Population pharmacokinetics and optimization of the dosing regimen of digoxin in adult patients
© Komatsu et al. 2015
Received: 1 May 2015
Accepted: 13 August 2015
Published: 25 September 2015
This study aimed to evaluate the population pharmacokinetics of digoxin in Japanese patients and establish a dosage regimen based on the pharmacokinetic data.
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).
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).
Our findings suggest that population parameters are useful for evaluating digoxin pharmacokinetics.
Digoxin is widely prescribed for the treatment of congestive heart failure and atrial fibrillation. Therapeutic drug monitoring of digoxin is recommended because of its narrow therapeutic range [1, 2]. Previous studies have reported several equations and nomograms to enable physicians to determine the appropriate dosage of digoxin for individual patients [3, 4]. In addition, population pharmacokinetic data indicate that clearance of digoxin is influenced by demographic variables such as age, total body weight, and serum creatinine levels [5–7]. However, population pharmacokinetics have not been studied thus far to evaluate the influence of concomitant administration of drugs such as amiodarone, verapamil, and tolvaptan on relative digoxin clearance. Recently, the therapeutic range for digoxin in patients with heart failure with a normal sinus rhythm has been revised to a low and narrow range (0.5–0.8 ng/mL) based on the findings from the digitalis investigation group trial . In this study, we aimed to analyze the population pharmacokinetics of digoxin in the presence of concomitant administration of other drugs. Furthermore, we determined initial dosing regimens to achieve concentrations (0.5–0.8 ng/mL) according to our population pharmacokinetic data.
Blood samples were collected as part of the routine patient care for therapeutic drug monitoring and laboratory testing when we collect blood samples from patients. This study was approved by the Ethics Committee of Kitasato University Hospital (B13-99: approved on July 24, 2013).
Number of Patients
EF (%) (>=40: <40)
0.125 mg /3 days
0.125 mg /2 days
0.0625 mg /day
Digoxin concentration (ng/mL)
Antiarrhythmic agent; Class I
(Aprindine, Cibenzoline, Flecainide, Pilsicainde, Procainamide)
Antiarrhythmic agent;Class IV
(Bepridil, Diltiazem, Verapamil)
Data analysis was performed using the nonlinear mixed effects model (NONMEM) program (version VI, level 1.0) developed by Beal and Sheiner . Because the trough serum concentration of digoxin achieved steady-state, we used a simple pharmacokinetic model as follows: Cssij = Dij/(CLij τij), where Cssi is the steady-state serum digoxin concentration measured in the jth patient while he or she received the ith dosage, Dij is the dosage of digoxin in the jth patient, CLij is the total body clearance of digoxin in the jth patient, and τij is the dosing interval for the ith dosage in the jth patient. We used the first-order conditional estimation method (FOCE) for modeling. The inter-individual variability of the parameters was assessed using an exponential error model: Pi = TV(Pi) × exp(ηi), where Pi indicated the individual value, TV(Pi) was the population value for the parameters described in the equation, and ηi was the random deviation of Pi from TV(Pi). The value of ηi was assumed to be independently and normally distributed with a mean of 0 and a variance of ω2. The residual (intra-individual) variability of the parameters was assessed using a proportional error model: Cobs,ij = Cpred,ij × (1 + εij), where Cobs,ij and Cpred,ij denote the jth observed and predicted concentrations for the ith subject, respectively, and ε is a random intra-individual error that is normally distributed with a mean of 0 and variance σ2.
We examined the covariance of the variables, including age, CLcr, BW, EF, systolic blood pressure, and concomitant use of drugs to improve the population pharmacokinetic model. The influence of continuous covariates on the pharmacokinetic parameter TV(P) was modeled according to the following equations: TV(P) = θp + θc × (covariance), and TV(P) = θp × θc(covariance). The covariance that showed a correlation with the pharmacokinetic parameters was introduced into the model. The significance of the influence of covariates was evaluated by a change of -2 log likelihood (the minimum value of the objective function: OBJ). An OBJ decrease of more than 3.84 from the basic structural model (χ2: degree of freedom = 1, P < 0.05) was considered statistically significant during the forward inclusion process. The full model was structured by incorporating the significant covariates, and the final model was developed using a backward elimination method. When one covariate factor was excluded from the full model, an OBJ that increased more than 6.63 from the full model (χ2: degree of freedom = 1, P < 0.01) was considered statistically significant.
The adequacy of fitting was examined by plotting the predicted concentration versus the observed concentration, individual predicted concentration after Bayesian step versus observed concentration, and the weighted residual concentration versus the predicted one. The accuracy and robustness of the final model were assessed using the bootstrap method . A bootstrap sample was generated by repeated random sampling of the original data set, and the size of the bootstrap sample was the same as that of the original sample. We reconstructed 200 bootstrap samples, and the final model was determined by repeatedly testing the 200 bootstrap samples. The mean parameter estimates obtained from replication and calculated normally were compared with those obtained from the original data set.
Determination of the dosing regimen
Simulation of pharmacokinetics was performed to determine the dosing regimen based on our population pharmacokinetics data. Digoxin concentration was simulated for 1000 patients by using the final population pharmacokinetics model.
The probability that the trough digoxin concentration was in the range of 0.5–0.8 ng/mL in the steady state was calculated as the ratio of the number of simulated patients to the total number of patients. The simulation was performed using Microsoft Excel® 2013.
Hypothesis testing for fixed effect model of digoxin pharmacokinetics
θ1×θ2Ejection function (EF>41=1,EF<40=0)
θ1×θ2Drug (0;Cocomitant adminstaraion,1;otherwise)
Antiarrhythmic agent;Class I (Aprindine,Cibenzoline, Flecainide,Pilsicainde,Procainamide)
Antiarrhythmic agent;Class IV (Bepridil,Diltiazem,Verapamil)
Final estimates for the population pharmacokinetic parameters of digoxin
Population mean parameters
CL(L/h)= (1.21+0.0532×CLcr(mL/min)) ×(1+0.787×AMD)
(AMD = 0 for cocomitant adminstarion of amiodarone, otherwise 1)
Results of bootstrap validation
95% Confidence interval
Mean ± S.E.
Mean ± S.E.
1.21 ± 0.21
1.30 ± 0.05
0.0532 ± 0.0068
0.0543 ± 0.0016
0.787 ± 0.187
0.803 ± 0.018
0.104 ± 0.017
0.324 ± 0.006
0.065 ± 0.010
0.340 ± 0.086
To our knowledge, this is the first study in which a dosing regimen based on population pharmacokinetics has been proposed. We analyzed the sampling data for digoxin obtained from routine clinical data by using the NONMEM. This study focused on how drugs interact with digoxin. Our results showed that amiodarone and CLcr were significant covariants for the systemic clearance of digoxin. The systemic clearance consists of elimination rate constant and distribution volume. However, our model can’t individually assess these parameters because our data is trough concentration sampling. The rate constant is mainly affected by creatinine clearance and amiodarone. Typically, digoxin is thought to be eliminated by the kidney. Additionally, previous studies showed that CLcr is an affective factor [5–7]. Regarding drug interactions, amiodarone, but not amlodipine, atorvastatin, bisoprolol, carvedilol, or tolvaptan, remained during the backward deletion. Beta blockers such as bisoprolol and carvedilol increase the maximum plasma concentration of digoxin by about 1.3-fold . Similarly, administration of 10 mg atorvastatin increases the maximum plasma concentration of digoxin by about 1.2-fold . Administration of 60 mg tolvaptan increases the maximum plasma concentration of digoxin by about 1.3-fold . Schwartz JB reported that amlodipine does not significantly influence steady-state digoxin concentrations . Chen R  and Yukawa E  showed that spironolactone and calcium channel blockers affect the clearance of digoxin. However these factors result in about a 20 % decrease in digoxin clearance, and also these studies didn’t examine amiodarone. Therefore, we concluded that these medications were not included as final factors. Amiodarone increased the trough level of digoxin concentration by approximately two-fold . The main reason that amiodarone increases the serum concentration of digoxin is the inhibition of digoxin secretion from renal tubules and the inhibition of the P-glycoprotein membrane transporter system . Our results showed that the population means showed a good predictive performance. The final model lacked bias, despite the drug concentration and the observed drug concentration being almost identical to the individual predicted concentration after the Bayesian steps. The weighted residuals were acceptable to within three standard deviations, which are generally recognized as criteria for no selection biases. In addition, the convergence ratio on bootstrap data was significantly high (Table 4). Thus, the robustness of the model was sufficiently confirmed. The difference between θ of the final model estimate and that of the bootstrap means was relatively small. Therefore, we concluded that the final model had a good predictive performance. In future studies, we will perform external validation for our new population pharmacokinetics model. The probability of the trough concentration of digoxin being in the therapeutic range of 0.5–0.8 ng/mL for congestive heart failure is shown in Fig. 3. This regimen suggests the typical initial dosage of digoxin in patients at various CLcr with co-administration of amiodarone. The different initial dosages of digoxin calculated using the Koup  and Jusko  method depending on different values of CLcr to achieve a target digoxin concentration of 0.7 ng/mL were as follows: CLcr < 30 mL/min, start at 0.0625 mg every day; CLcr 30–80 mL/min, start at 0.125 mg every day; and CLcr > 120 mL/min, start at 0.25 mg every day. In addition, Bauman JL et al  reported a nomogram for achieving a steady-state concentration of 0.7 ng/mL on the basis of creatinine clearance and IBW or height. These findings are similar to those obtained using our new dosing regimen without amiodarone. This initial dosage regimen will be useful for reasonable therapies using oral digoxin for congestive heart failure. Some limitations exist in this study. Some studies have shown that the serum concentration of digoxin increases after concomitant administration with other antiarrhythmic drugs such as verapamil and bepridil [20, 21]. We did not include these factors because very few patients in our study received concomitant administration of these drugs. Additionally, we didn’t examine the effect of digoxin transportation role of P-glycoprotein inhibitors such as itraconazole, cyclosporine and clarithromycin [22–24] because these medicines are not administered. Therefore our nomogram is not adapted with concomitant with these medications. Our population model didn’t consider volume of distribution or absorption phase. Therefore our model didn’t simulate these phase. A prospective study using this regimen is necessary to investigate the robustness and reliability of our model.
Our results suggested that these dosage regimens would provide maximum therapeutic benefit of digoxin, and achieve the overall goal of reducing the toxicity in patients in whom the dosage of digoxin exceeds the therapeutic range.
We thank Kazuo Yago, Ph.D., for his support in this study. We would like to thank all the staff members of Kitasato University Hospital involved in this study.
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