PARENT SESSION

1F - QSAR
Poster Hall
8:30 AM - Tuesday, 29 April 2003
Chair: Schüürmann, G.¹, ¹
Co-chair: Verhaar, H.J.M.², Cronin, M.³, ^{2 3}

(TUP/50) Identification of the structural requirements for mutagencitiy: I. Incorporating molecular flexibility and metabolic activation of chemicals.

Mekenyan, Ovanes¹, Serafimova, Rossitsa¹, Thompson, Ed², Dimitrov, Sabcho¹, Kotov, Stefan¹, Dimitrova, Nadezhda¹, Walker, John³, ¹ Laboratory of Mathematical Chemistry, University “Prof. As. Zlatarov”, Bourgas, Bulgaria, Bulgaria² Human & Environ. Safety, The Procter & Gamble Company, MVL, Cincinnati, OH 45239-8707, USA³ USEPA TSCA Interagency Testing Committee, 401 M Street, SW, Washington, DC 20460, USA

ABSTRACT- The COmmon REactivity PAttern (COREPA) is a pattern-recognition method for identifying common stereoelectronic (reactivity) patterns of structurally diverse chemicals, which exert similar biological effects. The approach is not dependent upon predetermined toxicophores or alignment of conformers to a lead compound. COREPA has been used to identify structural requirements for eliciting mutagenic effects. Elucidation of this pattern requires examination of the conformational flexibility of the compounds, revealing areas in the multidimensional descriptor space which are most populated by the conformers of mutagenic chemicals and least populated by non-mutagenic ones (including chemicals which become mutagenic after metabolic activation). The QSAR analysis was based on Salmonella data from the National Toxicology Program. Because of the complexity of the data, the training set was confined to a single strain - TA100. The mutagenicity profile is described as a hierarchically ordered set of rules based on ranges of parameter variations. The structural factors controlling the effect are global reactivity of chemicals (Egap=E(HOMO)-E(LUMO)) combined with their ability to take part in SE2 (local electronic charges) and SE1 (reactive fragments) electrophilic reactions. These significant factors were tuned by additional structural requirements associated with molecular polarity and surface. Based on derived reactivity patterns, a descriptor profile (decision tree) was established for identifying mutagenic chemicals. The mutagenicity model correctly identified 137 of 148 (93%) of the direct acting mutagens in the training set, and 789 of 820 (96%) of the nonmutagens in the training set. A system, which identifies those chemicals, which require metabolic activation, has also been developed. The system applies a simulator for metabolic transformation of chemicals under S-9 generating stable metabolites that are submitted to mutagenicity models. This model correctly identified 201 of 229 (88%) of the chemicals in a training set suggesting explicitly the mutagenic metabolites.

Key words: metabolic activation, mutagenicity, S9 metabolism, TA100