Pharmacokinetics Of Digoxin Research Articles

Abstract 4135497: Model-informed dosing recommendations for digoxin in interstage single ventricle heart disease

Digoxin is commonly used in infants with single ventricle heart disease (SVD) and may decrease mortality in the interstage period. While labeled for the treatment of heart failure in infants, dosing recommendations do not account for alterations in drug disposition in SVD. We aimed to characterize digoxin pharmacokinetics (PK) in interstage infants with SVD. Infants <3 months with SVD who received enteral digoxin per standard of care after stage 1 and prior to stage 2 palliation were included in this prospective, multicenter, open-label PK study. Preterm infants and those with creatinine >2mg/dL or on extracorporeal support at enrollment were excluded. PK samples were prospectively collected and digoxin plasma concentrations quantitated by HPLC/MS/MS assay validated per FDA guidance. Population PK analysis was performed in NONMEM. Effects of covariates on PK parameters were evaluated. Dosing simulations were conducted in a virtual cohort of infants with SVD that optimized dosing to achieve trough concentrations of 0.5-2 ng/mL, the label-endorsed reference range for heart failure. Across 10 sites, 50 infants contributed 207 PK samples. Table 1 shows cohort characteristics. A 2-compartment model with transit compartment absorption and linear elimination best described the data. Renal function and weight were significant covariates. Infants with SVD had lower apparent clearance than previously reported in infants without SVD. Current labeled dosing resulted in simulated exposures above the reference range ( Figure 1 ). A new dosing regimen for infants with SVD is proposed. Digoxin PK is altered in infants with SVD. Dosing should account for renal function, age, and weight.

Digoxin loading doses for atrial fibrillation in critically Ill patients

Abstract Introduction The use of digoxin, a cardiac glycoside, for inotropic effects and electrophysiologic actions date back as far as the 18th century. Loading doses (LD) of digoxin are recommended when used for rate control in the setting of atrial fibrillation or atrial flutter (AF/AFL), though the optimal serum drug concentration (SDC) remains unknown. Pharmacokinetic based formulas used to calculate a LD target a SDC of 0.8-1.2 ng/mL. Still, digoxin pharmacokinetics remain unpredictable and are influenced by age, weight, renal impairment, and critical illness. Purpose The purpose of this study was to assess the safety and efficacy of digoxin LD used in critically ill patients to rate control AF/AFL and to assess the SDC achieved after a LD. Methods We conducted an Institutional Review Board approved retrospective cohort study at New York University Langone Health between January 2013 and March 2018. Adult patients (18 years of age or older) were included if they received an intravenous digoxin LD for AF/AFL in any intensive care unit and had a digoxin SDC measured between 6 and 24 hours after the LD. Patients were excluded if they received the LD orally. The primary endpoint was the SDC achieved after the LD. Secondary safety endpoints were the incidence of supratherapeutic digoxin SDC (> 1.2 ng/mL). Secondary efficacy endpoints included the incidence of rate control success, defined as a heart rate (HR) less than 110 beats per minute (bpm) within 24 hours of the digoxin LD. Results A total of 92 patients were included in the final analysis. The cohort was 53% male with a median age of 72 years, median BMI of 27, a median creatinine clearance (CrCL) of 47 mL/min at the time of IV digoxin LD and 66% of the cohort had AF. The highest HR associated with the AF/AFL 24 hours prior to digoxin was 133 (121, 145) bpm. The median total LD of digoxin for the entire cohort was 11 mcg/kg (750 mcg). In those with impaired renal function (CrCl < 30 mL/min), the median LD of digoxin was 8.3 mcg/kg. For 61% of the cohort, the LD was distributed over 6-hour intervals. The median SDC after completion of the IV digoxin LD was 1.3 ng/mL (0.9, 1.7), with a range of 0.4-4.1 ng/mL. A SDC > 1.2 ng/mL was observed in 51% of the cohort. Secondary outcomes of rate control success occurred in 60% of the cohort. No patients experienced any digoxin associated dysrhythmias or required reversal. Conclusion We found a SDC of 1.3 ng/mL post a digoxin LD in critically ill patients with AF/AFL to be successful in achieving rate control in 60% of our cohort. Despite 51% of the cohort achieving a SDC > 1.2 ng/mL, digoxin related adverse events were uncommon. Our analysis support previous data suggesting dose adjustment of digoxin in patients with impaired renal function but suggest a higher SDC may be acceptable in the acute setting when used for rate control. Our sample size is limited, and future studies should confirm our findings in critically ill patients.

Quantitatively Predicting Effects of Exercise on Pharmacokinetics of Drugs Using a Physiologically Based Pharmacokinetic Model.

Exercise significantly alters human physiological functions, such as increasing cardiac output and muscle blood flow and decreasing glomerular filtration rate (GFR) and liver blood flow, thereby altering the absorption, distribution, metabolism, and excretion of drugs. In this study, we aimed to establish a database of human physiological parameters during exercise and to construct equations for the relationship between changes in each physiological parameter and exercise intensity, including cardiac output, organ blood flow (e.g., muscle blood flow and kidney blood flow), oxygen uptake, plasma pH and GFR, etc. The polynomial equation P = ΣaiHRi was used for illustrating the relationship between the physiological parameters (P) and heart rate (HR), which served as an index of exercise intensity. The pharmacokinetics of midazolam, quinidine, digoxin, and lidocaine during exercise were predicted by a whole-body physiologically based pharmacokinetic (WB-PBPK) model and the developed database of physiological parameters following administration to 100 virtual subjects. The WB-PBPK model simulation results showed that most of the observed plasma drug concentrations fell within the 5th-95th percentiles of the simulations, and the estimated peak concentrations (Cmax) and area under the curve (AUC) of drugs were also within 0.5-2.0 folds of observations. Sensitivity analysis showed that exercise intensity, exercise duration, medication time, and alterations in physiological parameters significantly affected drug pharmacokinetics and the net effect depending on drug characteristics and exercise conditions. In conclusion, the pharmacokinetics of drugs during exercise could be quantitatively predicted using the developed WB-PBPK model and database of physiological parameters. SIGNIFICANCE STATEMENT: This study simulated real-time changes of human physiological parameters during exercise in the WB-PBPK model and comprehensively investigated pharmacokinetic changes during exercise following oral and intravenous administration. Furthermore, the factors affecting pharmacokinetics during exercise were also revealed.

A phase I drug-drug interaction study to assess the effect of futibatinib on P-gp and BCRP substrates and of P-gp inhibition on the pharmacokinetics of futibatinib.

Futibatinib, an inhibitor of fibroblast growth factor receptor 1-4, is approved for the treatment of patients with advanced cholangiocarcinoma with FGFR2 fusions/rearrangements. In this phase I drug-drug interaction study, the effects of futibatinib on P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) substrates, and of P-gp inhibition on futibatinib pharmacokinetics (PK) were investigated in healthy adults aged 18-55 years. In part 1, 20 participants received digoxin (P-gp substrate) and rosuvastatin (BCRP substrate). Following a ≥10-day washout, futibatinib was administered for 7 days, with digoxin and rosuvastatin coadministered on the third day. In part 2, 24 participants received futibatinib. Following a ≥3-day washout, quinidine (P-gp inhibitor) was administered for 4 days, with futibatinib coadministered on day 4. Blood samples were collected predose and for 24 (futibatinib), 72 (rosuvastatin), and 120 h (digoxin) postdose. Urine samples (digoxin) were collected predose and for 120 h postdose. PK parameters were compared between treatments using analysis of variance. Coadministration with futibatinib had no effect on the PK of digoxin and rosuvastatin, and coadministration with quinidine had minimal effects on the PK of futibatinib. Differences in Cmax and AUC with and without futibatinib and quinidine, respectively, were <20%. The most common treatment-emergent adverse events were diarrhea (80%) and increased blood phosphorous (75%) in part 1 and prolonged electrocardiogram QT interval (38%) in part 2. The data show that futibatinib has no clinically meaningful effects on the PK of P-gp or BCRP substrates and that the effect of P-gp inhibition on futibatinib PK is not clinically relevant.

Digoxin Pharmacokinetics in Patients with Obesity Before and After a Gastric Bypass or a Strict Diet Compared with Normal Weight Individuals.

Several drugs on the market are substrates for P-glycoprotein (P-gp), an efflux transporter highly expressed in barrier tissues such as the intestine. Body weight, weight loss, and a Roux-en-Y gastric bypass (RYGB) may influence P-gp expression and activity, leading to variability in the drug response. The objective of this study was therefore to investigate digoxin pharmacokinetics as a measure of the P-gp phenotype in patients with obesity before and after weight loss induced by an RYGB or a strict diet and in normal weight individuals. This study included patients with severe obesity preparing for an RYGB (n = 40) or diet-induced weight loss (n = 40) and mainly normal weight individuals scheduled for a cholecystectomy (n = 18). Both weight loss groups underwent a 3-week low-energy diet (<1200 kcal/day) followed by an additional 6 weeks of <800 kcal/day induced by an RYGB (performed at week 3) or a very-low-energy diet. Follow-up time was 2 years, with four digoxin pharmacokinetic investigations at weeks 0, 3, and 9, and year 2. Hepatic and jejunal P-gp levels were determined in biopsies obtained from the patients undergoing surgery. The RYGB group and the diet group had a comparable weight loss in the first 9 weeks (13 ± 2.3% and 11 ± 3.6%, respectively). During this period, we observed a minor increase (16%) in the digoxin area under the concentration-time curve from zero to infinity in both groups: RYGB: 2.7 µg h/L [95% confidence interval (CI) 0.67, 4.7], diet: 2.5 µg h/L [95% CI 0.49, 4.4]. In the RYGB group, we also observed that the time to reach maximum concentration decreased after surgery: from 1.0 ± 0.33 hours at week 3 to 0.77 ± 0.08 hours at week 9 (-0.26 hours [95% CI -0.47, -0.05]), corresponding to a 25% reduction. Area under the concentration-time curve from zero to infinity did not change long term (week 0 to year 2) in either the RYGB (1.1 µg h/L [-0.94, 3.2]) or the diet group (0.94 µg h/L [-1.2, 3.0]), despite a considerable difference in weight loss from baseline (RYGB: 30 ± 7%, diet: 3 ± 6%). At baseline, the area under the concentration-time curve from zero to infinity was -5.5 µg h/L [95% CI -8.5, -2.5] (-26%) lower in patients with obesity (RYGB plus diet) than in normal weight individuals scheduled for a cholecystectomy. Further, patients undergoing an RYGB had a 0.05 fmol/µg [95% CI 0.00, 0.10] (29%) higher hepatic P-gp level than the normal weight individuals. Changes in digoxin pharmacokinetics following weight loss induced by a pre-operative low-energy diet and an RYGB or a strict diet (a low-energy diet plus a very-low-energy diet) were minor and unlikely to be clinically relevant. The lower systemic exposure of digoxin in patients with obesity suggests that these patients may have increased biliary excretion of digoxin possibly owing to a higher expression of P-gp in the liver.

Population Pharmacokinetics of Digoxin in Nonagenarian Patients: Optimization of the Dosing Regimen.

The aim of this study was to develop a population pharmacokinetic model of digoxin in patients over 90 years old and to propose an equation for adjusting digoxin dose in this population. We included 326 nonagenarian patients admitted to Severo Ochoa University Hospital (Spain) who received digoxin and were under therapeutic drug monitoring. All data were retrospectively collected, and population modeling was performed with non-linear mixed-effect modeling software (NONMEM®). One- and two-compartment models were tested to calculate digoxin clearance (Cl), volume of distribution (Vd), absorption rate constant (Ka), and bioavailability (bioavailable fraction, F). The covariates were evaluated by stepwise covariate model building, and the final model was internally validated by bootstrap analysis with 1000 resamples. External validation was performed with another population of 95 patients with the same characteristics as the modeling group. The population was 26% males, with a mean age of 93.2 years (90-103 years), mean creatinine 1.11 mg/dL (0.42-3.81 mg/dL), and mean total body weight 61.2 kg (40-100 kg). The pharmacokinetics of digoxin were best described by a one-compartment model (ADVAN2 TRANS2), with first-order conditional estimation with interaction. The covariates with influence on our model were creatinine clearance based on the Cockcroft-Gault equation (CG), serum potassium (K), co-administration of loop diuretics, and sex: Cl/F = 4.55 · (CG/36.4)0.468 · 0.83LD · 1.21SEX; Vd/F = 355 · (K/4.3)-0.849; Ka = 1.22 h-1 [where LD indicates loop diuretics (1 for administered, 0 for otherwise) and SEX indicates patient sex (1 for male, 0 for female)]. Based on our results, we proposed an equation to adjust the digoxin dosing regimen in nonagenarian patients: dose (mg) = 0.144 · (CG/36.4)0.468 · 0.83LD · 1.21SEX. The greatest influence on digoxin clearance came from renal function calculated by the Cockcroft-Gault equation. Vd was decreased by K. The model developed showed a precise predictive performance to be applied for therapeutic drug monitoring.

Dosage Optimization of Digoxin in Older Patients with Heart Failure and Chronic Kidney Disease: A Population Pharmacokinetic Analysis.

Renal function is an important index for digoxin dose adjustment, especially in patients with chronic kidney disease (CKD). Decreased glomerular filtration rate is common in older patients with cardiovascular disease. The aim of this study was to establish a digoxin population pharmacokinetic model in older patients with heart failure and CKD and to optimize the digoxin dose strategy. Older patients with heart failure and CKD aged > 60years from January 2020 to January 2021 and who had an estimated glomerular filtration rate (eGFR) < 90mL/min/1.73m2 or urine protein production were enrolled in this retrospective study. Population pharmacokinetic analysis and Monte Carlo simulations (n = 1000) were performed using NONMEN software. The precision and stability of the final model were analyzed by graphical and statistical methods. Overall, 269 older patients with heart failure were enrolled. A total of 306 digoxin concentrations were collected, with a median value of 0.98ng/mL (interquartile range [IQR] 0.62-1.61, range 0.04-4.24). The median age was 68years (IQR 64-71, range 60-94) and eGFR was 53.6mL/min/1.73m2 (IQR 38.1-65.2, range 11.4-89.8). A one-compartment model with first-order elimination was developed to describe the digoxin pharmacokinetics. Typical values for clearance and volume of distribution were 2.67L/h and 36.9L, respectively. Dosage simulations were stratified by eGFR and metoprolol. Doses of 62.5 and 125μg were recommended for older patients with eGFR < 60mL/min/1.73m2. A population pharmacokinetic model of digoxin in older patients with heart failure and CKD was established in this study. A novel digoxin dosage strategy was recommended in this vulnerable population.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling.

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Effects of rifampin coadministration on the pharmacokinetics of digoxin: a real-world data approach.

Digoxin, a cardiac glycoside, is commonly prescribed to treat heart failure and atrial fibrillation. Because digoxin acts as a substrate of P-glycoprotein (P-gp), its blood concentration may be reduced by P-gp inducers such as rifampin. To assess the real-world implications of this drug-drug interaction, a retrospective analysis was carried out on the Clinical Data Warehouse at Seoul National University Hospital between 2012 and 2017. Eleven patients who received both digoxin and rifampin with satisfying the inclusion/exclusion criteria were identified. The Ctrough values of digoxin monotherapy were compared to those of the combination therapy with rifampin. Results demonstrated that the systemic exposure of orally administered digoxin decreased by 40% with the concurrent use of rifampin. Clinicians should be aware of potential drug interactions between digoxin and rifampin, as adjustments to digoxin dosage might be necessary for patients receiving rifampin or other P-gp inducer drugs.

Evaluation of the Effect of Isobutyl Paraben and 2-ethyl Hexyl Paraben on P-glycoprotein Functional Expression in Rats: A Pharmacokinetic Study.

Pharmaceutical excipients have been shown to influence drug disposition through modulating transport protein. This study assessed the effect of single dose administration of parabens on the pharmacokinetics (PK) of digoxin, a probe substrate of p-glycoprotein (p-gp), in vivo. Also, the effect of multiple dosing of parabens on p-gp expression was examined. Rats were randomized into four groups that received either the vehicle, 25 mg/ kg verapamil, 100 mg/ kg isobutyl paraben, or 100 mg/ kg 2-ethyl hexyl paraben, which was followed by giving 0.2 mg/ kg digoxin via oral gavage. Blood samples were collected at different time points, digoxin concentration was measured using LC/MS-MS, and digoxin PK parameters were estimated. Another set of rats received multiple doses of parabens for 14 days, followed by measuring intestinal and hepatic mRNA expression of p-gp using qRT-PCR. Single dose administration of verapamil significantly increased Cmax (by 60.4 %) and AUC0-t (by 61.7 %) of digoxin compared to the control group, while the PK parameters of digoxin in rats exposed to parabens were not significantly different from the control. Consistently, the mRNA expression of p-gp in the intestine and liver was not affected by parabens treatment. The lack of isobutylparaben and 2-ethylhexyl paraben effect on p-gp may suggest the insignificant interaction of parabens with p-gp drug substrates, which could be considered for safety when designing pharmaceutical formulations.

Optimization and Validation of Glycerol-induced Acute Renal Failure Model in Rats in Predicting the Pharmacokinetics in Patients with Acute Renal Failure

The study standardized the glycerol induced (intra-peritoneal and intra-muscular) acute renal failure (ARF) models in Sprague-Dawley rats and validated its potential to predict the change in pharmacokinetics of the selected probe drugs in renal impairment. We assessed the induction of ARF by measuring the serum creatinine and blood-urea-nitrogen (BUN) levels at different time points (baseline, 6 h, 12 h, 24 h, 48 h and 72 h). Glycerol administrations via both intra-peritoneal and intra-muscular routes caused a significant increase in serum creatinine and BUN levels in rats at 6 h; however, the levels decreased within 24 h with intra-peritoneal administration. In contrast, with intra-muscular administration, a sustained increase in serum creatinine and BUN levels was observed up to 72 h post glycerol treatment demonstrating a sustained ARF like condition in rats that was suitable to evaluate the pharmacokinetics of drugs. Therefore, intra-muscular glycerol administration model was used to evaluate the pharmacokinetics of selected probe drugs including metformin, digoxin, atorvastatin and linezolid. The pharmacokinetics of metformin and digoxin were significantly altered in ARF versus control rats with increase in plasma exposure, prolonged elimination half-life and decreased systemic clearance. However, the pharmacokinetics of atorvastatin and linezolid did not show significant differences between ARF and control rats. These results demonstrated the utility of the ARF model in rats in predicting pharmacokinetic changes in patients with renal impairment. Such models may prove useful in early discovery work to screen and prioritize compounds requiring dose adjustment in patients with renal impairment.

THE DANGER OF DIGOXIN: TOXICITY WITHIN THE "THERAPEUTIC” RANGE

THE DANGER OF DIGOXIN: TOXICITY WITHIN THE "THERAPEUTIC” RANGE

Evaluation of Safety and Clinically Relevant Drug-Drug Interactions with Tucatinib in Healthy Volunteers.

Background and ObjectiveTucatinib is approved for treatment of human epidermal growth factor receptor 2-positive metastatic breast cancer. Understanding potential drug–drug interactions (DDIs) informs proper dosing when co-administering tucatinib with other therapies. The aim of this study was to evaluate DDIs between tucatinib and metabolizing enzymes and transporters in healthy volunteers.MethodsParts A–C assessed the impact of itraconazole (cytochrome P450 [CYP] 3A4 inhibitor), rifampin (CYP3A4/CYP2C8 inducer), or gemfibrozil (CYP2C8 inhibitor) on the pharmacokinetics of a single 300 mg dose of tucatinib administered orally and its primary metabolite, ONT-993. Parts D and E assessed the effect of steady-state tucatinib on the pharmacokinetics of repaglinide (CYP2C8 substrate), tolbutamide (CYP2C9 substrate), midazolam (CYP3A4 substrate), and digoxin (P-glycoprotein substrate).ResultsTucatinib area under the concentration–time curve from time 0 extrapolated to infinity (AUC0–inf) increased by ~ 1.3- and 3.0-fold with itraconazole and gemfibrozil, respectively, and decreased by 48% with rifampin, indicating that tucatinib is metabolized primarily by CYP2C8, and to a lesser extent via CYP3A. Tucatinib was a strong inhibitor of CYP3A (midazolam AUC0–inf increased 5.7-fold), a weak inhibitor of CYP2C8 and P-glycoprotein, and had no impact on CYP2C9-mediated metabolism in humans. Tucatinib was well tolerated, alone and with co-administered drugs.ConclusionThe potential DDIs identified here may be mitigated by avoiding concomitant use of tucatinib with strong CYP3A inducers, moderate CYP2C8 inducers, CYP3A substrates with a narrow therapeutic window (modifying substrate dose where concomitant use is unavoidable), and strong CYP2C8 inhibitors (decreasing tucatinib dose where concomitant use is unavoidable), or by reducing the dose of P-glycoprotein substrates with a narrow therapeutic window.Trial RegistrationThis trial (NCT03723395) was registered on October 29, 2018.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-022-01144-z.

Robust physiologically based pharmacokinetic model of rifampicin for predicting drug-drug interactions via P-glycoprotein induction and inhibition in the intestine, liver, and kidney.

P‐glycoprotein (P‐gp) is an efflux transporter that plays an important role in the pharmacokinetics of its substrate, and P‐gp activities can be altered by induction and inhibition effects of rifampicin. This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of rifampicin to predict the P‐gp‐mediated drug–drug interactions (DDIs) and assess the DDI impact in the intestine, liver, and kidney. The induction and inhibition parameters of rifampicin for P‐gp were estimated using two of seven DDI cases of rifampicin and digoxin and incorporated into our previously constructed PBPK model of rifampicin. The constructed rifampicin model was verified using the remaining five DDI cases with digoxin and five DDI cases with other P‐gp substrates (talinolol and quinidine). Based on the established PBPK model, following repeated dosing of 600 mg rifampicin, the deduced net effect was an approximately threefold induction in P‐gp activities in the intestine, liver, and kidney. Furthermore, in all 12 cases the predicted area under the plasma concentration–time curve ratios of the P‐gp substrates were within the predefined acceptance criteria with various dosing regimens. Intestinal effects of P‐gp–mediated DDIs had their greatest impact on the pharmacokinetics of digoxin and talinolol, with a minimal impact on the liver and kidney. For quinidine, predicted intestinal P‐gp/cytochrome P450 3A–mediated DDIs were slightly underestimated because of the complexity of nonlinearity and transporter‐enzyme interplay. These findings demonstrate that our rifampicin model can be applicable to quantitatively predict the net impact of P‐gp induction and/or inhibition on diverse P‐gp substrates and investigate the magnitude of DDIs in each tissue.

Evaluating the regulation of transporter proteins and P-glycoprotein in rats with cholestasis and its implication for digoxin clearance.

BACKGROUNDCardiac and hepatic functionality are intertwined in a multifaceted relationship. Pathologic processes involving one may affect the other through a variety of mechanisms, including hemodynamic and membrane transport effects.AIMTo better understand the effect of extrahepatic cholestasis on regulations of membrane transporters involving digoxin and its implication for digoxin clearance.METHODSTwelve adult rats were included in this study; baseline hepatic and renal laboratory values and digoxin pharmacokinetic (PK) studies were established before evenly dividing them into two groups to undergo bile duct ligation (BDL) or a sham procedure. After 7 d repeat digoxin PK studies were completed and tissue samples were taken to determine the expressions of cell membrane transport proteins by quantitative western blot and real-time polymerase chain reaction. Data were analyzed using SigmaStat 3.5. Means between pre-surgery and post-surgery in the same experimental group were compared by paired t-test, while independent t-test was employed to compare the means between sham and BDL groups.RESULTSDigoxin clearance was decreased and liver function, but not renal function, was impaired in BDL rats. BDL resulted in significant up-regulation of multidrug resistance 1 expression in the liver and kidney and its down-regulation in the small intestine. Organic anion transporting polypeptides (OATP)1A4 was up-regulated in the liver but down-regulated in intestine after BDL. OATP4C1 expression was markedly increased in the kidney following BDL.CONCLUSIONThe results suggest that cell membrane transporters of digoxin are regulated during extrahepatic cholestasis. These regulations are favorable for increasing digoxin excretion in the kidney and decreasing its absorption from the intestine to compensate for reduced digoxin clearance due to cholestasis.

The System Profile of Renal Drug Transporters in Tubulointerstitial Fibrosis Model and Consequent Effect on Pharmacokinetics.

With the widespread clinical use of drug combinations, the incidence of drug–drug interactions (DDI) has significantly increased, accompanied by a variety of adverse reactions. Drug transporters play an important role in the development of DDI by affecting the elimination process of drugs in vivo, especially in the pathological state. Tubulointerstitial fibrosis (TIF) is an inevitable pathway in the progression of chronic kidney disease (CKD) to end-stage renal disease. Here, the dynamic expression changes of eleven drug transporters in TIF kidney have been systematically investigated. Among them, the mRNA expressions of Oat1, Oat2, Oct1, Oct2, Oatp4C1 and Mate1 were down-regulated, while Oat3, Mrp2, Mrp4, Mdr1-α, Bcrp were up-regulated. Pearson correlation analysis was used to analyze the correlation between transporters and Creatinine (Cr), OCT2 and MATE1 showed a strong negative correlation with Cr. In contrast, Mdr1-α exhibited a strong positive correlation with Cr. In addition, the pharmacokinetics of cimetidine, ganciclovir, and digoxin, which were the classical substrates for OCT2, MATE1 and P-glycoprotein (P-gp), respectively, have been studied. These results reveal that changes in serum creatinine can indicate changes in drug transporters in the kidney, and thus affect the pharmacokinetics of its substrates, providing useful information for clinical use.

The Effect of Oral Letermovir Administration on the Pharmacokinetics of a Single Oral Dose of P-Glycoprotein Substrate Digoxin in Healthy Volunteers.

Letermovir is a human cytomegalovirus (CMV) terminase inhibitor approved in the United States, Canada, Japan, and the European Union for prophylaxis of CMV infection and disease in CMV-seropositive, allogeneic, hematopoietic stem-cell transplant recipients. In vitro, letermovir is a substrate and potential modulator of P-glycoprotein. The potential of letermovir to alter the pharmacokinetics of digoxin (a P-glycoprotein substrate) upon coadministration in healthy subjects was therefore investigated in a phase 1 trial (EudraCT: 2011-004516-39). Oral letermovir 240mg was administered twice daily for 12days with a single oral digoxin 0.5-mg dose on day7; after a washout period, oral digoxin 0.5mg was administered on day35 (sequence 1). The period order was reversed after a 28-day washout for sequence 2. Pharmacokinetics and safety were evaluated. The presence of steady-state letermovir reduced digoxin area under the plasma concentration-time curve from administration until last quantifiable measurement by 12% and maximum plasma concentration by 22% compared with digoxin alone; digoxin half-life and elimination rate remained similar in both conditions. The between-subject variability of digoxin maximum plasma concentration was higher with letermovir than without (42% vs 31%) and similar for digoxin area under the plasma concentration-time curve in both periods. No specific safety or tolerability concerns were identified. Overall, letermovir had no clinically relevant effect on concomitant administration with digoxin.

Pharmacokinetic Drug-Drug Interaction Study of Omecamtiv Mecarbil With Amiodarone and Digoxin in Healthy Subjects.

Omecamtiv mecarbil (OM), a novel cardiac myosin activator, is being evaluated for the treatment of heart failure with reduced ejection fraction. In vitro studies demonstrate OM as a substrate and inhibitor of P‐glycoprotein (P‐gp), which can result in drug‐drug interactions. Two phase 1, open‐label studies assessed the effect of coadministration of OM (50‐mg single dose) on the pharmacokinetics of digoxin (0.5‐mg single dose; N = 15), a P‐gp substrate, and the effect of coadministration of amiodarone (600‐mg single dose), a P‐gp inhibitor, on the pharmacokinetics of OM (50‐mg single dose; N = 14) in healthy subjects. The ratios of the geometric least squares mean (90% confidence interval [CI]) of digoxin coadministered with OM vs digoxin alone for area under the plasma concentration–time curve (AUC) from time 0 to infinity, AUC from time 0 to the time of the last quantifiable concentration, and maximum observed plasma concentration were 1.06 (90%CI, 0.99‐1.14), 1.06 (90%CI, 0.98‐1.14), and 1.08 (90%CI, 0.92‐1.26), respectively. The ratios of the geometric least squares mean of OM coadministered with amiodarone vs OM alone for AUC from time 0 to infinity, AUC from time 0 to the time of the last quantifiable concentration, and maximum observed plasma concentration were 1.21 (90%CI, 1.08‐1.36), 1.21 (90%CI, 1.07‐1.36), and 1.08 (90%CI, 0.96‐1.22), respectively. In conclusion, OM coadministered with digoxin or amiodarone did not result in any clinically relevant pharmacokinetic drug‐drug interactions.

Evaluation of the Influence of Zhenwu Tang on the Pharmacokinetics of Digoxin in Rats Using HPLC-MS/MS.

Digoxin (DIG) is a positive inotropic drug with a narrow therapeutic window that is used in the clinic for heart failure. The active efflux transporter of DIG, P-glycoprotein (P-gp), mediates DIG absorption and excretion in rats and humans. Up to date, several studies have shown that the ginger and Poria extracts in Zhenwu Tang (ZWT) affect P-gp transport activity. This study aimed to explore the effects of ZWT on the tissue distribution and pharmacokinetics of DIG in rats. The deionized water or ZWT (18.75 g/kg) was orally administered to male Sprague–Dawley rats once a day for 14 days as a pretreatment. On day 15, 1 hour after receiving deionized water or ZWT, the rats were given the solution of DIG at 0.045 mg/kg dose, and the collection of blood samples was carried out from the fundus vein or excised tissues at various time points. HPLC-MS/MS was used for the determination of the DIG concentrations in the plasma and the tissues under investigation. The pharmacokinetic interactions between DIG and ZWT after oral coadministration in rats revealed significant reductions in DIG Cmax and AUC0-∞, as well as significant increases in T1/2 and MRT0-∞. When coadministered with ZWT, the DIG concentration in four of the investigated tissues statistically decreased at different time points except for the stomach. This study found that combining DIG with ZWT reduced not only DIG plasma exposure but also DIG accumulation in tissues (heart, liver, lungs, and kidneys). The findings of our study could help to improve the drug's validity and safety in clinical applications and provide a pharmacological basis for the combined use of DIG and ZWT.

Impact of fedratinib on the pharmacokinetics of transporter probe substrates using a cocktail approach

Fedratinib, an oral, selective Janus kinase 2 inhibitor, has been shown to inhibit P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, OATP1B3, organic cation transporter (OCT) 2, and multidrug and toxin extrusion (MATE) 1 and MATE2-K in vitro. The objective of this study was to evaluate the influence of fedratinib on the pharmacokinetics (PK) of digoxin (P-gp substrate), rosuvastatin (OATP1B1/1B3 and BCRP substrate), and metformin (OCT2 and MATE1/2-K substrate). In this nonrandomized, fixed-sequence, open-label study, 24 healthy adult participants received single oral doses of digoxin 0.25mg, rosuvastatin 10mg, and metformin 1000mg administered as a drug cocktail (day 1, period 1). After a 6-day washout, participants received oral fedratinib 600mg 1h before the cocktail on day 7 (period 2). An oral glucose tolerance test (OGTT) was performed to determine possible influences of fedratinib on the antihyperglycemic effect of metformin. Plasma exposure to the three probe drugs was generally comparable in the presence or absence of fedratinib. Reduced metformin renal clearance by 36% and slightly higher plasma glucose levels after OGTT were observed in the presence of fedratinib. Single oral doses of the cocktail ± fedratinib were generally well tolerated. These results suggest that fedratinib has minimal impact on the exposure of P-gp, BCRP, OATP1B1/1B3, OCT2, and MATE1/2-K substrates. Since renal clearance of metformin was decreased in the presence of fedratinib, caution should be exercised in using coadministered drugs that are renally excreted via OCT2 and MATEs. Clinicaltrials.gov NCT04231435 on January 18, 2020.