Received for publication April 27, 2004.
Revised July 8, 2004.
Accepted for publication July 12, 2004.

QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF THE METABOLIC N-DEALKYLATION REACTION RATES

Abstract

It is widely recognized that pre-clinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (CYP), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid and the leaving group then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared and modular neural networks with 2 hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of CYP mediated metabolism and may be applied and integrated with other models for CYP binding in order to produce a systems-based approach for predicting drug metabolism.

Key words: computational models, computer modeling and simulation, CYP2D, CYP3A, cytochrome P450, structure-activity relationships

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