Huixin Yu (1), Neeltje Steeghs (2,3), Jacqueline S. L. Kloth (4), Djoeke de Wit (5), Nielka P. van Erp (6), Ron H. J. Mathijssen (4), Jos H. Beijnen (1,3,7), Jan H. M. Schellens (2,3,7), Alwin D. R. Huitema (1,3)
(1) Department of Pharmacy & Pharmacology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, NL; (2) Department of Medical Oncology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, NL; (3) Department of Clinical Pharmacology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, NL; (4) Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, NL; (5) Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, NL; (6) Department of Clinical Pharmacy, Radboud University Medical Center, Nijmegen, NL; (7) Department of Pharmaceutical Sciences, Utrecht University, Utrecht, NL.
Objectives: Sunitinib is a multi-targeted tyrosine kinase inhibitor used in the treatment of several malignancies. The application of therapeutic drug monitoring (TDM) in clinical practice for sunitinib and its active metabolite SU12662 requires a pharmacokinetic (PK) model for adequate interpretation of data. However, published sunitinib models either neglected correlations between sunitinib and its metabolite [1] or were based on a very limited dataset [2]. Therefore, we developed a population PK model for both sunitinib and SU12662.
Methods: In this analysis, PK data of 70 patients were collected from three PK studies of sunitinib [3–5]. A mechanism-based PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using nonlinear mixed-effects modelling. According to the model, sunitinib is firstly absorbed by a first-order process to a hypothetical enzyme compartment, whereby sunitinib remains unchanged or is metabolised into SU12662. Subsequently, unchanged sunitinib and metabolite distribute to the central compartment. Sunitinib is further metabolised from the systemic circulation by the same enzyme. Allometric scaling based on body weight was applied for the estimation of clearance and volume of distribution. Graphical model assessment was performed by goodness-of fit plots and prediction-corrected visual predictive check (pcVPC).
Results: Both sunitinib as SU12662 PK were best described by one compartment models. Introduction of pre-systemic formation of SU12662 strongly improved the model (∆OFV ~400). The clearances of parent and metabolite drugs were estimated at 35.3±1.59 L•h-1 and 17.3±0.98 L•h-1 for 70 kg individuals. Correlation efficient were estimated between inter-individual variability of both clearances, both volume of distributions, and between clearance and volume of distribution of SU12662 as 0.60, 0.38 and 0.30, respectively. The pcVPC indicated the developed model appropriately captured the PK and variability for both sunitinib and SU12662.
Conclusions: An adequate PK model for sunitinib and its active metabolite SU12662 has been developed and evaluated. Incorporation of pre-systemic metabolism strongly improved the model. Further studies in application of TDM service and PK-pharmacodynamic correlations can base on the developed PK model.
References:
[1] Houk BE, Bello CL, Kang D, et al. A population pharmacokinetic meta-analysis of sunitinib malate (SU11248) and its primary metabolite (SU12662) in healthy volunteers and oncology patients. Clin cancer Res. 2009;15(7):2497–506.
[2] Lindauer A, Di Gion P, Kanefendt F, et al. Pharmacokinetic/pharmacodynamic modeling of biomarker response to sunitinib in healthy volunteers. Clin Pharmacol Ther. 2010;87(5):601–8.
[3] Kloth JSL, Klümpen H-J, Yu H, et al. Predictive Value of CYP3A and ABCB1 Phenotyping Probes for the Pharmacokinetics of Sunitinib: the ClearSun Study. Clin Pharmacokinet. 2014;53:261–9.
[4] Kloth JS, Binkhorst L, de Bruijn P, et al. Effect of dosing time on sunitinib pharmacokinetics. Eur J Cancer. 2013;49(#692).
[5] De Wit D, Gelderblom H, Sparreboom A, et al. Midazolam as a phenotyping probe to predict sunitinib exposure in patients with cancer. Cancer Chemother Pharmacol. 2014;73(1):87–96.
Reference: PAGE 23 (2014) Abstr 3056 [www.page-meeting.org/?abstract=3056]
Poster: Drug/Disease modeling - Oncology