Fan Zhang (1), Peiming Ma (1), Huafang Li (2), Chao Chen (3)
(1) Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, China; (2) Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, China; (3) Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, UK
Objectives: The apparent bimodal distribution of lamotrigine clearance, previously described by a mixture model, has led to regulatory acceptance of bioequivalence (BE) between two formulations despite a BE study that missed the acceptance criterion. The objective of the present work was to describe lamotrigine population pharmacokinetics (PK) in Chinese population using data from multiple studies, applying a mixture model as appropriate to investigate clearance (CL) distribution characteristics in Chinese population.
Methods: Four GSK funded PK studies were conducted in healthy Chinese subjects (196 males and 6 female) in the mainland and Hong Kong for two oral formulations, lamotrigine dispersible/chewable (DC) and compressed tablets, at 25 and 100 mg doses. PK models were fitted to plasma concentrations using a non-linear mixed-effect model implemented in NONMEM V7.3.0 and Monolix V4.33. Effects of body weight, age, sex, dose, formulation, and region on PK parameters were evaluated. A mixture model with two subpopulations in clearance (CL) was tested on the dataset. Parameter estimates from the final runs in NONMEM and Monolix were compared.
Results: A two-compartment model with first-order absorption (with lag-time), linear elimination and combined errors adequately described lamotrigine PK data. Region was identified as a covariate for ka, sex was identified as a covariate for bioavailability (F), formulation was identified as a covariate for t-lag, and body weight was identified as a covariate for CL and V. Adding mixture model separating subpopulation with different CL was able to significantly reduce objective function value. Cross-study comparison suggested no major ethnic difference between Chinese and Caucasian subjects.
Conclusion: A mixture model was further supported when applying to the Chinese study dataset. No major ethnic difference was found between Chinese and Caucasians.
Reference: PAGE 25 () Abstr 5841 [www.page-meeting.org/?abstract=5841]
Poster: Drug/Disease modeling - Other topics