Comparison of two population pharmacokinetic programs, NONMEM and P-PHARM, for tacrolimus

Abstract

Objectives: To compare the population modelling programs NONMEM and P-PHARM during investigation of the pharmacokinetics of tacrolimus in paediatric liver-transplant recipients.

Methods: Population pharmacokinetic analysis was performed using NONMEM and P-PHARM on retrospective data from 35 paediatric liver-transplant patients receiving tacrolimus therapy. The same data were presented to both programs. Maximum likelihood estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F). Covariates screened for influence on these parameters were weight, age, gender, post-operative day, days of tacrolimus therapy, transplant type, biliary reconstructive procedure, liver function tests, creatinine clearance, haematocrit, corticosteroid dose, and potential interacting drugs.

Results: A satisfactory model was developed in both programs with a single categorical covariate--transplant type--providing stable parameter estimates and small, normally distributed (weighted) residuals. In NONMEM, the continuous covariates--age and liver function tests--improved modelling further. Mean parameter estimates were CL/F (whole liver) = 16.3 l/h, CL/F (cut-down liver) = 8.5 l/h and V/F = 565 l in NONMEM, and CL/F = 8.3 l/h and V/F = 155 l in P-PHARM. Individual Bayesian parameter estimates were CL/F (whole liver) = 17.9 +/- 8.8 l/h, CL/F (cut-down liver) = 11.6 +/- 8.8 l/h and V/F = 712 +/- 792 l in NONMEM, and CL/F (whole liver) = 12.8 +/- 3.5 l/h, CL/F (cut-down liver) = 8.2 +/- 3.4 l/h and V/F = 221 +/- 164 l in P-PHARM. Marked interindividual kinetic variability (38-108%) and residual random error (approximately 3 ng/ml) were observed. P-PHARM was more user friendly and readily provided informative graphical presentation of results. NONMEM allowed a wider choice of errors for statistical modelling and coped better with complex covariate data sets.

Conclusion: Results from parametric modelling programs can vary due to different algorithms employed to estimate parameters, alternative methods of covariate analysis and variations and limitations in the software itself.