A Population Pharmacokinetic Meta-analysis of Sunitinib Malate (SU11248) and Its Primary Metabolite (SU12662) in Healthy Volunteers and Oncology Patients

Purpose: Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P 450 3A4 to an active metabolite (SU...

Full description

Saved in:
Bibliographic Details
Published in:Clinical cancer research 2009-04, Vol.15 (7), p.2497-2506
Main Authors: HOUK, Brett E, BELLO, Carlo L, KANG, Dongwoo, AMANTEA, Michael
Format: Article
Language:English
Subjects:
Administration, Oral
Adolescent
Adult
Aged
Aged, 80 and over
Antineoplastic agents
Antineoplastic Agents - administration & dosage
Antineoplastic Agents - pharmacokinetics
Biological and medical sciences
Female
Healthy volunteers
Humans
Indoles - administration & dosage
Indoles - pharmacokinetics
Male
Medical sciences
Middle Aged
Models, Biological
Neoplasms - metabolism
Oncology patients
Pharmacology. Drug treatments
Population pharmacokinetics
Protein Kinase Inhibitors - administration & dosage
Protein Kinase Inhibitors - pharmacokinetics
Protein-Tyrosine Kinases - antagonists & inhibitors
Pyrroles - administration & dosage
Pyrroles - pharmacokinetics
SU12662
Sunitinib malate (SU11248)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Purpose: Sunitinib malate is an oral multitargeted tyrosine kinase inhibitor approved for advanced renal cell carcinoma and imatinib-resistant or imatinib-intolerant gastrointestinal stromal tumor. Following administration, sunitinib is metabolized by cytochrome P 450 3A4 to an active metabolite (SU12662). The objective of this analysis was to assess sunitinib and SU12662 pharmacokinetics and to identify covariates that might explain variability in exposure following oral administration. Experimental Design: Data from 590 subjects (73 volunteers and 517 patients) in 14 studies were analyzed. Plasma concentration-time data were analyzed using nonlinear mixed-effects modeling to estimate population pharmacokinetic parameters, as well as relationships between these parameters and gender, race, age, weight, creatinine clearance, Eastern Cooperative Oncology Group score, and tumor type. Simulations were done to determine the predicted effect of these covariates on exposure. Results: Separate models were developed for sunitinib and SU12662 (each a two-compartment model with first-order absorption and elimination). Sunitinib parameters were estimated as CL/ F , 51.8 L/h and Vd/ F central , 2,030 liters. SU12662 parameters were estimated as CL/ F , 29.6 L/h and Vd/ F central , 3,080 liters. Tumor type (except acute myeloid leukemia), Asian race, gender, body weight, and elevated Eastern Cooperative Oncology Group score described a portion of the variability in CL/ F for sunitinib and metabolite; gender and body weight explained some of the variability in Vd/ F central for sunitinib and metabolite. Among patients, the predicted changes in sunitinib and metabolite AUC and C max as a result of the individual covariates ranged up to 17%. Conclusion: The magnitude of the predicted changes in exposure with the covariates studied minimizes the necessity for dose adjustment in any of these subpopulations.
ISSN:1078-0432
1557-3265
DOI:10.1158/1078-0432.CCR-08-1893