PKPD modeling of predictors for adverse effects and overall survival in sunitinib-treated patients with GIST

CPT: Pharmacometrics and Systems Pharmacology

Volumn 2, Issue 12, 2013, Pages

  Ahansson, E K ^a   Ma, G ^a,b   Amantea, M A ^b   French, J ^b,c   Milligan, P A ^b   Friberg, L E ^a   Karlsson, M O ^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

PLACEBO; SUNITINIB; VASCULOTROPIN RECEPTOR 2; VASCULOTROPIN RECEPTOR 3;

AREA UNDER THE CURVE; ARTICLE; BLOOD PRESSURE MEASUREMENT; BONE MARROW SUPPRESSION; CANCER PATIENT; CANCER SIZE; DIASTOLIC BLOOD PRESSURE; DISEASE SEVERITY; DRUG EXPOSURE; FATIGUE; GASTROINTESTINAL STROMAL TUMOR; HAND FOOT SYNDROME; HUMAN; HYPERTENSION; INDIVIDUALIZATION; MAJOR CLINICAL STUDY; MEAN RESIDENCE TIME; MULTIPLE CYCLE TREATMENT; NEUTROPENIA; NEUTROPHIL COUNT; OVERALL SURVIVAL; PHASE 1 CLINICAL TRIAL (TOPIC); PHASE 2 CLINICAL TRIAL (TOPIC); PHASE 3 CLINICAL TRIAL (TOPIC); PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL (TOPIC); TREATMENT RESPONSE;

EID: 84891800143     PISSN: None     EISSN: 21638306     Source Type: Journal    
DOI: 10.1038/psp.2013.62     Document Type: Article

References (28)

1. Klümpen, H.J., Samer, C.F., Mathijssen, R.H., Schellens, J.H. & Gurney, H. Moving towards dose individualization of tyrosine kinase inhibitors. Cancer Treat. Rev. 37, 251-260 (2011).
2. Hansson, E.K. et al. PKPD modeling of VEGF, sVEGFR-2,-3 and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst. Pharmacol. 2: e84. (2013).
3. 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).
4. Eskens, F.A. & Verweij, J. The clinical toxicity profile of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) targeting angiogenesis inhibitors; a review. Eur. J. Cancer 42, 3127-3139 (2006). (Pubitemid 44869016)
5. Rini, B.I. et al. Hypertension as a biomarker of efficacy in patients with metastatic renal cell carcinoma treated with sunitinib. J. Natl. Cancer Inst. 103, 763-773 (2011).
6. Rixe, O., Billemont, B. & Izzedine, H. Hypertension as a predictive factor of Sunitinib activity. Ann. Oncol. 18, 1117 (2007). (Pubitemid 47050505)
7. Donskov, F. et al. Neutropenia as a biomarker of sunitinib efficacy in patients (Pts) with gastrointestinal stromal tumour (GIST). Eur. J. Cancer 47, S135 (2011).
8. 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)
9. 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).
10. 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).
11. Maki, R.G. et al. Results from a continuation trial of SU11248 in patients (pts) with imatinib (IM)-resistant gastrointestinal stromal tumor (GIST). 41st Annual meeting of the American Society of Clinical Oncology, Orlando, FL, 13-17 May 2005. http://www.asco.org (2005).
12. Friberg, L.E., Henningsson, A., Maas, H., Nguyen, L. & Karlsson, M.O. Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J. Clin. Oncol. 20, 4713-4721 (2002). (Pubitemid 36025290)
13. Keizer, R.J. et al. A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080. J. Pharmacokinet. Pharmacodyn. 37, 347-363 (2010).
14. Hénin, E. et al. A dynamic model of hand-and-foot syndrome in patients receiving capecitabine. Clin. Pharmacol. Ther. 85, 418-425 (2009).
15. Zingmark, P.H., Kågedal, M. & Karlsson, M.O. Modelling a spontaneously reported side effect by use of a Markov mixed-effects model. J. Pharmacokinet. Pharmacodyn. 32, 261-281 (2005). (Pubitemid 41712743)
16. Houk, B.E., Bello, C.L., Poland, B., Rosen, L.S., Demetri, G.D. & Motzer, R.J. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother. Pharmacol. 66, 357-371 (2010).
17. 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).
18. van Kesteren, C. et al. Semi-physiological model describing the hematological toxicity of the anti-cancer agent indisulam. Invest. New Drugs 23, 225-234 (2005). (Pubitemid 40684947)
19. Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 92, 205-216 (2000).
20. 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)
21. 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)
22. 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)
23. 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).
24. 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).
25. Cartwright, G.E., Athens, J.W. & Wintrobe, M.M. The kinetics of granulopoiesis in normal man. Blood 24, 780-803 (1964).
26. Karlsson, M.O., Port, R.E., Ratain, M.J. & Sheiner, L.B. A population model for the leukopenic effect of etoposide. Clin. Pharmacol. Ther. 57, 325-334 (1995).
27. Friberg, L.E., Brindley, C.J., Karlsson, M.O. & Devlin, A.J. Models of schedule dependent haematological toxicity of 2'-deoxy-2'-methylidenecytidine (DMDC). Eur. J. Clin. Pharmacol. 56, 567-574 (2000).
28. Sheiner, L.B., Stanski, D.R., Vozeh, S., Miller, R.D. & Ham, J. Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin. Pharmacol. Ther. 25, 358-371 (1979). (Pubitemid 9164953)