Population pharmacokinetic model of digoxin in older Chinese patients and its application in clinical practice
Abstract
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=5.9×[1–0.412×SPI]×[1–0.0101×(WT–62.9)]×[1–0.0012×(Cr–126.8)] (L/h); Ka=1.63 (h−1);Vd/F=550 (L). The population estimates for Cl/F and Vd/F were 5.9 L/h and 550 L, respectively. The interindividual variabilities (CV) were 49.0% for Cl/F and 94.3% for Vd/F. The residual variability (SD) between observed and predicted concentrations was 0.365 μg/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 observated 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.
Keywords:
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=5.9×[1–0.412×SPI]×[1–0.0101×(WT–62.9)]×[1–0.0012×(Cr–126.8)] (L/h); Ka=1.63 (h−1);Vd/F=550 (L). The population estimates for Cl/F and Vd/F were 5.9 L/h and 550 L, respectively. The interindividual variabilities (CV) were 49.0% for Cl/F and 94.3% for Vd/F. The residual variability (SD) between observed and predicted concentrations was 0.365 μg/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 observated 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.