IN VITRO-IN VIVO EXTRAPOLATION OF CYP2D6 INACTIVATION BY PAROXETINE: PREDICTION OF NONSTATIONARY PHARMACOKINETICS AND DRUG INTERACTION MAGNITUDE

Impact of ignoring extraction ratio when predicting drug-drug interactions, fraction metabolized, and intestinal first-pass contribution

Many mathematical models for in vitro to in vivo prediction of drug-drug interactions (DDIs) of orally administered victim drugs have been developed. However, to date, none of these models have been applicable to all intravenously administered victim drugs. We derived and conducted a sensitivity/error analysis of a modification to the existing multiple mode interaction prediction model such that it is applicable to all intravenously administered victim drugs. Using this model we showed that ignoring the hepatic extraction ratio (EH) (as low as 0.3) of intravenously administered victim drugs can result in 1) substantial underestimation of f(m, CYPi) (the fraction of hepatic clearance of the victim drug via a given enzymatic pathway) and 2) error in dissecting the contribution of intestinal and hepatic components of DDIs for orally administered drugs. Using this model we describe DDI boundaries (degree of inhibition or induction) at which ignoring the EH of commonly used victim drugs results in ≥30% error in the predicted area under the concentration-time curve (AUC) ratio or contribution of intestinal interaction to a DDI (CYP3A probes only). For the most widely used victim drug midazolam, these boundaries for AUC ratio are net inhibition (I/K(i) or λ/k(deg)) ≥1.3 or fold induction ≥2.1; for intestinal contribution the boundaries are 0.37 and 1.5, respectively. To accurately predict the intravenous AUC ratio, intestinal contribution, or f(m, CYPi) 1) for all induction DDIs irrespective of EH of the victim drug and 2) for modest to potent inhibition DDIs even when the EH is moderate (≥0.3), we propose that our model be used.

Selective serotonin reuptake inhibitors and breast cancer mortality in women receiving tamoxifen: a population based cohort study

OBJECTIVE

To characterise whether some selective serotonin reuptake inhibitor (SSRI) antidepressants reduce tamoxifen's effectiveness by inhibiting its bioactivation by cytochrome P450 2D6 (CYP2D6).

DESIGN

Population based cohort study.

PARTICIPANTS

Women living in Ontario aged 66 years or older treated with tamoxifen for breast cancer between 1993 and 2005 who had overlapping treatment with a single SSRI.

MAIN OUTCOME MEASURES

Risk of death from breast cancer after completion of tamoxifen treatment, as a function of the proportion of time on tamoxifen during which each SSRI had been co-prescribed.

RESULTS

Of 2430 women treated with tamoxifen and a single SSRI, 374 (15.4%) died of breast cancer during follow-up (mean follow-up 2.38 years, SD 2.59). After adjustment for age, duration of tamoxifen treatment, and other potential confounders, absolute increases of 25%, 50%, and 75% in the proportion of time on tamoxifen with overlapping use of paroxetine (an irreversible inhibitor of CYP2D6) were associated with 24%, 54%, and 91% increases in the risk of death from breast cancer, respectively (P<0.05 for each comparison). By contrast, no such risk was seen with other antidepressants. We estimate that use of paroxetine for 41% of tamoxifen treatment (the median overlap in our sample) would result in one additional breast cancer death within five years of cessation of tamoxifen for every 19.7 (95% confidence interval 12.5 to 46.3) patients so treated; the risk with more extensive overlap would be greater.

CONCLUSION

Paroxetine use during tamoxifen treatment is associated with an increased risk of death from breast cancer, supporting the hypothesis that paroxetine can reduce or abolish the benefit of tamoxifen in women with breast cancer.

Drug metabolism and pharmacokinetics

In this article, aspects of absorption, distribution, metabolism, and excretion have been described bearing in mind the pathogenesis of allergic diseases and their possible therapeutic opportunities. The importance of the routes of administration of the different therapeutic groups has been emphasized. The classical aspects of drug metabolism and disposition related to oral administration have been reviewed, but special emphasis has been given to intranasal, cutaneous, transdermal, and ocular administration as well as to the absorption and the subsequent bioavailability of drugs. Drug-metabolizing enzymes and transporters present in extrahepatic tissues, such as nasal mucosa and the respiratory tract, have been particularly discussed. As marketed antiallergic drugs include both racemates and enantiomers, aspects of stereoselective absorption, distribution, metabolism, and excretion have been discussed. Finally, a new and promising methodology, microdosing, has been presented, although it has not yet been applied to drugs used in the treatment of allergic diseases.

Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin

The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.

Cocktail-substrate assay system for mechanism-based inhibition of CYP2C9, CYP2D6, and CYP3A using human liver microsomes at an early stage of drug development

We established a mechanism-based inhibition cocktail-substrate assay system using human liver microsomes and drug-probe substrates that enabled simultaneous estimation of the inactivation of main cytochrome P450 (CYP) enzymes, CYP2C9, CYP2D6, and CYP3A, in drug metabolism. The inactivation kinetic parameters of typical mechanism-based inhibitors, tienilic acid, paroxetine, and erythromycin, for each enzyme in the cocktail-substrate assay were almost in agreement with the values obtained in the single-substrate assay. Using this system, we confirmed that multiple CYP inactivation caused by mechanism-based inhibitors such as isoniazid and amiodarone could be detected simultaneously. Mechanism-based inhibition potency can be estimated by the determination of the observed inactivation rate constants (k(obs)) at a single concentration of test compounds because the k(obs) of eleven CYP3A inactivators at 10 microM in the assay system nearly corresponded to k(inact)/K(I) values, an indicator of a compound's propensity to alter the activity of a CYP in vivo (R(2) = 0.97). Therefore, this cocktail-substrate assay is considered to be a powerful tool for evaluating mechanism-based inhibition at an early stage of drug development.

Application of mechanism-based CYP inhibition for predicting drug-drug interactions

BACKGROUND

A mechanism-based inhibition of CYPs is characterized by NADPH-, time- and concentration-dependent enzyme inactivation and substrate protection. A significant inactivation of CYPs and particularly the main human hepatic and intestinal CYPs could result in clinical drug-drug interactions (DDIs) and adverse drug reactions.

OBJECTIVE

To address whether DDIs owing to mechanism-based CYP inhibition is predictable based on in vitro inhibitory data.

METHOD

Medline (by means of PubMed up to 26 March 2009) has been searched using proper relevant terms.

RESULT/CONCLUSION

It is possible to predict DDIs caused by mechanism-based CYP inhibition, although the in vitro data do not necessarily translate directly into relative extents of inhibition in vivo because in vivo clinical consequences depend on additional factors that are not easily accounted for in vitro and for reversible inhibition. Incorporation of other important parameters such as CYP degradation rate (k(deg)), relative contribution of the CYP inactivated to the victim drug elimination (f(m(CYP))) and inhibition of intestinal CYP-mediated first-pass metabolism of the object drug (F'(gut)/F(gut) ratio) into the prediction models significantly improves the prediction. Uncertainty of the prediction is mainly from the variability in the estimates of these critical parameters.

Semiphysiologically based pharmacokinetic models for the inhibition of midazolam clearance by diltiazem and its major metabolite

Prediction of the extent and time course of drug-drug interactions (DDIs) between the mechanism-based inhibitor diltiazem (DTZ) and the CYP3A4 substrate midazolam (MDZ) is confounded by time- and concentration-dependent clearance of the inhibitor. Semiphysiologically based pharmacokinetic (PBPK) models were developed for DTZ and MDZ with the major metabolite of DTZ, N-desmethyldiltiazem (nd-DTZ), incorporated in the DTZ model. Enzyme kinetic parameters (k(inact) and K(I)) for DTZ and nd-DTZ were estimated in vitro and used to model the time course of changes in the amount of CYP3A4 in the liver and gut wall, which in turn, determined the nonlinear elimination of MDZ and DTZ, and the corresponding DDI. The robustness of the model prediction was assessed by comparing the results of the prediction to published DTZ pharmacokinetic and DTZ/MDZ interaction data. A clinical study was conducted to further validate the predicted increase of MDZ exposure after DTZ treatment. The model predicted the nonlinear disposition of DTZ after single and multiple oral doses. The clinical study showed that DTZ treatment resulted in 4.1- and 1.6-fold increases in MDZ exposure after oral and intravenous MDZ administration, respectively, suggesting that the DDI in the gut wall plays an important role in the DTZ/MDZ interaction. The semi-PBPK model incorporating the DDI at the gut wall, and the effect of nd-DTZ successfully predicted the nonlinear disposition of DTZ and its interaction with MDZ. Moreover, model simulation suggested that both DTZ and nd-DTZ contributed to the overall inhibitory effect after DTZ administration, and the values of the in vitro estimated inhibition parameters and CYP3A4 turnover rate are critical for the prediction.

Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods

The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.

The consequences of 3,4-methylenedioxymethamphetamine induced CYP2D6 inhibition in humans

3,4-Methylenedioxymethamphetamine (MDMA, ecstasy) is a widely abused substituted amphetamine. MDMA is predominantly O-demethylenated in humans by cytochrome P450 isoforms 2D6 and 1A2 (CYP2D6 and CYP CYP1A2, respectively). MDMA is also a mechanism-based inhibitor of CYP2D6. A controlled clinical trial was conducted in 15 healthy male subjects whereby a probe drug, dextromethorphan (DEX), was administered after an oral dose of 1.5 mg/kg MDMA. The pharmacokinetics of DEX and its metabolites were used to evaluate changes in CYP2D6 activity. The urinary metabolic ratio of DEX and dextrorphan was used to calculate a recovery half-life of CYP2D6. After MDMA, DEX Cmax and area under the curve increased approximately 10-fold with corresponding decreases in dextrorphan pharmacokinetic parameters. The metabolic ratio increased almost 100-fold from 0.0061 +/- 0.0056 to 0.4322 +/- 0.2848 after MDMA administration, with 67% of the subjects having a value greater than the antimode of 0.3 for assigning the poor metabolizer phenotype. CYP2D6 activity recovered after 10 days with a recovery half-life of 46.6 hours. In addition to the possible long-term serotonergic effects of MDMA, users must be warned of the consequences of such an inhibition.

Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction

CYP3A4 induction is not generally considered to be a concern for safety; however, serious therapeutic failures can occur with drugs whose exposure is lower as a result of more rapid metabolic clearance due to induction. Despite the potential therapeutic consequences of induction, little progress has been made in quantitative predictions of CYP3A4 induction-mediated drug-drug interactions (DDIs) from in vitro data. In the present study, predictive models have been developed to facilitate extrapolation of CYP3A4 induction measured in vitro to human clinical DDIs. The following parameters were incorporated into the DDI predictions: 1) EC(50) and E(max) of CYP3A4 induction in primary hepatocytes; 2) fractions unbound of the inducers in human plasma (f(u, p)) and hepatocytes (f(u, hept)); 3) relevant clinical in vivo concentrations of the inducers ([Ind](max, ss)); and 4) fractions of the victim drugs cleared by CYP3A4 (f(m, CYP3A4)). The values for [Ind](max, ss) and f(m, CYP3A4) were obtained from clinical reports of CYP3A4 induction and inhibition, respectively. Exposure differences of the affected drugs in the presence and absence of the six individual inducers (bosentan, carbamazepine, dexamethasone, efavirenz, phenobarbital, and rifampicin) were predicted from the in vitro data and then correlated with those reported clinically (n = 103). The best correlation was observed (R(2) = 0.624 and 0.578 from two hepatocyte donors) when f(u, p) and f(u, hept) were included in the predictions. Factors that could cause over- or underpredictions (potential outliers) of the DDIs were also analyzed. Collectively, these predictive models could add value to the assessment of risks associated with CYP3A4 induction-based DDIs by enabling their determination in the early stages of drug development.

Pharmacokinetic interaction of flecainide and paroxetine in relation to the CYP2D6*10 allele in healthy Korean subjects

AIMS

The objectives were to evaluate the effect of CYP2D6 genetic polymorphism on the pharmacokinetics of flecainide, and also on the extent of drug interaction with paroxetine as a CYP2D6 inhibitor after a single oral administration in healthy subjects.

METHODS

An open-label, two-period, single-sequence, cross-over study was performed in 21 healthy Korean male volunteers (seven for CYP2D6*1/*1 or *1/*2, group 1; seven for CYP2D6*1/*10, group 2; seven for CYP2D6*10/*10 or *10/*36, group 3). Subjects were administered 200 mg of flecainide on day 1. After a 7-day wash-out period, subjects were administered 20 mg of paroxetine from day 8 to 14, and 200 mg of flecainide on day 15. Blood sampling was performed up to 72 h after flecainide administration.

RESULTS

Terminal elimination half-life and mean residence time (MRT) were significantly different among three genotype groups after a single oral administration of flecainide (P = 0.021, 0.011, respectively). Area under the concentration-time curve, terminal elimination half-life and MRT increased significantly after paroxetine co-administration only in groups 1 and 2.

CONCLUSIONS

This study reports that the extent of drug interaction between flecainide and paroxetine is influenced by the CYP2D6*10 allele in healthy subjects, which is frequent in Asians.

The use of hepatocytes in evaluating time-dependent inactivation of P450 in vivo

BACKGROUND

Time-dependent inactivation (TDI) of P450 is an important mechanism of drug interactions. The quantitative in vitro - in vivo correlation of TDI using systems such as human liver microsomes requires a comprehensive understanding of in vitro kinetics, pharmacokinetics, inhibition mechanisms, and homeostasis of the enzyme being inactivated.

OBJECTIVE

To evaluate the use of hepatocytes in predicting TDI.

METHODS

The theoretical basis of in vitro - in vivo correlation of TDI and the progress in using microsomes and hepatocytes to predict TDI in vivo are reviewed.

RESULTS/CONCLUSION

Factors that may impact prediction accuracy, such as nonspecific binding, metabolism of inactivator, active transport, and sequential inhibitory metabolites, can be assessed by performing 'in vitro-in vitro' correlation between microsomes and hepatocytes. Together with microsomal data and the aid of computer modeling and simulation, hepatocytes provide a powerful tool to optimize the integrated approaches aimed at quantitatively predicting TDI in vivo.

Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor.

WHAT THIS STUDY ADDS

Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions.

AIMS

The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques.

METHODS

The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP.

RESULTS

When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs.

CONCLUSIONS

The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor.

WHAT THIS STUDY ADDS

Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions.

AIMS

The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques.

METHODS

The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP.

RESULTS

When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs.

CONCLUSIONS

The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

Mechanism-based inactivation of human cytochrome P450 enzymes: strategies for diagnosis and drug-drug interaction risk assessment

Among drugs that cause pharmacokinetic drug-drug interactions, mechanism-based inactivators of cytochrome P450 represent several of those agents that cause interactions of the greatest magnitude. In vitro inactivation kinetic data can be used to predict the potential for new drugs to cause drug interactions in the clinic. However, several factors exist, each with its own uncertainty, that must be taken into account in order to predict the magnitude of interactions reliably. These include aspects of in vitro experimental design, an understanding of relevant in vivo concentrations of the inactivator, and the extent to which the inactivated enzyme is involved in the clearance of the affected drug. Additionally, the rate of enzyme degradation in vivo is also an important factor that needs to be considered in the prediction of the drug interaction magnitudes. To address mechanism-based inactivation for new drugs, various in vitro experimental approaches have been employed. The selection of approaches for in vitro kinetic characterization of inactivation as well as in vitro-in vivo extrapolation should be guided by the purpose of the exercise and the stage of drug discovery and development, with an increase in the level of sophistication throughout the research and development process.

NADPH-dependent covalent binding of [3H]paroxetine to human liver microsomes and S-9 fractions: identification of an electrophilic quinone metabolite of paroxetine

The primary pathway of clearance of the methylenedioxyphenyl-containing compound and selective serotonin reuptake inhibitor paroxetine in humans involves P450 2D6-mediated demethylenation to a catechol intermediate. The process of demethylenation also results in the mechanism-based inactivation of the P450 isozyme. While the link between P450 2D6 inactivation and pharmacokinetic interactions of paroxetine with P450 2D6 substrates has been firmly established, there is a disconnect in terms of paroxetine's excellent safety record despite the potential for bioactivation. In the present study, we have systematically assessed the NADPH-dependent covalent binding of [(3)H]paroxetine to human liver microsomes and S-9 preparations in the absence and presence of cofactors of the various phase II drug-metabolizing enzymes involved in the downstream metabolism/detoxification of the putative paroxetine-catechol intermediate. Incubation of [(3)H]paroxetine with human liver microsomes and S-9 preparations resulted in irreversible binding of radioactive material to macromolecules by a process that was NADPH-dependent. The addition of reduced glutathione (GSH) to the microsomal and S-9 incubations resulted in a dramatic reduction of covalent binding. Following incubations with NADPH- and GSH-supplemented human liver microsomes and S-9, three sulfydryl conjugates with MH(+) ions at 623 Da (GS1), 779 Da (GS2), and 928 Da (GS3), respectively, were detected by LC-MS/MS. The collision-induced dissociation spectra allowed an insight into the structure of the GSH conjugates, based on which, bioactivation pathways were proposed. The formation of GS 1 was consistent with Michael addition of GSH to the quinone derived from two-electron oxidation of paroxetine-catechol. GS 3 was formed by the addition of a second molecule of GSH to the quinone species obtained via the two-electron oxidation of GS 1. The mechanism of formation of GS 2 can be rationalized via (i) further two-electron oxidation of the catechol motif in GS 3 to the ortho-quinone, (ii) loss of a glutamic acid residue from one of the adducted GSH molecules, and (iii) condensation of a cysteine-NH 2 with an adjacent carbonyl of the ortho-quinone to yield an ortho-benzoquinoneimine structure. Inclusion of the catechol-O-methyltransferase cofactor S-adenosylmethionine (SAM) in S-9 incubations also dramatically reduced the covalent binding of [(3)H]paroxetine, a finding that was consistent with O-methylation of the paroxetine-catechol metabolite to the corresponding guaiacol regioisomers in S-9 incubations. While the NADPH-dependent covalent binding was attenuated by GSH and SAM, these reagents did not alter paroxetine's ability to inactivate P450 2D6, suggesting that the reactive intermediate responsible for P450 inactivation did not leave the active site to react with other proteins. The results of our studies indicate that in addition to the low once-a-day dosing regimen (20 mg) of paroxetine, efficient scavenging of the catechol and quinone metabolites by SAM and GSH, respectively, serves as an explanation for the excellent safety record of paroxetine despite the fact that it undergoes bioactivation.

Inactivation of CYP2D6 by methylenedioxymethamphetamine in different recombinant expression systems

Recombinantly expressed CYP450 systems (rCYPs) are often used to screen for irreversible/quasi-irreversible enzyme inhibitors during drug development. The concentration- and time-dependent inactivation of CYP2D6 by methylenedioxymethamphetamine (MDMA) was compared in three different rCYP2D6 systems (yeast microsomes, Supersomestrade mark and Bactosomestrade mark) under the conditions of the most commonly used protocols in assessing mechanism-based inactivation (MBI). MDMA (2-20microM) was pre-incubated with enzyme for 0, 2.5 and 5min followed by a five-fold dilution and further incubation with dextromethorpan (DEX) (50microM). The formation of dextrorphan (DOR) from DEX was used as a specific marker of CYP2D6 activity. Concentration- and time-dependent inactivation of CYP2D6 by MDMA was observed with each rCYP system. However, the apparent kinetic parameters for MBI (k(inact), the maximum inactivation rate constant and K(I), the inhibitor concentration associated with half maximal rate of inactivation) were significantly greater (p<0.05) for Bactosomestrade mark (0.95+/-0.33min(-1), 42.9+/-20.1microM) than those found using yeast microsomes (0.28+/-0.04min(-1), 2.86+/-1.18microM) and Supersomestrade mark (0.38+/-0.05min(-1), 3.66+/-0.10microM). After correction for depletion of MDMA during pre-incubation, k(inact) and K(I) values determined using Bactosomestrade mark decreased significantly but remained higher than for the other rCYP systems (p<0.05). Substantial metabolism of DOR after its formation from DEX was also observed using Supersomestrade mark and Bactosomestrade mark. Sub-optimal study design when investigating MBI may compromise the quantitative characterization of inhibitory characteristics using some rCYP systems.

In vitroapproaches to investigate mechanism-based inactivation of CYP enzymes

Mechanism-based inactivation (MBI) of human drug-metabolising CYP enzymes is an important consideration in the preclinical ADME evaluation of new drug candidates. In this report, the in vitro approaches used to investigate MBI of CYP enzymes are described, with an emphasis on the characterisation required to assess potential drug-drug interactions. Recent disparities in MBI data between in vitro test systems are also reviewed, highlighting the limitations of Escherichia coli-expressed human recombinant CYP in the prediction of drug-drug interactions that arise via MBI.

The Evolving Role of Drug Metabolism in Drug Discovery and Development

Drug metabolism in pharmaceutical research has traditionally focused on the well-defined aspects of absorption, distribution, metabolism and excretion, commonly-referred to ADME properties of a compound, particularly in the areas of metabolite identification, identification of drug metabolizing enzymes (DMEs) and associated metabolic pathways, and reaction mechanisms. This traditional emphasis was in part due to the limited scope of understanding and the unavailability of in vitro and in vivo tools with which to evaluate more complex properties and processes. However, advances over the past decade in separate but related fields such as pharmacogenetics, pharmacogenomics and drug transporters, have dramatically shifted the drug metabolism paradigm. For example, knowledge of the genetics and genomics of DMEs allows us to better understand and predict enzyme regulation and its effects on exogenous (pharmacokinetics) and endogenous pathways as well as biochemical processes (pharmacology). Advances in the transporter area have provided unprecedented insights into the role of transporter proteins in absorption, distribution, metabolism and excretion of drugs and their consequences with respect to clinical drug-drug and drug-endogenous substance interactions, toxicity and interindividual variability in pharmacokinetics. It is therefore essential that individuals involved in modern pharmaceutical research embrace a fully integrated approach and understanding of drug metabolism as is currently practiced. The intent of this review is to reexamine drug metabolism with respect to the traditional as well as current practices, with particular emphasis on the critical aspects of integrating chemistry and biology in the interpretation and application of metabolism data in pharmaceutical research.

Mechanism-Based Inactivation of Human Cytochrome P450 Enzymes and the Prediction of Drug-Drug Interactions

The ability to use vitro inactivation kinetic parameters in scaling to in vivo drug-drug interactions (DDIs) for mechanism-based inactivators of human cytochrome P450 (P450) enzymes was examined using eight human P450-selective marker activities in pooled human liver microsomes. These data were combined with other parameters (systemic C(max), estimated hepatic inlet C(max), fraction unbound, in vivo P450 enzyme degradation rate constants estimated from clinical pharmacokinetic data, and fraction of the affected drug cleared by the inhibited enzyme) to predict increases in exposure to drugs, and the predictions were compared with in vivo DDIs gathered from clinical studies reported in the scientific literature. In general, the use of unbound systemic C(max) as the inactivator concentration in vivo yielded the most accurate predictions of DDI with a mean -fold error of 1.64. Abbreviated in vitro approaches to identifying mechanism-based inactivators were developed. Testing potential inactivators at a single concentration (IC(25)) in a 30-min preincubation with human liver microsomes in the absence and presence of NADPH followed by assessment of P450 marker activities readily identified those compounds known to be mechanism-based inactivators and represents an approach that can be used with greater throughput. Measurement of decreases in IC(50) occurring with a 30-min preincubation with liver microsomes and NADPH was also useful in identifying mechanism-based inactivators, and the IC(50) measured after such a preincubation was highly correlated with the k(inact)/K(I) ratio measured after a full characterization of inactivation. Overall, these findings support the conclusion that P450 in vitro inactivation data are valuable in predicting clinical DDIs that can occur via this mechanism.

The impact of experimental design on assessing mechanism-based inactivation of CYP2D6 by MDMA (Ecstasy)

MDMA (3-4-methylenedioxymethamphetamine, commonly known as Ecstasy) is a potent mechanism-based inhibitor (MBI) of cytochrome P450 2D6 (CYP2D6), causing quasi-irreversible inhibition of the enzyme in vitro. An evaluation of the in vivo implications of this phenomenon depends on the accuracy of the estimates of the parameters that define the inhibition in vitro, namely k(inact) (the maximal inhibition rate) and KI (the inactivation constant). These values are determined in two steps, pre-incubation of the enzyme with the inhibitor (enzyme inactivation), followed by dilution and further incubation to measure residual enzyme activity with a probe substrate. The aim of this study was to assess the impact of different dilutions and probe substrate concentrations on the estimates of k(inact) and KI using recombinantly expressed CYP2D6. Enzyme activity was measured by the conversion of dextromethorphan (DEX) to dextrorphan (DOR). Dilution factors of 1.25, 2, 5, 10, 25 and 50 (DEX at 30 microM) gave mean (+/-SE) values of k(inact) (min-1) of 0.20+/-0.06, 0.21+/-0.05, 0.31+/-0.06, 0.37+/-0.11, 0.51+/-0.10 and 0.58+/-0.08, respectively, and KI (microM) values (after correction for non-specific microsomal binding) of 2.22+/-1.90, 2.80+/-1.34, 5.78+/-2.07, 6.36+/-2.93, 3.99+/-1.57 and 4.86+/-1.37, respectively. Accordingly, high (e.g. 50 fold) and low (e.g. 1.25 fold) dilutions were associated with statistically significant differences in kinetic values (p <0.05). Varying DEX concentration (10-100 microM) was not associated with significant changes in k(inact) and KI values when a five-fold dilution was used (with the exception of a lower KI at 10 microM DEX). High dilution was also shown to reduce non-specific microsomal binding of MDMA. The changes in the two kinetic parameters were dependent on the experimental procedure and shown to be unlikely to have a material influence on the maximum inhibition of CYP2D6 expected in vivo after typical recreational doses of MDMA (50-100 mg), since the potency of inhibition was high. The different values of the kinetic parameters were predicted to have a marginal influence on the time for recovery of enzyme activity following re-synthesis of CYP2D6.

An evaluation of potential mechanism-based inactivation of human drug metabolizing cytochromes P450 by monoamine oxidase inhibitors, including isoniazid

AIMS

To characterize potential mechanism-based inactivation (MBI) of major human drug-metabolizing cytochromes P450 (CYP) by monoamine oxidase (MAO) inhibitors, including the antitubercular drug isoniazid.

METHODS

Human liver microsomal CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A activities were investigated following co- and preincubation with MAO inhibitors. Inactivation kinetic constants (KI and kinact) were determined where a significant preincubation effect was observed. Spectral studies were conducted to elucidate the mechanisms of inactivation.

RESULTS

Hydrazine MAO inhibitors generally exhibited greater inhibition of CYP following preincubation, whereas this was less frequent for the propargylamines, and tranylcypromine and moclobemide. Phenelzine and isoniazid inactivated all CYP but were most potent toward CYP3A and CYP2C19. Respective inactivation kinetic constants (KI and kinact) for isoniazid were 48.6 microm and 0.042 min-1 and 79.3 microm and 0.039 min-1. Clorgyline was a selective inactivator of CYP1A2 (6.8 microm and 0.15 min-1). Inactivation of CYP was irreversible, consistent with metabolite-intermediate complexation for isoniazid and clorgyline, and haeme destruction for phenelzine. With the exception of phenelzine-mediated CYP3A inactivation, glutathione and superoxide dismutase failed to protect CYP from inactivation by isoniazid and phenelzine. Glutathione partially slowed (17%) the inactivation of CYP1A2 by clorgyline. Alternate substrates or inhibitors generally protected against CYP inactivation.

CONCLUSIONS

These data are consistent with mechanism-based inactivation of human drug-metabolizing CYP enzymes and suggest that impaired metabolic clearance may contribute to clinical drug-drug interactions with some MAO inhibitors.

Quantitative prediction of macrolide drug-drug interaction potential from in vitro studies using testosterone as the human cytochrome P4503A substrate

OBJECTIVE

Macrolide antibiotics are mechanism-based inactivators of CYP3A enzymes that exhibit varying degrees of inhibitory potency. Our aim was to predict quantitatively the drug-drug interaction (DDI) potential of five macrolides from in vitro studies using testosterone as the CYP3A substrate, and to compare the predictions generated from human liver microsomal and recombinant CYP3A4 data.

METHODS

The in vitro kinetic constants of CYP3A inactivation (K (I) and k (inact)) were estimated by varying the time of pre-incubation and the concentration of troleandomycin, erythromycin, clarithromycin, roxithromycin or azithromycin. CYP3A activity was determined from the measurement of testosterone 6beta-hydroxylation with human liver microsomes (HLM) and recombinant CYP3A4 as the enzyme sources. The mechanism-based pharmacokinetic model was fitted with inactivation data to predict the increase in oral area under the plasma concentration-time curve (AUC) for midazolam.

RESULTS

All five macrolides inactivated testosterone 6beta-hydroxylation by HLM and recombinant CYP3A4 with k (inact) values in the range of 0.023 to 0.058 min(-1). The potency of inactivation (K (I)) was higher using recombinant CYP3A4 as the enzyme source. The oral AUCs for midazolam were predicted from HLM data to increase 16.6, 5.3, 4.6, 1.6 and 1.2-fold due to the inhibition of metabolic clearance by troleandomycin, erythromycin, clarithromycin, roxithromycin and azithromycin, respectively. These results are within the range of the AUC ratios reported for clinical DDI studies. The predicted AUC increases generated using recombinant CYP3A4 overestimated the magnitude of the DDIs.

CONCLUSIONS

The DDI potential of five macrolide antibiotics was quantitatively predicted from in vitro studies using testosterone as the CYP3A substrate with HLM as the enzyme source.

Drug disposition of chiral and achiral drug substrates metabolized by cytochrome P450 2D6 isozyme: case studies, analytical perspectives and developmental implications

The concepts of drug development have evolved over the last few decades. Although number of novel chemical entitities belonging to varied classes have made it to the market, the process of drug development is challenging, intertwined as it is with complexities and uncertainities. The intention of this article is to provide a comprehensive review of novel chemical entities (NCEs) that are substrates to cytochrome P450 (CYP) 2D6 isozyme. Topics covered in this review aim: (1) to provide a framework of the importance of CYP2D6 isozyme in the biotransformation of NCEs as stand-alones and/or in conjunction with other CYP isozymes; (2) to provide several case studies of drug disposition of important drug substrates, (3) to cover key analytical perspectives and key assay considerations to assess the role and involvement of CYP2D6, and (4) to elaborate some important considerations from the development point of view. Additionally, wherever applicable, special emphasis is provided on chiral drug substrates in the various subsections of the review.

PREDICTION OF TIME-DEPENDENT CYP3A4 DRUG-DRUG INTERACTIONS: IMPACT OF ENZYME DEGRADATION, PARALLEL ELIMINATION PATHWAYS, AND INTESTINAL INHIBITION

Time-dependent inhibition of CYP3A4 often results in clinically significant drug-drug interactions. In the current study, 37 in vivo cases of irreversible inhibition were collated, focusing on macrolides (erythromycin, clarithromycin, and azithromycin) and diltiazem as inhibitors. The interactions included 17 different CYP3A substrates showing up to a 7-fold increase in AUC (13.5% of studies were in the range of potent inhibition). A systematic analysis of the impact of CYP3A4 degradation half-life (mean t1/2deg = 3 days, ranging from 1 to 6 days) on the prediction of the extent of interaction for compounds with a differential contribution from CYP3A4 to the overall elimination (defined by fmCYP3A4) was performed. Although the prediction accuracy was very sensitive to the CYP3A4 degradation rate for substrates mainly eliminated by this enzyme fm(CYP3A4 >or= 0.9), minimal effects are observed when CYP3A4 contributes less than 50% to the overall elimination in cases when the parallel elimination pathway is not subject to inhibition. Use of the mean CYP3A4 t1/2deg (3 days), average unbound systemic plasma concentration of the inhibitor, and the corresponding fm(CYP3A4) resulted in 89% of studies predicted within 2-fold of the in vivo value. The impact of the interaction in the gut wall was assessed by assuming maximal intestinal inhibition of CYP3A4. Although a reduced number of false-negative predictions was observed, there was an increased number of overpredictions, and generally, a loss of prediction accuracy was observed. The impact of the possible interplay between CYP3A4 and efflux transporters on the intestinal interaction requires further evaluation.

The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions

The accuracy of in vitro inhibition parameters in scaling to in vivo drug-drug interactions (DDI) was examined for over 40 drugs using seven human P450-selective marker activities in pooled human liver microsomes. These data were combined with other parameters (systemic C(max), estimated hepatic inlet C(max), fraction unbound, and fraction of the probe drug cleared by the inhibited enzyme) to predict increases in exposure to probe drugs, and the predictions were compared with in vivo DDI gathered from clinical studies reported in the scientific literature. For drugs that had been tested as precipitants of drug interactions for more than one P450 in vivo, the order of inhibitory potencies in vitro generally aligned with the magnitude of the in vivo interactions. With the exception of many drugs known to be mechanism-based inactivators, the use of in vitro IC(50), the fraction of the affected drug metabolized by the target enzyme [f(m(CYP))] and an estimate of free hepatic inlet C(max), was generally successful in identifying those drugs that cause at least a 2-fold increase in the exposure to P450 marker substrate drugs. For CYP3A, incorporation of inhibition of both hepatic and intestinal metabolism was needed for the prediction of DDI. Many CYP3A inhibitors showed a different inhibitory potency for three different CYP3A marker activities; however, these differences generally did not alter the conclusions regarding whether a drug would cause a CYP3A DDI in vivo. Overall, these findings support the conclusion that P450 in vitro inhibition data are valuable in designing clinical DDI study strategies and can be used to predict the magnitudes of DDI.