Herbert S. Rosenkranz
SAR Model Development, Characterization, and Application.
Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA.
The adoption of SAR techniques for risk assessment purposes requires that the predictive performances of the model be characterized and optimized. The development of such methods with respect to CASE/ MULTICASE will be described. Moreover the effects of size, informational content, ratio of actives/ inactives in the model on predictivity must be determined.
Characterized SAR models can provide mechanistic insights: nature of toxicophore, reactivity, receptor binding. Comparison of toxicophores among SAR models allows a determination of mechanistic overlaps (e.g. mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity).
Methods have been developed to combine SAR submodels and thereby improve predictive performance.
Now that SAR methods are gaining acceptance, the development of GLPs is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional. These issues are being addressed in our Department at the University of Pittsburgh.
The research described has been carried out in collaboration with Drs. Gilles Klopman, Albert Cunningham, Jessica Zhang, H. Gregg Claycamp, Orest Macina, Nancy Sussman and Steve Grant.
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