Development, Characterization and Application of Predictive-Toxicology Models

Abstract

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.

Elevated Levels of Somatic Mutation as a Biomarker of Environmental Effects Contributing to Breast Carcinogenesis

“…Many approaches have been tried, from attempts to reproduce the logic of a working toxicologist through hierarchical sets of rules and decision trees, to correlation of chemical structure or physicochemical properties with biological activity, to artificial intelligence systems such as neural networks that attempt to combine the best features of each approach (Benfenati and Gini, 1997;Benigni and Richard, 1998;Rosenkranz et al, 1999). The challenge is much greater for toxicologists than for drug designers, however, since identification of a single successful lead compound can make the approach successful in the latter case, whereas missing a single toxic compound in the former could result in tragedy.…”

Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals

“…TOPKAT does not directly compare the atomic components of substructural molecular fragments, as CASE does [Rosenkranz et al, 1999], but rather it statistically compares unknown chemicals to those in its database of identified carcinogens and noncarcinogens according to descriptors of one-and two-atom-fragment electrotopological states [Hall et al, 1991] as well as molecular shape and symmetry indices. Furthermore, consideration of metabolic pathways is not part of TOPKAT's analyses, although enzyme-mediated metabolism is generally understood to be essential in the "activation" of many chemicals to their final reactive, carcinogenic form.…”

“…SAR analysis can be performed either by human experts [see, e.g., Ashby, 1996] or by computers. Computer-based systems fall into two general categories: those that attempt to mimic the reasoning processes of human experts [see, e.g., Woo et al, 1995;Marchant, 1996] and those that perform statistical comparisons between the characteristics of the chemical of interest and those of known carcinogens and noncarcinogens in the system's database [see, e.g., Enslein et al, 1994;Rosenkranz et al, 1999]. One widely used system of this latter type is TOPKAT (Oxford Molecular, Beaverton, OR).…”

Estimating the extent of the health hazard posed by high-production volume chemicals.

“…The resulting list of biophores can then be used in mechanistic studies or to predict the activity of yet untested molecules (10). For example, upon submission for evaluation, MULTICASE will determine if an unknown molecule contains a biophore.…”

“…Although SAR projections may not have perfect predictivity, the current study seeks to assess the prevalence of toxicants among HPV chemicals. Such estimates based on SAR techniques can be derived for populations of molecules provided the SAR model has been validated and its predictivity is known (10)(11)(12).…”