Population Pharmacokinetic Analysis Of Digoxin In Chinese Neonates And Infants

BackgroundDigoxin is a cardiac glycoside, which has been widely prescribed for the treatment of congestive heart failure, atrial fibrillation. With the narrow therapeutics range of digoxin, routine therapeutic drug monitoring is essential for digoxin in clinical use. Digoxin serum concentrations differ largely among pediatric individuals after the administration of the drug, which relate to the process of drug disposition and physical’development characteristics of patients. Digoxin is excreted primarily by the kidney in the unchanged form, but organ function (such as the renal function) of children is in the process of development during infancy. Normally, the number of Nephron hit adult level at the 36th week of gestation, but the function of Nephron keep developing during the whole pregnancy and early time of life. Renal function is not mature in neonates. In the few months after birth, renal blood flow, glomerular filtration, tubular secretion and renal tubular recidivism in infants are increasing. That is to say, the renal function is developing remarkably; the ability of renal excretion is improving continuously. The renal function in children hit the adult level until about 1 year old. So it is inappropriate to calculate the digoxin dosages based on the body weight or the body surface area in the children under the age of 1, which may lead to the over-dose or under-dose of the drug. It is necessary to employ the pharmacokinetic analysis of digoxin in children (age< 1) to obtain pharmacokinetic parameter values, so as to reduce the drug administration risk in clinical.Frequent and repeated blood sampling can do physical and mental harm to the pediatric patients. So the traditional pharmacokinetic analysis is restricted because of the ethics, which lead to the shortage of therapeutic data in neonates and infants. Recently, population pharmacokinetic analysis have been applied to the clinical therapeutic study area, it can analyze the rich or sparse data sets and evaluate impact of the fixed and random effects to population pharmacokinetic model. In this study, we establish the population pharmacokinetic model of digoxin in Chinese neonates and infants using nonlinear mixed effects model, aimed to optimize the dosing regimen of the drug.Objective:To obtain more information regarding the influence of various covariates on the disposition of digoxin in Chinese neonates and infants using therapeutic drug monitoring data, and to provide information for individualized drug dosing.Methods:Routine clinical pharmacokinetic data (125 observations) were retrospectively collected from 107 patients (age< 1) in Children’s Hospital of Fudan University (2011-2012). Nonlinear mixed effects model, first-order absorption and one compartment model were employed to establish the population pharmacokinetic model. Total body weight (allometric power model), postnatal age, serum creatinine, gender, presence of heart congestive failure and concomitant medications influencing on apparent total clearance and apparent drug distribution volume are investigated in this study. In the internal model evaluation process, bootstrap, visual predictive check (VPC) and normalized prediction distribution error (NPDE) are employed to evaluate the stable and predictive performance of the model; in the external model evaluation process,24 patients were enrolled to evaluate extrapolation effect of the model.Results:Pharmacokinetic parameter population typical estimates for CL/F and V/F were 0.147 L/h/kg and 15.7L/kg, respectively. Total body weight and postnatal age were identified as the important factors affecting total clearance of digoxin; Total body weight was the covariate identified that influenced the apparent distribution volume. In the model developing process, ka was fixed as 0.718 h-1. The bootstrap re-sampling successful rate was 92.9%. The values of estimated parameters from the bootstrap procedure were closed to those estimated from the original dataset, which indicated a good stability profile for the final model. The results of VPC and NPDE validation supported the predictive performance of the final model. In addition, results of the external evaluation meet the requirement of validation.Conclusions:The population pharmacokinetic model established in this study was stable and predictive, which can provide information for the individualized dosing of digoxin.