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

(SCH-1117-829762) In vitro tests, mechanistic hypotheses, and iterative model development to build QSARs for risk assessment.

Schmieder, P¹, Mekenyan, O², Veith, G³, ¹ U.S. EPA, ORD, NHEERL, Mid-Continent Ecology Division, Duluth, MN, USA² Laboratory Mathematical Chemistry, Bourgas As. Zlatarov University, Bourgas, Bulgaria³ International QSAR Foundation to Reduce Animal Testing, Two Harbors, MN, USA

ABSTRACT- Assessing the risks of chemicals when measured data are not available is a major challenge for regulatory bodies, and the identification of potential endocrine disruptors highlights the need for hypothesis-driven approaches to estimate adverse effects at lower cost. One approach is the development of quantitative structure-activity relationships (QSAR) for major toxicity pathways such as the estrogen signaling pathway. Statistical models of complex receptor binding without classification of binding mechanisms are often unreliable, and yet the elucidation of binding mechanisms for many receptors is still in its infancy. This paper presents an iterative approach for discovery of the structural requirements for distinct mechanisms which involves QSAR-based hypothesis generation, strategic chemical selection for hypothesis testing, QSAR evaluation and improvement of mechanistic classification. The approach is grounded in seeking mechanistic understanding of the underlying interactions, and defining chemical similarity in terms of biological activity. Strategic selection of chemicals for testing is an essential component, given the numerous diverse chemicals for model regulatory applications. The immediate goal is to determine structural requirements for chemical binding to the estrogen receptor, a more complex interaction than previously appreciated. The larger objective is to present a process applicable to recurring issues surrounding determining structural attributes associated with toxicity leading to adverse biological consequence. Determinations must be made with enough specificity to result in reliable predictions but broad applicability to numerous diverse chemicals. The process strives for mechanistic interpretability and transparency, with measures of coverage within inventories to which applied. The presentation introduces key aspects (detailed in posters) integrating information through successive iterations, including: i) in vitro assays (effects, concentrations, metabolites) linked to adverse outcomes; ii) QSAR hypothesis-generation, model refinement; iii) strategic chemical selection in the context of regulatory inventories; and, iv)prioritization of chemicals for the endpoint of concern.[Abstract does not necessarily reflect EPA policy].

Key words: in vitro effects assays, in vitro metabolism assay, regulatory applicability