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

(ALA-1117-833478) Chemical selection for endpoint related hierarchical chemical categorization and QSAR model development.

Aladjov, H¹, Kolanczyk, R², Nikolov, N³, Mekenyan, O⁴, Veith, G⁵, Akahori, Y⁶, Nakai, M⁶, ¹ NRC Post-doctoral associate – US EPA, Mid-Continent Ecology Division, Duluth, Minnesota, USA² US EPA, Mid-Continent Ecology Division, Duluth, Minnesota, USA³ Centre of Biomedical Engineering "Ivan Daskalov" – Bulgarian Academy of Science, Sofia, Bulgaria⁴ Laboratory of Mathematical Chemistry, Asen Zlatarov University, Bourgas, Bulgaria⁵ International QSAR Foundation to Reduce Animal Testing, Two Harbors, Minnesota, USA⁶ Chemicals Evaluation Research Institute, Sugito-machi, Japan

ABSTRACT- While building a single model for estrogen binding affinity may be statistically possible it can be argued that such a model will lack mechanistic interpretation and be extremely sensitive to training set selection. Alternatively, attempts to group chemicals by receptor interaction types allows resultant QSARs to be more specific and reveal structural requirements associated with underlying interaction mechanisms. To achieve this an iterative approach to model development was adopted. Based on available literature four major types of ER interaction were outlined and representative chemicals selected. From this, rules for mechanistic classifications were proposed. When rules were applied to chemical inventories many chemicals remained unclassified, presumably due to incompleteness of the initial rules. New categories were introduced paying attention to first principles and functional groups, and used to parse: a training set designed in-house with a large representation of industrial chemicals (EPA/MED); other existing training sets from CERI and literature sources; and, chemical inventories of interest. By comparing the number of chemicals in each category within training sets and inventories it was possible to outline the relevance of the classes with respect to inventories and assess test data availability for building models. Approaches for selecting and testing chemicals to further investigate structure-activity hypotheses where ambiguous or no information exists were developed. Models built where data permitted were used to further refine and evaluate mechanistic hypotheses and aid further chemical selection. Where particular categories of chemicals are found to be consistent model outliers, new interaction mechanisms can be hypothesized and explored. Structural requirements established in the models are used to further screen inventories and select and test chemicals, eventually stabilizing the number of categories and interaction mechanisms. This iterative process of refining models and categorization rules is continued until a target inventory is adequately covered to the extent needed for regulatory acceptance.

Key words: QSAR, strategic chemical selection, prioritization of inventori