent developments , 1990 .

[16]  S. Weisberg.  Plots, transformations, and regression , 1985 .

[17]  Alexandru T. Balaban,et al.  From chemical topology to three-dimensional geometry , 2002 .

[18]  R. Todeschini,et al.  The WHIM Theory: New 3D-molecular descriptors for QSAR in environmental modelling , 1997 .

[19]  Paola Gramatica,et al.  SD-modelling and Prediction by WHIM Descriptors. Part 5. Theory Development and Chemical Meaning of WHIM Descriptors , 1997 .

[20]  D. Cvetkovic,et al.  Graph theory and molecular orbitals , 1974 .

[21]  I. Gutman,et al.  Graph theory and molecular orbitals. XII. Acyclic polyenes , 1975 .

[22]  Marco Vighi,et al.  QSARs for toxicity of organophosphorous pesticides to Daphnia and honeybees , 1991 .

[23]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .