OBJECTIVE: To estimate individual and population postinduction pharmacokinetics of @entity2049 ( @entity2050 ) in @entity302 adult and paediatric @entity1 who received chronic @entity2050 monotherapy. METHODS: We have used the USC*PACK collection of PC programs for the estimations. The preinduction @entity2050 metabolism was also estimated in 16 volunteers after a single dose of @entity2050 (200 mg). We used a linear one-compartmental model with oral absorption and found the pharmacokinetic parameter values of @entity2050 behaviour to be in good agreement with those reported earlier. RESULTS: Serum @entity2050 concentrations correlated poorly with daily doses in both the adult and @entity1 populations. Because of the diversity within the population, use of the mean population model without knowledge of an individual @entity1 's pharmacokinetic characteristics gives poor prediction. In contrast, the individual Bayesian posterior models gave good prediction for all subjects in the population, due to the removal of the interindividual variability. CONCLUSION: This approach permits one to individualize drug therapy for @entity1 even when only sparse therapeutic drug monitoring (TDM) data are available. Future individual @entity2050 serum level predictions were acceptable from a clinical point of view (mean absolute error = 13.2 +/- 9.7%). The optimal sampling strategy approach helped to design an optimal cost-effective TDM protocol for @entity2050 therapy management.

Population pharmacokinetic modelling of XXXX by using the iterative Bayesian (IT2B) and the nonparametric EM (NPEM) algorithms: implications for dosage.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient', 'child'] @entity2049 :: ('MESH:D002220', 'Chemical') :: ['carbamazepine'] @entity302 :: ('MESH:D004827', 'Disease') :: ['epileptic'] @entity2050 :: ('CHEBI:51097', 'Chemical') :: ['CBZ']
@entity2049 :: (MESH:D002220,Chemical) :: ['carbamazepine']
