Huixin Yu (1), Nielka P. van Erp (2), Sander Bins (3), Ron H. J. Mathijssen (3), Jan H. M. Schellens (4, 5, 6), Jos H. Beijnen (1, 5, 6), Neeltje Steeghs (4,5), Alwin D. R. Huitema (1,5)
(1) Department of Pharmacy & Pharmacology, Netherlands Cancer Institute-Antoni van Leeuwenhoek and MC Slotervaart, Amsterdam, NL; (2) Department of Clinical Pharmacy, Radboud University Medical Center, Nijmegen, NL; (3) Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, NL; (4) Department of Medical Oncology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, NL; (5) Department of Clinical Pharmacology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, NL; (6) Department of Pharmaceutical Sciences, Utrecht University, Utrecht, NL.
Objectives: Pazopanib is a tyrosine kinase inhibitor that has been approved for the treatment of renal cell carcinoma and soft tissue sarcoma at an oral dose of 800 mg daily. Pazopanib is insoluble at neutral pH in water and, consequently, has a complex absorption profile. The primary aim of this study was to develop a pharmacokinetic (PK) model for pazopanib. The secondary aims were to understand the absorption profile, dose-concentration relationship, inter- and intra- patient variability, and change over time in concentrations of pazopanib.
Methods: This analysis included the PK data of pazopanib collected in 96 patients from three clinical studies [1–3]. Various numbers of compartments and different absorption models were explored. The relationship between relative bioavailability (rF) and dose was investigated. It was also explored whether a decrease over time in the concentrations of pazopanib could be identified. In one of the three clinical studies [3], the inducing effects of ifosfamide on pazopanib clearance was modelled with fixed parameter estimates based on previously published enzyme turn-over model of ifosfamide auto-induction [4]. In addition, inter- and intra- patient variability on rF was estimated.
Results: A two-compartment model best described the PK of pazopanib. The absorption phase of pazopanib was best modelled by two first-order processes: firstly, 36% (relative standard error (RSE) 34%) of pazopanib was absorbed at a relatively fast rate (0.4 h-1 (RSE 31%)); after a lag time of 1 hour (RSE 29%), the remaining part of the pazopanib dose was absorbed at a slower rate (0.1 h-1 (RSE 28%)). The rF at 200 mg dose level was fixed to 1; with increasing dose rF was found to reduce by an Emax manner with Emax fixed to 1 and the dose at half of maximum effect estimated as 480 mg (RSE 23%). The rF of pazopanib was found to decrease with a maximum magnitude of 50% (RSE 27%) and an exponential-decay constant of 0.15 day-1 (RSE 43%). The inter- and intra -patient variability on rF were estimated as 35.6% and 74.5%, respectively. The residual error model was a combined model estimated with proportional and additive errors.
Conclusions: A PK model for pazopanib was successfully developed. This model studied and illustrated the complex absorption process, the non-linear dose-concentration relationship, high inter- and intra- patient variability, and the exponential-decay of concentration of pazopanib in time.
References:
[1] de Wit D, van Erp NP, den Hartigh J, et al. Therapeutic Drug Monitoring to individualize the dosing of pazopanib: a pharmacokinetic feasibility study. Ther Drug Monit. 2014;37:331–8.
[2] Hamberg P, Mathijssen RHJ, de Bruijn P, et al. Impact of pazopanib on docetaxel exposure: results of a phase I combination study with two different docetaxel schedules. Cancer Chemother Pharmacol. 2014;75:365–71.
[3] Hamberg P, Boers-Sonderen MJ, van der Graaf WTA, et al. Pazopanib exposure decreases as a result of an ifosfamide-dependent drug-drug interaction: results of a phase I study. Br J Cancer. 2014;110:888–93.
[4] Kerbusch T, Huitema ADR, Ouwerkerk J, et al. Evaluation of the autoinduction of ifosfamide metabolism by a population pharmacokinetic approach using NONMEM. Br J Clin Pharmacol. 2000;49:555–61.
Reference: PAGE 25 (2016) Abstr 5926 [www.page-meeting.org/?abstract=5926]
Poster: Drug/Disease modeling - Oncology