Population-based investigation of relative clearance of digoxin in Japanese neonates and infants by multiple-trough screen analysis

• Published in: European journal of clinical pharmacology 2001-04, Vol.57 (1), p.19-24
• Main Authors: SUEMATSU, Fumihiro, YUKAWA, Eiji, YUKAWA, Miho, MINEMOTO, Masao, OHDO, Shigehiro, HIGUCHI, Shun, GOTO, Yoshinobu
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Cardiotonic agents; Cardiotonic Agents - blood; Cardiotonic Agents - pharmacokinetics; Cardiovascular system; Chi-Square Distribution; Digoxin - blood; Digoxin - pharmacokinetics; Diuretics - pharmacokinetics; Drug Monitoring - methods; Drug Monitoring - statistics & numerical data; Female; Humans; Infant; Infant, Newborn; Japan; Male; Medical sciences; Models, Biological; Pharmacology. Drug treatments; Regression Analysis; Reproducibility of Results; Spironolactone - pharmacokinetics; Studies
• ISSN: 0031-6970; 1432-1041
• DOI: 10.1007/s002280100274

The steady-state concentrations of digoxin at trough levels were studied to establish the role of patient characteristics in estimating doses for digoxin using routine therapeutic drug monitoring data. The data (n = 448) showing steady state after repetitive oral administration in 172 hospitalized neonates and infants were analyzed using Nonlinear Mixed Effect Model (NONMEM), a computer program designed to analyze pharmacokinetics in study populations by allowing pooling of data. Analysis of the pharmacokinetics of digoxin was accomplished using a simple steady-state pharmacokinetic model. The effects of a variety of developmental and demographic factors on the clearance of digoxin were investigated. Estimates generated using NONMEM indicated that clearance of digoxin (l.h-1) was influenced by the demographic variables of age, total body weight, serum creatinine, the coadministration of spironolactone, and the presence or absence of congestive heart failure. The interindividual variability in digoxin clearance was modeled with proportional errors with an estimated coefficient of variation of 32.1%, and the residual variability was 28.9%. In the validation set of 66 patients, the performance (bias, precision) of the final population model was good (mean prediction error -0.04 ng.ml-1; mean absolute prediction error 0.20 ng.ml-1).