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.

Twenty-four organic compounds currently undergoing testing within cancer bioassays under the aegis of the U.S. National Toxicology Program (NTP) were submitted to the computer automated structure evaluation (CASE) and multiple computer automated structure evaluation (MULTICASE) system for predictions of activity. Individual predictions resulting from the NTP combined rodent, NTP mouse, Carcinogenic Potency Database (CPDB) combined rodent, and CPDB mouse databases were combined using Bayes' theorem to yield an overall probability of rodent carcinogenicity. Based upon an arbitrary probability cut-off of 0.50, nine compounds were predicted to be rodent carcinogens. The predicted carcinogens are chloroprene, 1-chloro-2-propanol, codeine, emodin, furfuryl alcohol, isobutyraldehyde, primaclone, sodium xylenesulfonate, and t-butylhydroquinone.