Population pharmacokinetics of digoxin in Egyptian pediatric patients: impact of one data point utilization

Accidental apixaban intoxication in a 23-month-old child: a case report

BACKGROUND

Direct oral anticoagulants, such as apixaban, are increasingly used in everyday practice in order to treat or prevent thromboembolic diseases. To date, there is no available data about apixaban pharmacokinetics in children, and no intoxication has previously been described.

CASE PRESENTATION

A 23-month-old boy, with no medical history, was admitted to the emergency department 2 h after accidentally ingesting 40 mg apixaban and 0.75 mg digoxin. No adverse event was observed. Digoxin trough level was within therapeutic values. Apixaban blood concentration increased up to 1712 μg/L at H + 6 (1000-2750 μg/L using 2-5 mg/kg of apixaban in adults). The terminal half-life was 8.2 h (6-15 h in adults). The rapid elimination may explain the absence of bleeding despite high concentrations.

CONCLUSIONS

Despite an important intake of apixaban and a real disturbance in routine coagulation assays, no clinical sign of bleeding was observed, perhaps due to wide therapeutic range of apixaban. It may also be explained by its rapid elimination. Considering the high Cmax and a possible enteroenteric recycling, the use of activated charcoal should be considered in such situations in order to prevent eventual bleeding.

A systematic review of population pharmacokinetic analyses of digoxin in the paediatric population

This is a PROSPERO registered systematic review (CRD42018105207), conducted to summarize the available knowledge regarding the population pharmacokinetics of digoxin in paediatrics and to identify the sources of variability in its disposition. PubMed, ISI Web of Science, SCOPUS and Science Direct databases were searched from inception to January 2019. All paediatric population pharmacokinetic studies of digoxin that utilized the nonlinear mixed-effect modelling approach were incorporated in this review, and data were synthesized descriptively. After application of the inclusion-exclusion criteria 8 studies were included. Most studies described digoxin pharmacokinetics as a 1-compartment model with only 1 study describing its pharmacokinetics as 2-compartments. Age was an important predictor of clearance in studies involving neonates or infants, other predictors of clearance were weight, height, serum creatinine, coadministration of spironolactone and presence of congestive heart failure. Congestive heart failure was also associated with an increased volume of distribution in 1 study. The estimated value of apparent clearance in a typical individual standardized by mean weight ranged between 0.24 and 0.56 L/h/kg, the interindividual variability in clearance ranged between 7.0 and 35.1%. Half of the studies evaluated the performance of their developed models via external evaluation. In conclusion, substantial predictors of digoxin pharmacokinetics in the paediatric population in addition to model characteristics and evaluation techniques are presented. For clinicians, clearance could be predicted using age especially in neonates or infants, weight, height, serum creatinine, coadministration of medications and disease status. For future researchers, designing pharmacokinetic studies that allow 2-compartment modelling and linking pharmacokinetics with pharmacodynamics is recommended.

Predicting the serum digoxin concentrations of infants in the neonatal intensive care unit through an artificial neural network

BACKGROUND

Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA).

METHODS

A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model.

RESULTS

Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations.

CONCLUSION

The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.

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.

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

AIM

To establish a population pharmacokinetic (PPK) model of digoxin in older Chinese patients to provide a reference for individual medication in clinical practice.

METHODS

Serum concentrations of digoxin and clinically related data including gender, age, weight (WT), serum creatinine (Cr), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), albumin (ALB), and co-administration were retrospectively collected from 119 older patients taking digoxin orally for more than 7 d. NONMEM software was used to get PPK parameter values, to set up a final model, and to assess the models in clinical practice.

RESULTS

Spironolactone (SPI), WT, and Cr markedly affected the clearance rate of digoxin. The final model formula is Cl/F=5.9x[1-0.412 x SPI] x [1-0.0101x(WT-62.9)] x [1-0.0012x(Cr-126.8)] (L/h); Ka=1.63 (h(-1)); V(d)/F=550 (L). The population estimates for Cl/F and V(d)/F were 5.9 L/h and 550 L, respectively. The interindividual variabilities (CV) were 49.0% for Cl/F and 94.3% for V(d)/F. The residual variability (SD) between observed and predicted concentrations was 0.365 microg/L. The difference between the objective function value and the primitive function value was less than 3.84 (P>0.05) by intra-validation. Clinical applications indicated that the percent of difference between the predicted concentrations estimated by the PPK final model and the observed concentrations were -4.3%-+25%. Correlation analysis displayed that there was a linear correlation between observed and predicted values (y=1.35x+0.39, r=0.9639, P<0.0001).

CONCLUSION

The PPK final model of digoxin in older Chinese patients can be established using the NONMEM software, which can be applied in clinical practice.

Application of two-point assay of digoxin serum concentration in studying population pharmacokinetics in Egyptian pediatric patients with heart failure: does it make sense?

Digoxin pharmacokinetics (PK) was studied among a selected group of Egyptian pediatric patients (n = 40) with an age range of 0.33 to 15 years. All the patients had heart failure and were maintained on i.v. digoxin (10 microg/kg/d in 2 equal doses). For population PK analysis, 2 serum samples of digoxin were taken per patient. From 30 patients' trough (before the next dose) and 4 hours postdose samples were obtained, while in the other 10 patients, 0.5- and 6-hour postdose samples were taken. Serum concentrations were measured by fluorescence polarization immunoassay. PK modeling was performed using NONMEM software on log-transformed serum digoxin data. The best structural covariate-free model was a linear 2-compartment model with an exponential error model for intersubject variability and an additive model for intrasubject variability. Serum creatinine (SCR) was a significant covariate for clearance. The final population PK parameters were CL (L/h) = 0.388 - [0.78 x (SCR-0.6)], V1 (L/kg) = 1.38, Q (L/h/kg) = 0.48, V2 (L/kg) = 9.11, where CL is the total body clearance, V1 and V2 are the apparent volumes of distribution in the central and peripheral compartments, and Q is intercompartment clearance. A bootstrap resampling for internal validation achieved excellent agreement with the original data sets for PK parameters. In conclusion, 2 points of digoxin concentration allow good regression analysis for clearance-covariate relationship. The inclusion of SCR into the final model might allow better selection of initial maintenance dose of the drug. A prospective study on larger sample size of pediatric patients is recommended for clinical validation of the final model.