The drug development process utilizes the parallel assessment of activity at a therapeutic target as well as absorption, distribution, metabolism, excretion, and toxicity properties of molecules. The development of novel, reliable, and inexpensive computational methods for the early assessment of metabolism and toxicity is becoming increasingly an important part of this process. We have used a computational approach for the assessment of drugs and drug-like compounds which bind to the cytochromes P450 (P450s) with experimentally determined Km values. The physicochemical properties of these compounds were calculated using molecular descriptor software and then analyzed using Kohonen self-organizing maps. This approach was applied to generate a P450-specific classification of nearly 500 drug compounds. We observed statistically significant differences in the molecular properties of low Km molecules for various P450s and suggest a relationship between 33 of these compounds and their CYP3A4-inhibitory activity. A test set of additional CYP3A4 inhibitors was used, and 13 of 15 of these molecules were colocated in the regions of low Km values. This computational approach represents a novel method for use in the generation of metabolism models, enabling the scoring of libraries of compounds for their Km values to numerous P450s.

ABSTRACT:It is widely recognized that preclinical 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 (P450), 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, and 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 crossvalidated 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 two 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 P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.Quantitative structure-metabolism relationship (QSMR) models allow the estimation of complex metabolism-related phenomena from relatively simple calculated molecular properties or descriptors. Such models can be used for the design of structural analogs of bioactive compounds with improved pharmacokinetic properties (Bouska et al., 1997;Madsen et al., 2002;Humphreys et al., 2003), evaluation of excretion kinetics (Holmes et al., 1995;Bollard et al., 1996;Cupid et al., 1996), estimation of approximate rates of metabolic conversion for prodrugs or soft drug candidates (Buchwald and Bodor, 1999;Bodor, 1999), and assessment of potential toxic effects of novel compounds. (Di Carlo et al., 1986a,b;Di Carlo, 1990;Altomare et al., 1992).The computational prediction of the metabolic fate of novel compounds is a nontrivial problem. First, an indiscriminate pooling of metabolic data from different species in the commercially available databases substantially distorts any attempt at generalization (Darvas, 1988). The metabolic pathways and corresponding networks can be very different even in close mammalian species, so that any use of such pooled data is problematic (Mulder, 1990). Second, in vitro and in vivo data may differ substantially even for the same species. The metabolic fate of a drug delivered to the human liver after intravenous administration is often quite different from that observed in the liver micros...