Electronic models for cytochrome P450 oxidations

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10(-1) to 10(-5) and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure-activity and physiochemical information.

Regioselectivity prediction of CYP1A2-mediated phase I metabolism

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs

The site of biotransformation, the extent and rate of metabolism and the number of active metabolic pathways are among the most important characteristics of the pharmacokinetics of a drug. The catalytic activity of drug metabolizing enzymes is likely the most influential determinant of the pharmacokinetic variability. Metabolic stability is the prerequisite for sustaining the therapeutically relevant concentrations. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. A variety of computational approaches are currently available for predicting different cytochrome P450 (CYP)-related metabolism endpoints. The present review will describe these approaches and their impact on drug development process. Indications on the available software for the implementation will also be given.

Regiospecificity of human cytochrome P450 1A1-mediated oxidations: the role of steric effects

Cytochrome P450 1A1 oxidizes a diverse range of substrates, including the procarcinogenic xenobiotic benzo[a]pyrene (B[a]P) and endogenous fatty acid precursors of prostaglandins, such as arachidonic acid (AA) and eicosapentaenoic acid (EA). We have investigated the extent to which enzyme-substrate interactions govern regio- and stereoselectivity of oxidation of these compounds by using docking and molecular dynamics (MD) simulations to examine the likelihood of substrate oxidation at various sites. Due to structural differences between the substrates analyzed, B[a]P and its diols (planar, rigid), and the fatty acids AA and EA (long, flexible), different docking strategies were required. B[a]P, B[a]P-7,8-diols, (+) 7S,8S- and (-) 7R,8R-diols, were docked into the active site of a homology model of P450 1A1 using an automated routine, Affinity (Accelrys, San Diego, CA). AA and EA, on the other hand, required a series of restrained MD simulations to obtain a variety of productive binding modes. All complexes were evaluated by MD-based in silico site scoring to predict product profiles based on certain geometric criteria, such as angle and distance of a given substrate atom from the ferryl oxygen. For all substrates studied, the in vitro profiles were generally reflected by the in silico scores, which suggests that steric factors play a key role in determining regiospecificity in P450 1A1-mediated oxidations. We have also shown that molecular dynamics simulations may be very useful in determination of product profiles for structurally diverse substrates of P450 enzymes.

Effect of carbonate anion on cytochrome P450 2D6-mediated metabolism in vitro: the potential role of multiple oxygenating species

Studies were designed to investigate various anions and their effects on cytochrome P450 2D6-mediated metabolism in vitro. Incubations were initially performed in buffered phosphate, carbonate, sulfate, and acetate solutions (50mM, pH 7.4), with CYP2D6 substrates dextromethorphan, 7-methoxy-4-(aminomethyl)-coumarin (MAMC), (S,S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine hydrochloride [(-)-OSU6162], and amitriptyline. Dextromethorphan and MAMC O-dealkylation activity in buffered carbonate was approximately 25 and 38%, respectively, relative to phosphate, while activity in sulfate and acetate buffers displayed minor differences. In contrast, N-dealkylation reactions for both (-)-OSU6162 and amitriptyline were unaffected by the presence of carbonate, and the other anions tested. Subsequent kinetic studies revealed that the basis of reduced turnover of dextromethorphan was primarily a V(max) effect, as the V(max) for the rate was 16.9 and 5.6 pmol/min/pmol P450 in phosphate and carbonate, respectively. Interestingly, similar rates of dextromethorphan O-demethylation in phosphate and carbonate were observed when reactions were supported by cumene hydroperoxide (CuOOH). Furthermore, it was observed that while CuOOH could equally support dextromethorphan O-demethylation compared to NADPH, amitriptyline N-demethylation was only minimally supported. Finally, intramolecular kinetic isotope effect (KIE) experiments with amitriptyline-d3 in CuOOH-supported reactions yielded a k(H)/k(D) of 5.2, substantially higher than in phosphate and carbonate supported by NADPH (k(H)/k(D)=1.5). Overall, results suggest that carbonate disrupts the relative ratios of the potential P450 oxygenating species, which differentially catalyze O- and N-dealkylation reactions mediated by CYP2D6.

Subcellular pharmacokinetics and its potential for library focusing

Subcellular pharmacokinetics (SP) optimizes biology-related factors in the design of libraries for high throughput screening by defining comparatively narrow ranges of properties (lipophilicity, amphiphilicity, acidity, reactivity, 3D-structural features) of the included compounds. The focusing ensures appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) in those test biosystems, which are more complex than isolated receptors, and in humans. The SP deploys conceptual models that include transport and accumulation in a series of membranes, protein binding, hydrolysis, and other reactions with cell constituents. The kinetics of drug disposition is described as a non-linear disposition function of drug structure and properties. The SP capabilities are illustrated here using a model-based quantitative structure-activity relationship of toxicity of phenolic compounds against Tetrahymena pyriformis as dependent on lipophilicity and acidity. The resulting SP models clearly outperform empirical models in predictive ability outside the parameter space, as revealed by the leave-extremes-out cross-validation technique with omission of compounds beyond pre-defined lipophilicity and acidity ranges. The SP models do not change substantially if the parameters space is shrunk within some limits. In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization. Once calibrated for a given biosystem, the SP models provide a detailed recipe for tailoring the drug properties to ensure optimum ADMET. The focusing is more accurate than with traditional empirical QSAR studies, assessment of drug-likeness, or the rules for identification of compounds with permeability problems.

(Q) SAR study on the metabolic stability of steroidal androgens

Metabolic stability is a key issue in the development of orally active androgens for Partial Androgen Deficiency in Aging Males (PADAM) and male contraception. Rates of metabolism in human hepatocyte suspensions provide useful information on the stability of compounds that undergo a first pass metabolism. We have derived a structure-pharmacokinetic relationship for a data set of 32 in-house steroidal androgens by means of the decision-trees technique. Volume, shape, number of rotatable bonds, and surface turned out to be the most important descriptors for classification. Only 2 of the 32 compounds were misclassified. The most stable compounds were classified in three leaf nodes on different branches of the tree, suggesting that higher metabolic stability can be achieved for the same substrate by different steric modifications. Further, it is generally assumed that the first step in cytochrome P450s oxidation reactions takes place by hydrogen abstraction to form a radical intermediate. An electronic model for hydrogen abstraction in steroidal androgens was, therefore, developed by means of ab initio calculations. Activation energies of steroid radical systems calculated as energy differences between the reactants equilibrium geometry energies and their corresponding transition states energies could be used to predict relative rates of metabolism to guide the design and redesign process of metabolically more stable steroidal androgens.

Competitive CYP2C9 inhibitors: enzyme inhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationship analysis

This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the K(i) values generated a model with r(2) = 0.947 and a cross-validation of q(2) = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental K(i) values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.