PKPD modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST

CPT: Pharmacometrics and Systems Pharmacology

Volumn 2, Issue 11, 2013, Pages

  Hansson, E K ^a   Amantea, M A ^b   Westwood, P ^a   Milligan, P A ^b   Houk, B E ^b   French, J ^b,c   Karlsson, M O ^a   Friberg, L E ^a  

^a UPPSALA UNIVERSITY   (Sweden)

^b Pf Izer Global Research and Development   (United States)

^c Metrum Research Group   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMATINIB; PLACEBO; STEM CELL FACTOR RECEPTOR; SUNITINIB; VASCULOTROPIN; VASCULOTROPIN RECEPTOR 2; VASCULOTROPIN RECEPTOR 3;

AREA UNDER THE CURVE; ARTICLE; CANCER INHIBITION; CONTROLLED STUDY; GASTROINTESTINAL STROMAL TUMOR; HUMAN; MAJOR CLINICAL STUDY; OVERALL SURVIVAL; PHASE 1 CLINICAL TRIAL; PHASE 2 CLINICAL TRIAL; PHASE 3 CLINICAL TRIAL; PREDICTIVE VALUE; PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL; SURVIVAL TIME; TREATMENT OUTCOME; TUMOR GROWTH; TUMOR VOLUME;

EID: 84891785804     PISSN: None     EISSN: 21638306     Source Type: Journal    
DOI: 10.1038/psp.2013.61     Document Type: Article

References (36)

1. Abrams, T.J. et al. Preclinical evaluation of the tyrosine kinase inhibitor SU11248 as a single agent and in combination with "standard of care" therapeutic agents for the treatment of breast cancer. Mol. Cancer Ther. 2, 1011-1021 (2003).
2. Mendel, D.B. et al. In vivo antitumor activity of SU11248, a novel tyrosine kinase inhibitor targeting vascular endothelial growth factor and platelet-derived growth factor receptors: determination of a pharmacokinetic/pharmacodynamic relationship. Clin. Cancer Res. 9, 327-337 (2003). (Pubitemid 36109749)
3. O'Farrell, A.M. et al. SU11248 is a novel FLT3 tyrosine kinase inhibitor with potent activity in vitro and in vivo. Blood 101, 3597-3605 (2003).
4. Kim, D.W. et al. An orally administered multitarget tyrosine kinase inhibitor, SU11248, is a novel potent inhibitor of thyroid oncogenic RET/papillary thyroid cancer kinases. J. Clin. Endocrinol. Metab. 91, 4070-4076 (2006).
5. Jubb, A.M., Oates, A.J., Holden, S. & Koeppen, H. Predicting benefit from anti-angiogenic agents in malignancy. Nat. Rev. Cancer 6, 626-635 (2006). (Pubitemid 44140860)
6. Eisenhauer, E.A. & Verweij, J. New response evaluation criteria in solid tumors: RECIST Guideline Version 1.1. EJC Supplements. 7, 5 (2009).
7. Atkinson, A.J. et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Therapeut. 69, 89-95 (2001). (Pubitemid 32225406)
8. Norden-Zfoni, A. et al. Blood-based biomarkers of SU11248 activity and clinical outcome in patients with metastatic imatinib-resistant gastrointestinal stromal tumor. Clin. Cancer Res. 13, 2643-2650 (2007). (Pubitemid 46788031)
9. DePrimo, S.E. & Bello, C. Surrogate biomarkers in evaluating response to anti-angiogenic agents: focus on sunitinib. Ann. Oncol. 18 (suppl. 10), x11-x19 (2007).
10. Kontovinis, L.F., Papazisis, K.T., Touplikioti, P., Andreadis, C., Mouratidou, D. & Kortsaris, A.H. Sunitinib treatment for patients with clear-cell metastatic renal cell carcinoma: clinical outcomes and plasma angiogenesis markers. BMC Cancer 9, 82 (2009).
11. Motzer, R.J. et al. Activity of SU11248, a multitargeted inhibitor of vascular endothelial growth factor receptor and platelet-derived growth factor receptor, in patients with metastatic renal cell carcinoma. J. Clin. Oncol. 24, 16-24 (2006). (Pubitemid 46630489)
12. Claret, L. et al. Model-based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. J. Clin. Oncol. 27, 4103-4108 (2009).
13. Claret, L., Lu, J.F., Sun, Y.N. & Bruno, R. Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer. Cancer Chemother. Pharmacol. 66, 1141-1149 (2010).
14. Bruno, R., Lu, J.F., Sun, Y.N. & Claret, L. A modeling and simulation framework to support early clinical drug development decisions in oncology. J. Clin. Pharmacol. 51, 6-8 (2011).
15. Wang, Y. et al. Elucidation of relationship between tumor size and survival in non-smallcell lung cancer patients can aid early decision making in clinical drug development. Clin. Pharmacol. Ther. 86, 167-174 (2009).
16. Lu, J.F. et al. Population pharmacokinetic/pharmacodynamic modeling for the time course of tumor shrinkage by motesanib in thyroid cancer patients. Cancer Chemother. Pharmacol. 66, 1151-1158 (2010).
17. Rock, E.P. et al. Food and Drug Administration drug approval summary: Sunitinib malate for the treatment of gastrointestinal stromal tumor and advanced renal cell carcinoma. Oncologist 12, 107-113 (2007). (Pubitemid 46143508)
18. Ibrahim, J.G., Chu, H. & Chen, L.M. Basic concepts and methods for joint models of longitudinal and survival data. J. Clin. Oncol. 28, 2796-2801 (2010).
19. Dansirikul, C., Silber, H.E. & Karlsson, M.O. Approaches to handling pharmacodynamic baseline responses. J. Pharmacokinet. Pharmacodyn. 35, 269-283 (2008).
20. Bocci, G. et al. Increased plasma vascular endothelial growth factor (VEGF) as a surrogate marker for optimal therapeutic dosing of VEGF receptor-2 monoclonal antibodies. Cancer Res. 64, 6616-6625 (2004). (Pubitemid 39297922)
21. Ebos, J.M. et al. Vascular endothelial growth factor-mediated decrease in plasma soluble vascular endothelial growth factor receptor-2 levels as a surrogate biomarker for tumor growth. Cancer Res. 68, 521-529 (2008). (Pubitemid 351380080)
22. Lindauer, A. et al. Pharmacokinetic/pharmacodynamic modeling of biomarker response to sunitinib in healthy volunteers. Clin. Pharmacol. Ther. 87, 601-608 (2010).
23. Deprimo, S.E. et al. Circulating levels of soluble KIT serve as a biomarker for clinical outcome in gastrointestinal stromal tumor patients receiving sunitinib following imatinib failure. Clin. Cancer Res. 15, 5869-5877 (2009).
24. Hansson, E.K. et al. PKPD modeling of predictors for side effects and overall survival in sunitinib treated patients with gastro intestinal stromal tumor. CPT Pharmacometrics Syst. Pharmacol. (in press).
25. Schindler, E., Amantea, M., Milligan, A.P., Karlsson, M.O. & Friberg, E.L. PKPDModeling of VEGF, sVEGFR-1, sVEGFR-2, sVEGFR-3 and tumor size following axitinib treatment in metastatic renal cell carcinoma (mRCC) patients. (2013).
26. Demetri, G.D. et al. Efficacy and safety of sunitinib in patients with advanced gastrointestinal stromal tumour after failure of imatinib: a randomised controlled trial. Lancet 368, 1329-1338 (2006). (Pubitemid 44528723)
27. George, S. et al. Clinical evaluation of continuous daily dosing of sunitinib malate in patients with advanced gastrointestinal stromal tumour after imatinib failure. Eur. J. Cancer 45, 1959-1968 (2009).
28. Shirao, K. et al. Phase I/II study of sunitinib malate in Japanese patients with gastrointestinal stromal tumor after failure of prior treatment with imatinib mesylate. Invest. New Drugs 28, 866-875 (2010).
29. Maki, R.G. et al. Results from a continuation trial of SU11248 in patients (pts) with imatinib (IM)-resistant gastrointestinal stromal tumor (GIST). 41 st Annual meeting of the american society of Clinical oncology, Orlando, FL, 13-17 May 2005. (2005).
30. Beal, S., Sheiner, L.B., Boekmann, A. & Bauer, R.J. NONMEM User's Guides (1989-2006). (USA Icon Development Solutions, Ellicott City, MD, 2009).
31. Jonsson, E.N. & Karlsson, M.O. Xpose-an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput. Methods Programs Biomed. 58, 51-64 (1999). (Pubitemid 29049052)
32. Lindbom, L., Ribbing, J. & Jonsson, E.N. Perl-speaks-NONMEM (PsN)-a Perl module for NONMEM related programming. Comput. Methods Programs Biomed. 75, 85-94 (2004). (Pubitemid 38798039)
33. Lindbom, L., Pihlgren, P., Jonsson, E.N. & Jonsson, N. PsN-Toolkit-a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput. Methods Programs Biomed. 79, 241-257 (2005). (Pubitemid 41196689)
34. Bergstrand, M., Hooker, A.C., Wallin, J.E. & Karlsson, M.O. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 13, 143-151 (2011).
35. Houk, B.E., Bello, C.L., Kang, D. & Amantea, M. A population pharmacokinetic metaanalysis of sunitinib malate (SU11248) and its primary metabolite (SU12662) in healthy volunteers and oncology patients. Clin. Cancer Res. 15, 2497-2506 (2009).
36. Dayneka, N.L., Garg, V. & Jusko, W.J. Comparison of four basic models of indirect pharmacodynamic responses. J. Pharmacokinet. Biopharm. 21, 457-478 (1993). (Pubitemid 24025269)