Ketoconazole inhibition of triazolam and alprazolam clearance: differential kinetic and dynamic consequences

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits.

Progress in the Consideration of Possible Sex Differences in Drug Interaction Studies

Background:

Anecdotal evidence suggests that there may be sex differences in Drug-drug Interactions (DDI) involving specific drugs. Regulators have provided general guidance for the inclusion of females in clinical studies. Some clinical studies have reported sex differences in the Pharmacokinetics (PK) of CYP3A4 substrates, suggesting that DDI involving CYP3A4 substrates could potentially show sex differences.

Objective:

The aim of this review was to investigate whether recent prospective DDI studies have included both sexes and whether there was evidence for the presence or absence of sex differences with the DDIs.

Methods:

The relevant details from 156 drug interaction studies within 124 papers were extracted and evaluated.

Results:

Only eight studies (five papers) compared the outcome of the DDI between males and females. The majority of the studies had only male volunteers. Five studies had females only while 60 had males only, with 7.7% of the studies having an equal proportion of both sexes. Surprisingly, four studies did not specify the sex of the subjects.

:

Based on the limited number of studies comparing males and females, no specific trends or conclusions were evident. Sex differences in the interaction were reported between ketoconazole and midazolam as well as clarithromycin and midazolam. However, no sex difference was observed with the interaction between clarithromycin and triazolam or erythromycin and triazolam. No sex-related PK differences were observed with the interaction between ketoconazole and domperidone, although sex-related differences in QT prolongation were observed.

Conclusion:

This review has shown that only limited progress had been made with the inclusion of both sexes in DDI studies.

Effect of CYP3A Inhibition and Induction on the Pharmacokinetics of Suvorexant: Two Phase I, Open-Label, Fixed-Sequence Trials in Healthy Subjects

BACKGROUND AND OBJECTIVES

Suvorexant is an orexin receptor antagonist indicated for the treatment of insomnia, characterized by difficulties with sleep onset and/or sleep maintenance. As suvorexant is metabolized primarily by Cytochrome P450 3A (CYP3A), and its pharmacokinetics may be affected by CYP3A modulators, the effects of CYP3A inhibitors (ketoconazole or diltiazem) or an inducer (rifampin [rifampicin]) on the pharmacokinetics, safety, and tolerability of suvorexant were investigated.

METHODS

In two Phase I, open-label, fixed-sequence trials (Studies P008 and P038), healthy subjects received a single oral dose of suvorexant followed by co-administration with multiple once-daily doses of strong/moderate CYP3A inhibitors (ketoconazole/diltiazem) or a strong CYP3A inducer (rifampin). Treatments were administered in the morning: suvorexant 4 mg with ketoconazole 400 mg (Study P008; N = 10), suvorexant 20 mg with diltiazem 240 mg (Study P038; N = 20), and suvorexant 40 mg with rifampin 600 mg (Study P038; N = 10). Area under the plasma concentration-time curve from time zero to infinity (AUC_0-∞), maximum plasma concentration (C_max), half-life (t_½), and time to C_max (t_max) were derived from plasma concentrations of suvorexant collected at prespecified time points up to 10 days following CYP3A inhibitor/inducer co-administration. Adverse events (AEs) were recorded.

RESULTS

Co-administration with ketoconazole resulted in increased exposure to suvorexant [AUC_0-∞: geometric mean ratio (GMR); 90% confidence interval (CI) 2.79 (2.35, 3.31)] while co-administration with diltiazem resulted in a lesser effect [GMR (90% CI): 2.05 (1.82, 2.30)]. Co-administration with rifampin led to a marked decrease (88%) in suvorexant exposure. Consistent with morning administration and known suvorexant pharmacology, somnolence was the most frequently reported AE.

CONCLUSIONS

These results are consistent with expectations that strong CYP3A inhibitors and inducers exert marked effects on suvorexant pharmacokinetics. In the context of a limited sample size, single suvorexant doses were generally well tolerated in healthy subjects when co-administered with/without a CYP3A inhibitor/inducer.

Analysis of Clinical Drug-Drug Interaction Data To Predict Magnitudes of Uncharacterized Interactions between Antiretroviral Drugs and Comedications

Despite their high potential for drug-drug interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also being ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required. We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes, such as CYP3A, and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers, and inhibitors of CYP3A to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs. The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and comedications were successfully quantified, meaning 53%, 85%, and 98% of the predictions were within 1.25-fold (0.80 to 1.25), 1.5-fold (0.66 to 1.48), and 2-fold (0.66 to 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or, conversely, minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs. The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs, being particularly relevant in an HIV setting, given the treatment's complexity, high DDI risk, and limited guidance on the management of DDIs.

Prediction of drug-drug interactions using physiologically-based pharmacokinetic models of CYP450 modulators included in Simcyp software

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio-pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug-drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism-based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism-based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp^® software. The aim of this report was to establish confidence in each CYP-specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.

Simulations of Cytochrome P450 3A4-Mediated Drug-Drug Interactions by Simple Two-Compartment Model-Assisted Static Method

In order to predict cytochrome P450 3A4 (CYP3A4)-mediated drug-drug interactions (DDIs), a simple 2-compartment model-assisted, overall inhibition activity (A_i,overall) method was derived based on 2 concepts. One concept was that the increase in blood victim level and fold increase in the area under the blood victim level curve produced by DDI are determined entirely by A_i,overall, the hepatic availability of the victim and fraction of urinary excreted unchanged victim, where A_i,overall is determined by the perpetrator-specific CYP isoform inhibition activities (A_i,CYPs, DDI predictor-1) and victim-specific fractional CYP isoform contributions (f_m,CYPs, predictor-2). The other concept was that a DDI can be bridged to other DDIs, so that any possible DDI produced by a given victim or a given perpetrator can be predicted by using these predictors. The A_i,CYP3A4s of 12 common CYP3A4 inhibitors were able to be determined and shown to be useful for the prediction of CYP3A4-mediated DDIs wherein victims were metabolized by multiple CYP isoforms. Additionally, it was demonstrated that f_m,CYP values with high confidence can be estimated by bridging DDIs produced by the same victim and different perpetrators. This bridging approach will accelerate prediction of DDIs produced by new chemical entities from the existing DDI database.

In vitro and in vivo characterisation of the metabolism and disposition of suvorexant in humans

1. Suvorexant (MK-4305, Belsomra®) is a first-in-class dual orexin receptor antagonist approved in the USA and Japan for the treatment of insomnia. The current studies describe suvorexant's absorption, disposition and potential for CYP-mediated drug interactions in humans. 2. Following single oral administration of [(14)C]suvorexant to healthy human subjects, 90% of the radioactivity was recovered (66% in faeces, 23% in urine), primarily as oxidative metabolites. 3. In plasma, suvorexant and M9 were predominant, accounting for 30 and 37% of the total radioactivity, respectively. Metabolite M17 became more prominent (approaching 10%) following multiple daily doses of unlabelled suvorexant. M9 and M17 are not expected to contribute to the pharmacological activity of suvorexant due to reduced orexin receptor binding affinity and limited brain penetration. 4. CYP3A was determined to be the predominant enzyme mediating suvorexant oxidation. In vitro, suvorexant demonstrated reversible inhibition of CYP3A4 and 2C19 (IC50 ∼ 4-5 μM), and weak time-dependent inhibition of CYP3A4 (KI = 12 μM, kinact = 0.14 min(-1)). Suvorexant was also a weak inducer of CYP3A4, 1A2 and 2B6. Given the low plasma concentrations at clinical doses, suvorexant was not anticipated to cause significant drug interactions via inhibition and/or induction of major CYPs in vivo.

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Liver injury associated with ketoconazole: review of the published evidence

The azole antifungal agent ketoconazole has been available since 1981 for the treatment of fungal infections. In 2013, the American Food and Drug Administration and the European Medicines Agency issued warnings or prohibitions against the clinical use of oral ketoconazole due to the risk of liver injury which may lead to liver transplantation or death. From the available published evidence it is difficult to determine the actual incidence or prevalence of liver injury during clinical use of ketoconazole as an antifungal. Hepatic injury, when it occurs, is generally evident as asymptomatic and reversible abnormalities of liver function tests. However, serious liver injury has been reported. Alternatives to ketoconazole (such as itraconazole, fluconazole, voriconazole, and terbinafine) are available, but improved safety with respect to liver injury risk is not clearly established. We are not aware of published reports of significant ketoconazole-associated liver injury in volunteer study participants when ketoconazole has been used as a CYP3A inhibitor in the context of clinical research on drug metabolism. Possible alternatives to ketoconazole as prototype CYP3A inhibitors include ritonavir and potentially itraconazole, but not clarithromycin.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.

WHAT THIS PAPER ADDS

Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates

The first objective of the present study was to predict the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human. The second objective was to predict pharmacokinetics of the selected drugs in presence of inhibitors of the intestinal and/or hepatic CYP3A activity. We developed a whole-body physiologically based pharmacokinetics (WB-PBPK) model accounting for presystemic elimination of midazolam (MDZ), alprazolam (APZ), triazolam (TRZ), and simvastatin (SMV). The model also accounted for concomitant administration of the above-mentioned drugs with CYP3A inhibitors, namely ketoconazole (KTZ), itraconazole (ITZ), diltiazem (DTZ), saquinavir (SQV), and a furanocoumarin contained in grape-fruit juice (GFJ), namely 6',7'-dihydroxybergamottin (DHB). Model predictions were compared to published clinical data. An uncertainty analysis was performed to account for the variability and uncertainty of model parameters when predicting the model outcomes. We also briefly report on the results of our efforts to develop a global sensitivity analysis and its application to the current WB-PBPK model. Considering the current criterion for a successful prediction, judged satisfied once the clinical data are captured within the 5th and 95th percentiles of the predicted concentration-time profiles, a successful prediction has been obtained for a single oral administration of MDZ and SMV. For APZ and TRZ, however, a slight deviation toward the 95th percentile was observed especially for C(max) but, overall, the in vivo profiles were well captured by the PBPK model. Moreover, the impact of DHB-mediated inhibition on the extent of intestinal pre-systemic elimination of MDZ and SMV has been accurately predicted by the proposed PBPK model. For concomitant administrations of MDZ and ITZ, APZ and KTZ, as well as SMV and DTZ, the in vivo concentration-time profiles were accurately captured by the model. A slight deviation was observed for SMV when coadministered with ITZ, whereas more important deviations have been obtained between the model predictions and in vivo concentration-time profiles of MDZ coadministered with SQV. The same observation was made for TRZ when administered with KTZ. Most of the pharmacokinetic parameters predicted by the PBPK model were successfully predicted within a two-fold error range either in the absence or presence of metabolism-based inhibition. Overall, the present study demonstrated the ability of the PBPK model to predict DDI of CYP3A substrates with promising accuracy.

Effects of ketoconazole on the biodistribution and metabolism of [11C]loperamide and [11C]N-desmethyl-loperamide in wild-type and P-gp knockout mice

INTRODUCTION

[(11)C]Loperamide and [(11)C]N-desmethyl-loperamide ([(11)C]dLop) have been proposed as radiotracers for imaging brain P-glycoprotein (P-gp) function. A major route of [(11)C]loperamide metabolism is N-demethylation to [(11)C]dLop. We aimed to test whether inhibition of CYP3A4 with ketoconazole might reduce the metabolism of [(11)C]loperamide and [(11)C]dLop in mice, and thereby improve the quality of these radiotracers.

METHODS

Studies were performed in wild-type and P-gp knockout (mdr-1a/b -/-) mice. During each of seven study sessions, one pair of mice, comprising one wild-type and one knockout mouse, was pretreated with ketoconazole (50 mg/kg, ip), while another such pair was left untreated. Mice were sacrificed at 30 min after injection of [(11)C]loperamide or [(11)C]dLop. Whole brain and plasma samples were measured for radioactivity and analyzed with radio-high-performance liquid chromatography.

RESULTS

Ketoconazole increased the plasma concentrations of [(11)C]loperamide and its main radiometabolite, [(11)C]dLop, by about twofold in both wild-type and knockout mice, whereas the most polar radiometabolite was decreased threefold. Furthermore, ketoconazole increased the brain concentrations of [(11)C]loperamide and the radiometabolite [(11)C]dLop by about twofold in knockout mice, and decreased the brain concentrations of the major and most polar radiometabolite in wild-type and knockout mice by 82% and 49%, respectively. In contrast, ketoconazole had no effect on plasma and brain distribution of administered [(11)C]dLop and its radiometabolites in either wild-type or knockout mice, except to increase the low plasma [(11)C]dLop concentration. The least polar radiometabolite of [(11)C]dLop was identified with LC-MS(n) as the N-hydroxymethyl analog of [(11)C]dLop and this also behaved as a P-gp substrate.

CONCLUSION

In this study, ketoconazole (50 mg/kg, ip) proved partially effective for inhibiting the N-demethylation of [(11)C]loperamide in mouse in vivo but had relatively smaller or no effect on [(11)C]dLop.

Effect of the CYP3A inhibitor ketoconazole on the pharmacokinetics and pharmacodynamics of bortezomib in patients with advanced solid tumors: a prospective, multicenter, open-label, randomized, two-way crossover drug-drug interaction study

BACKGROUND

The proteasome inhibitor bortezomib undergoes oxidative biotransformation via multiple cytochrome P450 (CYP) enzymes, with CYP3A4 identified as a partial, yet potentially important, contributor based on in vitro drug metabolism studies.

OBJECTIVE

The aim of this study was to assess the effect of concomitant administration of ketoconazole on the pharmacokinetics (PK) and pharmacodynamics (PD) of bortezomib.

METHODS

This was a prospective, multicenter, open-label, randomized, multiple-dose, 2-way crossover study in patients with advanced solid tumors. All patients received bortezomib 1.0 mg/m(2) IV (on days 1, 4, 8, and 11 of two 21-day cycles) and were randomized to receive concomitant ketoconazole 400 mg on days 6, 7, 8, and 9 of cycle 1 or 2. Serial blood samples were collected over the day-8 dosing interval (immediately prior to bortezomib administration, and from 5 minutes to 72 hours after administration) in cycles 1 and 2 for measurement of plasma bortezomib concentrations for noncompartmental PK analysis and blood 20S proteasome inhibition for PD analysis. All adverse events (AEs) were recorded during each cycle including serious AEs and all neurotoxicity events for up to 30 days after the last dose of bortezomib.

RESULTS

Twenty-one patients (median age, 57 years; sex, 67% male; race, 86% white; median body surface area, 2.01 m(2)) were randomized to treatment. Twelve patients completed the protocol-specified dosing and PK sampling in both cycles 1 and 2. Assessment of the effect of ketoconazole on bortezomib PK and PD was based on data in these 12 PK-evaluable patients. The ratio of geometric mean bortezomib AUC(0-tlast)(AUC from time 0 to last quantifiable concentration) for bortezomib plus ketoconazole versus bortezomib alone was 1.352 (90% CI, 1.032-1.772). Consistent with this observed mean increase in bortezomib exposure, concomitant administration of ketoconazole was associated with a corresponding increase (24%-46%) in the blood proteasome inhibitory effect.

CONCLUSION

Concomitant administration of the CYP3A inhibitor ketoconazole with bortezomib resulted in a mean increase of 35% in bortezomib exposure. ClinicalTrials.gov identifier: NCT00129207.

A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling

BACKGROUND AND OBJECTIVE

Pharmacokinetic drug-drug interactions (DDIs) are one of the major causes of adverse events in pharmacotherapy, and systematic prediction of the clinical relevance of DDIs is an issue of significant clinical importance. In a previous study, total exposure changes of many substrate drugs of cytochrome P450 (CYP) 3A4 caused by coadministration of inhibitor drugs were successfully predicted by using in vivo information. In order to exploit these predictions in daily pharmacotherapy, the clinical significance of the pharmacokinetic changes needs to be carefully evaluated. The aim of the present study was to construct a pharmacokinetic interaction significance classification system (PISCS) in which the clinical significance of DDIs was considered with pharmacokinetic changes in a systematic manner. Furthermore, the classifications proposed by PISCS were compared in a detailed manner with current alert classifications in the product labelling or the summary of product characteristics used in Japan, the US and the UK.

METHODS

A matrix table was composed by stratifying two basic parameters of the prediction: the contribution ratio of CYP3A4 to the oral clearance of substrates (CR), and the inhibition ratio of inhibitors (IR). The total exposure increase was estimated for each cell in the table by associating CR and IR values, and the cells were categorized into nine zones according to the magnitude of the exposure increase. Then, correspondences between the DDI significance and the zones were determined for each drug group considering the observed exposure changes and the current classification in the product labelling. Substrate drugs of CYP3A4 selected from three therapeutic groups, i.e. HMG-CoA reductase inhibitors (statins), calcium-channel antagonists/blockers (CCBs) and benzodiazepines (BZPs), were analysed as representative examples. The product labelling descriptions of drugs in Japan, US and UK were obtained from the websites of each regulatory body.

RESULTS

Among 220 combinations of drugs investigated, estimated exposure changes were more than 5-fold for 41 combinations in which ten combinations were not alerted in the product labelling at least in one country; these involved buspirone, nisoldipine and felodipine as substrates, and ketoconazole, voriconazole, telithromycin, clarithromycin and nefazodone as inhibitors. For those drug combinations, the alert classifications were anticipated as potentially inappropriate. In the current product labelling, many inter-country differences were also noted. Considering the relationships between previously observed exposure changes and the current alert classifications, the boundaries between 'contraindication' and 'warning/caution' were determined as a 7-fold exposure increase for statins and CCBs, and as a 4-fold increase for BZPs. PISCS clearly discriminated these drug combinations in accordance with the determined boundaries. Classifications by PISCS were expected to be valid even for future drugs because the classifications were made by zones, not by designating individual drugs.

CONCLUSION

The present analysis suggested that many current alert classifications were potentially inappropriate especially for drug combinations where pharmacokinetics had not been evaluated. It is expected that PISCS would contribute to constructing a leak-less alerting system for a broad range of pharmacokinetic DDIs. Further validation of PISCS is required in clinical studies with key drug combinations, and its extension to other CYP and metabolizing enzymes remains to be achieved.

Prediction of pharmacokinetic drug-drug interaction caused by changes in cytochrome P450 activity using in vivo information

The aim of the present paper was to present an overview of the current status of the methods used to predict the magnitude of pharmacokinetic drug-drug interactions (DDIs) which are caused by apparent changes in cytochrome P450 (CYP) activity with an emphasis on a method using in vivo information. In addition, more than a hundred representative CYP substrates, inhibitor and inducer drugs involved in significant pharmacokinetic DDIs were selected from the literature and are listed. Although the magnitude of DDIs has been conventionally predicted based on in vitro experiments, their predictability is restricted occasionally due to several difficulties, including a precise determination of the unbound inhibitor concentrations at the enzyme site and a reliable in vitro measurement of the inhibition constant (K(i)). Alternatively, a simple method has been recently proposed for the prediction of the magnitude of DDIs based on information fully available from in vivo clinical studies. The new in vivo-based method would be applicable to the adjustment of dose regimens in actual pharmacotherapy situations although it requires a prior clinical study for the prediction. In this review, theoretical and quantitative relationships between the in vivo- and the in vitro-based prediction methods are considered. One of the interesting outcomes of the consideration is that the K(i)-normalized dose (dose/in vitro K(i)) of larger than approximately 20L (2-200L, when variability is considered) may be a pragmatic index which predicts significant in vivo DDIs. In the last part of the article, the relevance of the inclusion of the in vivo-based method into the process of new drug development is discussed for good prediction of in vivo DDIs.

Prediction of human drug-drug interactions from time-dependent inactivation of CYP3A4 in primary hepatocytes using a population-based simulator

Time-dependent inactivation (TDI) of human cytochromes P450 3A4 (CYP3A4) is a major cause of clinical drug-drug interactions (DDIs). Human liver microsomes (HLM) are commonly used as an enzyme source for evaluating the inhibition of CYP3A4 by new chemical entities. The inhibition data can then be extrapolated to assess the risk of human DDIs. Using this approach, under- and overpredictions of in vivo DDIs have been observed. In the present study, human hepatocytes were used as an alternative to HLM. Hepatocytes incorporate the effects of other mechanisms of drug metabolism and disposition (i.e., phase II enzymes and transporters) that may modulate the effects of TDI on clinical DDIs. The in vitro potency (K(I) and k(inact)) of five known CYP3A4 TDI drugs (clarithromycin, diltiazem, erythromycin, verapamil, and troleandomycin) was determined in HLM (pooled, n = 20) and hepatocytes from two donors (D1 and D2), and the results were extrapolated to predict in vivo DDIs using a Simcyp population trial-based simulator. Compared with observed DDIs, the predictions derived from HLM appeared to be overestimated. The predictions based on TDI measured in hepatocytes were better correlated with the DDIs (n = 37) observed in vivo (R(2) = 0.601 for D1 and 0.740 for D2) than those from HLM (R(2) = 0.451). In addition, with the use of hepatocytes a greater proportion of the predictions were within a 2-fold range of the clinical DDIs compared with using HLM. These results suggest that DDI predictions from CYP3A4 TDI kinetics in hepatocytes could provide an alternative approach to balance HLM-based predictions that can sometimes substantially overestimate DDIs and possibly lead to erroneous conclusions about clinical risks.

Computing with evidence Part II: An evidential approach to predicting metabolic drug-drug interactions

We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.

Antibodies as a probe in cytochrome P450 research

Cytochrome P450 (P450) is the superfamily of enzymes responsible for biotransformation of endobiotics and xenobiotics. However, their large isoform multiplicity, inducibility, diverse structure, widespread distribution, polymorphic expression, and broad overlapping substrate specificity make it difficult to measure the precise role of each individual P450 to the metabolism of drugs (or carcinogens) and hamper the understanding of the relationship between the genetic/environmental factors that regulate P450 phenotype and the responses of the individual P450s to drugs. The antibodies against P450s have been useful tools for the quantitative determination of expression level and contribution of the epitope-specific P450 to the metabolism of a drug or carcinogen substrate in tissues containing multiple P450 isoforms and for implications in pharmacogenetics and human risk assessment. In particular, the inhibitory antibodies are uniquely suited for reaction phenotyping that helps to predict human pharmacokinetics for clinical drug-drug interaction potential in drug discovery and development.

Identification of human cytochrome P450 enzymes involved in the formation of 4-hydroxyestazolam from estazolam

To predict drug interactions with estazolam, the biotransformation of estazolam to its major hydoxylated metabolite, 4-hydroxyestazolam was studied in vitro using pooled human liver microsomes and individual expressed human cytochrome P450 (CYP) enzymes. Estazolam was metabolized to 4-hydroxyestazolam according to the Hill kinetic model in pooled human liver microsomes. The Km value for the 4-hydroxylation of estazolam was 24.1 microM, and the Vmax value was 52.6 pmol min(-1)mg(-1) protein. The formation of 4-hydroxyestazolam from estazolam in pooled human liver microsomes was significantly inhibited by itraconazole and erythromycin, specific CYP3A4 inhibitors, in a dose-dependent manner, with IC50 values of 1.1 and 12.8 microM, respectively. When estazolam was incubated with expressed human CYP enzymes (CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1 and CYP3A4), it was metabolized only by CYP3A4. In conclusion, the biotransformation of estazolam to 4-hydroxyestazolam was catalyzed by CYP3A4.

Microsomal prediction ofin vivoclearance and associated interindividual variability of six benzodiazepines in humans

The intrinsic clearances (CLint) of midazolam, triazolam, diazepam, nordiazepam, flunitrazepam and alprazolam were determined from two liver banks (n=21) by formation kinetics of ten metabolites. A literature-collated database of in vivo CLint values (811 subjects) was used to assess predictions and variability. The in vivo clearance of six benzodiazepines was generally underpredicted by in vitro data and the degree of bias was in agreement with a database of structurally diverse compounds (n=37). The variability observed for in vitro clearances (11--19--fold for midazolam, diazepam and nordiazepam in liver bank 1; 101--269--fold for triazolam, flunitrazepam and alprazolam in liver bank 2) exceeded the in vivo variability for the same compounds (4--59 and 10--29, respectively). This mismatch may contribute to the bias in microsomal predictions and it highlights the need for careful selection of representative livers for human liver banks.

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.

Intestinal first-pass metabolism of CYP3A4 substrates

Cytochrome P450 3A4 (CYP3A4) is present not only in the liver but also in the small intestine, where it functions as a barrier against xenobiotics. Some CYP3A4 substrates exhibit low bioavailability due to intestinal first pass metabolism. The AUCs of such CYP3A4 substrates are remarkably changed by the inhibition, induction, and saturation of CYP3A4 and so prediction of intestinal first-pass metabolism is important. In this article, factors affecting intestinal first-pass metabolism of drugs are reviewed, focusing on the intestinal metabolism by CYP3A. The methods to predict intestinal first-pass metabolism are also reviewed.

Prediction of metabolic drug clearance in humans: in vitro-in vivo extrapolation vs allometric scaling

Previously in vitro-in vivo extrapolation (IVIVE) with the Simcyp Clearance and Interaction Simulator has been used to predict the clearance of 15 clinically used drugs in humans. The criteria for the selection of the drugs were that they are used as probes for the activity of specific cytochromes P450 (CYPs) or have a single CYP isoform as the major or sole contributor to their metabolism and that they do not exhibit non-linear kinetics in vivo. Where data were available for the clearance of the drugs in at least three animal species, the predictions from IVIVE have now been compared with those based on allometric scaling (AS). Adequate data were available for estimating oral clearance (CLp.o.) in 9 cases (alprazolam, sildenafil, caffeine, clozapine, cyclosporine, dextromethorphan, midazolam, omeprazole and tolbutamide) and intravenous clearance in 6 cases (CLi.v.) (cyclosporine, diclofenac, midazolam, omeprazole, theophylline and tolterodine). AS predictions were based on five different methods: (1) simple allometry (clearance versus body weight); (2) correction for maximum life-span potential (CL x MLP); (3) correction for brain weight (CL x BrW); (4) the use of body surface area; and (5) the rule of exponents. A prediction accuracy was indicated by mean-fold error and the Pearson product moment correlation coefficient. Predictions were considered successful if the mean-fold error was <or=2. IVIVE predictions were accurate in 14 of 15 cases (mean-fold error range: 1.02-4.00). All five AS methods were accurate in 13, 11, 10, 10 and 14 cases, respectively. However, in some cases the error of AS exceeded fivefold. On the basis of the current results, IVIVE is more reliable than AS in predicting human clearance values for drugs mainly metabolized by CYP450 enzymes. This suggests that the place of AS methods in pre-clinical drug development warrants further scrutiny.

HepaRG cells as an in vitro model for evaluation of cytochrome P450 induction in humans

HepaRG is a highly differentiated cell line that displays several hepatocyte-like functions, including drug-metabolizing enzymes. In this study, the HepaRG cells were characterized and evaluated as an in vitro model to predict cytochrome P450 (P450) enzyme induction of drugs in humans. Exposure of HepaRG cells to prototypical inducers resulted in induction of CYP1A1, CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4 mRNA, as well as phenacetin O-dealkylase, bupropion hydroxylase, diclofenac 4'-hydroxylase, and midazolam 1'-hydroxylase activities. The observed induction is consistent with the previously reported expression of the nuclear receptors pregnane X receptor, constitutive androstane receptor, and aryl hydrocarbon receptor, which are necessary for a P450 induction response. To avoid problems with toxicity and solubility, the induction potency of test compounds was evaluated by calculating the concentrations leading to a 2-fold increase of baseline mRNA or enzyme activity levels (F(2) values) instead of EC(50) values from full dose-response curves. For CYP3A4 mRNA, the obtained F(2) values were related to the in vivo exposure [area under the plasma concentration versus time curve (AUC)] of the inducer (AUC/F(2)). This score was then correlated with the decrease in AUC for a CYP3A probe drug, administered before and after treatment with the inducing agent. By using this method an excellent correlation (R(2) = 0.863) was obtained, which implies that the degree of CYP3A induction in vivo can be predicted from CYP3A4 mRNA induction in HepaRG cells. The present study shows that HepaRG cells are a valuable model to be used for prediction of induction of drug-metabolizing P450 enzymes in vivo in humans.

Inhibition of CYP3A4 and CYP3A5 catalyzed metabolism of alprazolam and quinine by ketoconazole as racemate and four different enantiomers

OBJECTIVE

The antifungal drug ketoconazole (KTZ) is known as an inhibitor of, especially, the CYP3A subfamily, which catalyzes the metabolism of a large variety of drugs. Interactions between KTZ and CYP3A substrates have been reported both in vivo and in vitro. Most of them, however, involved the KTZ racemate. KTZ racemate and the separate enantiomers, 2R,4R; 2R,4S; 2S,4S, and 2S,4R, were evaluated for their selectivity in inhibiting alprazolam and quinine metabolism.

METHODS

The inhibition of alprazolam and quinine metabolism was studied in an in vitro system of human liver microsomes (HLM), recombinant of CYP3A4 and CYP3A5. The concentrations of formed 3-hydroxyquinine and 4- and alpha-hydroxyalprazolam were measured by HPLC and LC-MS, respectively.

RESULTS

Quinine 3-hydroxylation was catalyzed to a similar extent by CYP3A4 and CYP3A5. The formation rate of 4-hydroxyalprazolam was higher than that of alpha-hydroxyalprazolam for each HLM, CYP3A4 and CYP3A5. KTZ racemate and enantiomers showed differential inhibitory effects of quinine and alprazolam metabolism. Quinine metabolism catalyzed by HLM, CYP3A4 and CYP3A5 was potently inhibited by the trans-enantiomer KTZ 2S,4S, with IC(50) value of 0.16 microM for HLM, 0.04 microM for CYP3A4 and 0.11 microM for CYP3A5. The same enantiomer showed the lowest IC(50) values of 0.11 microM for HLM and 0.04 microM for CYP3A5 with respect to alprazoalm 4-hydroxylation and also the same pattern for alprazolamalpha-hydroxylation, 0.13 microM for HLM and 0.05 microM for CYP3A5. Alprazolam metabolism (both alpha- and 4- hydroxylations) catalyzed by CYP3A4 was inhibited potently by the cis-enantiomer KTZ 2S,4R, with IC(50) values of 0.03 microM.

CONCLUSIONS

Alprazolam and quinine metabolism is catalyzed by both CYP3A4 and CYP3A5. The present study showed that different KTZ enantiomers inhibit CYP3A4 and CYP3A5 to different degrees, indicating that structural differences among the enantiomers would be related to their inhibitory potency on these two enzymes.

General Framework for the Quantitative Prediction of CYP3A4-Mediated Oral Drug Interactions Based on the AUC Increase by Coadministration of??Standard Drugs

BACKGROUND

Cytochrome P450 (CYP) 3A4 is the most prevalent metabolising enzyme in the human liver and is also a target for various drug interactions of significant clinical concern. Even though there are numerous reports regarding drug interactions involving CYP3A4, it is far from easy to estimate all potential interactions, since too many drugs are metabolised by CYP3A4. For this reason, a comprehensive framework for the prediction of CYP3A4-mediated drug interactions would be of considerable clinical importance.

OBJECTIVE

The objective of this study was to provide a robust and practical method for the prediction of drug interactions mediated by CYP3A4 using minimal in vivo information from drug-interaction studies, which are often carried out early in the course of drug development.

DATA SOURCES

The analysis was based on 113 drug-interaction studies reported in 78 published articles over the period 1983-2006. The articles were used if they contained sufficient information about drug interactions. Information on drug names, doses and the magnitude of the increase in the area under the concentration-time curve (AUC) were collected.

METHODS

The ratio of the contribution of CYP3A4 to oral clearance (CR(CYP)(3A4)) was calculated for 14 substrates (midazolam, alprazolam, buspirone, cerivastatin, atorvastatin, ciclosporin, felodipine, lovastatin, nifedipine, nisoldipine, simvastatin, triazolam, zolpidem and telithromycin) based on AUC increases observed in interaction studies with itraconazole or ketoconazole. Similarly, the time-averaged apparent inhibition ratio of CYP3A4 (IR(CYP)(3A4)) was calculated for 18 inhibitors (ketoconazole, voriconazole, itraconazole, telithromycin, clarithromycin, saquinavir, nefazodone, erythromycin, diltiazem, fluconazole, verapamil, cimetidine, ranitidine, roxithromycin, fluvoxamine, azithromycin, gatifloxacin and fluoxetine) primarily based on AUC increases observed in drug-interaction studies with midazolam. The increases in the AUC of a substrate associated with coadministration of an inhibitor were estimated using the equation 1/(1 - CR(CYP)(3A4) x IR(CYP)(3A4)), based on pharmacokinetic considerations.

RESULTS

The proposed method enabled predictions of the AUC increase by interactions with any combination of these substrates and inhibitors (total 251 matches). In order to validate the reliability of the method, the AUC increases in 60 additional studies were analysed. The method successfully predicted AUC increases within 67-150% of the observed increase for 50 studies (83%) and within 50-200% for 57 studies (95%). Midazolam is the most reliable standard substrate for evaluation of the in vivo inhibition of CYP3A4. The present analysis suggests that simvastatin, lovastatin and buspirone can be used as alternatives. To evaluate the in vivo contribution of CYP3A4, ketoconazole or itraconazole is the selective inhibitor of choice.

CONCLUSION

This method is applicable to (i) prioritize clinical trials for investigating drug interactions during the course of drug development and (ii) predict the clinical significance of unknown drug interactions. If a drug-interaction study is carefully designed using appropriate standard drugs, significant interactions involving CYP3A4 will not be missed. In addition, the extent of CYP3A4-mediated interactions between many other drugs can be predicted using the current method.

Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability

The Simcyp Population-Based ADME Simulator was used to predict median drug clearances and their associated variance from in vitro data. Fifteen drugs satisfied the entry criteria for the study and the relevant information (in vitro metabolism data and in vivo human clearance values) were collated from the literature. Predicted values of median clearances fell within 2-fold of observed values for 73% of the drugs (oral route) and 78% of the drugs (intravenous route) when microsomal binding was disregarded, and for 93% (oral) and 100% (intravenous) when it was considered. Irrespective of whether microsomal binding was considered, the predicted fold variability fell within 2-fold of the observed variability for 80% (oral) and 67% (intravenous) of the drugs.

Kinetics and dynamics of intravenous adinazolam, N-desmethyl adinazolam, and alprazolam in healthy volunteers

The pharmacokinetics and pharmacodynamics of adinazolam mesylate (10 mg), N-desmethyl adinazolam mesylate (NDMAD, 10 mg), and alprazolam (1 mg) were investigated in 9 healthy male subjects in a randomized, blinded, single-dose, 4-way crossover study. All drugs were intravenously infused over 30 minutes. Plasma adinazolam, NDMAD, and alprazolam concentrations, electroencephalographic (EEG) activity in the beta (12-30 Hz) range, performance on the Digit Symbol Substitution Test (DSST), and subjective measures of mood and sedation were monitored for 12 to 24 hours. Mean pharmacokinetic parameters for adinazolam, NDMAD, and alprazolam, respectively, were as follows: volume of distribution (L), 106, 100, and 77; elimination half-life (hours), 2.9, 2.8, and 14.6; and clearance (mL/min), 444, 321, and 84. More than 80% of the total infused adinazolam dose was converted to systemically appearing NDMAD. All 3 benzodiazepine agonists significantly increased beta EEG activity, with alprazolam showing the strongest agonist activity and adinazolam showing the weakest activity. Alprazolam and NDMAD significantly decreased DSST performance, whereas adinazolam had no effect relative to placebo. Adinazolam, NDMAD, and alprazolam all produced significant observer-rated sedation. Plots of EEG effect versus plasma alprazolam concentration demonstrated counterclockwise hysteresis, consistent with an effect site delay. This was incorporated into a kinetic-dynamic model in which hypothetical effect site concentration was related to pharmacodynamic EEG effect via the sigmoid E(max) model, yielding an effect site equilibration half-life of 4.8 minutes. The exponential effect model described NDMAD pharmacokinetics and EEG pharmacodynamics. The relation of both alprazolam and NDMAD plasma concentrations to DSST performance could be described by a modified exponential model. Pharmacokinetic-dynamic modeling was not possible for adinazolam, as the data did not conform to any known concentration-effect model. Collectively, these results indicate that the benzodiazepine-like effects occurring after adinazolam administration are mediated by mainly NDMAD.

CYP3A4 substrate selection and substitution in the prediction of potential drug-drug interactions

The complexity of in vitro kinetic phenomena observed for CYP3A4 substrates (homo- or heterotropic cooperativity) confounds the prediction of drug-drug interactions, and an evaluation of alternative and/or pragmatic approaches and substrates is needed. The current study focused on the utility of the three most commonly used CYP3A4 in vitro probes for the prediction of 26 reported in vivo interactions with azole inhibitors (increase in area under the curve ranged from 1.2 to 24, 50% in the range of potent inhibition). In addition to midazolam, testosterone, and nifedipine, quinidine was explored as a more "pragmatic" substrate due to its kinetic properties and specificity toward CYP3A4 in comparison with CYP3A5. Ki estimates obtained in human liver microsomes under standardized in vitro conditions for each of the four probes were used to determine the validity of substrate substitution in CYP3A4 drug-drug interaction prediction. Detailed inhibitor-related (microsomal binding, depletion over incubation time) and substrate-related factors (cooperativity, contribution of other metabolic pathways, or renal excretion) were incorporated in the assessment of the interaction potential. All four CYP3A4 probes predicted 69 to 81% of the interactions with azoles within 2-fold of the mean in vivo value. Comparison of simple and multisite mechanistic models and interaction prediction accuracy for each of the in vitro probes indicated that midazolam and quinidine in vitro data provided the best assessment of a potential interaction, with the lowest bias and the highest precision of the prediction. Further investigations with a wider range of inhibitors are required to substantiate these findings.

Influence of ketoconazole on azimilide pharmacokinetics in healthy subjects

AIM

To assess the influence of ketoconazole on azimilide pharmacokinetics.

METHODS

A two-period randomized crossover study was conducted in healthy male and female subjects (19-45 years). Placebo or 200 mg ketoconazole were administered orally every 24 h for 29 days. On day 8, a single oral dose of 125 mg azimilide dihydrochloride was coadministered following an overnight fast. Blood samples were obtained prior to and for 22 days following azimilide dihydrochloride administration. The plasma protein binding of azimilide was also assessed at 6 h after dosing.

RESULTS

Following ketoconazole administration, a 16% increase in azimilide AUC (90% confidence interval (CI) 112%, 120%), a 12% increase in C(max) (95% CI 107%, 116%), a 13% increase in t(1/2,z) (95% CI 107%, 120%) and a 14% decrease in CL(o) (95% CI 82%, 90%) were observed.

CONCLUSIONS

The changes in azimilide pharmacokinetics following ketoconazole treatment are not clinically important since the 90% CI for the AUC fell within the prespecified range of 80-125%. Thus, no clinically important drug interactions are expected when azimilide dihydrochloride is coadministered with CYP3A4 inhibitors.

Co-prescription of cytochrome P450 2D6/3A4 inhibitor-substrate pairs in clinical practice. A retrospective analysis of data from Norwegian primary pharmacies

OBJECTIVE

Inhibition of cytochrome P (P450) (CYP) enzymes, in particular CYP3A4 and CYP2D6, is an important drug-interacting mechanism. The objective of our study was to assess how frequently CYP3A4 and CYP2D6 inhibitors are co-prescribed with substrates of the respective enzymes.

METHODS

Included inhibitors were clarithromycin, erythromycin, fluconazole, itraconazole, ketoconazole and nefazodone (CYP3A4 inhibitors) and bupropion, fluoxetine, paroxetine and terbinafine (CYP2D6 inhibitors). The inhibitors were combined with substrates shown to be pharmacokinetically sensitive towards inhibition (190 drug pairs in total). Lists of patients receiving inhibitors and substrates were drawn from prescription databases (approximately 43,500 patients) of three Norwegian primary pharmacies during a 6-month period (July 2002 to January 2003). The lists were matched on name and date of birth to identify patients using drug pairs. Concurrent use was made probable from dates of purchase and drug profiles.

RESULTS

Inhibitors were prescribed to 2,062 patients. Altogether, 369 events of substrate co-prescription were registered. The highest frequencies of co-prescribed substrates were found for paroxetine (101 events per 267 patients, 38%), fluoxetine (36 events per 110 patients, 33%) and clarithromycin (59 events per 242 patients, 24%). The drugs most often detected in combination with inhibitors were codeine (116 events) and metoprolol (38 events) for CYP2D6 and zopiclone (45 events) and simvastatin (26 events) for CYP3A4.

CONCLUSION

Several commonly used CYP2D6 and CYP3A4 inhibitors are frequently co-prescribed with substrates in Norwegian clinical practice. Alertness when inhibitors are prescribed would aid physicians and pharmacists to detect many drug combinations with potential interaction risk.

Clinical pharmacology, clinical efficacy, and behavioral toxicity of alprazolam: a review of the literature

Alprazolam is a benzodiazepine derivative that is currently used in the treatment of generalized anxiety, panic attacks with or without agoraphobia, and depression. Alprazolam has a fast onset of symptom relief (within the first week); it is unlikely to produce dependency or abuse. No tolerance to its therapeutic effect has been reported. At discontinuation of alprazolam treatment, withdrawal and rebound symptoms are common. Hence, alprazolam discontinuation must be tapered. An exhaustive review of the literature showed that alprazolam is significantly superior to placebo, and is at least equally effective in the relief of symptoms as tricyclic antidepressants (TCAs), such as imipramine. However, although alprazolam and imipramine are significantly more effective than placebo in the treatment of panic attacks, Selective Serotonin Reuptake Inhibitors (SSRIs) appear to be superior to either of the two drugs. Therefore, alprazolam is recommended as a second line treatment option, when SSRIs are not effective or well tolerated. In addition to its therapeutic effects, alprazolam produces adverse effects, such as drowsiness and sedation. Since alprazolam is widely used, many clinical studies investigated its cognitive and psychomotor effects. It is evident from these studies that alprazolam may impair performance in a variety of skills in healthy volunteers as well as in patients. Since the majority of alprazolam users are outpatients, this behavioral impairment limits the safe use of alprazolam in patients routinely engaged in potentially dangerous daily activities, such as driving a car.

Pharmacokinetic and pharmacodynamic evaluation of the inhibition of alprazolam by citalopram and fluoxetine

The selective serotonin reuptake inhibitor antidepressant fluoxetine inhibits alprazolam metabolism in vivo by inhibition of the cytochrome P450 3A4 enzyme. Citalopram is a selective serotonin reuptake inhibitor antidepressant that has not yet been fully evaluated with respect to its potential for cytochrome P450 3A4-mediated drug interactions in vivo. Building on the existing in vitro and in vivo evidence that suggest a minimal effect of citalopram on cytochrome P450 3A4, we hypothesized that therapeutic doses of citalopram (20 mg/d), as compared with fluoxetine (20 mg/d), would cause less impairment in the metabolism of the probe drug alprazolam (1 mg) through inhibition of the cytochrome P450 3A4 isozyme as measured by pharmacokinetic and pharmacodynamic parameters in vivo. We found that fluoxetine prolonged the half-life of alprazolam by 16% and increased the area under the curve 0-infinity of alprazolam by 32%, while citalopram did not affect these parameters, although the time of maximum concentration of alprazolam was prolonged by 30 minutes after citalopram administration. Neither selective serotonin reuptake inhibitor significantly affected the pharmacodynamic profile of alprazolam. This experiment suggests differential effects by citalopram and fluoxetine on alprazolam kinetics.

Clinically important drug interactions with zopiclone, zolpidem and zaleplon

Insomnia, an inability to initiate or maintain sleep, affects approximately one-third of the American population. Conventional benzodiazepines, such as triazolam and midazolam, were the treatment of choice for short-term insomnia for many years but are associated with adverse effects such as rebound insomnia, withdrawal and dependency. The newer hypnosedatives include zolpidem, zaleplon and zopiclone. These agents may be preferred over conventional benzodiazepines to treat short-term insomnia because they may be less likely to cause significant rebound insomnia or tolerance and are as efficacious as the conventional benzodiazepines. This review aims to summarise the published clinical drug interaction studies involving zolpidem, zaleplon and zopiclone. The pharmacokinetic and pharmacodynamic interactions that may be clinically important are highlighted. Clinical trials have studied potential interactions of zaleplon, zolpidem and zopiclone with the following types of drugs: cytochrome P450 (CYP) inducers (rifampicin), CYP inhibitors (azoles, ritonavir and erythromycin), histamine H(2) receptor antagonists (cimetidine and ranitidine), antidepressants, antipsychotics, antagonists of benzodiazepines and drugs causing sedation. Rifampicin significantly induced the metabolism of the newer hypnosedatives and decreased their sedative effects, indicating that a dose increase of these agents may be necessary when they are administered with rifampicin. Ketoconazole, erythromycin and cimetidine inhibited the metabolism of the newer hypnosedatives and enhanced their sedative effects, suggesting that a dose reduction may be required. Addition of ethanol to treatment with the newer hypnosedatives resulted in additive sedative effects without altering the pharmacokinetic parameters of the drugs. Compared with some of the conventional benzodiazepines, fewer clinically important interactions appear to have been reported in the literature with zaleplon, zolpidem and zopiclone. The fact that these drugs are newer to the market and have not been as extensively studied as the conventional benzodiazepines may be the reason for this. Another explanation may be a difference in CYP metabolism. While triazolam and midazolam are biotransformed almost entirely via CYP3A4, the newer hypnosedatives are biotransformed by several CYP isozymes in addition to CYP3A4, resulting in CYP3A4 inhibitors and inducers having a lesser effect on their biotransformation.

Short-term exposure to low-dose ritonavir impairs clearance and enhances adverse effects of trazodone

Antiretroviral agents may participate in drug interactions that influence the efficacy and toxicity of other antiretrovirals, as well as pharmacologic treatments of coincident or complicating diseases. The viral protease inhibitor, ritonavir, may cause drug interactions by inhibiting the activity of cytochrome P450-3A (CYP3A) isoforms. In a single-dose, blinded, four-way crossover study, 10 healthy volunteer subjects received 50 mg of trazodone hydrochloride or matching placebo concurrent with low-dose ritonavir (four doses of 200 mg each) or with placebo. Compared to the control condition, ritonavir significantly reduced apparent oral clearance of trazodone (155 +/- 23 vs. 75 +/- 12 ml/min, p < 0.001), prolonged elimination half-life (6.7 +/- 0.7 vs. 14.9 +/- 3.9 h, p < 0.05), and increased peak plasma concentrations (842 +/- 64 vs. 1125 +/- 111 ng/ml, p < 0.05) (mean +/- SE). Coadministration of trazodone with ritonavir increased sedation, fatigue, and performance impairment compared to trazodone plus placebo; differences reached significance only for the digitsymbol substitution test. Three subjects experienced nausea, dizziness, or hypotension when trazodone was given with ritonavir; 1 of these subjects also experienced syncope. Thus short-term low-dose administration of ritonavir impairs oral clearance of trazodone and increases the occurrence of adverse reactions. The findings are consistent with impairment of CYP3A-mediated trazodone metabolism by ritonavir.

Human variability in CYP3A4 metabolism and CYP3A4-related uncertainty factors for risk assessment

CYP3A4 constitutes the major liver cytochrome P450 isoenzyme and is responsible for the oxidation of more than 50% of all known drugs. Human variability in kinetics for this pathway has been quantified using a database of 15 compounds metabolised extensively (>60%) by this CYP isoform in order to develop CYP3A4-related uncertainty factors for the risk assessment of environmental contaminants handled via this route. Data were analysed from published pharmacokinetic studies (after oral and intravenous dosing) in healthy adults and other subgroups using parameters relating primarily to chronic exposure [metabolic and total clearances, area under the plasma concentration-time curve (AUC)] and acute exposure (Cmax). Interindividual variability in kinetics was greater for the oral route (46%, 12 compounds) than for the intravenous route (32%, 14 compounds). The physiological and molecular basis for the difference between these two routes of exposure is discussed. In relation to the uncertainty factors used for risk assessment, the default kinetic factor of 3.16 would be adequate for adults, whereas a CYP3A4-related factor of 12 would be required to cover up to 99% of neonates, which have lower CYP3A4 activity.

Effect of zolpidem on human cytochrome P450 activity, and on transport mediated by P-glycoprotein

The influence of high concentrations of zolpidem (100 microM, corresponding to approximately 200 times maximum therapeutic concentrations) on the activity of six human Cytochrome P450 (CYP) enzymes was evaluated in a model system using human liver microsomes. Zolpidem produced negligible or weak inhibition of human CYP1A2, 2B6, 2C9, 2C19, 2D6, and 3A. Transport of rhodamine 123, presumed to be mediated mainly by the energy-dependent efflux transport protein P-glycoprotein, was studied in a cell culture system using a human intestinal cell line. High concentrations of zolpidem (100 microM), exceeding the usual therapeutic range by more than 100-fold, produced only modest impairment of rhodamine 123 transport. The findings indicate that zolpidem is very unlikely to cause clinical drug interactions attributable to impairment of CYP activity or P-gp mediated transport.