Interaction between propranolol and propafenone in healthy volunteers

[Pharmacokinetic risk factors of beta-blockers overdose in the elderly: Case report and pharmacology approach]

Beta-blockers are widely prescribed in elderly patients and may induce severe adverse drug reactions. We report a case of bisoprolol-induced bradycardia in an elderly patient with impaired renal function and use of cytochrome P450 inhibitors. A literature review has been performed in order to analyze pharmacokinetic risk factors of beta-blockers overdosing in geriatrics. Various mechanisms can result in decreased elimination of beta-blockers. These mechanisms vary according to the beta-blocker agent and may be combined in some individuals, especially elderly patients. This can lead to unexpected overexposure. Knowledge about drug interactions and pharmacokinetic elimination pathways is important for preventing overexposure and adverse drug reactions when using beta-blockers.

Pharmacokinetic Variability of Drugs Used for Prophylactic Treatment of Migraine

In this review, we evaluate the variability in the pharmacokinetics of 11 drugs with established prophylactic effects in migraine to facilitate 'personalized medicine' with these drugs. PubMed was searched for 'single-dose' and 'steady-state' pharmacokinetic studies of these 11 drugs. The maximum plasma concentration was reported in 248 single-dose and 115 steady-state pharmacokinetic studies, and the area under the plasma concentration-time curve was reported in 299 single-dose studies and 112 steady-state pharmacokinetic studies. For each study, the coefficient of variation was calculated for maximum plasma concentration and area under the plasma concentration-time curve, and we divided the drug variability into two categories; high variability, coefficient of variation >40%, or low or moderate variability, coefficient of variation <40%. Based on the area under the plasma concentration-time curve in steady-state studies, the following drugs have high pharmacokinetic variability: propranolol in 92% (33/36), metoprolol in 85% (33/39), and amitriptyline in 60% (3/5) of studies. The following drugs have low or moderate variability: atenolol in 100% (2/2), valproate in 100% (15/15), topiramate in 88% (7/8), and naproxen and candesartan in 100% (2/2) of studies. For drugs with low or moderate pharmacokinetic variability, treatment can start without initial titration of doses, whereas titration is used to possibly enhance tolerability of topiramate and amitriptyline. The very high pharmacokinetic variability of metoprolol and propranolol can result in very high plasma concentrations in a small minority of patients, and those drugs should therefore be titrated up from a low initial dose, depending mainly on the occurrence of adverse events.

Selective regulation of cardiac organic cation transporter novel type 2 (OCTN2) in dilated cardiomyopathy

Organic cation transporters (OCT1-3 and OCTN1/2) facilitate cardiac uptake of endogenous compounds and numerous drugs. Genetic variants of OCTN2, for example, reduce uptake of carnitine, leading to heart failure. Whether expression and function of OCTs and OCTNs are altered by disease has not been explored in detail. We therefore studied cardiac expression, heart failure-dependent regulation, and affinity to cardiovascular drugs of these transporters. Cardiac transporter mRNA levels were OCTN2>OCT3>OCTN1>OCT1 (OCT2 was not detected). Proteins were localized in vascular structures (OCT3/OCTN2/OCTN1) and cardiomyocytes (OCT1/OCTN1). Functional studies revealed a specific drug-interaction profile with pronounced inhibition of OCT1 function, for example, carvedilol [half maximal inhibitory concentration (IC₅₀), 1.4 μmol/L], diltiazem (IC₅₀, 1.7 μmol/L), or propafenone (IC₅₀, 1.0 μmol/L). With use of the cardiomyopathy model of coxsackievirus-infected mice, Octn2mRNA expression was significantly reduced (56% of controls, 8 days after infection). Accordingly, in endomyocardial biopsy specimens OCTN2 expression was significantly reduced in patients with dilated cardiomyopathy, whereas the expression of OCT1-3 and OCTN1 was not affected. For OCTN2 we observed a significant correlation between expression and left ventricular ejection fraction (r = 0.53, P < 0.0001) and the presence of cardiac CD3⁺ T cells (r = -0.45, P < 0.05), respectively. OCT1, OCT3, OCTN1, and OCTN2 are expressed in the human heart and interact with cardiovascular drugs. OCTN2 expression is selectively reduced in dilated cardiomyopathy patients and predicts the impairment of cardiac function.

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.

Integrated in vitro analysis for the in vivo prediction of cytochrome P450-mediated drug-drug interactions

Unbound IC(50) (IC(50,u)) values of 15 drugs were determined in eight recombinantly expressed human cytochromes P450 (P450s) and human hepatocytes, and the data were used to simulate clinical area under the plasma concentration-time curve changes (deltaAUC) on coadministration with prototypic CYP2D6 substrates. Significant differences in IC(50,u) values between enzyme sources were observed for quinidine (0.02 microM in recombinant CYP2D6 versus 0.5 microM in hepatocytes) and propafenone (0.02 versus 4.1 microM). The relative contribution of individual P450s toward the oxidative metabolism of clinical probes desipramine, imipramine, tolterodine, propranolol, and metoprolol was estimated via determinations of intrinsic clearance using recombinant P450s (rP450s). Simulated deltaAUC were compared with those observed in vivo via the ratios of unbound inhibitor concentration at the entrance to the liver to inhibition constants determined against rP450s ([I](in,u)/K(i)) and incorporating parallel substrate elimination pathways. For this dataset, there were 20% false negatives (observed deltaAUC >or= 2, predicted deltaAUC < 2), 77% correct predictions, and 3% false positives. Thus, the [I](in,u)/K(i) approach appears relatively successful at estimating the degree of clinical interactions and can be incorporated into drug discovery strategies. Using a Simcyp ADME (absorption, metabolism, distribution, elimination) simulator (Simcyp Ltd., Sheffield, UK), there were 3% false negatives, 94% correct simulations, and 3% false positives. False-negative predictions were rationalized as a result of mechanism-based inhibition, production of inhibitory metabolites, and/or hepatic uptake. Integrating inhibition and reaction phenotyping data from automated rP450 screens have shown applicability to predict the occurrence and degree of in vivo drug-drug interactions, and such data may identify the clinical consequences for candidate drugs as both "perpetrators" and "victims" of P450-mediated interactions.

Pharmacokinetics and interactions of headache medications, part II: prophylactic treatments

The present part II review highlights pharmacokinetic drug-drug interactions (excluding those of minor severity) of medications used in prophylactic treatment of the main primary headaches (migraine, tension-type and cluster headache). The principles of pharmacokinetics and metabolism, and the interactions of medications for acute treatment are examined in part I. The overall goal of this series of two reviews is to increase the awareness of physicians, primary care providers and specialists regarding pharmacokinetic drug-drug interactions (DDIs) of headache medications. The aim of prophylactic treatment is to reduce the frequency of headache attacks using beta-blockers, calcium-channel blockers, antidepressants, antiepileptics, lithium, serotonin antagonists, corticosteroids and muscle relaxants, which must be taken daily for long periods. During treatment the patient often continues to take symptomatic drugs for the attack, and may need other medications for associated or new-onset illnesses. DDIs can, therefore, occur. As a whole, DDIs of clinical relevance concerning prophylactic drugs are a limited number. Their effects can be prevented by starting the treatment with low dosages, which should be gradually increased depending on response and side effects, while frequently monitoring the patient and plasma levels of other possible coadministered drugs with a narrow therapeutic range. Most headache medications are substrates of CYP2D6 (e.g., beta-blockers, antidepressants) or CYP3A4 (e.g., calcium-channel blockers, selective serotonin re-uptake inhibitors, corticosteroids). The inducers and, especially, the inhibitors of these isoenzymes should be carefully coadministered.

Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data?

When the metabolism of a drug is competitively or noncompetitively inhibited by another drug, the degree of in vivo interaction can be evaluated from the [I]u/Ki ratio, where [I]u is the unbound concentration around the enzyme and Ki is the inhibition constant of the inhibitor. In the present study, we evaluated the metabolic inhibition potential of drugs known to be inhibitors or substrates of cytochrome P450 by estimating their [I]u/Ki ratio using literature data. The maximum concentration of the inhibitor in the circulating blood ([I]max), its maximum unbound concentration in the circulating blood ([I]max,u), and its maximum unbound concentration at the inlet to the liver ([I]in,max,u) were used as [I]u, and the results were compared with each other. In order to calculate the [I]u/Ki ratios, the pharmacokinetic parameters of each drug were obtained from the literature, together with their reported Ki values determined in in vitro studies using human liver microsomes. For most of the drugs with a calculated [I]in,max,u/Ki ratio less than 0.25, which applied to about half of the drugs investigated, no in vivo interactions had been reported or "no interaction" was reported in clinical studies. In contrast, the [I]max,u/Ki and [I]max/Ki ratio was calculated to be less than 0.25 for about 90% and 65% of the drugs, respectively, and more than a 1.25-fold increase was reported in the area under the concentration-time curve of the co-administered drug for about 30% of such drugs. These findings indicate that the possibility of underestimation of in vivo interactions (possibility of false-negative prediction) is greater when [I]max,u or [I]max values are used compared with using [I]in,max,u values.

Cardiovascular drug-drug interactions

The drug-drug interactions discussed in this article have either documented or suspected clinical relevance for patients with cardiovascular disease and the clinician involved in the care of these patients. Oftentimes, drug-drug interactions are difficult, if not impossible, to predict because of the high degree of interpatient variability in drug disposition. Certain drug-drug interactions, however, may be avoided through knowledge and sound clinical judgment. Every clinician should maintain a working knowledge of reported drug-drug interactions and an understanding of basic pharmacokinetic and pharmacodynamic principles to help predict and minimize the incidence and severity of drug-drug interactions.

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.

Single-dose interaction study of diprafenone HCl and propranolol HCl in healthy volunteers

Using a 3 x 3 Latin Square design, a possible interaction between diprafenone HCl a class IC antiarrhythmic drug with nonspecific beta-antagonist activity and propranolol HCl was investigated in nine young, healthy, caucasian, male volunteers. The volunteers randomly received 3 single-dose treatments: (A) 200 mg DHCl, (B) 80 mg PHCl, and (C) 200 mg DHCl and 80 mg PHCl. Scheduled blood samples were taken and plasma concentrations of both diprafenone and propranolol were measured by sensitive and specific assay methods. Lead II electrocardiogram intervals at rest, heart rate during erect bicycle ergometry, and echocardiographic variables at rest and shortly after exercise were recorded. The data analysis used compartment model independent methods. There was no evidence for a pharmacokinetic interaction between the two drugs. With DHCl, two of the nine subjects showed greatly increased areas under the plasma concentration-time curves and apparent disposition half-lives in the presence and absence of PHCl, indicating that metabolism of diprafenone may be subject to pharmacogenetic polymorphism. There was evidence for a pharmacodynamic interaction between DHCl and PHCl regarding the negative chronotropic effect at rest and during exercise. There was no difference in the pharmacodynamics and tolerability of the three treatments in suspected "poor" and "extensive metabolizers" of DHCl.

Individual variation in first-pass metabolism

Individual variation in pharmacokinetics has long been recognised. This variability is extremely pronounced in drugs that undergo extensive first-pass metabolism. Drug concentrations obtained from individuals given the same dose could range several-fold, even in young healthy volunteers. In addition to the liver, which is the major organ for drug and xenobiotic metabolism, the gut and the lung can contribute significantly to variability in first-pass metabolism. Unfortunately, the contributions of the latter 2 organs are difficult to quantify because conventional in vivo methods for quantifying first-pass metabolism are not sufficiently specific. Drugs that are mainly eliminated by phase II metabolism (e.g. estrogens and progestogens, morphine, etc.) undergo significant first-pass gut metabolism. This is because the gut is rich in conjugating enzymes. The role of the lung in first-pass metabolism is not clear, although it is quite avid in binding basic drugs such as lidocaine (lignocaine), propranolol, etc. Factors such as age, gender, disease states, enzyme induction and inhibition, genetic polymorphism and food effects have been implicated in causing variability in pharmacokinetics of drugs that undergo extensive first-pass metabolism. Of various factors considered, age and gender make the least evident contributions, whereas genetic polymorphism, enzymatic changes due to induction or inhibition, and the effects of food are major contributors to the variability in first-pass metabolism. These factors can easily cause several-fold variations. Polymorphic disposition of imipramine and propafenone, an increase in verapamil first-pass metabolism by rifampicin (rifampin), and the effects of food on propranolol, metoprolol and propafenone, are typical examples. Unfortunately, the contributions of these factors towards variability are unpredictable and tend to be drug-dependent. A change in steady-state clearance of a drug can sometimes be exacerbated when first-pass metabolism and systemic clearance of a drug are simultaneously altered. Therefore, an understanding of the source of variability is the key to the optimisation of therapy.