Similar effect of oxidation deficiency (debrisoquine polymorphism) and quinidine on the apparent volume of distribution of (+/-)-metoprolol

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Volume of Distribution is Unaffected by Metabolic Drug-Drug Interactions

Introduction

It has been recognized that significant transporter interactions result in volume of distribution changes in addition to potential changes in clearance. For drugs that are not clinically significant transporter substrates, it is expected that drug–drug interactions would not result in any changes in volume of distribution.

Methods

An evaluation of this hypothesis proceeded via an extensive analysis of published intravenous metabolic drug–drug interactions, based on clinically recommended index substrates and inhibitors of major cytochrome P450 (CYP) isoforms.

Results

Seventy-two metabolic drug interaction studies were identified where volume of distribution at steady-state (V_ss) values were available for the CYP index substrates caffeine (CYP1A2), metoprolol (CYP2D6), midazolam (CYP3A4), theophylline (CYP1A2), and tolbutamide (CYP2C9). Changes in exposure (area under the curve) up to 5.1-fold were observed; however, ratios of V_ss changes have a range of 0.70–1.26, with one outlier displaying a V_ss ratio of 0.57.

Discussion

These results support the widely held founding tenant of pharmacokinetics that clearance and V_ss are independent parameters. Knowledge that V_ss is unchanged in metabolic drug–drug interactions can be helpful in discriminating changes in clearance from changes in bioavailability (F) when only oral dosing data are available, as we have recently demonstrated. As V_ss remains unchanged for intravenous metabolic drug–drug interactions, following oral dosing changes in V_ss/F will reflect changes in F alone. This estimation of F change can subsequently be utilized to assess changes in clearance alone from calculations of apparent clearance. Utilization of this simple methodology for orally dosed drugs will have a significant impact on how drug–drug interactions are interpreted from drug development and regulatory perspectives.

Development and validation of an HPLC-UV method for the simultaneous determination of the antipsychotics clozapine, olanzapine and quetiapine, several beta-blockers and their metabolites

A simple, accurate and selective column-switching high-performance liquid chromatography (HPLC) method was developed and validated for simultaneous quantification of six beta-blockers (metoprolol, timolol, bisoprolol, propranolol, carvedilol and nebivolol), three of their metabolites (α-hydroxy metoprolol, N-desisopropyl propranolol and 4'-hydroxy carvedilol 4-HCAR), three antipsychotics (olanzapine, clozapine and quetiapine) and three of their metabolites (N-desmethyl olanzapine, N-desmethyl clozapine and N-desalkyl quetiapine) in human serum. After pretreatment on a Merck LiChrospher RP-4 ADS column (25 μm), drugs were separated on a Phenomenex Gemini Phenyl Hexyl 110 A column (250 × 4.6 mm, 5 μm) using a gradient mixture of acetonitrile and potassium dihydrogen phosphate buffer pH 3.1 (containing 10% methanol) as a mobile phase at a flow rate of 1 mL/min. The total analysis time was 40 min. For detection of the analytes, four different UV wavelengths were used: 215, 226, 242 and 299 nm. The method was validated according to the guidelines of the Society of Toxicology and Forensic Chemistry in terms of selectivity, linearity, accuracy, precision and stability and successfully applied for the analysis of the 15 described analytes in human serum.

Influence of Chronic Renal Failure on Stereoselective Metoprolol Metabolism in Hypertensive Patients

The influence of chronic renal failure on the stereoselective metabolism of rac-metoprolol was investigated in 15 hypertensive patients, 7 of them with chronic renal failure and 8 with normal renal function. They were treated with rac-metoprolol (200 mg) for 7 days. The patients of both groups presented stereoselectivity in metoprolol metabolism, favoring the formation of 1'R-alpha-hydroxymetoprolol (AUC(1(')R/1(')S)(0-24) approximately 2.5) and (R)-metoprolol acidic metabolite (AUC((S)/(R))(0-24) = 0.8), the latter resulting in the plasma accumulation of (S)-metoprolol (AUC((S)/(R))(0-24) = 1.2). Patients with chronic renal failure presented plasma accumulation of the 4 alpha-hydroxymetoprolol isomers and of both metoprolol acidic metabolite enantiomers. A 50% reduction in Cl(R) does not explain the 3- to 4-fold plasma accumulation of metoprolol acidic metabolite in this group, suggesting that other pathways of metoprolol elimination are affected in chronic renal failure in addition to renal excretion. Chronic renal failure does not change the stereoselective kinetic disposition of metoprolol but modifies its stereoselective metabolism, inducing some of the CYP enzymes involved in the formation of the metoprolol acid metabolite.

Prediction of CYP2D6 drug interactions from in vitro data: evidence for substrate-dependent inhibition

Predicting the magnitude of potential drug-drug interactions is important for underwriting patient safety in the clinical setting. Substrate-dependent inhibition of cytochrome P450 enzymes may confound extrapolation of in vitro results to the in vivo situation. However, the potential for substrate-dependent inhibition with CYP2D6 has not been well characterized. The inhibition profiles of 20 known inhibitors of CYP2D6 were characterized in vitro against four clinically relevant CYP2D6 substrates (desipramine, dextromethorphan, metoprolol, and thioridazine) and bufuralol. Dextromethorphan exhibited the highest sensitivity to in vitro inhibition, whereas metoprolol was the least sensitive. In addition, when metoprolol was the substrate, inhibitors with structurally constrained amino moieties (clozapine, debrisoquine, harmine, quinidine, and yohimbine) exhibited at least a 5-fold decrease in inhibition potency when results were compared with those for dextromethorphan. Atypical inhibition kinetics were observed for these and other inhibitor-substrate pairings. In silico docking studies suggested that interactions with Glu216 and an adjacent hydrophobic binding pocket may influence substrate sensitivity and inhibition potency for CYP2D6. The in vivo sensitivities of the clinically relevant CYP2D6 substrates desipramine, dextromethorphan, and metoprolol were determined on the basis of literature drug-drug interaction (DDI) outcomes. Similar to the in vitro results, dextromethorphan exhibited the highest sensitivity to CYP2D6 inhibition in vivo. Finally, the magnitude of in vivo CYP2D6 DDIs caused by quinidine was predicted using desipramine, dextromethorphan, and metoprolol. Comparisons of the predictions with literature results indicated that the marked decrease in inhibition potency observed for the metoprolol-quinidine interaction in vitro translated to the in vivo situation.

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

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

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.

Enantioselectivity in the steady-state pharmacokinetics of metoprolol in hypertensive patients

In the present study we investigated the enantioselectivity in the pharmacokinetics of metoprolol administered in a multiple-dose regimen as the racemate. The study was conducted on 10 patients of both sexes with mild to severe essential hypertension, aged 28 to 76 years, with normal hepatic and renal function and phenotyped as extensive metabolizers of debrisoquine (urine debrisoquine to 4-hydroxydebrisoquine ratios of 0.28 to 6.56). The patients were treated with racemic metoprolol (two 100 mg tablets every 24 h) for 7 days. Serial blood samples were collected at times zero, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 10, 12, 16, 20, 22, and 24 h and urine at each 6 h period until 24 h after metoprolol administration. The plasma concentrations of the (-)-(S)- and (+)-(R)-metoprolol enantiomers were determined by HPLC using a chiral stationary phase (Chiralpak AD, 4.6 x 250 mm) and fluorescence detection. The enantiomeric ratios differing from one were evaluated by the paired t test and the results are reported as means (95% CI). No differences were observed between metoprolol enantiomers in half-lives and absorption, distribution and elimination rate constants. However, the following differences (p < 0.05) were observed between the (-)-(S) and (+)-(R) enantiomers: maximum plasma concentration, C(max), 179.99 (123. 33-236.64) versus 151.30 (95.04-207.57) ng/mL; area under the plasma concentration versus time curve, AUC(0-24)(SS), 929.85 (458.02-1401. 70) versus 782.11 (329.80-1234.40) ng h/mL; apparent total clearance, Cl(T)/f, 1.70 (0.79-2.61) versus 2.21 (1.06-3.36) L/h/kg, apparent distribution volume, Vd/f, 10.51 (6.35-14.68) versus 13.80 (6.93-20. 68) L/kg, and renal clearance, Cl(R), 0.06 (0.05-0.08) versus 0.07 (0.05-0.09) L/kg. The enantiomeric ratios AUC((-)-(S))/AUC((+)-(R)) ranged from 1.14 to 1.44, with a mean of 1.29. The data obtained demonstrate enantioselectivity in the kinetic disposition of metoprolol, with plasma accumulation of the pharmacologically more active (-)-(S)-metoprolol enantiomer in hypertensive patients phenotyped as extensive metabolizers of debrisoquine.

Pharmacokinetic and pharmacodynamic interactions between diltiazem and quinidine

OBJECTIVES

To examine the pharmacokinetic and pharmacodynamic interactions between quinidine and diltiazem because both drugs can inhibit drug metabolism.

METHODS

Twelve fasting, healthy male volunteers (age, 24 +/- 5 years; weight, 75 +/- 10 kg) received a single oral dose of diltiazem (60 mg) or quinidine (200 mg), alone and on a background of the other drug, in a crossover study. Background treatment consisted of 100 mg quinidine twice a day or 90 mg sustained-release diltiazem twice a day for 2 day before the study day.

RESULTS

Pretreatment with diltiazem significantly (p < 0.05) increased the area under the curve of quinidine from 7414 +/- 1965 to 11,213 +/- 2610 ng.hr/ml and increased its terminal elimination half-life (t1/2) from 6.8 +/- 1.1 to 9.3 +/- 1.5 hours. Its oral clearance was decreased from 0.39 +/- 0.1 to 0.25 +/- 0.1 L/hr/kg, whereas the maximal concentration was not significantly affected. Diltiazem disposition was not significantly affected by pretreatment with quinidine. Diltiazem pretreatment increased QTc and PR intervals and decreased heart rate and diastolic blood pressure. No significant pharmacodynamic differences were shown for diltiazem alone versus quinidine pretreatment.

CONCLUSION

Diltiazem significantly decreased the clearance and increased the t1/2 of quinidine, but quinidine did not alter the kinetics of diltiazem with the dose used. No significant pharmacodynamic interaction was shown for the combination that would not be predicted from individual drug administration.