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


CONCLUSIONS
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.
Staatz
Christine E
CE
School of Pharmacy, University of Queensland, Brisbane, Queensland 4072, Australia. c.staatz@pharmacy.uq.edu.au
Tett
Susan E
SE
D000293:Adolescent; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D004305:Dose-Response Relationship, Drug; D016903:Drug Monitoring; D005260:Female; D006801:Humans; D007166:Immunosuppressive Agents / Q000008:administration & dosage / Q000097:blood / Q000493:pharmacokinetics*; D007223:Infant; D016031:Liver Transplantation; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D012984:Software; D016559:Tacrolimus / Q000008:administration & dosage / Q000097:blood / Q000493:pharmacokinetics*
D007166:Immunosuppressive Agents; D016559:Tacrolimus