Development, Characterization and Application of Predictive-toxicology Models
ISBN or ISSN
SAR and QSAR in Environmental Research
Publication Date / Copyright Date
The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized 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 predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.
Medical Specialties | Medicine and Health Sciences | Osteopathic Medicine and Osteopathy
Rosenkranz, Herbert S.; Cunningham, Albert R.; Zhang, Ying Ping; Claycamp, H Gregg; Macina, Orest T.; Sussman, Nancy B.; Grant, Stephen G.; and Klopman, Gilles, "Development, Characterization and Application of Predictive-toxicology Models" (1999). College of Osteopathic Medicine Faculty Articles. 769.