Reversible and irreversible inhibition of CYP3A enzymes by tamoxifen and metabolites

Zhao, Xue; Jones, David R.; Wang, Ying Hong; Grimm, Scott; Hall, Stephen D.

doi:10.1080/00498250210158230

Cited by 63 publications

(32 citation statements)

References 27 publications

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“…For example, 3-hydroxytamoxifen and 4-hydroxytamoxifen do not form MIC, whereas tamoxifen and n-desmethyltamoxifen form MIC in vitro (Zhao et al, 2002). Some macrolide antibiotics such as clarithromycin form MIC (Mayhew et al, 2000), whereas others such as miocamycin do not (Kasahara et al, 2000), which suggests the amine may be sterically hindered in the latter case such that it cannot orientate correctly in the CYP3A4 (ϩb 5 ) binding site.…”

Section: Discussionmentioning

confidence: 99%

“…Testosterone 6␤-hydroxylation was determined to quantify time-and 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 k inact and K I 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).…”

Section: Methodsmentioning

confidence: 99%

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

Jones¹,

Ekins²,

Li³

et al. 2007

Drug Metab Dispos

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ABSTRACT: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 (؉b 5 ). 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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Jones¹,

Ekins²,

Li³

et al. 2007

Drug Metab Dispos

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“…The 6␤-OH TES concentration was determined by an HPLC system with ultraviolet detection at a wavelength of 254 nm as described previously (Zhao et al, 2002). For the determination of ERY and nd-ERY concentrations, 200 l of internal standard (0.5 ng/l troleandomycin in 1 M sodium cabonate-1 M sodium bicarbonate, 4:1 v/v, pH ϭ 9.6) was added to each sample, followed by the addition of 3 ml of hexane/ethyl acetate (1:1 v/v).…”

mentioning

confidence: 99%

Prediction of the Effect of Erythromycin, Diltiazem, and Their Metabolites, Alone and in Combination, on CYP3A4 Inhibition

Zhang¹,

Jones²,

Hall³

2008

Drug Metab Dispos

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ABSTRACT:Predictive models of complex drug-drug interactions between multiple inhibitors and their metabolites have not been evaluated. The purpose of this study was to evaluate an interaction model for cytochrome P450 3A4 (CYP3A4) that incorporated the simultaneous reversible and irreversible inhibition by multiple inhibitors. Erythromycin (ERY) and diltiazem (DTZ), and their major metabolites, N-desmethylerythromycin (nd-ERY) and N-desmethyldiltiazem (nd-DTZ), were chosen to evaluate the model. k inact (rate constant for maximal inactivation), K I (inhibitor concentration at 50% maximal inactivation), and K i (reversible inhibition constant) were estimated for ERY, DTZ, nd-ERY, and nd-DTZ, respectively, using cDNA-expressed CYP3A4 and human liver microsomes under optimal experimental conditions. To evaluate the interaction model, combinations of inhibitors and metabolites were incubated at concentrations equal to K I , 1 ⁄2K I , and 2K I of each inhibitor for specified durations in both enzyme systems. The models were further evaluated by the incubation of combinations of inhibitors with the substrate testosterone for 10 min. CYP3A4 inhibition in the presence of drug mixtures was predicted from the inhibition parameters determined for each drug or metabolite alone. The CYP3A4 activity in the presence of multiple inhibitors was well predicted by the model incorporating additive irreversible inhibition as modified by mutual competitive inhibition (percent mean error and percent mean absolute error ranged from ؊0.06 to 0.04 and from 0.03 to 0.09, respectively). In conclusion, the additive model predicted the combined effect of multiple inhibitors on CYP3A inhibition in vitro. However, simultaneous reversible and irreversible inhibition effects should be taken into account in a reaction mixture of substrate and multiple inhibitors of CYP3A4.Concomitant medications causing drug-drug interactions (DDIs) have led to serious adverse drug events during treatment and resulted in restrictions in prescribing drugs and withdrawal of drugs from the market (Jankel and Fitterman, 1993;Yuan et al., 1999). The incidence and extent of DDIs would be expected to increase when multiple inhibitors of a specific drug-metabolizing enzyme are administered simultaneously compared with administration of a single inhibitor alone. According to the U.S. Food and Drug Administration's Guidance for Industry Drug Interaction Studies, inhibitors of cytochrome P450 3A4 (CYP3A4) can be classified as potent, moderate, or weak if the area under the plasma concentration-time curve (AUC) fold increase of midazolam with the coadministered inhibitor is more than 5-fold, between 2-and 5-fold, or less than 2-fold, respectively (Guidance for Industry: In Vivo Drug Metabolism/Drug Interaction Studies: Study Design, Data Analysis, and Recommendations for Dosing and Labeling, U.S. Food and Drug Administration, 2006, http://www. fda.gov/cder). Thus, when two moderate inhibitors or one moderate and one weak inhibitor are given together, it is likely that they ...

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“…A detailed description of the kinetic characteristics of this type of inhibition can be found in the text by Silverman (27). Although TDI can arise for a number of reasons (including the formation of potent yet reversiblebinding metabolites (28)(29)(30)), the most important molecular mechanisms are:…”

Section: Cyp Inhibition Measurement: Determination Of Time-dependent mentioning

confidence: 99%

In Vitro Evaluation of Reversible and Irreversible Cytochrome P450 Inhibition: Current Status on Methodologies and their Utility for Predicting Drug–Drug Interactions

Fowler

Zhang

2008

AAPS J

161

108

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Abstract. It is widely accepted that today's practice of polypharmacy inevitably increases the incidence of drug-drug interactions (DDIs). Serious DDI is a major liability for any new chemical entity (NCE) entering the pharmaceutical market. As such, pharmaceutical companies employ various strategies to avoid problematic compounds for clinical development. A key cause for DDIs is the inhibition of cytochrome P450 enzymes (CYPs) that are responsible for metabolic clearance of many drugs. Screening for inhibition potency of CYPs by NCEs has therefore become a routine practice during the drug discovery stage. However, in order to make proper use of DDI data, an understanding of the strengths and weaknesses of the various experimental systems in current use is required. An illustrated review of experimental practices is presented with discussion of likely future developments. The combination of high quality in vitro data generation and the application of in vivo CYP inhibition modelling approaches should allow more informed decisions to be made in the search for drug molecules with acceptable DDI characteristics.

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Reversible and irreversible inhibition of CYP3A enzymes by tamoxifen and metabolites

Cited by 63 publications

References 27 publications

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

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

Prediction of the Effect of Erythromycin, Diltiazem, and Their Metabolites, Alone and in Combination, on CYP3A4 Inhibition

In Vitro Evaluation of Reversible and Irreversible Cytochrome P450 Inhibition: Current Status on Methodologies and their Utility for Predicting Drug–Drug Interactions

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Reversible and irreversible inhibition of CYP3A enzymes by tamoxifen and metabolites

Cited by 63 publications

References 27 publications

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

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

Prediction of the Effect of Erythromycin, Diltiazem, and Their Metabolites, Alone and in Combination, on CYP3A4 Inhibition

In Vitro Evaluation of Reversible and Irreversible Cytochrome P450 Inhibition: Current Status on Methodologies and their Utility for Predicting Drug–Drug Interactions

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

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