Population pharmacokinetic model of digoxin in older Chinese patients and its application in clinical practice

Influence of Weight and Body Size on the Pharmacokinetics of Heart Failure Pharmacotherapy: A Systematic Review

OBJECTIVE

To conduct a review of studies evaluating the influence of body size and weight (WT) on the pharmacokinetics (PK) of drugs recommended for heart failure (HF) treatment.

DATA SOURCES

A systematic search of the MEDLINE (1946 to April 2023) and EMBASE (1974 to April 2023) databases was conducted for articles that focused on the impact of WT or body size on the PK of drugs of interest used in HF patients.

STUDY SELECTION AND DATA EXTRACTION

Articles written in English or French related to the aim of our study were retained for analysis.

DATA SYNTHESIS

Of 6493 articles, 20 were retained for analysis. Weight was associated with the clearance of digoxin, carvedilol, enalapril, and candesartan as well as the volume of distribution of eplerenone and bisoprolol. There was no documented direct impact of WT on the PK of furosemide, valsartan, and metoprolol, although these studies were limited or confounded by the small sample size, adjustment of PK factors by WT, or the use of the Cockroff-Gault equation for the evaluation of creatinine clearance, which includes WT.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

This review highlights and summarizes the available data on the importance of WT on the PK of HF treatment.

CONCLUSION

Considering the significant impact of WT on most HF drugs in this review, it may be important to further investigate it in the context of personalized therapy, particularly in patients presenting extreme WTs.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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

BACKGROUND

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.

OBJECTIVE

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.

METHODS

Older patients with heart failure and CKD aged > 60 years from January 2020 to January 2021 and who had an estimated glomerular filtration rate (eGFR) < 90 mL/min/1.73 m² 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.

RESULTS

Overall, 269 older patients with heart failure were enrolled. A total of 306 digoxin concentrations were collected, with a median value of 0.98 ng/mL (interquartile range [IQR] 0.62-1.61, range 0.04-4.24). The median age was 68 years (IQR 64-71, range 60-94) and eGFR was 53.6 mL/min/1.73 m² (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 2.67 L/h and 36.9 L, respectively. Dosage simulations were stratified by eGFR and metoprolol. Doses of 62.5 and 125 μg were recommended for older patients with eGFR < 60 mL/min/1.73 m².

CONCLUSIONS

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.

Assessment of Aging-Related Function Variations of P-gp Transporter in Old-Elderly Chinese CHF Patients Based on Modeling and Simulation

BACKGROUND AND OBJECTIVES

P-glycoprotein (P-gp) is one of the most intensely studied transporters owing to its broad tissue distribution and substrate specificity. Existing research suggests that the risk of systemic exposure to dabigatran etexilate (DABE) and digoxin, two P-gp probe substrates in vivo, has significantly increased in elderly patients. We applied a model-based quantitative pharmacological approach to assess aging-related P-gp changes in the Chinese old-elderly population.

METHODS

Population pharmacokinetic (PopPK) modeling was first performed using clinical pharmacokinetic data to explore the effect of age on the pharmacokinetic characteristics of dabigatran (DAB, the active principle of DABE) and digoxin in elderly Chinese patients. Corresponding physiologically based pharmacokinetic (PBPK) models were established to further explain the elevated systemic exposure to these two drugs. Eventually, standard dosing regimens of DABE and digoxin were assessed in Chinese old-elderly patients with chronic heart failure (CHF) with different stages of renal impairment.

RESULTS

PopPK analysis suggested that age as a covariate had an additional effect on the apparent clearance of these two drugs after correcting for creatinine clearance. PBPK simulation results suggested that disease-specific pathophysiological changes could explain DAB exposure in the young elderly. In the elderly population, 17.1% of elevated DAB exposure remained unexplained, and 25.5% of the reduced P-gp function associated with aging was ultimately obtained using sensitivity analysis. This value was further validated using digoxin data obtained by PBPK modeling. The simulation results suggest that CHF patients with advanced age and moderate-to-severe renal impairment require heightened vigilance for elevated exposure risk during the use of DABE and digoxin.

CONCLUSIONS

Aging might be a significant risk factor for elevated systemic exposure to DAB and digoxin by reducing P-gp-mediated efflux in the Chinese old elderly population.

Pharmacokinetics Parameters of Diagoxin among Saudi Patients in Qassim Region, Saudi Arabia

BACKGROUND

The pharmacodynamic effects of digoxin are susceptible to multiple factors, most notably, heart uptake of the digoxin dose and its concentration in the serum. Another important factor to mention is the renal function state of an individual.

OBJECTIVE

In this study, we aimed to develop a simple algorithm based on subsets of clinically relevant information, which will help to personalize digoxin based on pharmacokinetic (PK) approach, which can help on marketing the appropriate utilization of this medication.

METHOD

This was a retrospective chart review and analysis of 48 patients who were admitted to the Drug and Poison Information Center in Buraidah, Saudi Arabia, between January 2016 and April 2019. All pharmacokinetic parameters were added according to the C-peaks and C-troughs. MONOLiX® was used for pharmacokinetic data analysis.

RESULTS

Twenty-seven (56%) were males, and twenty-one (44%) were females with an average age of 63.6 years across both genders. The mean volume of distribution was 496.6 litres with an average clearance of 6.6 L/h. For females, their average volume of distribution was slightly higher than that for males (526 litres compared to 473 liters). In addition, the clearance rate between both genders showed a 2.1 litre/hour discrepancy (7.8 L/h for females compared to 5.7 L/h for males).

CONCLUSION

In order to individualize the digoxin dosage regimens, this model can be used to predict digoxin serum concentration. Further studies are needed to clarify the effects of nutritional status and co-administration of medications on digoxin pharmacokinetics.

Population Pharmacokinetic Studies of Digoxin in Adult Patients: A Systematic Review

BACKGROUND

Digoxin is a cardiac glycoside that was introduced to cardiovascular medicine more than 200 years ago. Its use is associated with large variability, which complicates achieving the desired therapeutic outcomes.

OBJECTIVES

To present a synthesis of the available literature on the population pharmacokinetics of digoxin in adults and to identify the sources of variability in its pharmacokinetics.

METHODS

This is a PROSPERO registered systematic review (CRD42018105300). A literature search was conducted using the ISI Web of Science, Science Direct, PubMed, and SCOPUS databases to identify digoxin population pharmacokinetic studies of adults that utilized the nonlinear mixed-effect modeling approach.

RESULTS

Sixteen articles were included in the present analysis. Only two studies were conducted in elderly subjects as a separate population. Both the pharmacokinetics and pharmacodynamics of digoxin were investigated in one study. Furthermore, the reviewed studies were mostly conducted in East Asian populations (68.8%). Digoxin's pharmacokinetics were usually described by a one-compartment model because of the nature of the collected data. Weight, age, kidney function, presence of heart failure, and co-administered medications such as calcium channel blockers were the most commonly identified predictors of digoxin clearance. The value of apparent clearance in a typical study individual ranged from 0.005 to 0.2 l/h/kg, while the value of the apparent volume of distribution ranged from 3.14 to 15.2 l/kg. The quality of model evaluation was deemed excellent only in 31.3% of the studies.

CONCLUSION

This review provides information about variables that need to be considered when prescribing digoxin. The results highlight the need for prospective studies that allow two-compartment pharmacokinetic/pharmacodynamic models to be established, with a special focus on the elderly subpopulation.

A model for comprehensive oral biological age score with oral and systemic clinical parameters

BACKGROUND

Biological age reflects the functional status of an individual. The purpose of the study was to develop a model for estimating oral biological age with oral and systemic parameters.

METHODS

A total of 248 subjects who had a routine health check were assessed with oral and general clinical examination. Chi-square test was performed to screen oral clinical candidate indicators. General parameters were analyzed by Pearson correlation coefficient and principal component analysis to develop a general biological age score. A final comprehensive model of oral biological age score was established by combining oral and general biological age score.

RESULTS

A total of eight oral indicators (mucosal blood blister, mucosal dryness, impacted tooth, missing teeth, residual crowns, dental calculus, gingival hyperemia, and gingival recession) and 10 general clinical indicators (triglyceride, creatinine, blood urea nitrogen, glucose, total cholesterol, mean erythrocyte hemoglobin concentration, mean erythrocyte hemoglobin, uric acid, body weight, and systolic blood pressure) were selected for oral and general biological age score, respectively (r > 0.25, P < 0.05). A model of comprehensive oral biological age score was then formed by principal component analysis: 0.046 triglyceride + 0.010 creatinine + 0.141 blood urea nitrogen + 0.048 glucose + 0.068 total cholesterol + 0.014 mean erythrocyte hemoglobin concentration + 0.082 mean erythrocyte hemoglobin + 0.001 uric acid + 0.020 body weight + 0.005 systolic blood pressure + 0.037 oral biological age score -10.908. The score was increased accordingly with CA.

CONCLUSION

Oral biological age can be easily estimated clinically by the model of comprehensive oral biological age score using oral and systemic clinical parameters by general practitioners.

Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach

Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted. A patient with serum digoxin concentration being controlled at 0.5-0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin. Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models. Six machine learning techniques were considered, including decision tree (C4.5), k-nearest neighbors (kNN), classification and regression tree (CART), randomForest (RF), multilayer perceptron (MLP), and logistic regression (LGR). In the non-DDI group, the area under ROC curve (AUC) of RF (0.912) was excellent, followed by that of MLP (0.813), CART (0.791), and C4.5 (0.784); the remaining classifiers performed poorly. For the DDI group, the AUC of RF (0.892) was the best, followed by CART (0.795), MLP (0.777), and C4.5 (0.774); the other classifiers' performances were less than ideal. The decision tree-based approaches and MLP exhibited markedly superior accuracy performance, regardless of DDI status. Although digoxin is a high-alert medication, its initial dose can be accurately determined by using data mining techniques such as decision tree-based and MLP approaches. Developing a dosage decision support system may serve as a supplementary tool for clinicians and also increase drug safety in clinical practice.

A population pharmacokinetic analysis of the influence of nutritional status of digoxin in hospitalized Korean patients

BACKGROUND

Safe and effective use of digoxin in hospitalized populations requires information about the drug's pharmacokinetics and the influence of various factors on drug disposition. However, no attempts have been made to link an individual's digoxin requirements with nutritional status.

OBJECTIVES

The main goal of this study was to estimate the population pharmacokinetics of digoxin and to identify the nutritional status that explains pharmacokinetic variability in hospitalized Korean patients.

METHODS

Routine therapeutic drug-monitoring data from 106 patients who received oral digoxin at Seoul National University Bundang Hospital were retrospectively collected. The pharmacokinetics of digoxin were analyzed with a 1-compartment, open-label pharmacokinetic model by using a nonlinear mixed-effects modeling tool (NONMEM) and a multiple trough screening approach.

RESULTS

The effect of demographic characteristics and biochemical and nutritional indices were explored. Estimates generated by using NONMEM indicated that the CL/F of digoxin was influenced by renal function, serum potassium, age, and percentage of ideal body weight (PIBW). These influences could be modeled by following the equation CL/F (L/h) = 1.36 × (creatinine clearance/50)(1.580) × K(0.835) × 0.055 × (age/65) × (PIBW/100)(0.403). The interindividual %CV for CL/F was 34.3%, and the residual variability (SD) between observed and predicted concentrations was 0.225 μg/L. The median estimates from a bootstrap procedure were comparable and within 5% of the estimates from NONMEM. Correlation analysis with the validation group showed a linear correlation between observed and predicted values.

CONCLUSIONS

The use of this model in routine therapeutic drug monitoring requires that certain conditions be met which are consistent with the conditions of the subpopulations in the present study. Therefore, further studies are needed to clarify the effects of nutritional status on digoxin pharmacokinetics. The present study established important sources of variability in digoxin pharmacokinetics and highlighted the relationship between pharmacokinetic parameters and nutritional status in hospitalized Korean patients.