Atorvastatin coadministration may increase digoxin concentrations by inhibition of intestinal P-glycoprotein-mediated secretion

Statins Increase the Bioavailability of Fixed-Dose Combination of Sofosbuvir/Ledipasvir by Inhibition of P-glycoprotein

BACKGROUND

Coadministration of statins and direct acting antiviral agents is frequently used. This study explored the effects of both atorvastatin and lovastatin on pharmacokinetics of a fixed-dose combination of sofosbuvir/ledipasvir "FDCSL".

METHODS

12 healthy volunteers participated in a randomized, three-phase crossover trial and were administered a single atorvastatin dose 80 mg plus tablet containing 400/90 mg FDCSL, a single lovastatin dose 40 mg plus tablet containing 400/90 mg FDCSL, or tablets containing 400/90 mg FDCSL alone. Liquid chromatography-tandem mass spectrometry was used to analyze plasma samples of sofosbuvir, ledipasvir and sofosbuvir metabolite "GS-331007" and their pharmacokinetic parameters were determined.

RESULTS

Atorvastatin caused a significant rise in sofosbuvir bioavailability as explained by increasing in AUC0-∞ and Cmax by 34.36% and 11.97%, respectively. In addition, AUC0-∞ and Cmax of GS-331007 were increased by 73.73% and 67.86%, respectively after atorvastatin intake. Similarly, co-administration of lovastatin with FDCSL increased the bioavailability of sofosbuvir, its metabolite (AUC0-∞ increase by 17.2%, 17.38%, respectively, and Cmax increase by 12.03%, 22.24%, respectively). However, neither atorvastatin nor lovastatin showed a change in ledipasvir bioavailability. Hepatic elimination was not affected after statin intake with FDCSL. Compared to lovastatin, atorvastatin showed significant increase in AUC0-∞ and Cmax of both sofosbuvir and its metabolite.

CONCLUSIONS

Both atorvastatin and lovastatin increased AUC of sofosbuvir and its metabolite after concurrent administration with FDCSL. Statins' P-glycoprotein inhibition is the attributed mechanism of interaction. The increase in sofosbuvir bioavailability was more pronounced after atorvastatin intake. Close monitoring is needed after co-administration of atorvastatin and FDCSL.

Drug Interactions Affecting Antiarrhythmic Drug Use

Antiarrhythmic drugs (AAD) play an important role in the management of arrhythmias. Drug interactions involving AAD are common in clinical practice. As AADs have a narrow therapeutic window, both pharmacokinetic as well as pharmacodynamic interactions involving AAD can result in serious adverse drug reactions ranging from arrhythmia recurrence, failure of device-based therapy, and heart failure, to death. Pharmacokinetic drug interactions frequently involve the inhibition of key metabolic pathways, resulting in accumulation of a substrate drug. Additionally, over the past 2 decades, the P-gp (permeability glycoprotein) has been increasingly cited as a significant source of drug interactions. Pharmacodynamic drug interactions involving AADs commonly involve additive QT prolongation. Amiodarone, quinidine, and dofetilide are AADs with numerous and clinically significant drug interactions. Recent studies have also demonstrated increased morbidity and mortality with the use of digoxin and other AAD which interact with P-gp. QT prolongation is an important pharmacodynamic interaction involving mainly Vaughan-Williams class III AAD as many commonly used drug classes, such as macrolide antibiotics, fluoroquinolone antibiotics, antipsychotics, and antiemetics prolong the QT interval. Whenever possible, serious drug-drug interactions involving AAD should be avoided. If unavoidable, patients will require closer monitoring and the concomitant use of interacting agents should be minimized. Increasing awareness of drug interactions among clinicians will significantly improve patient safety for patients with arrhythmias.

Current Evidence, Challenges, and Opportunities of Physiologically Based Pharmacokinetic Models of Atorvastatin for Decision Making

Atorvastatin (ATS) is the gold-standard treatment worldwide for the management of hypercholesterolemia and prevention of cardiovascular diseases associated with dyslipidemia. Physiologically based pharmacokinetic (PBPK) models have been positioned as a valuable tool for the characterization of complex pharmacokinetic (PK) processes and its extrapolation in special sub-groups of the population, leading to regulatory recognition. Several PBPK models of ATS have been published in the recent years, addressing different aspects of the PK properties of ATS. Therefore, the aims of this review are (i) to summarize the physicochemical and pharmacokinetic characteristics involved in the time-course of ATS, and (ii) to evaluate the major highlights and limitations of the PBPK models of ATS published so far. The PBPK models incorporate common elements related to the physicochemical aspects of ATS. However, there are important differences in relation to the analyte evaluated, the type and effect of transporters and metabolic enzymes, and the permeability value used. Additionally, this review identifies major processes (lactonization, P-gp contribution, ATS-Ca solubility, simultaneous management of multiple analytes, and experimental evidence in the target population), which would enhance the PBPK model prediction to serve as a valid tool for ATS dose optimization.

Edoxaban and the Issue of Drug-Drug Interactions: From Pharmacology to Clinical Practice

Edoxaban, a direct factor Xa inhibitor, is the latest of the non-vitamin K antagonist oral anticoagulants (NOACs). Despite being marketed later than other NOACs, its use is now spreading in current clinical practice, being indicated for both thromboprophylaxis in patients with non-valvular atrial fibrillation (NVAF) and for the treatment and prevention of venous thromboembolism (VTE). In patients with multiple conditions, the contemporary administration of several drugs can cause relevant drug-drug interactions (DDIs), which can affect drugs' pharmacokinetics and pharmacodynamics. Usually, all the NOACs are considered to have significantly fewer DDIs than vitamin K antagonists; notwithstanding, this is actually not true, all of them are affected by DDIs with drugs that can influence the activity (induction or inhibition) of P-glycoprotein (P-gp) and cytochrome P450 3A4, both responsible for the disposition and metabolism of NOACs to a different extent. In this review/expert opinion, we focused on an extensive report of edoxaban DDIs. All the relevant drugs categories have been examined to report on significant DDIs, discussing the impact on edoxaban pharmacokinetics and pharmacodynamics, and the evidence for dose adjustment. Our analysis found that, despite a restrained number of interactions, some strong inhibitors/inducers of P-gp and drug-metabolising enzymes can affect edoxaban concentration, just as it happens with other NOACs, implying the need for a dose adjustment. However, our analysis of edoxaban DDIs suggests that given the small propensity for interactions of this agent, its use represents an acceptable clinical decision. Still, DDIs can be significant in certain clinical situations and a careful evaluation is always needed when prescribing NOACs.

Pharmacokinetic interaction between atorvastatin and fixed-dose combination of sofosbuvir/ledipasvir in healthy male Egyptian volunteers

PURPOSE

Comorbid conditions of heart and liver disorders added to HCV-induced hepatic steatosis make co-administration of statins, and direct-acting antivirals is common in clinical practice. This study aimed to evaluate the pharmacokinetic interaction of atorvastatin and fixed-dose combination of sofosbuvir/ledipasvir "FDCSL" with rationalization to the underlying mechanism.

METHODS

A randomized, three-phase crossover study that involves 12 healthy volunteers was performed. Participants received a single-dose of atorvastatin 80 mg alone, atorvastatin 80-mg plus tablets containing 400/90 mg FDCSL, or tablets containing 400/90 mg FDCSL alone. Plasma samples were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) for atorvastatin, sofosbuvir, ledipasvir, and sofosbuvir metabolite "GS-331007," and their pharmacokinetics parameters were determined.

RESULTS

Compared to atorvastatin alone, the administration of FDCSL caused a significant increase in both areas under the concentration-time curve from time zero to infinity (AUC_0-∞) and maximum plasma concentration (C_max) of atorvastatin by 65.5% and 156.0%, respectively. Also, atorvastatin caused a significant increase in the AUC_0-∞ and C_max of sofosbuvir by 32.0% and 11.0%, respectively. Similarly, AUC_0-∞ and C_max of sofosbuvir metabolite significantly increased by 84.0% and 74.0%, respectively. However, ledipasvir AUC_0-∞ showed no significant change after atorvastatin intake. The elimination rate in all drugs revealed no significant changes.

CONCLUSION

After concurrent administration of FDCSL with atorvastatin, the AUC_0-∞ of both atorvastatin and sofosbuvir were increased. Caution should be taken with close monitoring for possible side effects after co-administration of atorvastatin and FDCSL in clinical practice.

Strategy for the Prediction of Steady-State Exposure of Digoxin to Determine Drug-Drug Interaction Potential of Digoxin With Other Drugs in Digitalization Therapy

Digoxin, a narrow therapeutic index drug, is widely used in congestive heart failure. However, the digitalization therapy involves dose titration and can exhibit drug-drug interaction. Ctrough versus area under the plasma concentration versus time curve in a dosing interval of 24 hours (AUC0-24h) and Cmax versus AUC0-24h for digoxin were established by linear regression. The predictions of digoxin AUC0-24h values were performed using published Ctrough or Cmax with appropriate regression lines. The fold difference, defined as the quotient of the observed/predicted AUC0-24h values, was evaluated. The mean square error and root mean square error, correlation coefficient (r), and goodness of the fold prediction were used to evaluate the models. Both Ctrough versus AUC0-24h (r = 0.9215) and Cmax versus AUC0-24h models for digoxin (r = 0.7781) showed strong correlations. Approximately 93.8% of the predicted digoxin AUC0-24h values were within 0.76-fold to 1.25-fold difference for Ctrough model. In sharp contrast, the Cmax model showed larger variability with only 51.6% of AUC0-24h predictions within 0.76-1.25-fold difference. The r value for observed versus predicted AUC0-24h for Ctrough (r = 0.9551; n = 177; P < 0.001) was superior to the Cmax (r = 0.6134; n = 275; P < 0.001) model. The mean square error and root mean square error (%) for the Ctrough model were 11.95% and 16.2% as compared to 67.17% and 42.3% obtained for the Cmax model. Simple linear regression models for Ctrough/Cmax versus AUC0-24h were derived for digoxin. On the basis of statistical evaluation, Ctrough was superior to Cmax model for the prediction of digoxin AUC0-24h and can be potentially used in a prospective setting for predicting drug-drug interaction or lack of it.

Pharmacokinetic interaction between fimasartan and atorvastatin in healthy male volunteers

Introduction

Major cardiovascular risk factors, including hypertension and dyslipidemia, are often comorbidities, frequently leading to concurrent prescription of angiotensin receptor blockers and 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins). The study’s objective was to evaluate the effect of coadministration of fimasartan and atorvastatin on their pharmacokinetics (PKs).

Subjects and methods

In a randomized, open-label, three-period, six-sequence, crossover, multiple-dose study, 36 healthy subjects received 120 mg fimasartan, 40 mg atorvastatin, or both (based on their assigned sequence) once daily for 7 days in each period, with a 7-day washout between periods. Blood samples for the PK analysis of fimasartan, atorvastatin, and the 2-hydroxy atorvastatin metabolite were collected up to 48 h after the last dose.

Results

The coadministration of fimasartan and atorvastatin was well tolerated and led to an increase in the peak concentration and area under the concentration–time curve at steady state of fimasartan by 2.18-fold (95% confidence interval [CI], 1.79–2.65) and 1.35-fold (95% CI, 1.26–1.43) and those of atorvastatin increased by 1.82-fold (95% CI, 1.51–2.18) and 1.12-fold (95% CI, 1.04–1.22), respectively.

Conclusion

Coadministration increased the systemic exposures of fimasartan and atorvastatin, but the clinical significance of this finding needs to be evaluated with respect to exposure responses and clinical outcomes.

Statin drug-drug interactions in a Romanian community pharmacy

Background and aim

Statins are frequently prescribed for patients with dyslipidemia and have a well-established safety profile. However, when associated with interacting dugs, the risk of adverse effects, especially muscular toxicity, is increased.

The objective of this study was to identify, characterize and quantify the prevalence of the potential drug-drug interactions (pDDIs) of statins in reimbursed prescriptions from a community pharmacy in Bucharest.

Methods

We analyzed the reimbursed prescriptions including statins collected during one month in a community pharmacy. The online program Medscape Drug Interaction Checker was used for checking the drug interactions and their classification based on severity: Serious – Use alternative, Significant – Monitor closely and Minor.

Results

132 prescriptions pertaining to 125 patients were included in the analysis. Our study showed that 25% of the patients who were prescribed statins were exposed to pDDIs: 37 Serious and Significant interactions in 31 of the statins prescriptions. The statins involved were atorvastatin, simvastatin and rosuvastatin.

Conclusions

Statin pDDIs have a high prevalence and patients should be monitored closely in order to prevent the development of adverse effects that result from statin interactions.

Population pharmacokinetics and optimization of the dosing regimen of digoxin in adult patients

Background

This study aimed to evaluate the population pharmacokinetics of digoxin in Japanese patients and establish a dosage regimen based on the pharmacokinetic data.

Methods

We analyzed 287 serum digoxin samples from 192 individuals by using the nonlinear mixed effects model. We used simulations to optimize the dosage regimen of digoxin to achieve a high likelihood of the target concentration (0.5–0.8 ng/mL).

Results

The total body clearance (CL/F ([L/h]) was calculated using the following formula: CL/F = (1.21 + 0.0532 × CLcr [(mL/min]) × (1 + 0.787 × AMD), where CLcr is the creatinine clearance and AMD is 0 in the case of concomitant administration of amiodarone and 1 otherwise. To achieve the target concentration (0.5–0.8 ng/mL), the dosage of digoxin was 0.0625 mg/day (CLcr < 35 mL/min and AMD = 0); 0.125 mg/day (CLcr, 35–65 mL/min and AMD = 0); 0.1875 mg/day (CLcr, 65–100 mL/min and AMD = 0); 0.0625 mg/every other day (CLcr < 30 mL/min and AMD = 1); and 0.0625 mg/day (CLcr, 30–85 mL/min and AMD = 1).

Conclusions

Our findings suggest that population parameters are useful for evaluating digoxin pharmacokinetics.

Potential drug interactions with statins: Estonian register-based study

In Estonia, HMG-CoA reductase inhibitors are widely used to modify lipid levels but there are no current data on additional medicines prescribed alongside the statins. The aim of this study was to identify the frequency of potential clinically relevant interactions at a national level among an outpatient population treated with statins between January and June 2008, based on the prescription database of the Estonian Health Insurance Fund. This retrospective prevalence study included 203,646 outpatients aged 50 years or older, of whom 29,367 received statin therapy. The study analysed individuals who had used at least one prescription medicine for a minimum of 7 days concomitantly with statins. Potential drug interactions were analysed using Epocrates online, Stockley's Drug Interactions, and the drug interaction database developed in Estonia. Statins metabolised by the CYP3A4 isoenzyme were prescribed to 64% of all statin users. Medicines known to have potentially clinically significant interactions with statins were prescribed to 4.6% of patients. The drugs prescribed concomitantly most often with simvastatin were warfarin (5.7%) and amiodarone (3.9%), whereas digoxin (1.2%) and ethinylestradiol (2%) were prescribed with atorvastatin. Potential interactions were not detected in the treatment regimens of rosuvastatin, pravastatin, and fluvastatin users.

Effect of steady-state atorvastatin on the pharmacokinetics of a single dose of colchicine in healthy adults under fasted conditions

BACKGROUND AND OBJECTIVE

Colchicine is commonly prescribed for gout. While minimally metabolized by the cytochrome P450 (CYP) 3A4 isoenzyme, colchicine is a substrate for P-glycoprotein (P-gp). Atorvastatin is metabolized primarily by CYP3A4 and is a P-gp inhibitor. Patients with gout often have dyslipidemia; therefore, the potential for co-administration of atorvastatin and colchicine exists. The objective of this study was to determine the effect of oral atorvastatin on the pharmacokinetics of a single, oral dose of colchicine.

METHODS

Twenty-four healthy adult subjects were enrolled in this single-center, open-label, non-randomized, one-sequence, two-period drug-drug interaction study. On day 1, subjects received a single oral dose of colchicine 0.6 mg. After a 14-day washout, subjects received atorvastatin 40 mg once daily for 14 days followed by a single dose of colchicine 0.6 mg co-administered with atorvastatin 40 mg on day 28. Main outcome measures were colchicine maximum plasma concentration (C max), area under the plasma concentration-time curve (AUC) from time zero to the last measurable concentration (AUC last), and AUC from time zero to infinity (AUC∞), which were compared with and without concurrent atorvastatin.

RESULTS

Colchicine AUC last, AUC∞, and C max increased by 27, 24, and 31 %, respectively, when co-administered with atorvastatin. Corresponding 90 % confidence intervals around the ratios were outside the established no-effect 80-125 % interval.

CONCLUSION

Increased colchicine exposure was observed after a single dose of colchicine was administered with steady-state atorvastatin. Additional studies with multiple dosing of both drugs are needed to further determine the clinical implications of these results.

Pravastatin in HIV-Infected Patients Treated with Protease Inhibitors: A Placebo-Controlled Randomized Study

PURPOSE

The objectives of the study were to assess the effects of pravastatin on plasma HIV RNA, lipid parameters, and protease inhibitor (PI) concentrations in patients treated with PI-containing regimens and with total cholesterol (TC) > or = 5.5 mmol/L.

METHOD

A clinical trial including patients randomized to receive pravastatin or matching placebo for 12 weeks was implemented.

RESULTS

Twelve patients were included in the pravastatin group and 9 in the placebo group. At week 12 (W12), no patient had experienced virological failure. Between week 0 (W0) and W12, the median differences for TC were -1.4 mmol/L in the pravastatin group and +0.2 mmol/L in the placebo group (p = .005); for LDL, they were -1.0 mmol/L and +0.3 (p = .007), respectively. A significant decrease of the PI concentration (12 hours after administration) ratio W12 - W0/W0 was noticed in the pravastatin group (-0.2 [interquartile range, -0.3 to -0.1] as compared with the placebo group (0.1 [IQR, 0.0 to 0.3]) (p = .03). When the study was restricted to patients treated with lopinavir/ritonavir, a decrease from 3.8 microg/mL at baseline to 2.9 mug/mL at W12 was noticed in the pravastatin arm (p = .04) but not in the control arm (p = 1.00). No clinical adverse event reached a severity of grade 3.

CONCLUSION

We observed in this study that the use of pravastatin in PI-treated patients was not associated with major change in the plasma HIV RNA on 12 weeks of follow-up. However, we found a trend of decrease of the trough PI concentration at W12, suggesting a possible drug-drug interaction of pravastatin on PI metabolism.

Association between statin-induced creatine kinase elevation and genetic polymorphisms in SLCO1B1, ABCB1 and ABCG2

PURPOSE

Treatment with statins requires close monitoring of serum creatine kinase (CK) levels to prevent myopathy, a common and potentially serious dose-dependent adverse effect of these drugs. We have investigated the correlation between elevated CK levels and polymorphisms in the genes encoding transporters involved in statin disposition.

METHODS

Patients with and without statin-induced elevated serum CK levels were genotyped for polymorphisms in SLCO1B1 (SLCO1B1 A388G and SLCO1B1 T521C), ABCB1 (ABCB1 C1236T and ABCB1 C3435T) and ABCG2 (ABCG2 C421A).

RESULTS

Patients carrying SLCO1B1 T521C or ABCB1 C1236T single nucleotide polymorphisms (SNPs) had an odds ratio (OR) for statin-induced elevated serum CK levels of 8.86 (p<0.01) and 4.67 (p<0.05), respectively, while patients carrying the SLCO1B1 A388G SNP had an OR of 0.24 (p<0.05). An arbitrary score based on genotype combination discriminated patients with and without CK elevation at a specificity of 97 % and a sensitivity of 39 %.

CONCLUSION

Genotyping of the SLCO1B1, ABCB1 and ABCG2 genes deserves consideration as a clinical approach to improve statin safety while concomitantly reducing the burden of blood tests for CK measurements.

Intestinal drug transporters: an overview

The importance of drug transporters as one of the determinants of pharmacokinetics has become increasingly evident. While much research has been conducted focusing the role of drug transporters in the liver and kidney less is known about the importance of uptake and efflux transporters identified in the intestine. Over the past years the effects of intestinal transporters have been studied using in vivo models, in situ organ perfusions, in vitro tissue preparations and cell lines. This review aims to describe up to date findings regarding the importance of intestinal transporters on drug absorption and bioavailability, highlighting areas in need of further research. Wu and Benet proposed a Biopharmaceutics Drug Disposition Classification System (BDDCS) that allows the prediction of transporter effects on the drug disposition of orally administered drugs. This review also discusses BDDCS predictions with respect to the role of intestinal transporters and intestinal transporter-metabolizing enzyme interplay on oral drug pharmacokinetics.

Drug-drug interaction studies of cardiovascular drugs involving P-glycoprotein, an efflux transporter, on the pharmacokinetics of edoxaban, an oral factor Xa inhibitor

Background

Edoxaban, an oral direct factor Xa inhibitor, is in development for thromboprophylaxis, including prevention of stroke and systemic embolism in patients with atrial fibrillation (AF). P-glycoprotein (P-gp), an efflux transporter, modulates absorption and excretion of xenobiotics. Edoxaban is a P-gp substrate, and several cardiovascular (CV) drugs have the potential to inhibit P-gp and increase drug exposure.

Objective

To assess the potential pharmacokinetic interactions of edoxaban and 6 cardiovascular drugs used in the management of AF and known P-gp substrates/inhibitors.

Methods

Drug-drug interaction studies with edoxaban and CV drugs with known P-gp substrate/inhibitor potential were conducted in healthy subjects. In 4 crossover, 2-period, 2-treatment studies, subjects received edoxaban 60 mg alone and coadministered with quinidine 300 mg (n = 42), verapamil 240 mg (n = 34), atorvastatin 80 mg (n = 32), or dronedarone 400 mg (n = 34). Additionally, edoxaban 60 mg alone and coadministered with amiodarone 400 mg (n = 30) or digoxin 0.25 mg (n = 48) was evaluated in a single-sequence study and 2-cohort study, respectively.

Results

Edoxaban exposure measured as area under the curve increased for concomitant administration of edoxaban with quinidine (76.7 %), verapamil (52.7 %), amiodarone (39.8 %), and dronedarone (84.5 %), and exposure measured as 24-h concentrations for quinidine (11.8 %), verapamil (29.1 %), and dronedarone (157.6 %) also increased. Administration of edoxaban with amiodarone decreased the 24-h concentration for edoxaban by 25.7 %. Concomitant administration with digoxin or atorvastatin had minimal effects on edoxaban exposure.

Conclusion

Coadministration of the P-gp inhibitors quinidine, verapamil, and dronedarone increased edoxaban exposure. Modest/minimal effects were observed for amiodarone, atorvastatin, and digoxin.

Drug interactions with statins

The 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) are generally well tolerated as monotherapy. Statins are associated with two important adverse effects, asymptomatic elevation in liver enzymes and myopathy. Myopathy is most likely to occur when statins are administered with other drugs. Statins are substrates of multiple drug transporters (including OAT- -P1B1, BCRP and MDR1) and several cytochrome P450 (CYP) enzymes (including CYP3A4, CYP2C8, CYP2C19, and CYP2C9). Possible adverse effects of statins can occur due to interactions in concomitant use of drugs that substantially inhibit or induce their methabolic pathway. This review summarizes the most important interactions of statins.

Statin-associated polymyositis following omeprazole treatment

Statins are an extensively used class of drugs, and myopathy is an uncommon, but well-described side effect of statin therapy. Inflammatory myopathies, including polymyositis, dermatomyositis, and necrotizing autoimmune myopathy, are even more rare, but debilitating, side effects of statin therapy that are characterized by the persistence of symptoms even after discontinuation of the drug. It is important to differentiate statin-associated inflammatory myopathies from other self-limited myopathies, as the disease often requires multiple immunosuppressive therapies. Drug interactions increase the risk of statin-associated toxic myopathy, but no risk factors for statin-associated inflammatory myopathies have been established. Here we describe the case of a man, age 59 years, who had been treated with a combination of atorvastatin and gemfibrozil for approximately 5 years and developed polymyositis after treatment with omeprazole for 7 months. Symptoms did not resolve after discontinuation of the atorvastatin, gemfibrozil, and omeprazole. The patient was treated with prednisone and methotrexate followed by intravenous immunoglobulin, which resulted in normalization of creatinine kinase levels and resolution of symptoms after 14 weeks. It is unclear if polymyositis was triggered by interaction of the statin with omeprazole and/or gemfibrozil, or if it developed secondary to long-term use of atorvastatin only.

The P-glycoprotein transport system and cardiovascular drugs

Permeability glycoprotein (P-gp) mediates the export of drugs from cells located in the small intestine, blood-brain barrier, hepatocytes, and kidney proximal tubule, serving a protective function for the body against foreign substances. Intestinal absorption, biliary excretion, and urinary excretion of P-gp substrates can therefore be altered by either the inhibition or induction of P-gp. A wide spectrum of drugs, such as anticancer agents and steroids, are known P-gp substrates and/or inhibitors, and many cardiovascular drugs have recently been observed to have clinically relevant interactions as well. We review the interactions among commonly prescribed cardiovascular drugs that are P-gp substrates and observe interactions involving P-gp that may be relevant to clinical practice. Cardiovascular drugs with narrow therapeutic indexes (e.g., antiarrhythmic agents, anticoagulant agents) have demonstrated large increases in concentrations when coadministered with potent P-gp inhibitors, thus increasing the risk for drug toxicity. Therefore, dose adjustment or use of alternative agents should be considered when strong P-gp-mediated drug-drug interactions are present. Finally, interactions between novel drugs and known P-gp inhibitors are now being systematically evaluated during drug development, and recommended guidelines for the administration of P-gp substrate drugs will be expanded.

Effect of Lasofoxifene on the Pharmacokinetics of Digoxin in Healthy Postmenopausal Women

Lasofoxifene is in late-stage development for the prevention and treatment of osteoporosis. Digoxin is commonly prescribed for arrhythmias and congestive heart failure, has a narrow therapeutic index, and may be coadministered with lasofoxifene. This study was conducted to determine the effect of lasofoxifene (4-mg loading dose on day 11 followed by 0.5 mg/d on days 12-20) on the steady-state pharmacokinetics of digoxin (0.25 mg/d on days 1-20) in 12 healthy postmenopausal women. On days 10 and 20, blood and urine samples were collected for 24 hours to determine digoxin concentrations. The 90% confidence interval (CI) of least squares mean ratio for maximum concentration (C(max)) and area under the plasma concentration-time curve (AUC) was calculated. Lasofoxifene had no effect on digoxin plasma pharmacokinetics with a ratio (90% CI) of 95.4% (84.6%-107%) and 103% (97.7%-108%) for C(max) and AUC(0-24), respectively. The ratio of the percentage of dose eliminated unchanged in urine in 24 hours was 127% (116% to 142%). Coadministration of lasofoxifene had no effect on the steady state pharmacokinetics of digoxin.

Effect of ABCB1 polymorphisms and atorvastatin on sitagliptin pharmacokinetics in healthy volunteers

OBJECTIVES

The objectives of this study were to determine if ABCB1 polymorphisms are associated with interindividual variability in sitagliptin pharmacokinetics and if atorvastatin alters the pharmacokinetic disposition of sitagliptin in healthy volunteers.

METHODS

In this open-label, randomized, two-phase crossover study, healthy volunteers were prospectively stratified according to ABCB1 1236/2677/3435 diplotype (n = 9, CGC/CGC; n = 10, CGC/TTT; n = 10, TTT/TTT). In one phase, participants received a single 100 mg dose of sitagliptin; in the other phase, participants received 40 mg of atorvastatin for 5 days, with a single 100 mg dose of sitagliptin administered on day 5. A 24-h pharmacokinetic study followed each sitagliptin dose, and the study phases were separated by a 14-day washout period.

RESULTS

Sitagliptin pharmacokinetic parameters did not differ significantly between ABCB1 CGC/CGC, CGC/TTT, and TTT/TTT diplotype groups during the monotherapy phase. Atorvastatin administration did not significantly affect sitagliptin pharmacokinetics, with geometric mean ratios (90 % confidence intervals) for sitagliptin maximum plasma concentration, plasma concentration-time curve from zero to infinity, renal clearance, and fraction of sitagliptin excreted unchanged in the urine of 0.93 (0.86-1.01), 0.96 (0.91-1.01), 1.02 (0.93-1.12), and 0.98 (0.90-1.06), respectively.

CONCLUSIONS

ABCB1 CGC/CGC, CGC/TTT, and TTT/TTT diplotypes did not influence sitagliptin pharmacokinetics in healthy volunteers. Furthermore, atorvastatin had no effect on the pharmacokinetics of sitagliptin in the setting of ABCB1 CGC/CGC, CGC/TTT, and TTT/TTT diplotypes.

Psychotropic drug-drug interactions involving P-glycoprotein

Multidrug resistance P-glycoprotein (P-gp; also known as MDR1 and ABCB1) is expressed in the luminal membrane of the small intestine and blood-brain barrier, and the apical membranes of excretory cells such as hepatocytes and kidney proximal tubule epithelia. P-gp regulates the absorption and elimination of a wide range of compounds, such as digoxin, paclitaxel, HIV protease inhibitors and psychotropic drugs. Its substrate specificity is as broad as that of cytochrome P450 (CYP) 3A4, which encompasses up to 50 % of the currently marketed drugs. There has been considerable interest in variations in the ABCB1 gene as predictors of the pharmacokinetics and/or treatment outcomes of several drug classes, including antidepressants and antipsychotics. Moreover, P-gp-mediated transport activity is saturable, and is subject to modulation by inhibition and induction, which can affect the pharmacokinetics, efficacy or safety of P-gp substrates. In addition, many of the P-gp substrates overlap with CYP3A4 substrates, and several psychotropic drugs that are P-gp substrates are also CYP3A4 substrates. Therefore, psychotropic drugs that are P-gp substrates may cause a drug interaction when P-gp inhibitors and inducers are coadministered, or when psychotropic drugs or other medicines that are P-gp substrates are added to a prescription. Hence, it is clinically important to accumulate data about drug interactions through studies on P-gp, in addition to CYP3A4, to assist in the selection of appropriate psychotropic medications and in avoiding inappropriate combinations of therapeutic agents. There is currently insufficient information available on the psychotropic drug interactions related to P-gp, and therefore we summarize the recent clinical data in this review.

Interaction of digitalis-like compounds with p-glycoprotein

Digitalis-like compounds (DLCs), or cardiac glycosides, are produced and sequestered by certain plants and animals as a protective mechanism against herbivores or predators. Currently, the DLCs digoxin and digitoxin are used in the treatment of cardiac congestion and some types of cardiac arrhythmia, despite a very narrow therapeutic index. P-glycoprotein (P-gp; ABCB1) is the only known ATP-dependent efflux transporter that handles digoxin as a substrate. Ten alanine mutants of human P-gp drug-binding amino acids-Leu(65), Ile(306), Phe(336), Ile(340), Phe(343), Phe(728), Phe(942), Thr(945), Leu(975), and Val(982)-were generated and expressed in HEK293 cells with a mammalian baculovirus system. The uptake of [(3)H]-N-methyl-quinidine (NMQ), the P-gp substrate in vesicular transport assays, was determined. The mutations I306A, F343A, F728A, T945A, and L975A abolished NMQ transport activity of P-gp. For the other mutants, the apparent affinities for six DLCs (cymarin, digitoxin, digoxin, peruvoside, proscillaridin A, and strophanthidol) were determined. The affinities of digoxin, proscillaridin A, peruvoside, and cymarin for mutants F336A and I340A were decreased two- to fourfold compared with wild type, whereas that of digitoxin and strophanthidol did not change. In addition, the presence of a hydroxyl group at position 12β seems to reduce the apparent affinity when the side chain of Phe(336) and Phe(942) is absent. Our results showed that a δ-lactone ring and a sugar moiety at 3β of the steroid body are favorable for DLC binding to P-gp. Moreover, DLC inhibition is increased by hydroxyl groups at positions 5β and 19, whereas inhibition is decreased by those at positions 1β, 11α, 12β, and 16β. The understanding of the P-gp-DLC interaction improves our insight into DLCs toxicity and might enhance the replacement of digoxin with other DLCs that have less adverse drug effects.

Statin drug interactions and related adverse reactions

INTRODUCTION

Statin monotherapy is generally well tolerated, with a low frequency of adverse events. The most important adverse effects associated with statins are myopathy and an asymptomatic increase in hepatic transaminases, both of which occur infrequently. Because statins are prescribed on a long-term basis, their possible interactions with other drugs deserve particular attention, as many patients will typically receive pharmacological therapy for concomitant conditions during the course of statin treatment.

AREAS COVERED

This review summarizes the pharmacokinetic properties of statins and emphasizes their clinically relevant drug interactions and related adverse reactions.

EXPERT OPINION

Avoiding drug-drug interactions and consequent adverse drug reactions is essential in order to optimize compliance, and thus improve the treatment of patients at high cardiovascular risk. The different pharmacokinetic profiles among statins should be carefully considered, in order to understand the possible spectrum of drug interactions. The growing trend toward earlier statin treatment for the prevention of cardiovascular disease means that physicians must anticipate future polypharmacy when their patients require additional medications for comorbid conditions.

Effect of multidose cilostazol on pharmacokinetic and lipid profile of atorvastatin in male Wistar rats

Objectives

Atorvastatin (ATV) and cilostazol (CLZ) are often co-prescribed to treat conditions such as peripheral arterial disease. In the present study, the drug–drug interaction potential of multi-dose CLZ on both pharmacokinetics and the lipid-lowering ability of single-dose ATV is demonstrated.

Method

The pharmacokinetic parameters of ATV were determined in Wistar rats after per-oral pre-treatment with CLZ for 7 days in order to assess the interaction potential between ATV and CLZ. In-vitro metabolic inhibition and everted gut sac studies were conducted to elucidate the mechanism of this interaction. Biochemistry analyser was used to estimate lipid profiles in Wistar rats. A validated LC-MS/MS method was employed to simultaneously quantify both ATV and CLZ in rat plasma matrix.

Key findings

A statistically significant increase in systemic exposure to ATV after a single dose was observed in CLZ pre-treated rats. In-vitro metabolism studies using rat liver microsome (RLM) demonstrated statistically significant inhibition of ATV metabolism when co-incubated with CLZ. No change in apparent permeability of ATV was observed in the presence of CLZ. The blood lipid profile study after ATV administration indicated a statistically significant decrease in total cholesterol, triglycerides and LDL-cholesterol.

Conclusions

Multi-dose administration of CLZ influences the pharmacokinetics and lipid-lowering properties of ATV. Collectively, an apparent interaction between selected drugs was evident.

Drug-drug interactions with statins: will pitavastatin overcome the statins' Achilles' heel?

BACKGROUND

As the clinical complexity of patients at high cardiovascular risk and with multiple comorbid conditions increases, so does the potential for drug-drug interactions (DDIs). Large retrospective studies in various clinical settings have shown that an unacceptably large proportion of patients are coprescribed a statin with potentially interacting therapies, suggesting that the impact of polypharmacy on the safety profile of statins may be underappreciated.

SCOPE

To assess the evidence for the burden of DDIs and related adverse drug reactions (ADRs) with current statins relative to pitavastatin, a new agent recently approved in the USA and EU.

METHODS

Structured review of the PubMed and EMBASE databases (to 15 October 2010) for literature on statins in the areas of ADRs, polypharmacy and DDIs; pharmacokinetics, and pitavastatin clinical safety and efficacy.

FINDINGS

Patients who are on statin therapy are often receiving multiple medications for comorbid conditions, and so are at increased risk of ADRs, such as myopathy, because of pharmacokinetic interactions at the level of cytochrome P450 (CYP) enzymes and/or organic anion-transporting polypeptides. Pitavastatin has a distinctive metabolic profile that means it is marginally metabolised by CYP enzymes, and is therefore expected to have a low risk of DDIs and related ADRs. A large post-marketing study conducted in more than 20,000 patients in Japan has demonstrated that the rate of DDIs with pitavastatin treatment may compare favourably with that observed with atorvastatin and rosuvastatin.

CONCLUSIONS

The addition of pitavastatin to the range of available statins provides prescribing physicians with a new treatment option that is expected to have a low risk of DDIs and related ADRs. This, coupled with the demonstrated efficacy of pitavastatin in reducing low-density lipoprotein cholesterol, should help physicians individualise lipid-lowering regimens based on the patient profile and concomitant medications.

A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes

Background

The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.

Results

In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.

Conclusions

A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.

Modeling the kinetics of digoxin absorption: enhancement by P-glycoprotein inhibition

An increase in the area under the curve (AUC) after oral digoxin due to coadministration of drugs known as P-glycoprotein (P-gp) inhibitors has been reported in several studies, but there is very little information on the rate of absorption after P-gp inhibition. Based on an inverse Gaussian density absorption model and using a population approach, the authors reanalyzed data showing an increase in oral digoxin AUC in healthy volunteers after coadministration of talinolol. The model fitted the data well, and the results revealed that the maximum rate of digoxin absorption increased nearly 2-fold, whereas bioavailability increased only by 21%. It is concluded that the increase in the rate of absorption seems to be a better indicator of intestinal P-gp inhibition than the increase in extent of absorption. Furthermore, the authors use a simulation study to demonstrate the ability of the method to estimate bioavailability based on the population characteristics of digoxin disposition kinetics obtained from a different group of healthy volunteers.

Loperamide and P-glycoprotein inhibition: assessment of the clinical relevance

OBJECTIVES

Loperamide is a peripherally acting mu opioid receptor agonist and an avid substrate for P-glycoprotein. This may give rise to drug-drug interactions and increased risk for central adverse effects. The objective of this study was to re-evaluate the predictability of non-clinical data using loperamide as a probe P-glycoprotein substrate. We searched the literature for papers containing data on drug-drug interactions of loperamide-containing products in humans. We also reviewed the internal worldwide safety database of Johnson & Johnson for spontaneous case reports suggestive of a central opioid effect after coadministration of loperamide with a P-glycoprotein inhibitor or substrate.

KEY FINDINGS

Only one of the ten studies in our review supported the finding that inhibition of P-glycoprotein is associated with clinically relevant signs or symptoms of central nervous system (CNS) depression/opioid toxicity of loperamide. None of the 25 spontaneous case reports of interest were suggestive of signs or symptoms of CNS depression/opioid toxicity due to coadministration of loperamide and a P-glycoprotein inhibitor or substrate.

SUMMARY

Based on a review of the literature and a cumulative review of the spontaneous case reports, there is insufficient evidence that an interaction between loperamide and a P-glycoprotein inhibitor or substrate is associated with clinical symptoms of CNS depression/opioid toxicity when loperamide is taken at the recommended dose.

Refining the in vitro and in vivo critical parameters for P-glycoprotein, [I]/IC50 and [I2]/IC50, that allow for the exclusion of drug candidates from clinical digoxin interaction studies

The objective of this work was to further investigate the reasons for disconcordant clinical digoxin drug interactions (DDIs) particularly for false negative where in vitro data suggests no P-glycoprotein (P-gp) related DDI but a clinically relevant DDI is evident. Applying statistical analyses of binary classification and receiver operating characteristic (ROC), revised cutoff values for ratio of [I]/IC(50) < 0.1 and [I(2)]/IC(50) < 5 were identified to minimize the error rate, a reduction of false negative rate to 9% from 36% (based on individual ratios). The steady state total C(max) at highest dose of the inhibitor is defined as [I] and the ratio of the nominal maximal gastrointestinal concentration determined for highest dose per 250 mL volume defined [I(2)](.) We also investigated the reliability of the clinical data to see if recommendations can be made on values that would allow predictions of 25% change in digoxin exposure. The literature derived clinical digoxin interaction studies were statistically powered to detect relevant changes in exposure associated with digitalis toxicities. Our analysis identified that many co-meds administered with digoxin are cardiovascular (CV) agents. Moreover, our investigations also suggest that the presence of CV agents may alter cardiac output and/or kidney function that may act alone or are additional components to enhance digoxin exposure along with P-gp interaction. While we recommend digoxin as the probe substrate to define P-gp inhibitory potency for clinical assessment, we observed high concordance in P-gp inhibitory potency for calcein AM as a probe substrate.

Amlodipine/Atorvastatin: a review of its use in the treatment of hypertension and dyslipidaemia and the prevention of cardiovascular disease

Amlodipine/atorvastatin (Caduet) is a single-tablet, fixed-dose combination of the dihydropyridine calcium channel antagonist amlodipine and the HMG-CoA reductase inhibitor atorvastatin. The bioavailability of amlodipine and atorvastatin with a single-tablet, fixed-dose amlodipine/atorvastatin combination was not significantly different to that with coadministered separate amlodipine and atorvastatin tablets. In well controlled clinical trials in patients with hypertension and dyslipidaemia, once-daily amlodipine and atorvastatin (administered as the single-tablet, fixed-dose combination or coadministered as two separate tablets) effectively reduced systolic BP (SBP) and low-density lipoprotein cholesterol (LDL-C) levels, and enabled more patients to achieve BP and LDL-C goals than single-agent or placebo therapy. There was no modification of the effect of amlodipine on SBP when administered in combination with atorvastatin and there was no modification of the effect of atorvastatin on LDL-C when administered in combination with amlodipine. In noncomparative, titration-to-goal, open-label 'real-world' trials, the single-tablet, fixed-dose combination of amlodipine/atorvastatin enabled patients with hypertension and dyslipidaemia to achieve both BP and LDL-C goals. Administration of a single tablet of amlodipine/atorvastatin, compared with coadministration of these agents as two separate tablets, improved patient adherence, according to a retrospective study that utilized prescription refill rates from a large US insurance database. Data from the large, randomized, double-blind, placebo-controlled ASCOT-LLA trial also demonstrated that the combination of amlodipine-based therapy and atorvastatin was effective in preventing cardiovascular (CV) endpoints in hypertensive patients at risk of CV disease (CVD). In summary, amlodipine/atorvastatin offers a convenient and effective approach to improving adherence and managing CV risk in hypertensive patients with dyslipidaemia or at risk of CVD.

Expression and function of p-glycoprotein in normal tissues: effect on pharmacokinetics

ATP-binding cassette (ABC) drug efflux transporters limit intracellular concentration of their substrates by pumping them out of cell through an active, energy dependent mechanism. Several of these proteins have been originally associated with the phenomenon of multidrug resistance; however, later on, they have also been shown to control body disposition of their substrates. P-glycoprotein (Pgp) is the first detected and the best characterized of ABC drug efflux transporters. Apart from tumor cells, its constitutive expression has been reported in a variety of other tissues, such as the intestine, brain, liver, placenta, kidney, and others. Being located on the apical site of the plasma membrane, Pgp can remove a variety of structurally unrelated compounds, including clinically relevant drugs, their metabolites, and conjugates from cells. Driven by energy from ATP, it affects many pharmacokinetic events such as intestinal absorption, brain penetration, transplacental passage, and hepatobiliary excretion of drugs and their metabolites. It is widely believed that Pgp, together with other ABC drug efflux transporters, plays a crucial role in the host detoxication and protection against xenobiotic substances. On the other hand, the presence of these transporters in normal tissues may prevent pharmacotherapeutic agents from reaching their site of action, thus limiting their therapeutic potential. This chapter focuses on P-glycoprotein, its expression, localization, and function in nontumor tissues and the pharmacological consequences hereof.

Myopathy with statin-fibrate combination therapy: clinical considerations

Many patients who receive statin therapy for hyperlipidemia-such as patients with diabetes mellitus and metabolic syndrome--have residual cardiovascular risk. These patients often have dyslipidemia, including low levels of HDL cholesterol and elevated levels of triglycerides and small, dense LDL. For such patients, combination treatment with statins and fibrates is a potentially useful strategy to improve lipid and lipoprotein profiles and reduce cardiovascular risk. However, statin-fibrate combination regimens have potential adverse effects on skeletal muscle, including myopathy. To date, no large-scale, prospective, randomized, controlled trial has evaluated the safety and efficacy of statin-fibrate combination therapy; one such trial is underway but will not report data until 2010. Until then, clinicians need to consider pharmacokinetic, pharmacodynamic, metabolic, pathophysiologic and other factors that can increase the systemic exposure of statins and/or fibrates and hence heighten the risk of toxic effects on muscles, as well as data from clinical trials and recommendations of consensus panels to optimize the safety of such combination regimens. On the basis of currently available data, fenofibrate or fenofibric acid is the fibrate of choice when used in combination with a statin because each is, in theory, associated with a lower risk of myopathy than gemfibrozil.

Long-term use of rosuvastatin: a critical risk benefit appraisal and comparison with other antihyperlipidemics

Rosuvastatin represents the latest inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase introduced in clinical practice for the treatment of hypercholesterolemia. In comparative trials, across dose ranges this statin reduced low-density lipoprotein (LDL) cholesterol and total cholesterol significantly more than atorvastatin, simvastatin, and pravastatin, and triglycerides significantly more than simvastatin and pravastatin. In healthy subjects with normal LDL cholesterol and elevated C-reactive protein, rosuvastatin treatment significantly decreased the incidence of cardiovascular events. Its chemical and pharmacokinetic properties (with a low lipophilicity and poor capacity to inhibit cytochrome P450 enzymes) suggest a very limited penetration in extrahepatic tissues with a lower risk of muscle toxicity and unlike metabolically mediated drug–drug interactions. This article reviews the most recent data on the pharmacologic and clinical properties of rosuvastatin, in order to enable the correct use of this statin for the treatment of hypercholesterolemia.

The expression of efflux and uptake transporters are regulated by statins in Caco-2 and HepG2 cells

AIM

Statin disposition and response are greatly determined by the activities of drug metabolizing enzymes and efflux/ uptake transporters. There is little information on the regulation of these proteins in human cells after statin therapy. In this study, the effects of atorvastatin and simvastatin on mRNA expression of efflux (ABCB1, ABCG2 and ABCC2) and uptake (SLCO1B1, SLCO2B1 and SLC22A1) drug transporters in Caco-2 and HepG2 cells were investigated.

METHODS

Quantitative real-time PCR was used to measure mRNA levels after exposure of HepG2 and Caco-2 cells to statins.

RESULTS

Differences in mRNA basal levels of the transporters were as follows: ABCC2>ABCG2>ABCB1>SLCO1B1>>>SLC22A1>SLC O2B1 for HepG2 cells, and SLCO2B1>>ABCC2>ABCB1>ABCG2>>>SLC22A1 for Caco-2 cells. While for HepG2 cells, ABCC2, ABCG2 and SLCO2B1 mRNA levels were significantly up-regulated at 1, 10 and 20 micromol/L after 12 or 24 h treatment, in Caco-2 cells, only the efflux transporter ABCB1 was significantly down-regulated by two-fold following a 12 h treatment with atorvastatin. Interestingly, whereas treatment with simvastatin had no effect on mRNA levels of the transporters in HepG2 cells, in Caco-2 cells the statin significantly down-regulated ABCB1, ABCC2, SLC22A1, and SLCO2B1 mRNA levels after 12 or 24 h treatment.

CONCLUSION

These findings reveal that statins exhibits differential effects on mRNA expression of drug transporters, and this effect depends on the cell type. Furthermore, alterations in the expression levels of drug transporters in the liver and/or intestine may contribute to the variability in oral disposition of statins.Acta Pharmacologica Sinica (2009) 30: 956-964; doi: 10.1038/aps.2009.85; published online 22 June 2009.