Sequential metabolism is responsible for diltiazem-induced time-dependent loss of CYP3A

Mechanism-based inactivation of cytochromes P450: implications in drug interactions and pharmacotherapy

Cytochrome P40 (CYP) enzymes dominate the metabolism of numerous endogenous and xenobiotic substances. While it is commonly believed that CYP-catalysed reactions result in the detoxication of foreign substances, these reactions can also yield reactive intermediates that can bind to cellular macromolecules to cause cytotoxicity or irreversibly inactivate CYPs that create them.Mechanism-based inactivation (MBI) produces either irreversible or quasi-irreversible inactivation and is commonly caused by CYP metabolic bioactivation to an electrophilic reactive intermediate. Many drugs that have been known to cause MBI in CYPs have been discovered as perpetrators in drug-drug interactions throughout the last 20-30 years.This review will highlight the key findings from the recent literature about the mechanisms of CYP enzyme inhibition, with a focus on the broad mechanistic elements of MBI for widely used drugs linked to the phenomenon. There will also be a brief discussion of the clinical or pharmacokinetic consequences of CYP inactivation with regard to drug interaction and toxicity risk.Gaining knowledge about the selective inactivation of CYPs by common therapeutic drugs helps with the assessment of factors that affect the systemic clearance of co-administered drugs and improves comprehension of anticipated interactions with other drugs or xenobiotics.

Power of Integrating PBPK with PBBM (PBPK-BM): A Single Model Predicting Food Effect, Gender Impact, Drug-Drug Interactions and Bioequivalence in Fasting & Fed Conditions

Over the past few years, PBPK and PBBM modeling have proven their significance in drug development. PBPK modeling is traditionally used to predict drug-drug interactions, exposures in special populations whereas PBBM modeling is a part of PBPK modeling that is used for a range of biopharmaceutics applications.Because of these differences in utilities, often PBPK and PBBM models are developed separately. When both models are combined, they serve multiple purposes through unified model. In the present case, an integrated PBPK-PBBM model for an IR product has been utilized for bioequivalence prediction in fasting & fed conditions, evaluating gender impact and food effect, prediction of drug-drug interactions.Model was built using physicochemical properties, enzymes and transporter kinetics, bio-predictive dissolution and has been validated with passing and failed pilot BE studies. The validated model predicted pivotal bioequivalence outcome in fasting & fed conditions accurately, predicted gender impact and food effect in line with literature. Drug-drug interactions arising from transporter and metabolizing enzymes were predicted accurately.Overall, this work demonstrates utility of combining PBPK and PBBM model that can yield a single model which can be used for multiple purposes, regulatory justifications and can reduce regulatory review timelines.

Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers

Encorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

The effect of the very low dosage diltiazem on tacrolimus exposure very early after kidney transplantation: a randomized controlled trial

The objective of this study was to assess the effect of the very low dosage of diltiazem on tacrolimus exposure during the first week post-kidney transplantation, among cytochrome P450 (CYP) 3A5 expressers who did not receive diltiazem (EXplb), CYP3A5 expressers who received the very low dose diltiazem (EXdtz), CYP3A5 nonexpressers who did not receive diltiazem (NEplb), and CYP3A5 nonexpressers who received the very low dose diltiazem (NEdtz). Forty kidney recipients who receive tacrolimus-based immunosuppressive regimen were randomly assigned, with stratification on the CYP3A5 genotypes, to receive either diltiazem 30 mg every 12 h or a matched placebo. The observed median dose-adjusted area under the 12-h curve of tacrolimus concentration (AUC/D) at day 7 post-transplantation was lowest in the EXplb group followed by EXdtz, NEplb, and NEdtz at 34.9, 43.6, 49.4, and 71.1 ng*h/mL per mg, respectively. A Kruskal-Wallis test showed a significant difference in the mean ranks of AUC/D among groups. Significant differences between EXplb and NEplb, and between EXplb and NEdtz were demonstrated, whereas no sufficient evidence of significant differences was detected between the other pairs. In conclusion, coadministration of diltiazem 30 mg twice daily may be advantageous for increasing tacrolimus exposure early after kidney transplantation among CYP3A5 expressers.

Reversible Mechanisms of Enzyme Inhibition and Resulting Clinical Significance

Inhibition of a drug-metabolizing enzyme by the reversible interaction of a drug with the enzyme, thus decreasing the metabolism of another drug, is a major cause of clinically significant drug-drug interactions. This chapter defines the four reversible mechanisms of inhibition exhibited by drugs: competitive, noncompetitive, uncompetitive, and mixed competitive/noncompetitive. An in vitro procedure to determine the potential of a drug to be a reversible inhibitor is also provided. Finally, a number of examples of clinically significant drug-drug interactions resulting from reversible inhibition are described.

Anemarsaponin BII inhibits the activity of CYP3A4, 2D6, and 2E1 with human liver microsomes

CONTEXT

Anemarsaponin BII is one of the most active saponins isolated from Anemarrhena asphodeloides Bunge (Asparagaceae), a commonly used Chinese traditional paediatric medicine.

OBJECTIVE

This study investigates the effects of anemarsaponin BII on the activity of CYP450s to provide more guidance for the clinical use of anemarsaponin BII.

MATERIALS AND METHODS

Using various diagnostic substrates, the effects of a fixed concentration of anemarsaponin BII (100 μM) on the activity of eight main isoforms of CYP450s (CYP1A2, 2A6, 3A4, 2C8, 2C9, 2C19, 2D6 and 2E1) was first studied with pooled human liver microsomes (HLMs). Then, dose-dependent (0, 2.5, 5, 10, 25, 50 and 100 μM anemarsaponin BII) and time-dependent (0, 5, 10, 15 and 30 min) experiments were performed to obtain corresponding kinetic parameters.

RESULTS

Anemarsaponin BII showed significant inhibitory effects on the activity of CYP3A4, 2D6 and 2E1 with the IC₅₀ values of 13.67, 16.26 and 19.72 μM. Anemarsaponin BII acted as a non-competitive inhibitor of CYP3A4 with the K_I value of 6.72 μM and competitive inhibitors of CYP2D6 and 2E1 with the K_I values of 8.26 and 9.82 μM, respectively. Additionally, the inhibition of CYP3A4 was revealed to be time-dependent with the K_I value of 4.88 μM and the K_inact value of 0.053/min.

CONCLUSIONS

The inhibitory effect of anemarsaponin BII on the activity of CYP3A4, 2D6 and 2E1 indicated the potential drug-drug interaction between anemarsaponin BII and drugs metabolized by these CYP450s. Further in vivo experiments are needed to validate the potential drug-drug interactions.

Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions

Cytochrome P450 (CYP) enzyme kinetics often do not conform to Michaelis-Menten assumptions, and time-dependent inactivation (TDI) of CYPs displays complexities such as multiple substrate binding, partial inactivation, quasi-irreversible inactivation, and sequential metabolism. Additionally, in vitro experimental issues such as lipid partitioning, enzyme concentrations, and inactivator depletion can further complicate the parameterization of in vitro TDI. The traditional replot method used to analyze in vitro TDI datasets is unable to handle complexities in CYP kinetics, and numerical approaches using ordinary differential equations of the kinetic schemes offer several advantages. Improvement in the parameterization of CYP in vitro kinetics has the potential to improve prediction of clinical drug-drug interactions (DDIs). This manuscript discusses various complexities in TDI kinetics of CYPs, and numerical approaches to model these complexities. The extrapolation of CYP in vitro TDI parameters to predict in vivo DDIs with static and dynamic modeling is discussed, along with a discussion on current gaps in knowledge and future directions to improve the prediction of DDI with in vitro data for CYP catalyzed drug metabolism.

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min-1, the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min-1. These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Investigation of diltiazem metabolism in fish using a hybrid quadrupole/orbital trap mass spectrometer

RATIONALE

Diltiazem, a calcium channel blocker drug, is widespread in the environment because of its incomplete elimination during water treatment. It can cause negative effects on aquatic organisms; thus, a rapid and sensitive liquid chromatography/mass spectrometry (LC/MS) method to detect its presence was developed. Our approach is based on accurate mass measurements using a hybrid quadrupole-orbital trap mass spectrometer that was used to measure diltiazem and its metabolites in fish tissue.

METHODS

Blood plasma, muscle, liver, and kidney tissues of rainbow trout (Oncorhynchus mykiss), exposed for 42 days to 30 μg L(-1) diltiazem, were used for the method development. No metabolite standards were required to identify the diltiazem biotransformation products in the fish tissue.

RESULTS

Overall, 17 phase I diltiazem metabolites (including isomeric forms) were detected and tentatively identified using the MassFrontier spectral interpretation software. A semi-quantitative approach was used for organ-dependent comparison of the metabolite concentrations.

CONCLUSIONS

These data increase our understanding about diltiazem and its metabolites in aquatic organisms, such as fish. These encompass desmethylation, desacetylation and hydroxylation as well as their combinations. This study represents the first report of the complex diltiazem phase I metabolic pathways in fish.

Contribution of metabolites to P450 inhibition-based drug-drug interactions: scholarship from the drug metabolism leadership group of the innovation and quality consortium metabolite group

Recent European Medicines Agency (final) and US Food and Drug Administration (draft) drug interaction guidances proposed that human circulating metabolites should be investigated in vitro for their drug-drug interaction (DDI) potential if present at ≥ 25% of the parent area under the time-concentration curve (AUC) (US Food and Drug Administration) or ≥ 25% of the parent and ≥ 10% of the total drug-related AUC (European Medicines Agency). To examine the application of these regulatory recommendations, a group of scientists, representing 18 pharmaceutical companies of the Drug Metabolism Leadership Group of the Innovation and Quality Consortium, conducted a scholarship to assess the risk of contributions by metabolites to cytochrome P450 (P450) inhibition-based DDIs. The group assessed the risk of having a metabolite as the sole contributor to DDI based on literature data and analysis of the 137 most frequently prescribed drugs, defined structural alerts associated with P450 inhibition/inactivation by metabolites, and analyzed current approaches to trigger in vitro DDI studies for metabolites. The group concluded that the risk of P450 inhibition caused by a metabolite alone is low. Only metabolites from 5 of 137 drugs were likely the sole contributor to the in vivo P450 inhibition-based DDIs. Two recommendations were provided when assessing the need to conduct in vitro P450 inhibition studies for metabolites: 1) consider structural alerts that suggest P450 inhibition potential, and 2) use multiple approaches (e.g., a metabolite cut-off value of 100% of the parent AUC and the R(met) strategy) to predict P450 inhibition-based DDIs caused by metabolites in the clinic.

Reversible mechanisms of enzyme inhibition and resulting clinical significance

Inhibition of a drug-metabolizing enzyme by the reversible interaction of a drug with the enzyme, thus decreasing the metabolism of another drug, is a major cause of clinically significant drug-drug interactions. This chapter defines the four reversible mechanisms of inhibition exhibited by drugs: competitive, noncompetitive, uncompetitive, and mixed competitive/noncompetitive. An in vitro procedure to determine the potential of a drug to be a reversible inhibitor is also provided. Finally, a number of examples of clinically significant drug-drug interactions resulting from reversible inhibition are described.

Dynamic modeling of cytochrome P450 inhibition in vitro: impact of inhibitor depletion on IC₅₀ shift

The impact of inhibitor depletion on the determination of shifted IC₅₀ (IC₅₀ determined after 30 minutes of preincubation with inhibitor) is examined. In addition, IC₅₀-shift data are analyzed using a mechanistic model that incorporates the processes of inhibitor depletion, as well as reversible and time-dependent inhibition. Anomalies such as a smaller-than-expected shift in IC₅₀ and even increases in IC₅₀ with preincubation were explained by the depletion of inhibitor during the preincubation. The IC₅₀-shift assay remains a viable approach to characterizing a wide range of reversible and time-dependent inhibitors. However, as with more traditional time-dependent inactivation methods, it is recommended that IC₅₀-shift experimental data be interpreted with some knowledge of the magnitude of inhibitor depletion. For the most realistic classification of time-dependent inhibitors using IC₅₀-shift methods, shifted IC₅₀ should be calculated using observed inhibitor concentrations at the end of the incubation rather than nominal inhibitor concentrations. Finally, a mechanistic model that includes key processes, such as competitive inhibition, enzyme inactivation, and inhibitor depletion, can be used to describe accurately the observed IC₅₀ and shifted IC₅₀ curves. For compounds showing an IC₅₀ fold shift >1.5 based on the observed inhibitor concentrations, reanalyzing the IC₅₀-shift data using the mechanistic model appeared to allow for reasonable estimation of Ki, KI, and kinact directly from the IC₅₀ shift experiments.

Effect of the CYP3A inhibitors, diltiazem and ketoconazole, on ticagrelor pharmacokinetics in healthy volunteers

OBJECTIVES

Two open-label, two-period, crossover studies in healthy volunteers were designed to determine the pharmacokinetic interactions between ticagrelor, a P2Y12 receptor antagonist, and a moderate (diltiazem) and a strong (ketoconazole) cytochrome P450 (CYP) 3A inhibitor.

METHODS

Seventeen volunteers received diltiazem (240 mg once daily) for 14 days. In the second study, ketoconazole (n = 14) 200 mg twice daily was given for 10 days. A single oral 90-mg ticagrelor dose was administered on day 8 (diltiazem) or day 4 (ketoconazole). In each study, volunteers received a single 90-mg oral dose of ticagrelor before or after washout (≥14 days). Pharmacokinetic parameters for ticagrelor, AR-C124910XX (primary metabolite), diltiazem, and ketoconazole were assessed.

RESULTS

Compared with ticagrelor alone, diltiazem co-administration significantly increased the mean maximum concentration (C max) and mean area under the plasma concentration-time curve (AUC) for ticagrelor by 69% and 174%, respectively. Diltiazem co-administration reduced C max by 38% but had no significant effect on AUC for AR-C124910XX. C max and AUC for ticagrelor were increased by 135% and 632%, respectively, by ketoconazole co-administration, whereas these parameters were reduced by 89% and 56%, respectively, for AR-C124910XX. Diltiazem and ketoconazole pharmacokinetic parameters were not significantly affected by the presence of ticagrelor.

CONCLUSIONS

These results suggest that ticagrelor can be co-administered with moderate CYP3A inhibitors. However, co-administration of strong CYP3A inhibitors with ticagrelor is not recommended.

Optimized experimental design for the estimation of enzyme kinetic parameters: an experimental evaluation

A set of compounds (n = 30), including traditional cytochrome P450 substrates and compounds from AstraZeneca's compound library, was used in an experimental evaluation of an optimal design approach (ODA) for the estimation of enzyme kinetic parameters (CL(int), V(max), and K(m)). A depletion method previously shown to provide reliable results, the multiple depletion curves method (MDCM), was used as reference. Experiments were conducted with human liver microsomes, and samples were analyzed using liquid chromatography-tandem mass spectrometry. CL(int) estimated with the ODA were in >90% of the cases within a 2-fold difference compared with MDCM estimates. In addition, good agreement was generally seen for V(max) and K(m) estimates between the two methods as >80% of the estimates were within or almost within a 2-fold difference. The variability in V(max) and K(m) estimates were generally higher than for CL(int) estimates. In addition, decreased substrate turnover considerably increased the variability in V(max) and K(m) estimates, whereas only a modest increase was observed for CL(int) estimates. The experimental design of using multiple starting concentrations for the estimation of enzyme kinetics was shown to be appropriate even when there was a limitation to the number of samples. The method allowed for good estimates of CL(int) and also for V(max) and K(m) in many cases. Hence, this approach is a good alternative for the estimation of enzyme kinetic parameters, especially if enzyme saturation and an assessment of a potential risk for nonlinear metabolism are of interest.

Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4

A progress curve method for assessing time-dependent inhibition of CYP3A4 is based on simultaneous quantification of probe substrate metabolite and inhibitor concentrations during the experiment. Therefore, it may overcome some of the issues associated with the traditional two-step method and estimation of inactivation rate (k(inact)) and irreversible inhibition (K(I)) constants. In the current study, seven time-dependent inhibitors were investigated using a progress curve method and recombinant CYP3A4. A novel mechanistic modeling approach was applied to determine inhibition parameters using both inhibitor and probe metabolite data. Progress curves generated for clarithromycin, erythromycin, diltiazem, and N-desmethyldiltiazem were described well by the mechanistic mechanism-based inhibition (MBI) model. In contrast, mibefradil, ritonavir, and verapamil required extension of the model and inclusion of competitive inhibition term for the metabolite. In addition, this analysis indicated that verapamil itself causes minimal MBI, and the formation of inhibitory metabolites was responsible for the irreversible loss of CYP3A4 activity. The k(inact) and K(I) estimates determined in the current study were compared with literature data generated using the conventional two-step method. In the current study, the inactivation efficiency (k(inact)/K(I)) for clarithromycin, ritonavir, and erythromycin were up to 7-fold higher, whereas k(inact)/K(I) for mibefradil, N-desmethyldiltiazem, and diltiazem were, on average, 2- to 4.8-fold lower than previously reported estimates. Use of human liver microsomes instead of recombinant CYP3A4 resulted in 5-fold lower k(inact)/K(I) for erythromycin. In conclusion, the progress curve method has shown a greater mechanistic insight when determining kinetic parameters for MBI in addition to providing a more comprehensive experimental protocol.

Sequential metabolism of AMG 487, a novel CXCR3 antagonist, results in formation of quinone reactive metabolites that covalently modify CYP3A4 Cys239 and cause time-dependent inhibition of the enzyme

CYP3A4-mediated biotransformation of (R)-N-(1-(3-(4-ethoxyphenyl)-4-oxo-3,4-dihydropyrido[2,3-d]pyrimidin-2-yl)ethyl)-N-(pyridin-3-ylmethyl)-2-(4-(trifluoromethoxy)phenyl)acetamide (AMG 487) was previously shown to generate an inhibitory metabolite linked to dose- and time-dependent pharmacokinetics in humans. Although in vitro activity loss assays failed to demonstrate CYP3A4 time-dependent inhibition (TDI) with AMG 487, its M2 phenol metabolite readily produced TDI when remaining activity was assessed using either midazolam or testosterone (K(I) = 0.73-0.74 μM, k(inact) = 0.088-0.099 min(-1)). TDI investigations using an IC(50) shift method successfully produced inhibition attributable to AMG 487, but only when preincubations were extended from 30 to 90 min. The shift magnitude was ∼3× for midazolam activity, but no shift was observed for testosterone activity. Subsequent partition ratio determinations conducted for M2 using recombinant CYP3A4 showed that inactivation was a relatively inefficient process (r = 36). CYP3A4-mediated biotransformation of [(3)H]M2 in the presence of GSH led to identification of two new metabolites, M4 and M5, which shifted focus away from M2 being directly responsible for TDI. M4 (hydroxylated M2) was further metabolized to form reactive intermediates that, upon reaction with GSH, produced isomeric adducts, collectively designated M5. Incubations conducted in the presence of [(18)O]H(2)O confirmed incorporation of oxygen from O(2) for the majority of M4 and M5 formed (>75%). Further evidence of a primary role for M4 in CYP3A4 TDI was generated by protein labeling and proteolysis experiments, in which M4 was found to be covalently bound to Cys239 of CYP3A4. These investigations confirmed a primarily role for M4 in CYP3A4 inactivation, suggesting that a more complex metabolic pathway was responsible for generation of inhibitory metabolites affecting AMG 487 human pharmacokinetics.

Time-dependent inhibition and estimation of CYP3A clinical pharmacokinetic drug-drug interactions using plated human cell systems

The current studies assessed the utility of freshly plated hepatocytes, cryopreserved plated hepatocytes, and cryopreserved plated HepaRG cells for the estimation of inactivation parameters k(inact) and K(I) for CYP3A. This was achieved using a subset of CYP3A time-dependent inhibitors (fluoxetine, verapamil, clarithromycin, troleandomycin, and mibefradil) representing a range of potencies. The estimated k(inact) and K(I) values for each time-dependent inhibitor were compared with those obtained using human liver microsomes and used to estimate the magnitude of clinical pharmacokinetic drug-drug interaction (DDI). The inactivation kinetic parameter, k(inact), was most consistent across systems tested for clarithromycin, verapamil, and troleandomycin, with a high k(inact) of 0.91 min(-1) observed for mibefradil in HepaRG cells. The apparent K(I) estimates derived from the various systems displayed a range of variability from 3-fold for clarithromycin (5.4-17.7 μM) to 6-fold for verapamil (1.9-12.6 μM). In general, the inactivation kinetic parameters derived from the cell systems tested fairly replicated what was observed in time-dependent inhibition studies using human liver microsomes. Despite some of the observed differences in inactivation kinetic parameters, the estimated DDIs derived from each of the tested systems generally agreed with the clinically reported DDI within approximately 2-fold. In addition, a plated cell approach offered the ability to conduct longer primary incubations (greater than 30 min), which afforded improved ability to identify the weak time-dependent inhibitor fluoxetine. Overall, results from these studies suggest that in vitro inactivation parameters generated from plated cell systems may be a practical approach for identifying time-dependent inhibitors and for estimating the magnitude of clinical DDIs.

Mechanism-based inactivation of cytochrome P450 3A4 by mibefradil through heme destruction

Mibefradil (Posicor) was developed as a calcium channel blocker for the treatment of chronic hypertension. The compound was withdrawn from the market in 1998 because of the potential for rhabdomyolysis, renal failure, or bradycardia when it was coadministered with other drugs. Mibefradil has previously been shown to be a potent reversible (IC(50) = 0.3-2 μM) and mechanism-based (K(i) = 2.3 μM; k(inact) = 0.4 min(-1)) inhibitor of CYP3A4-catalyzed statin metabolism. At present, the mechanism of CYP3A4 inactivation by mibefradil is not known. Mechanism-based inactivation experiments and spectral studies were used to examine the mechanism of CYP3A4 inactivation by mibefradil and its major metabolite, des-methoxyacetyl mibefradil (Ro 40-5966), in vitro. Both mibefradil and Ro 40-5966 were shown to exhibit type I binding characteristics (K(s) = 0.69 ± 0.06 and 1.39 ± 0.04 μM, respectively) toward CYP3A4. Complete K(i)/k(inact) experiments were performed, revealing a rapid and irreversible decrease in CYP3A4-catalyzed 1'-hydroxymidazolam formation. Approximately 70% of CYP3A4 activity was lost in the first minute of incubation with mibefradil, and inactivation was nonlinear after 2 min. Ro 40-5966 also resulted in time-dependent inhibition of CYP3A4, albeit to a lesser extent than mibefradil. The decrease in CYP3A4 activity in the presence of mibefradil and NADPH was subsequently shown to have a good correlation with the time-dependent loss of CO binding, which, coupled with the lack of stable heme and/or apoprotein adducts, suggests heme destruction as the mechanism of inactivation of CYP3A4 by mibefradil.

Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanism-based CYP3A inhibitors

Accurate prediction of the extent of mechanism-based CYP3A inhibition is critical in determining the timing of clinical drug interaction studies in drug development. To evaluate the prediction accuracy of the static and Simcyp time-based approaches, 54 clinical drug interactions involving mechanism-based CYP3A inhibitors were predicted using both methods. The Simcyp time-based approach generated better prediction when 0.03 h(-1) was used as the hepatic CYP3A enzyme degradation rate constant (k(deg)) value. Of the predictions 87 and 55% had an error less than 2 and 0.5, respectively, relative to the observed values, compared with 57 and 20%, respectively, when the Simcyp default k(deg) value of 0.0077 h(-1) was used. Accuracy improvement using the k(deg) value of 0.03 over 0.0077 h(-1) was most evident for trials with observed magnitude of interaction greater than 2-fold; predictions with an error less than 0.5 relative to clinical observations increased from 8 to 48%. For the static approach, 76 and 35% of the predictions had an error less than 2 and 0.5, respectively. Both methods generated good predictions for weak and moderate inhibitors. The prediction accuracy could be affected by our knowledge of disposition of a substrate compound, in vitro inactivation parameter estimates, and the ability of Simcyp to accurately simulate the pharmacokinetics of inhibitors. Nonetheless, both the Simcyp and static approaches are useful tools for assessing the drug-drug interaction potential of a mechanism-based CYP3A inhibitor, especially when human pharmacokinetics of the inhibitor is known and 0.03 h(-1) is used as the hepatic CYP3A k(deg) value.

Sequential metabolism of secondary alkyl amines to metabolic-intermediate complexes: opposing roles for the secondary hydroxylamine and primary amine metabolites of desipramine, (s)-fluoxetine, and N-desmethyldiltiazem

Three secondary amines desipramine (DES), (S)-fluoxetine [(S)-FLX], and N-desmethyldiltiazem (MA) undergo N-hydroxylation to the corresponding secondary hydroxylamines [N-hydroxydesipramine, (S)-N-hydroxyfluoxetine, and N-hydroxy-N-desmethyldiltiazem] by cytochromes P450 2C11, 2C19, and 3A4, respectively. The expected primary amine products, N-desmethyldesipramine, (S)-norfluoxetine, and N,N-didesmethyldiltiazem, are also observed. The formation of metabolic-intermediate (MI) complexes from these substrates and metabolites was examined. In each example, the initial rates of MI complex accumulation followed the order secondary hydroxylamine > secondary amine >> primary amine, suggesting that the primary amine metabolites do not contribute to formation of MI complexes from these secondary amines. Furthermore, the primary amine metabolites, which accumulate in incubations of the secondary amines, inhibit MI complex formation. Mass balance studies provided estimates of the product ratios of N-dealkylation to N-hydroxylation. The ratios were 2.9 (DES-CYP2C11), 3.6 [(S)-FLX-CYP2C19], and 0.8 (MA-CYP3A4), indicating that secondary hydroxylamines are significant metabolites of the P450-mediated metabolism of secondary alkyl amines. Parallel studies with N-methyl-d(3)-desipramine and CYP2C11 demonstrated significant isotopically sensitive switching from N-demethylation to N-hydroxylation. These findings demonstrate that the major pathway to MI complex formation from these secondary amines arises from N-hydroxylation rather than N-dealkylation and that the primary amines are significant competitive inhibitors of MI complex formation.

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.

An inhibitory metabolite leads to dose- and time-dependent pharmacokinetics of (R)-N-{1-[3-(4-ethoxy-phenyl)-4-oxo-3,4-dihydro-pyrido[2,3-d]pyrimidin-2-yl]-ethyl}-N-pyridin-3-yl-methyl-2-(4-trifluoromethoxy-phenyl)-acetamide (AMG 487) in human subjects after multiple dosing

(R)-N-{1-[3-(4-Ethoxy-phenyl)-4-oxo-3,4-dihydro-pyrido[2,3-d]-pyrimidin-2-yl]-ethyl}-N-pyridin-3-yl-methyl-2-(4-trifluoromethoxyphenyl)-acetamide (AMG 487) is a potent and selective orally bioavailable chemokine (C-X-C motif) receptor 3 (CXCR3) antagonist that displays dose- and time-dependent pharmacokinetics in human subjects after multiple oral dosing. Although AMG 487 exhibited linear pharmacokinetics on both days 1 and 7 at the 25-mg dose, dose- and time-dependent kinetics were evident at the two higher doses. Nonlinear kinetics were more pronounced after multiple dosing. Area under the plasma concentration-time curve from 0 to 24 h [AUC((0-24 h))] increased 96-fold with a 10-fold increase in dose on day 7 compared with a 28-fold increase in AUC((0-24 h)) on day 1. These changes were correlated with time- and dose-dependent decreases in the metabolite to parent plasma concentrations, suggesting that these changes result from a decrease in the oral clearance (CL) of AMG 487 (e.g., intestinal/hepatic first-pass metabolism and systemic CL). The biotransformation of AMG 487 is dependent on CYP3A and results in the formation of two primary metabolites, a pyridyl N-oxide AMG 487 (M1) and an O-deethylated AMG 487 (M2). One of these metabolites, M2, undergoes further metabolism by CYP3A. M2 has also been demonstrated to inhibit CYP3A in a competitive (K(i)=0.75 microM) manner as well as via mechanism-based inhibition (unbound K(I)=1.4 microM, k(inact)=0.041 min(-1)). Data from this study implicate M2-mediated CYP3A mechanism-based inhibition as the proximal cause for the time-dependent pharmacokinetics of AMG 487. However, the sequential metabolism of M2, nonlinear AMG 487 pharmacokinetics, and the inability to accurately determine the role of intestinal AMG 487 metabolism complicates the correlation between M2 plasma concentrations and the time-dependent AMG 487 pharmacokinetic changes.

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.