Population pharmacokinetic model and Bayesian estimator for two XXXX formulations--twice daily Prograf and once daily Advagraf.

@entity1 @entity5396 @entity1140
AIM: To investigate the differences in the pharmacokinetics of Prograf and the prolonged release formulation Advagraf and to develop a Bayesian estimator to estimate @entity1140 inter-dose area under the curve (AUC) in renal transplant @entity1 receiving either Prograf or Advagraf. METHODS: @entity1140 concentration-time profiles were collected, in adult renal transplant recipients, at weeks 1 and 2, and at months 1, 3 and 6 post-transplantation from 32 Prograf treated @entity1 , and one profile was collected from 41 Advagraf @entity1 more than 12 months post-transplantation. Population pharmacokinetic (popPK) parameters were estimated using nonmem. In a second step, the popPK model was used to develop a single Bayesian estimator for the two @entity1140 formulations. RESULTS: A two-compartment model with Erlang absorption (n= 3) and first-order elimination best described the data. In Advagraf @entity1 , a bimodal distribution was observed for the absorption rate constant (K(tr) ): one group with a K(tr) similar to that of Prograf treated @entity1 and the other group with a slower absorption. A mixture model for K(tr) was tested to describe this bimodal distribution. However, the data were best described by the nonmixture model including covariates ( @entity5396 , haematocrit and drug formulation). Using this model and @entity1140 concentrations measured at 0, 1 and 3h post-dose, the Bayesian estimator could estimate @entity1140 AUC accurately (bias = 0.1%) and with good precision (8.6%). CONCLUSIONS: The single Bayesian estimator developed yields good predictive performance for estimation of individual @entity1140 inter-dose AUC in Prograf and Advagraf treated @entity1 and is suitable for clinical practice.
