MP6 Quantitative Structure Activity Relationships (QSARs)
Monday, 14 November 2005: 8:00 AM - 5:30 PM in Exhibit Hall

(LAM-1117-748399) New methods in QSAR: Use of structural equation modeling to describe PAH photoinduced toxicity.

Lampi, M¹, Reynolds, M², Dixon, D¹, Greenberg, B¹, ¹ Department of Biology, University of Waterloo, Waterloo, ON, Canada² Department of Psychology, University of Waterloo, Waterloo, ON, Canada

ABSTRACT- Recent advances in the technological fields related to computing have led to a large increase in the use of sophisticated modeling techniques. Ecotoxicology is no different, and indeed there has been wide application of such technology. A recent introduction to the field has been the use of complex methods, particularly neural network models. A complementary technique that is not widely employed in ecotoxicology is structural equation modeling (SEM). This method encompasses many statistical techniques including regression, factor and path analysis. Structural equation modeling allows for testing of hypotheses regarding relationships between observed and latent (unobserved) variables. Latent variables are theoretical concepts that unite phenomena under a single term, and are expressed in terms of directly measured variables. A novel application of structural equation modeling was used to validate the assumption of a bipartite mechanism for photoinduced toxicity of PAHs that includes photosensitization and photomodification. Two latent variables were created to represent the processes of photosensitization and photomodification, which both contribute to phototoxicity. These were based solely on physicochemical and photodynamic properties of the PAHs. The use of SEM enables the weighting of these properties, and their contribution to each latent variable individually, as well as the contribution of the latent variables to toxicity. Six existing PAH phototoxicity data sets were used and structural equation models were estimated using SEM software. These models accounted for a high amount of variance in six different sets of PAH phototoxicity data from different organisms, while providing insight regarding the contribution of different physicochemical and photodynamic descriptors to toxicity. The flexibility of SEM is evident as the relative contributions of each factor could be determined and compared. This study illustrates the promise for this type of modeling in ecotoxicology, potential future uses which include assessment of synergism, and to predictive models developed for other contaminants.

Key words: structural equation modeling, PAHs, photoinduced toxicity, predictive modeling