QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLICN-DEALKYLATION REACTION RATES

Abstract

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...

Computational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)

“…In and of itself, this may be valuable, as our approach therefore does not require the a priori knowledge or prediction of the metabolite/s involved. However, once the metabolite(s) is known, it may be possible to use combined descriptors for the parent and metabolites as demonstrated previously for predicting N-dealkylation (Balakin et al, 2004). Future work may assess the chemical reactivity of the molecules that form MIC and attempt to determine the binding interactions with heme and elsewhere in the protein using site-directed mutagenesis.…”

Molecular Characterization of CYP2B6 Substrates

“…The majority of the 64 metabolic reactions catalyzed were N-demethylation and hydroxylations, although several other reactions were less frequently observed. This information could additionally be useful in generating a probability for CYP2B6 metabolizing a molecule at these corresponding functional groups [34] or building models to predict rate of metabolism [35]. Ultimately models for V max , clearance and/or K cat for this enzyme would also be useful.…”

In silico prediction of cytochrome P450 inhibitors

“…The neural network and Bayesian model in that study is based on E-state key descriptors and Barnard 4096 bit fingerprints as descriptors and 600 molecules are included in the test set. The best regression model is the neural network model presented by Balakin et al [2004b].…”