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.
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 ureanitrogen (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.
Authors: S Ferrara; F Pazzucconi; A Bondioli; G Mombelli; A Agrati; G Ferraro; F Zoppi; C De Rosa; L Calabresi; C R Sirtori Journal: Pharmacol Res Date: 2004-07 Impact factor: 7.658
Authors: J W Cheng; S L Charland; L M Shaw; S Kobrin; S Goldfarb; E J Stanek; S A Spinler Journal: Pharmacotherapy Date: 1997 May-Jun Impact factor: 4.705
Authors: A Martín-Suárez; A C Falcao; M Outeda; F J Hernández; M C González; M Quero; I Arranz; J M Lanao Journal: Ther Drug Monit Date: 2002-12 Impact factor: 3.681