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

(MEK-1117-802127) Performance, reliability, and improvement of a tissue-specific metabolic simulator.

Mekenyan, O¹, Jones, J², Schmieder, P³, Kotov, S¹, Pavlov, T¹, Dimitrov, S¹, ¹ Laboratory of Mathematical Chemistry, "Prof. Assen Zlatarov" University, Bourgas, Bulgaria² US EPA, ORD, NERL, Ecosystems Research Division, Athens, GA, USA³ US EPA, ORD, NHEERL, Mid-Continent Ecology Division, Duluth, MN, USA

ABSTRACT- A methodology is described that has been used to build and enhance a simulator for rat liver metabolism providing reliable predictions within a large chemical domain. The tissue metabolism simulator (TIMES) utilizes a heuristic algorithm to generate plausible metabolic maps using a library of measured biotransformations and abiotic reactions. The TIMES simulator prioritizes the order of implementation of metabolic transformation reactions applied to a parent chemical by using probabilities determined from measured data, a significant improvement over earlier approaches. A transformation hierarchy is defined to objectively reproduce a training set of documented metabolic maps thus reducing over-propagation of metabolic maps and enhancing prioritization of generated metabolites according to their stability, amount, solubility, toxicity, etc. The reliability of simulated pathways and metabolites is assessed in comparison to observed metabolism data, thus one can prioritise competing pathways, as well as metabolites, by probability of occurrence and reliability. Reliability estimates are further used to strategically select chemicals for testing to most effectively expand the domain of the simulator. The performance of the simulator is assessed by the extent to which documented maps are reproduced by simulated maps and by the breadth of coverage of chemical structures and their metabolites within the simulator domain. A new iterative procedure has been developed to improve the performance of the simulator while expanding the domain where it is applied with highest reliability. When the simulator is re-trained with new metabolic maps, the system automatically identifies false negative (documented but not predicted) and false positive (predicted but not documented) metabolites. Subsequently, the transformation reaction database is updated by expert knowledge (new transformations are added and/or existing transformations modified) and the simulator performance is enhanced (using the software module SimBuilder) by re-defining the probabilities of the simulator transformation library.

Key words: QSAR, metabolism simulator, metabolite prediction, applicability domain