Population pharmacokinetics of oxcarbazepine and its metabolite 10-hydroxycarbazepine in healthy subjects

Oxcarbazepine is indicated for the treatment of partial or generalised tonic-clonic seizures. Most of the absorbed oxcarbazepine is converted into its active metabolite, 10-hydroxycarbazepine (MHD), which can exist as R-(-)- and S-(+)-MHD enantiomers. Here we describe the influence of the P-glycoprotein (P-gp) inhibitor verapamil, on the disposition of oxcarbazepine and MHD enantiomers, both of which are P-gp substrates. Healthy subjects (n=12) were randomised to oxcarbazepine or oxcarbazepine combined with verapamil at doses of 300mg b.i.d. and 80mg t.i.d., respectively. Blood samples (n=185) were collected over a period of 12h post oxcarbazepine dose. An integrated PK model was developed using nonlinear mixed effects modelling using a meta-analytical approach. The pharmacokinetics of oxcarbazepine was described by a two-compartment model with absorption transit compartments and first-order elimination. The concentration-time profiles of both MHD enantiomers were characterised by a one-compartment distribution model. Clearance estimates (95% CI) were 84.9L/h (69.5-100.3) for oxcarbazepine and 2.0L/h (1.9-2.1) for both MHD enantiomers. The volume of distribution was much larger for oxcarbazepine (131L (97-165)) as compared to R-(-)- and S-(+)-MHD (23.6L (14.4-32.8) vs. 31.7L (22.5-40.9), respectively). Co-administration of verapamil resulted in a modest increase of the apparent bioavailability of oxcarbazepine by 12% (10-28), but did not affect parent or metabolite clearances. Despite the evidence of comparable systemic levels of OXC and MHD following administration of verapamil, differences in brain exposure to both moieties cannot be excluded after P-glycoprotein inhibition

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662

AimsPreviously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice. MethodsOne thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens. ResultsSunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy. ConclusionsA semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects