Computational Approaches That Predict Metabolic Intermediate Complex Formation

Some mechanism-based inhibitors cause irreversible inhibition by forming a metabolic intermediate complex (MIC) with cytochrome P450. In the present study, 54 molecules (substrates of CYP3A and amine-containing compounds that are not known substrates of CYP3A) were spectrophotometrically assessed for their propensity to cause MIC formation with recombinant CYP3A4 ( b5). Comparisons of common physicochemical properties showed that mean ( S.D.) mol. wt. of MIC-forming compounds was significantly greater than mean mol. wt. of non-MIC-forming compounds, 472 ( 173) versus 307 ( 137), respectively. Computational pharmacophores, logistic regression, and recursive partitioning (RP) approaches were applied to predict MIC formation from molecular structure and to generate a quantitative structure activity relationship. A pharmacophore built with SKF-525A (2-diethylaminoethyl 2:2-diphenylvalerate hydrochloride), erythromycin, amprenavir, and norverapamil indicated that four hydrophobic features and a hydrogen bond acceptor were important for these MIC-forming compounds. Two different RP methods using either simple descriptors or 2D augmented atom descriptors indicated that hydrophobic and hydrogen bond acceptor features were required for MIC formation. Both of these RP methods correctly predicted the MIC formation status with CYP3A4 for 10 of 12 literature molecules in an independent test set. Logistic multiple regression and a third classification tree model predicted 11 of 12 molecules correctly. Both models possessed a hydrogen bond acceptor and represent an approach for predicting CYP3A4 MIC formation that can be improved using more data and molecular descriptors. The preliminary pharmacophores provide structural insights that complement those for CYP3A4 inhibitors and substrates. The cytochrome P450 (P450) enzymes (EC 1.14.14.1) are membrane-bound proteins that catalyze many oxidations of hydrophobic endobiotics and xenobiotics. The catalytic activity of P450s may be reduced by reversible and irreversible inhibition upon administration of xenobiotics. One type of irreversible inhibition, mechanism-based inhibition, has been the focus of many studies related to P450s recently due to its clinical implications for predicting drug-drug interactions. A mechanism-based inhibitor is one that binds to the active site and then becomes catalytically activated by the enzyme (Silverman, 1988). The activated form of the molecule will irreversibly bind to the enzyme to remove it from the active enzyme pool. Some mechanism-based inhibitors cause this irreversible inhibition by forming a metabolic intermediate complex (MIC) with the heme of the P450 (Franklin, 1977). Primary, secondary, or tertiary amines or methylenedioxy constituents (Supplemental Data Fig. 1) in the molecule are prerequisites for compounds that chelate the heme of the P450 (Franklin, 1977). More recent studies with metabolites of molecules such as indinavir and nelfinavir that lack these functional groups yet still display MIC formation may indicate that other chemical moieties are also involved (Ernest et al., 2005). The CYP3A family of enzymes is recognized as perhaps the most important for human drug metabolism because they metabolize most commercially available drugs (Wrighton et al., 2000). These P450s are expressed in numerous tissues but affect xenobiotic metabolism and clearance mainly in the liver and small intestine. There are four differentially regulated CYP3A genes in humans, CYP3A4, CYP3A5, CYP3A7, and CYP3A43. Of these genes, CYP3A4 is the most abundant form in the adult liver (Wrighton et al., 2000). The CYP3A forms demonstrate regioselectivity differences for some biotransformations of the same compounds, whereas CYP3A5 generally has lower or comparable metabolic capability than CYP3A4 for common probe substrates (Williams et al., 2002). In early pharmaceutical drug discovery, the assessment of inhibitory potency with CYP3A4 for new chemical entities is often included This work was supported by grants from the National Institutes of Health (Grants AG13718 to S.D.H. and GM74217 to S.D.H.) and the U.S. Food and Drug Administration (Grant FD-T-001756-01 to S.D.H.). Article, publication date, and citation information can be found at http://dmd.aspetjournals.org. doi:10.1124/dmd.106.014613. □S The online version of this article (available at http://dmd.aspetjournals.org) contains supplemental material. ABBREVIATIONS: P450, cytochrome P450; MIC, metabolic intermediate complex; QSAR, quantitative structure activity relationship; SKF-525A, 2-diethylaminoethyl 2:2-diphenylvalerate hydrochloride; MDMA, methylenedioxymethamphetamine; MDE, methylenedioxyethylamphetamine; MBDB, 2-methylamino-1-(3,4-methylenedioxyphenyl)butane; CSAR, classification structure activity relationship; glm, generalized linearized model; DDB, dimethyl-4,4 -dimethoxy-5,6,5 ,6 -dimethylenedioxybiphenyl-2,2 decarboxylate. 0090-9556/07/3509-1466–1475$20.00 DRUG METABOLISM AND DISPOSITION Vol. 35, No. 9 Copyright © 2007 by The American Society for Pharmacology and Experimental Therapeutics 14613/3238301 DMD 35:1466–1475, 2007 Printed in U.S.A. 1466 http://dmd.aspetjournals.org/content/suppl/2007/05/30/dmd.106.014613.DC1 Supplemental material to this article can be found at: at A PE T Jornals on Jne 1, 2017 dm d.aspurnals.org D ow nladed from as a first tier in vitro screen using fluorescent probes (Crespi and Stresser, 2000). This type of screen is useful for identifying potential inhibition of coadministered drugs and is commonly followed by time-dependent inhibition studies (Wu et al., 2003). These studies may be supplemented with additional in vitro assays with more traditional drug substrate probes using liquid chromatography/mass spectrometry analysis (Ekins et al., 2000b). Data from all of these in vitro screens are increasingly applied to predictive algorithm development (Riley et al., 2001; Gao et al., 2002; Ekins et al., 2003a,b). The interest in computational models based on in vitro data for predicting potential drug interactions via this protein and others (Ekins and Swaan, 2004) represents a possible means to improve productivity of the drug discovery process and remove potential bottlenecks caused by in vitro testing. Due to their highly parallel nature, computational methods are also probably the fastest and most cost-effective method for indicating likely toxic consequences (Ekins et al., 2000a) and suggesting new hypotheses for testing in vitro. A recent review of computational methods for P450s has documented how these approaches have been used over nearly 20 years alongside empirical methods (de Graaf et al., 2005). Others have described and compared the many pharmacophores that have been generated for P450s (Ekins et al., 2001; de Groot and Ekins, 2002), providing insight into the important features for the interaction of ligands and proteins. Computational pharmacophores for CYP3A4 have therefore been derived for substrates and inhibitors using kinetic constants Km, Ki (apparent), and IC50 data. Several studies have shown differential MIC formation for compounds between CYP3A4 and CYP3A5. For example, CYP3A5 did not form a MIC with verapamil (Wang et al., 2004) or saquinavir (Ernest et al., 2005). These differences suggest that the binding sites (and catalytic rate) for both enzymes are subtly different, accommodating some molecules and not others as has been suggested by recent pharmacophores for inhibitors of both enzymes (Ekins et al., 2003b). In the present study, 54 molecules were assessed for their propensity to form MIC, whereas 27 molecules possess kinact data with recombinant CYP3A4 in vitro. These data were analyzed along with the generation of simple molecular descriptors to understand any possible relationships between MIC, kinact, and molecule structure. Several computational approaches, e.g., pharmacophores, quantitative structure activity relationships (QSARs) (Ekins and Swaan, 2004), classification trees, and multiple regression methods, were used to generate models to predict these properties separately. These models were applied to the prediction of the probability of a molecule forming an MIC with CYP3A4 using a series of test molecules, whereas the kinact models were internally validated by omitting molecules at random. Materials and Methods Chemicals. All of the following classes of study drugs were purchased from Sigma-Aldrich (St. Louis, MO) or United States Pharmacopeia (Rockville, MD) or kindly donated by companies or investigators. NADPH was purchased from Roche Diagnostics (Indianapolis, IN). All other chemicals were at least analytical grade. Antibiotics: triacetyloleandomycin; erythromycin; N-desmethylerythromycin (United States Pharmacopeia); and clarithromycin, N-desmethylclarithromycin, and 14-hydroxyclarithromycin (donated by Abbott Laboratories, Chicago, IL). Calcium channel blockers: amlodipine; diltiazem; N-desmethyldiltiazem, desacetyldiltiazem, and desacetyl-N-desmethyldiltiazem (donated by Tanabe Seiyaku Co., Osaka, Japan); R-verapamil; S-verapamil; norverapamil; D-617; and nicardipine. Central nervous system drugs: amitriptyline; d,l-amphetamine; benzphetamine; brompheniramine; chlorpheniramine; desipramine; diphenhydramine; fenfluramine; fluoxetine; fluvoxamine; imipramine; loperamide; meperidine; methamphetamine; methylenedioxymethamphetamine (MDMA); methylenedioxyethylamphetamine (MDE); 2-methylamino-1-(3,4-methylenedioxyphenyl)butane (MBDB); methylphenidate; mirtazapine; nefazodone; norfluoxetine; nortriptyline; orphenhydramine; paroxetine; phencyclidine; propoxyphene (United States Pharmacopeia); sertraline; and tranylcypromine. Human immunodeficiency virus protease inhibitors: amprenavir (donated by GlaxoSmithKline, Research Triangle Park, NC); indinavir (donated by Merck, Whitehouse Station, NJ); nelfinavir (donated by Agouron Pharmaceuticals, Inc., New York, NY); ritonavir (donated by Abbott Laboratories); saquinavir (donated by F. Hoffmann-La Roche, Nutley, NJ); and lopinavir (donated by Abbott Laboratories). Anticancer drugs: tamoxifen; N-desmethyltamoxifen (donated by Zeneca Pharmaceuticals, Wilmington, DE); 4-hydroxytamoxifen; and 3-hydroxytamoxifen. Miscellaneous drugs: mifepristone and SKF-525A. Enzyme Preparation. Insect cell membranes containing baculovirus cDNA-expressed CYP3A4 ( b5) were purchased from BD Gentest (Bedford, MA). The P450 content was provided by the manufacturer at the time of purchase. Estimation of Mechanism-Based Inactivation Parameters. Testosterone 6 -hydroxylation was determined to quantify timeand concentration-dependent loss of CYP3A4 activity in the presence of inactivator (Ernest et al., 2005). The concentration of 6 -hydroxytestosterone was determined by highperformance liquid chromatography with UV detection as described previously (Zhao et al., 2002). The mechanism-based inactivation parameters kinact and KI were obtained from the pseudo first-order decline in the percentage of remaining CYP3A4 activity after preincubation with inactivator by nonlinear regression without weighting using WinNonlin Professional version 4.0 (Pharsight, Mountain View, CA). Details of the data fitting and parameter estimation have been described previously (Ernest et al., 2005). Metabolic Intermediate Complex Measurements. MIC formation was identified using dual wavelength spectroscopy (UVIKON 933 Double-Beam UV/VIS Spectrophotometer; Research Instruments International, San Diego, CA) by scanning from 380 to 500 nm. The sample cuvette contained protein [200 pmol of CYP3A4 ( b5)], 100 mM sodium phosphate buffer (pH 7.4), inhibitor, and 1 mM NADPH (made with the phosphate buffer), whereas the reference cuvette contained protein, 100 mM phosphate buffer, vehicle, and 1 mM NADPH. All MIC formation experiments were initiated by the addition of NADPH and maintained at 37°C. The absorbance difference spectra for the identification of MIC formation were estimated by subtracting the absorbance at 490 nm of the absorbance scan from the difference of the absorbance scan at 60 min and a background absorbance scan. MIC formation was quantified from absorbance difference spectra using an extinction coefficient of 65 mM 1 cm 1 (Pershing and Franklin, 1982). Computational Modeling. An initial assessment was undertaken to determine whether MIC and non-MIC-forming compounds could be differentiated with simple calculated molecular properties. Calculated log P (octanol-water partition coefficient) and mol. wt. were determined for each compound with ChemDraw for Excel (CambridgeSoft, Cambridge, MA). Statistical parameters were calculated with SPSS version 12.0 (SPSS Inc., Chicago, IL). In addition, several different computational techniques were evaluated: a pharmacophore method (Catalyst; Accelrys Inc., San Diego, CA), three recursive partitioning (tree) methods, which included ChemTree (Golden Helix Inc., Bozeman, MT), Cerius (Accelrys Inc.), and tree function in R software version 2.2.1 (http:// www.r-project.org) using different types of descriptors, a linear model (multiple regression and regression tree model) using R software version 2.2.1, and a logistical model using R software version 2.2.1. Pharmacophores for MIC Prediction. The Catalyst software was used on a Silicon Graphics Octane workstation (Silicon Graphics, Sunnyvale, CA). After importing the molecular structures, conformers were generated for each compound using the BEST functionality for each molecule and limited to a maximum of 255 conformers with an energy range of 20 kcal/mol. Structural relationships between compounds that form MIC and those that do not form MIC were assessed separately using the common features function (HipHop). The following groups of molecules were selected to test the fit of the hypothesis with unknowns: compounds that form MIC (SKF-525A, erythromycin, amprenavir, and norverapamil); compounds that do not form MIC (mifepris1467 METABOLIC INTERMEDIATE COMPLEX FORMATION WITH CYP3A4 ( b5) at A PE T Jornals on Jne 1, 2017 dm d.aspurnals.org D ow nladed from tone, sertraline, 4-hydroxytamoxifen, and paroxetine); and compounds that do not form MIC but inactivate CYP3A4 (lopinavir, saquinavir, and nefazodone). These alignments were then used to fit molecules to the hypothesis. Recursive Partitioning (ChemTree) for MIC Prediction. The ChemTree recursive partitioning software was run on a Pentium 4 processor. The 54 molecules and experimental data (binary response: 1, compound that forms MIC; 0, compound that does not form detectable MIC) were imported as an .sdf file into ChemTree to generate over 330 path-length descriptors (Young et al., 2002). These descriptors were used to generate either single-tree or 100 random-tree models with the following options: p value threshold for splits, 0.99; maximum segments, 3; parallel threads, 1; and resampling iterations, 10,000. Recursive Partitioning (Cerius CSAR) for MIC Prediction. The Cerius 4.8 software was used to generate the following default descriptors: sum of atomic polarizabilities, dipole magnitude, radius of gyration, area, mol. wt., molecular volume, density, principal moment of inertia, rotatable bonds, hydrogen bond acceptors, hydrogen bond donors, and AlogP98 (a second method for calculating the octanol-water partition coefficient). The CSAR recursive partitioning method (Hawkins et al., 1997) was used with the 54-molecule training set with the following settings: equally weighted observations, Gini scoring, and scaled prune (0). This model was internally validated using cross-validation: e.g., 10-fold, 5-fold, or 2-fold. A 10-fold cross-validation leaves out 10% test data. Recursive Partitioning (Tree Function in R) for MIC Prediction. The R version 2.2.1 software was used to predict MIC formation based on the 12 prescribed descriptors. The recursive partitioning method (Breiman et al., 1984) was used with the 54-molecule training set (with equally weighted observations and Gini scoring). This model was internally validated using 5-fold cross-validation, and the tree is pruned based on misclassification. Logistic Regression for MIC Prediction. The logistic regression model was implemented in an R function, generalized linearized model (glm) (R version 2.2.1). The model was generated with the 12 Cerius descriptors to predict MIC formation. Fifty-four compounds served as the training set. The glm() algorithm is based on the maximizing likelihood approach. A forward stepwise forward variable selection strategy was used to select descriptors. The optimal model was internally validated based on a 5-fold cross-validation. Pharmacophore, tree models, and the logistic regression model were tested with molecules outside of the training set using recently published data for 12 compounds (Yamazaki and Shimada, 1998; Kasahara et al., 2000; Kim et al., 2001; Kajita et al., 2002; Chatterjee and Franklin, 2003; Wu et al., 2003) with MIC formation for CYP3A4. Linear Multiple Regression and Regression Tree Models for kinact, KI, and kinact/KI Prediction. The kinact and KI values were estimated from in vitro studies with 27 compounds as described previously. Linear multiple regression and regression tree models were fit with the 12 descriptors listed under “Recursive Partitioning (Tree Function in R) for MIC Prediction” to predict kinact, KI, kinact/KI. Stratified randomization was used to divide samples into three strata based on 33 percentile and 67 percentile of kinact and/or KI values. Within each stratum, 2/3 of the samples (n 18) were randomly selected into the training set, and the remaining 1/3 (n 9) were selected into the external validation set. All variables were log-transformed. Two R functions, linear model (lm), and tree() (R version 2.2.1) were implemented for regression tree model construction. These two predictive models were based on 5-fold cross-validation on training set samples.