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Integrated semi‐physiological pharmacokinetic model for both sunitinib and its active metabolite SU 12662
Author(s) -
Yu Huixin,
Steeghs Neeltje,
Kloth Jacqueline S. L.,
Wit Djoeke,
Hasselt J. G. Coen,
Erp Nielka P.,
Beijnen Jos H.,
Schellens Jan H. M.,
Mathijssen Ron H. J.,
Huitema Alwin D. R.
Publication year - 2015
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.12550
Subject(s) - sunitinib , pharmacokinetics , metabolite , active metabolite , nonmem , pharmacology , volume of distribution , medicine , chemistry , cancer
Aims Previously published pharmacokinetic ( PK ) models for sunitinib and its active metabolite SU 12662 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. Methods One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU 12662. A semi‐physiological PK model for sunitinib and SU 12662 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. Results Sunitinib and SU 12662 PK were best described by a one and two compartment model, respectively. Introduction of pre‐systemic formation of SU 12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU 12662 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 70 kg patients. Correlation coefficients were estimated between inter‐individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU 12662 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. Conclusions A semi‐physiological PK model for sunitinib and SU 12662 in cancer patients was presented including pre‐systemic metabolism. The model was superior to previous PK models in many aspects.