Pharmacokinetic interaction between intravenous phenytoin and amiodarone in healthy volunteers

Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus® to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

Amiodarone: A Comprehensive Guide for Clinicians

Amiodarone is an effective antiarrhythmic medication frequently used in practice for both ventricular and atrial arrhythmias. Though classified as a class III antiarrhythmic, it affects all phases of the cardiac action potential. However, the drug has several side effects, including thyroid abnormalities, pulmonary fibrosis, and transaminitis, for which routine monitoring is recommended. It also interacts with several medications, such as warfarin, simvastatin, and atorvastatin, and many HIV antiretroviral medications. Given the common use of this medication in medical practice, it is vital that clinicians understand the indications, contraindications, dosing, side effects, and interactions of this medication. A thorough understanding of these topics is essential for clinicians to ensure safe and effective use of amiodarone.

Drug Interactions in Neurocritical Care

Drug-drug interactions (DDIs) are common and avoidable complications that are associated with poor patient outcomes. Neurocritical care patients may be at particular risk for DDIs due to alterations in pharmacokinetic profiles and exposure to medications with a high DDI risk. This review describes the principles of DDI pharmacology, common and severe DDIs in Neurocritical care, and recommendations to minimize adverse outcomes. A review of published literature was performed using PubMed by searching for 'Drug Interaction' and several high DDI risk and common neurocritical care medications. Key medication classes included anticoagulants, antimicrobials, antiepileptics, antihypertensives, sedatives, and selective serotonin reuptake inhibitors. Additional literature was also reviewed to determine the risk in neurocritical care and potential therapeutic alternatives. Clinicians should be aware of interactions in this setting, the long-term complications, and therapeutic alternatives.

In vivo information-guided prediction approach for assessing the risks of drug-drug interactions associated with circulating inhibitory metabolites

The in vivo drug-drug interaction (DDI) risks associated with cytochrome P450 inhibitors that have circulating inhibitory metabolites cannot be accurately predicted by conventional in vitro-based methods. A novel approach, in vivo information-guided prediction (IVIP), was recently introduced for CYP3A- and CYP2D6-mediated DDIs. This technique should be applicable to the prediction of DDIs involving other important cytochrome P450 metabolic pathways. Therefore, the aims of this study were to extend the IVIP approach to CYP2C9-mediated DDIs and evaluate the IVIP approach for predicting DDIs associated with inhibitory metabolites. The analysis was based on data from reported DDIs in the literature. The IVIP approach was modified and extended to CYP2C9-mediated DDIs. Thereafter, the IVIP approach was evaluated for predicting the DDI risks of various inhibitors with inhibitory metabolites. Although the data on CYP2C9-mediated DDIs were limited compared with those for CYP3A- and CYP2D6-mediated DDIs, the modified IVIP approach successfully predicted CYP2C9-mediated DDIs. For the external validation set, the prediction accuracy for area under the plasma concentration-time curve (AUC) ratios ranged from 70 to 125%. The accuracy (75-128%) of the IVIP approach in predicting DDI risks of inhibitors with circulating inhibitory metabolites was more accurate than in vitro-based methods (28-805%). The IVIP model accommodates important confounding factors in the prediction of DDIs, which are difficult to handle using in vitro-based methods. In conclusion, the IVIP approach could be used to predict CYP2C9-mediated DDIs and is easily modified to incorporate the additive effect of circulating inhibitory metabolites.

Amiodarone analog-dependent effects on CYP2C9-mediated metabolism and kinetic profiles

CYP2C9 substrates can exhibit both hyperbolic and atypical kinetic profiles, and their metabolism can be activated or inhibited depending on the effector studied. CYP2C9 genetic variants can also affect both substrate turnover and kinetic profile. The present study assessed whether analogs of the effector amiodarone differentially altered the atypical kinetic profile of the substrate naproxen and whether this effect was genotype-dependent. Amiodarone, desethylamiodarone, benzbromarone, and its dimethyl analog (benz(meth)arone) were incubated with naproxen and either CYP2C9.1 or CYP2C9.3. Amiodarone activated naproxen demethylation at lower concentrations, regardless of the CYP2C9 allele, and inhibited metabolism at higher concentrations without altering the kinetic profile. Desethylamiodarone was a potent inhibitor of naproxen demethylation, irrespective of the CYP2C9 allele. Benzbromarone altered naproxen demethylation kinetics from a biphasic profile to that of a hyperbolic form in CYP2C9.1 and CYP2C9.3, resulting in inhibition and activation, respectively. In contrast, benz(meth)arone activated naproxen demethylation in both CYP2C9.1 and CYP2C9.3. In addition, the kinetic profile of naproxen demethylation became more hyperbolic at lower concentrations of benz(meth)arone and then reverted back to biphasic as the benz(meth)arone was increased further. Equilibrium binding and multiple-ligand docking studies were used to propose how such similar compounds exerted very different effects on naproxen metabolism. In summary, effectors of CYP2C9 metabolism can alter not only the degree of substrate turnover (activation or inhibition) but also the kinetic profile of metabolism of CYP2C9 substrates through effects on substrate binding and orientation. In addition, these kinetics effects are concentration- and genotype-dependent.

Effect of Amiodarone on the Serum Concentration/Dose Ratio of Metoprolol in Patients with Cardiac Arrhythmia

Amiodarone has pharmacokinetic interactions with a number of therapeutic drugs, including warfarin, phenytoin, flecainide, and cyclosporine. Metoprolol is mainly metabolized by CYP2D6, and desethylamiodarone, a metabolite of amiodarone, has a markedly greater inhibitory effect on CYP2D6 than amiodarone. Therefore, the goal of this study was to evaluate the effect of amiodarone and desethylamiodarone on the serum concentration/dose ratio (C/D) of metoprolol in 120 inpatients with cardiac arrhythmias that received either metoprolol and amiodarone (MET+AMD group, n=30) or metoprolol alone (MET group, n=90). The ratio of administered metoprolol was compared between the MET and the MET+AMD groups. The dose of metoprolol and patient age were significantly higher in the MET group when compared with the MET+AMD group (1.00+/-0.480 versus 0.767+/-0.418 mg/kg/day, p<0.050; 68.6+/-10.6 versus 57.6+/-14.1 years, p<0.001, respectively), but the C/D ratio was significantly lower in the MET group than in the MET+AMD group (90.8+/-64.0 versus 136+/-97.8, p<0.01). Furthermore, a significant correlation was found between the C/D ratio and desethylamiodarone concentration (n=30, r=0.371, p<0.01). The results suggest that there is a significant interaction between amiodarone and metoprolol via desethylamiodarone-induced inhibition of CYP2D6. Therefore, careful monitoring of metoprolol concentrations/bioactivity of CYP2D6 is required in the context of co-administration of amiodarone and metoprolol.

Drug bioactivation, covalent binding to target proteins and toxicity relevance

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targets of reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.

Effect of benzodiazepines on the metabolism of buprenorphine in human liver microsomes

OBJECTIVE

To determine whether enzyme inhibition explains the clinical adverse interaction of benzodiazepines and buprenorphine.

METHODS

Buprenorphine was incubated in the presence of benzodiazepines (or metabolites) with human liver microsomes (HLMs). A number of benzodiazepines were screened at therapeutic concentrations after 0-min and 15-min preincubation times. For tentative metabolically activated inhibitors, the kinetics of inhibition was studied in a secondary incubation system. Buprenorphine and norbuprenorphine were quantified by means of liquid chromatography-mass spectrometry.

RESULTS

Buprenorphine elimination and norbuprenorphine formation were at most reduced by 26% (i.e., weak or negligible inhibition). Evidence of metabolically activated inhibition suggested the need for further studies on the inhibitory kinetics. Midazolam caused time- and concentration-dependent inhibition of norbuprenorphine formation with pseudo-first-order kinetics, and K(I) and k(inact) values of 10.5 microM and 0.045 min(-1), respectively. Mixed-type inhibition of buprenorphine elimination (K(i) = 30-35 microM) and a noncompetitive inhibition of norbuprenorphine formation were also observed. For clonazepam (up to 10 microM), 3-hydroxy-7-acetamidoclonazepam (up to 10 microM), and alpha-hydroxy-triazolam (up to 1.0 microM), no time- or concentration-dependent inhibition of buprenorphine metabolism was found.

CONCLUSION

A single benzodiazepine, midazolam, is a moderate mechanism-based inactivator of buprenorphine N-dealkylation. It is anticipated that repeated exposures to midazolam might alter the in vivo metabolism of buprenorphine.

Pharmacokinetic Characteristics of Amiodarone in Long-Term Oral Therapy in Japanese Population

To evaluate the pharmacokinetic properties and an optimum dose schedule of amiodarone in long-term oral therapy, serum concentrations of amiodarone and its metabolite, desethylamiodarone, were monitored from 345 Japanese inpatients who received amiodarone therapy for a variety of cardiac arrhythmias. Serum amiodarone and desethylamiodarone concentrations were determined by high performance liquid chromatography system. It was observed that the amiodarone and desethylamiodarone concentrations gradually increased with time. The frequency distribution in the amiodarone clearance of 245 subjects, who received fixed maintenance amiodarone therapy for at least 6 months, was nearly a unimodal one. The variation in the ratio of desetylamiodarone to amiodarone concentration in serum was very small. Although no differences in age, dose, dose duration, amiodarone or desethyamiodarone concentration or ratio were observed between men and women: however, the mean amiodarone clearance of women was significantly higer than that of men. The laboratory data were mostly within normal values and no significant relations were observed between serum amiodarone concentration and clinical laboratory data. These results suggest that the individual variation in pharmacokinetics of amiodarone is comparatively small, which might be sufficient to decide that the maintenance dose was the same one (200 mg/d) in long-term oral amiodarone therapy.

Stereoselective Effect of Amiodarone on the Pharmacokinetics of Racemic Carvedilol

We investigated whether there was a stereoselective effect of amiodarone on the pharmacokinetics of carvedilol. Among a series of 106 inpatients with heart failure, 52 received carvedilol monotherapy (carvedilol group) and 54 received carvedilol plus amiodarone (carvedilol+amiodarone group). The serum carvedilol concentration administered/dose ratio was compared between the two groups based on HPLC measurement of the serum levels of carvedilol, amiodarone, and desethylamiodarone. In 6 patients from the carvedilol group, serum carvedilol levels were compared before and after coadministration of amiodarone. There was no significant between-group difference of the serum concentration to dose (C/D ratio) for the R-enantiomer carvedilol, however, the C/D ratio for the S-enantiomer and the serum S-carvedilol to R-carvedilol (S/R) ratio were both significantly lower in the carvedilol group than in the carvedilol+amiodarone group(47.8+/-56.7 versus 95.3+/-105 ng/mg/kg, P=0.0048 and 0.460+/-0.207 versus 0.879+/-0.377 ng/mg/kg, P<0.001), respectively. Furthermore, the mean S-carvedilol concentration over 14 days of coadministration with amiodarone was higher than that before coadministration (6.54+/-1.73 ng/mL versus 3.03+/-0.670 ng/mL, P<0.001). These results suggest that metabolism of S-carvedilol was markedly inhibited by coadministration of amiodarone.

Potentially significant drug interactions of class III antiarrhythmic drugs

Class III antiarrhythmic drugs, especially amiodarone (a broad-spectrum antiarrhythmic agent), have gained popularity for use in clinical practice in recent years. Other class III antiarrhythmic drugs include bretylium, dofetilide, ibutilide and sotalol. These agents are effective for the management of various types of cardiac arrhythmias both atrial and ventricular in origin. Class III antiarrhythmic drugs may interact with other drugs by two major processes: pharmacodynamic and pharmacokinetic interactions. The pharmacodynamic interaction occurs when the pharmacological effects of the object drug are stimulated or inhibited by the precipitant drug. Pharmacokinetic interactions can result from the interference of drug absorption, metabolism and/or elimination of the object drug by the precipitant drug. Among the class III antiarrhythmic drugs, amiodarone has been reported to be involved in a significant number of drug interactions. It is mainly metabolised by cytochrome P450 (CYP)3A4 and it is a potent inhibitor of CYP1A2, 2C9, 2D6 and 3A4. In addition, amiodarone may interact with other drugs (such as digoxin) via the inhibition of the P-glycoprotein membrane transporter system, a recently described pharmacokinetic mechanism of drug interactions. Bretylium is not metabolised; it is excreted unchanged in the urine. Therefore the interactions between bretylium and other drugs (including other antiarrhythmic drugs) is primarily through the pharmacodynamic mechanism. Dofetilide is metabolised by CYP3A4 and excreted by the renal cation transport system. Drugs that inhibit CYP3A4 (such as erythromycin) and/or the renal transport system (such as triamterene) may interact with dofetilide. It appears that the potential for pharmacokinetic interactions between ibutilide and other drugs is low. This is because ibutilide is not metabolised by CYP3A4 or CYP2D6. However, ibutilide may significantly interact with other drugs by a pharmacodynamic mechanism. Sotalol is primarily excreted unchanged in the urine. The potential for drug interactions due to hepatic enzyme induction or inhibition appears to be less likely. However, a number of drugs (such as digoxin) have been reported to interact with sotalol pharmacodynamically. If concurrent use of a class III antiarrhythmic agent and another drug cannot be avoided or no published studies for that particular drug interaction are available, caution should be exercised and close monitoring of the patient should be performed in order to avoid or minimise the risks associated with a possible adverse drug interaction.

Amiodarone Interaction Time Differences with Warfarin and Digoxin

Background:

Amiodarone has pharmacokinetic and pharmacodynamic interactions with various therapeutic agents. The mechanism of interaction between warfarin and amiodarone is the inhibition of warfarin metabolism by amiodarone, and that between digoxin and amiodarone is the inhibition of digoxin transport by amiodarone.

Objective:

To investigate the pharmacokinetic magnitude of the time differences between amiodarone–warfarin and amiodarone–digoxin interactions.

Methods:

Amiodarone was administered concomitantly to 79 inpatients who had been receiving fixed-maintenance doses of warfarin or digoxin. Seventy-seven inpatients were prescribed warfarin therapy, and 54 inpatients were prescribed digoxin therapy. To determine serum concentrations of the warfarin enantiomers digoxin, amiodarone, and desethylamiodarone blood samples were obtained with coadministration of amiodarone. Serum S- and R-warfarin, amiodarone, and desethylamiodarone concentrations were measured by HPLC methods, and serum digoxin concentrations were measured by a fluorescence polarization immunoassay.

Results:

A remarkable decrease of S-warfarin clearance was observed within approximately the first 2 weeks after coadministration of amiodarone. Only a small decrease in R-warfarin clearance was observed. Digoxin clearance was gradually decreased with time, and a good reverse correlation was obtained between amiodarone or desethylamiodarone concentrations and digoxin clearance.

Conclusions:

Relatively short-term monitoring of warfarin clearance is required when amiodarone is coadministered. Long-term monitoring of digoxin serum amiodarone and desethylamiodarone concentrations is necessary to detect the amiodarone–digoxin interaction.

Lack of interaction between amiodarone and mexiletine in cardiac arrhythmia patients

Amiodarone has pharmacokinetic interactions with various therapeutic agents, including phenytoin, flecainide, and cyclosporine. Mexiletine is metabolized by CYP2D6 and CYP1A2. The objective of this study is to evaluate the effect of amiodarone on the pharmacokinetics of mexiletine through its inhibition of various cytochrome P450 (CYP) subtypes. In a series of 181 inpatients with supraventricular tachyarrhythmias, 26 inpatients received mexiletine and amiodarone therapy (MEX + AMD group), and the others received mexiletine therapy (MEX group). In 10 inpatients of the MEX + AMD group, the mexiletine clearance (CL(MEX)/F) before and after coadministration of amiodarone was compared. CL(MEX)/F was also compared in the MEX and MEX + AMD groups after the start of amiodarone therapy. Serum mexiletine, amiodarone, and desethylamiodarone concentrations were measured by an HPLC method. The CL(MEX)/F was estimated by the Bayesian method using population pharmacokinetic analysis. There was no significant difference in CL(MEX)/F before and after 1-month coadministration of amiodarone in 10 inpatients of the MEX + AMD group. Although serum amiodarone and desethylamiodarone concentrations gradually increased with time after the start of amiodarone therapy in these patients, CL(MEX)/F showed no change at 3 and 5 months after the start of amiodarone therapy. There was no significant difference in CL(MEX)/F of the MEX group and the MEX + AMD group. The results suggest that the pharmacokinetics of mexiletine is not affected by amiodarone in patients with cardiac arrhythmias.

Effect of mild therapeutic hypothermia on phenytoin pharmacokinetics

The authors examined the effect of mild therapeutic hypothermia on phenytoin pharmacokinetics in 14 patients with brain damage. Each patient was given phenytoin during and after mild therapeutic hypothermia. Plasma concentrations of total phenytoin, unbound phenytoin, and 5-(p-hydroxyphenyl)-5-phenylhydantoin (5-p-HPPH), the major metabolite of phenytoin, were measured. Pharmacokinetic parameters during and after mild therapeutic hypothermia were compared. Plasma concentrations of total and unbound phenytoin were significantly higher during hypothermia than after hypothermia. The area under the plasma concentration-time curve (zero to infinity) was increased by 180% and mean residence time was prolonged by 180% during hypothermia compared with the corresponding values after hypothermia. Moreover, the elimination constant (ke) was decreased by 50% and elimination clearance of phenytoin was decreased by 67% during hypothermia compared with the corresponding values after hypothermia. The plasma concentration of 5-p-HPPH was significantly lower during hypothermia than after hypothermia. These findings suggest that phenytoin metabolism is inhibited by mild therapeutic hypothermia.

Antiarrhythmic agents: drug interactions of clinical significance

The management of cardiac arrhythmias has grown more complex in recent years. Despite the recent focus on nonpharmacological therapy, most clinical arrhythmias are treated with existing antiarrhythmics. Because of the narrow therapeutic index of antiarrhythmic agents, potential drug interactions with other medications are of major clinical importance. As most antiarrhythmics are metabolised via the cytochrome P450 enzyme system, pharmacokinetic interactions constitute the majority of clinically significant interactions seen with these agents. Antiarrhythmics may be substrates, inducers or inhibitors of cytochrome P450 enzymes, and many of these metabolic interactions have been characterised. However, many potential interactions have not, and knowledge of how antiarrhythmic agents are metabolised by the cytochrome P450 enzyme system may allow clinicians to predict potential interactions. Drug interactions with Vaughn-Williams Class II (beta-blockers) and Class IV (calcium antagonists) agents have previously been reviewed and are not discussed here. Class I agents, which primarily block fast sodium channels and slow conduction velocity, include quinidine, procainamide, disopyramide, lidocaine (lignocaine), mexiletine, flecainide and propafenone. All of these agents except procainamide are metabolised via the cytochrome P450 system and are involved in a number of drug-drug interactions, including over 20 different interactions with quinidine. Quinidine has been observed to inhibit the metabolism of digoxin, tricyclic antidepressants and codeine. Furthermore, cimetidine, azole antifungals and calcium antagonists can significantly inhibit the metabolism of quinidine. Procainamide is excreted via active tubular secretion, which may be inhibited by cimetidine and trimethoprim. Other Class I agents may affect the disposition of warfarin, theophylline and tricyclic antidepressants. Many of these interactions can significantly affect efficacy and/or toxicity. Of the Class III antiarrhythmics, amiodarone is involved in a significant number of interactions since it is a potent inhibitor of several cytochrome P450 enzymes. It can significantly impair the metabolism of digoxin, theophylline and warfarin. Dosages of digoxin and warfarin should empirically be decreased by one-half when amiodarone therapy is added. In addition to pharmacokinetic interactions, many reports describe the use of antiarrhythmic drug combinations for the treatment of arrhythmias. By combining antiarrhythmic drugs and utilising additive electrophysiological/pharmacodynamic effects, antiarrhythmic efficacy may be improved and toxicity reduced. As medication regimens grow more complex with the aging population, knowledge of existing and potential drug-drug interactions becomes vital for clinicians to optimise drug therapy for every patient.

Inhibitory effects of amiodarone and its N-deethylated metabolite on human cytochrome P450 activities: prediction of in vivo drug interactions

AIMS

To predict the drug interactions of amiodarone and other drugs, the inhibitory effects and inactivation potential for human cytochrome P450 (CYP) enzymes by amiodarone and its N-dealkylated metabolite, desethylamiodarone were examined.

METHODS

The inhibition or inactivation potency of amiodarone and desethylamiodarone for human CYP activities were investigated using microsomes from B-lymphoblastoid cell lines expressing CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. The in vivo drug interactions of amiodarone and desethylamiodarone were predicted in vitro using the 1+Iu/Ki values.

RESULTS

Amiodarone weakly inhibited CYP2C9, CYP2D6, and CYP3A4-mediated activities with Ki values of 45.1-271.6 microm. Desethylamiodarone competitively inhibited the catalytic activities of CYP2D6 (Ki=4.5 microm ) and noncompetitively inhibited CYP2A6 (Ki=13.5 microm ), CYP2B6 (Ki=5.4 microm ), and CYP3A4 (Ki=12.1 microm ). The catalytic activities of CYP1A1 (Ki=1.5 microm, alpha=5.7), CYP1A2 (Ki=18.8 microm, alpha=2.6), CYP2C9 (Ki=2.3 microm, alpha=5.9), and CYP2C19 (Ki=15.7 microm, alpha=4.5) were inhibited by desethylamiodarone with mixed type. The 1+Iu/Ki values of desethylamiodarone were higher than those of amiodarone. Amiodarone inactivated CYP3A4, while desethylamiodarone inactivated CYP1A1, CYP1A2, CYP2B6, and CYP2D6.

CONCLUSIONS

The interactions between amiodarone and other drugs might occur via the inhibition of CYP activities by its N-dealkylated metabolite, desethylamiodarone, rather than by amiodarone itself. In addition, the inactivation of CYPs by desethylamiodarone as well as by amiodarone would also contribute to the drug interactions.

Cytochrome P4502C9: an enzyme of major importance in human drug metabolism

Accumulating evidence indicates that CYP2C9 ranks amongst the most important drug metabolizing enzymes in humans. Substrates for CYP2C9 include fluoxetine, losartan, phenytoin, tolbutamide, torsemide, S-warfarin, and numerous NSAIDs. CYP2C9 activity in vivo is inducible by rifampicin. Evidence suggests that CYP2C9 substrates may also be induced variably by carbamazepine, ethanol and phenobarbitone. Apart from the mutual competitive inhibition which may occur between alternate substrates, numerous other drugs have been shown to inhibit CYP2C9 activity in vivo and/or in vitro. Clinically significant inhibition may occur with coadministration of amiodarone, fluconazole, phenylbutazone, sulphinpyrazone, sulphaphenazole and certain other sulphonamides. Polymorphisms in the coding region of the CYP2C9 gene produce variants at amino acid residues 144 (Arg144Cys) and 359 (Ile359Leu) of the CYP2C9 protein. Individuals homozygous for Leu359 have markedly diminished metabolic capacities for most CYP2C9 substrates, although the frequency of this allele is relatively low. Consistent with the modulation of enzyme activity by genetic and other factors, wide interindividual variability occurs in the elimination and/or dosage requirements of prototypic CYP2C9 substrates. Individualisation of dose is essential for those CYP2C9 substrates with a narrow therapeutic index.

Interaction between amiodarone and lidocaine

We investigated the in vitro and in vivo interaction between amiodarone and lidocaine. The interaction on a molecular level was first studied in microsomes from 11 human livers. Close correlations between amiodarone N-monodesethylase activities and (a) the amounts of cytochrome P-4503A4 (CYP3A4), and (b) the rates of lidocaine N-monodesethylation were observed. Lidocaine inhibited amiodarone N-monodesethylation (Ki = 120 microM) competitively; inversely, amiodarone suppressed lidocaine N-monodesethylase activity in the same manner (Ki = 47 microM). Moreover, the metabolite N-monodesethylamiodarone (DEA) was stable and inhibited lidocaine metabolism in a concentration-dependent manner. The in vivo interaction was investigated in 6 cardiac patients. Each of them received a dose of 1 mg/kg lidocaine hydrochloride intravenously (i.v.) on three different occasions: before amiodarone treatment (control), and after cumulative doses of 3 g (phase I) and 13 g (phase II), respectively, amiodarone hydrochloride. The analysis of lidocaine pharmacokinetics showed an increase in lidocaine area under the curve (AUC) when amiodarone was administered, whereas that of N-monodesethylated lidocaine decreased. Moreover, the systemic clearance of lidocaine decreased, while the elimination half-life (t1/2) and the distribution volume at steady state of lidocaine remained unchanged. The pharmacokinetic parameters during phase II were the same as those during phase 1, indicating that the interaction had already occurred early in the loading phase of amiodarone administration. The interaction between amiodarone and lidocaine may be explained by the inhibition of CYP3A4 by amiodarone and/or by its main metabolite DEA.

The effect of tenidap sodium on the disposition and plasma protein binding of phenytoin in healthy male volunteers

1. The effects of tenidap sodium 120 mg day-1 at steady state and placebo on the plasma protein binding and pharmacokinetics of phenytoin were compared in this randomised, double-blind, placebo-controlled, parallel-group study, involving 12 healthy young men, conducted over 34 days. 2. Single oral doses of phenytoin 200 mg were given on days 1-3 and 29-31, and intravenous phenytoin, 250 mg infused over 20 min, was given on days 4 and 32. Tenidap (120 mg day-1), or matching placebo, was administered as single oral daily doses from days 8 to 34 inclusive. 3. The plasma protein binding of phenytoin was determined immediately before oral phenytoin administration on days 1 and 29. Pharmacokinetic parameters were estimated from the serum phenytoin concentration-time curves derived on days 4 and 32 following the phenytoin infusions. The differences between the pre- and post-treatment mean percentage of unbound plasma phenytoin and mean pharmacokinetic parameters were compared between treatment groups. 4. Tenidap sodium 120 mg day-1, at steady state, increased the percentage of unbound phenytoin in plasma by approximately 25%, but did not significantly affect AUC(0,48h) or Cmax. 5. Since tenidap increases the percentage of unbound phenytoin in plasma, when monitoring phenytoin plasma concentrations free concentrations of phenytoin should be considered. 6. Tenidap was well tolerated throughout the study.

In vitro forecasting of drugs which may interfere with the biotransformation of midazolam

The biotransformation of midazolam is mediated by a cytochrome P-450 isozyme (P-450 IIIA) whose activity is highly variable. The kinetics of the 1'- and 4-hydroxylation of midazolam, the major routes of midazolam oxidation, by human liver microsomes have been examined to characterize further the cytochrome isozyme(s) catalysing these reactions, and to screen for drugs that might interfere with them. In hepatic microsomal preparation from two kidney donors (extensive and poor metabolisers of debrisoquine) KM values for 1'-hydroxylation were 4.2 and 6.1 microM (extensive and poor metabolisers, respectively), and for the 4-hydroxylation they were 14.7 and 18.1 microM, respectively. The corresponding Vmax values were 25.8 and 29.8 and 17.0 and 18.1 nmol.mg P-1.h-1. Both reactions appeared to be catalysed by the same or by coregulated isozymes. Midazolam hydroxylations in vitro are inhibited by many drugs, including nifedipine and other dihydropyridine-type calcium channel blockers, ergot alkaloids, cyclosporine, erythromycin and phenothiazine-type neuroleptics. A clinical case report illustrates the consequence of such a drug-drug interference with hepatic biotransformation; midazolam-induced sleep in a patient lasted for 6 days (t1/2 = 25 h).

Effect of phenytoin on the clinical pharmacokinetics of amiodarone

Five healthy male volunteers were given oral amiodarone hydrochloride, 200 mg per day for 6 1/2 weeks, to determine its effects on the pharmacokinetics of both intravenous and oral phenytoin. Predose amiodarone and N-desethylamiodarone serum concentrations were obtained weekly during weeks 2-6. Amiodarone serum concentrations (ASC) increased during weeks 2-4 and then decreased sharply during weeks 5-6 when oral phenytoin, 2-4 mg/kg/day, was co-administered. In addition, N-desethylamiodarone serum concentrations (DEASC) exceeded corresponding ASC during weeks 5-6 whereas during weeks 2-4, DEASC were less than ASC. Because of the long elimination half-life for amiodarone previously reported in healthy volunteers after single doses of amiodarone and the frequent administration of amiodarone associated with this half-life, a modified equation for a continuous infusion was used to describe each subject's ASC versus time data. Pre-phenytoin ASC were fitted to an appropriate function to predict ASC during weeks 5-6 assuming no interaction. Observed versus predicted ASC were compared for weeks 5 and 6. Observed ASC during weeks 5 and 6 were (mean +/- SD) 0.25 +/- 0.09 micrograms/mL and 0.19 +/- 0.07 micrograms/mL, respectively. Corresponding predicted ASC were 0.36 +/- 0.12 micrograms/mL (P = .011) and 0.38 +/- 0.13 micrograms/mL (P = .004). These represented percent differences of 32.2 +/- 12.5% and 49.3 +/- 5.6% for weeks 5 and 6, respectively. Assuming there were no changes in the bioavailability of amiodarone during continuous administration, these findings strongly suggest induction of amiodarone metabolism by phenytoin. The clinical significance of this interaction remains to be determined.

Steady-state interaction between amiodarone and phenytoin in normal subjects

Amiodarone has been reported to increase phenytoin levels. This study was designed to evaluate the pharmacokinetic basis of this interaction at steady-state. Pharmacokinetic parameters for phenytoin were determined after 14 days of oral phenytoin, 2 to 4 mg/kg/day, before and after oral amiodarone, 200 mg daily for 6 weeks in 7 healthy male subjects. During amiodarone therapy, area under the serum concentration time curve for phenytoin was increased from 208 +/- 82.8 (mean +/- standard deviation) to 292 +/- 108 mg.hr/liter (p = 0.015). Both the maximum and 24-hour phenytoin concentrations were increased from 10.75 +/- 3.75 and 6.67 +/- 3.51 micrograms/ml to 14.26 +/- 3.97 (p = 0.016) and 10.27 +/- 4.67 micrograms/ml (p = 0.012), respectively, during concomitant amiodarone treatment. Amiodarone caused a decrease in the oral clearance of phenytoin from 1.29 +/- 0.30 to 0.93 +/- 0.25 liters/hr (p = 0.002). These results were due to a reduction in phenytoin metabolism by amiodarone as evidenced by a decrease in the urinary excretion of the principal metabolite of phenytoin, 5-(p-hydroxyphenyl)-5-phenylhydantoin, 149 +/- 39.7 to 99.3 +/- 40.0 mg (p = 0.041) and no change in the unbound fraction of the total phenytoin concentration expressed as a percentage, 10.3 +/- 2.7 versus 10.7 +/- 2.1% (p = 0.28) during coadministration of amiodarone. The alterations in phenytoin pharmacokinetics suggest that steady-state doses of phenytoin of 2 to 4 mg/kg/day should be reduced at least 25% when amiodarone is concurrently administered. All dosage reductions should be guided by clinical and therapeutic drug monitoring.

Relation of amiodarone hepatic and pulmonary toxicity to serum drug concentrations and superoxide dismutase activity

Hepatic enzymes, pulmonary function, serum amiodarone and desethylamiodarone (DEA) concentrations and erythrocyte superoxide dismutase (SOD) activity were monitored at regular intervals for 1 year in 30 patients receiving amiodarone. Subclinical hepatotoxicity developed in 5 patients. These patients had higher baseline alanine transaminase values (42.6 +/- 6.8 vs 22.9 +/- 1.8 U/liter) and had an increase in serum aspartate transaminase from 27 +/- 4.1 at baseline to 147 +/- 77.3 U/liter at 12 months. The other patients had little variation in aspartate transaminase. Six patients with normal baseline carbon monoxide diffusing capacity had subclinical pulmonary toxicity develop with a mean decrease in diffusing capacity to 0.7 +/- 0.05 of the baseline value, which correlated with decreasing erythrocyte SOD activity. Mean carbon monoxide diffusing capacity and SOD activity remained unchanged in the other patients. The mechanisms of hepatic and pulmonary injury remain unknown, but appear to be associated with exposure to higher total serum concentrations of amiodarone plus DEA. Patients who had hepatic and/or pulmonary abnormalities develop received higher doses of amiodarone (440 +/- 27 vs 340 +/- 18 mg/day), but also had a higher amiodarone:DEA ratio suggesting that dose-dependent kinetics contributed to the higher concentrations. Elevated baseline alanine transaminase may indicate increased risk for hepatotoxicity while a progressive decrease in erythrocyte SOD may be an early indication of pulmonary toxicity. The latter finding indicates a need to investigate the role of free radicals in the pathogenesis of amiodarone pulmonary toxicity.