AIMS: Axitinib is a potent and selective second generation inhibitor of vascular endothelial growth factor receptors 1, 2 and 3 approved for second line treatment of advanced renal cell carcinoma. The objectives of this analysis were to assess plasma pharmacokinetics and identify covariates that may explain variability in axitinib disposition following single dose administration in healthy volunteers. METHODS: Plasma concentration-time data from 337 healthy volunteers in 10 phase I studies were analyzed, using non-linear mixed effects modelling (nonmem) to estimate population pharmacokinetic parameters and evaluate relationships between parameters and food, formulation, demographic factors, measures of renal and hepatic function and metabolic genotypes (UGT1A1*28 and CYP2C19). RESULTS: A two compartment structural model with first order absorption and lag time best described axitinib pharmacokinetics. Population estimates for systemic clearance (CL), central volume of distribution (Vc ), absorption rate constant (ka ) and absolute bioavailability (F) were 17.0 l h(-1) , 45.3 l, 0.523 h(-1) and 46.5%, respectively. With axitinib Form IV, ka and F increased in the fasted state by 207% and 33.8%, respectively. For Form XLI (marketed formulation), F was 15% lower compared with Form IV. CL was not significantly influenced by any of the covariates studied. Body weight significantly affected Vc , but the effect was within the estimated interindividual variability for Vc . CONCLUSIONS: The analysis established a model that adequately characterizes axitinib pharmacokinetics in healthy volunteers. Vc was found to increase with body weight. However, no change in plasma exposures is expected with change in body weight; hence no dose adjustment is warranted.
AIMS: Axitinib is a potent and selective second generation inhibitor of vascular endothelial growth factor receptors 1, 2 and 3 approved for second line treatment of advanced renal cell carcinoma. The objectives of this analysis were to assess plasma pharmacokinetics and identify covariates that may explain variability in axitinib disposition following single dose administration in healthy volunteers. METHODS: Plasma concentration-time data from 337 healthy volunteers in 10 phase I studies were analyzed, using non-linear mixed effects modelling (nonmem) to estimate population pharmacokinetic parameters and evaluate relationships between parameters and food, formulation, demographic factors, measures of renal and hepatic function and metabolic genotypes (UGT1A1*28 and CYP2C19). RESULTS: A two compartment structural model with first order absorption and lag time best described axitinib pharmacokinetics. Population estimates for systemic clearance (CL), central volume of distribution (Vc ), absorption rate constant (ka ) and absolute bioavailability (F) were 17.0 l h(-1) , 45.3 l, 0.523 h(-1) and 46.5%, respectively. With axitinib Form IV, ka and F increased in the fasted state by 207% and 33.8%, respectively. For Form XLI (marketed formulation), F was 15% lower compared with Form IV. CL was not significantly influenced by any of the covariates studied. Body weight significantly affected Vc , but the effect was within the estimated interindividual variability for Vc . CONCLUSIONS: The analysis established a model that adequately characterizes axitinib pharmacokinetics in healthy volunteers. Vc was found to increase with body weight. However, no change in plasma exposures is expected with change in body weight; hence no dose adjustment is warranted.
Authors: A Maeda; H Ando; T Asai; H Ishiguro; N Umemoto; M Ohta; M Morishima; A Sumida; T Kobayashi; K Hosohata; K Ushijima; A Fujimura Journal: Clin Pharmacol Ther Date: 2010-12-22 Impact factor: 6.875
Authors: Joan H Schiller; Timothy Larson; S-H Ignatius Ou; Steven Limentani; Alan Sandler; Everett Vokes; Sinil Kim; Katherine Liau; Paul Bycott; Anthony J Olszanski; Joachim von Pawel Journal: J Clin Oncol Date: 2009-07-13 Impact factor: 44.544
Authors: Nielka van Erp; Hans Gelderblom; Martine van Glabbeke; Allan Van Oosterom; Jaap Verweij; Henk-Jan Guchelaar; Maria Debiec-Rychter; Bin Peng; Jean-Yves Blay; Ian Judson Journal: Clin Cancer Res Date: 2008-12-15 Impact factor: 12.531
Authors: W Zhao; V Elie; G Roussey; K Brochard; P Niaudet; V Leroy; C Loirat; P Cochat; S Cloarec; J L André; F Garaix; A Bensman; M Fakhoury; E Jacqz-Aigrain Journal: Clin Pharmacol Ther Date: 2009-10-28 Impact factor: 6.875
Authors: Brian I Rini; George Wilding; Gary Hudes; Walter M Stadler; Sinil Kim; Jamal Tarazi; Brad Rosbrook; Peter C Trask; Laura Wood; Janice P Dutcher Journal: J Clin Oncol Date: 2009-08-03 Impact factor: 44.544
Authors: Y K Pithavala; M Tortorici; M Toh; M Garrett; B Hee; U Kuruganti; G Ni; K J Klamerus Journal: Cancer Chemother Pharmacol Date: 2009-07-15 Impact factor: 3.333
Authors: Ying Chen; Akiyuki Suzuki; Michael A Tortorici; May Garrett; Robert R LaBadie; Yoshiko Umeyama; Yazdi K Pithavala Journal: Invest New Drugs Date: 2015-02-08 Impact factor: 3.850
Authors: Jason Shafrin; Jeffrey Sullivan; Jacquelyn W Chou; Michael N Neely; Justin F Doan; J Ross Maclean Journal: Cancer Manag Res Date: 2017-11-29 Impact factor: 3.989
Authors: Joyson J Karakunnel; Nam Bui; Latha Palaniappan; Keith T Schmidt; Kenneth W Mahaffey; Briggs Morrison; William D Figg; Shivaani Kummar Journal: J Transl Med Date: 2018-12-04 Impact factor: 5.531
Authors: Michael Ehrhardt; Rogerio B Craveiro; Julia Velz; Martin Olschewski; Anna Casati; Stefan Schönberger; Torsten Pietsch; Dagmar Dilloo Journal: J Cell Mol Med Date: 2018-01-29 Impact factor: 5.310
Authors: Christopher P Wilding; Mark L Elms; Ian Judson; Aik-Choon Tan; Robin L Jones; Paul H Huang Journal: Expert Rev Anticancer Ther Date: 2019-11-13 Impact factor: 4.512