Laurent Claret (1), Jenny Zheng (2), Francois Mercier (1), Pascal Chanu (1), Ying Chen (3), Brad Rosbrook (4), Pithavala, Yazdi (3), Peter A. Milligan (2), Rene Bruno (1)
(1) Pharsight Consulting Services, Pharsight, a CertaraTM Company, France, (2) Pfizer Pharmacometrics, Global Clinical Pharmacology, USA and UK, (3) Pfizer Clinical Pharmacology, La Jolla, CA, USA, (4) Pfizer Statistics, La Jolla, CA, USA
Objectives: To assess the link between early tumor shrinkage (ETS) and progression-free survival (PFS) based on historical first-line metastatic renal cell carcinoma (mRCC) data.
Methods: Tumor size data from 921 patients with first-line mRCC who received interferon-alpha, sunitinib, sorafenib or axitinib in 2 Phase III studies [1, 2] were fit using a simplified tumor growth inhibition model [3]. The relationship between model-based estimates of ETS at week 8 [4], as well as the baseline prognostic factors and PFS were tested in a multivariate log-logistic distribution model. Both models were implemented in NONMEM version 7.3.0 [5]. PFS model performance was evaluated by comparing the simulated distributions of PFS and treatment hazard ratio (HR) with the observed PFS and HR in the two studies. In addition, an external validation was conducted using data from an independent Phase II mRCC study [6]. The relationship between the differences in ETS and expected HR of an investigational treatment vs. sunitinib was simulated.
Results: A model with a non-linear ETS-PFS link, using albumin level and presence of bone metastases at baseline as prognostic factors, was qualified to predict PFS distribution by ETS quantiles. Additionally, the model was used to predict the HRs of sunitinib vs. interferon-alpha and axitinib vs. sorafenib. For sunitinib vs. interferon alfa, the median predicted HR was 0.58 with a 95% prediction interval (PI) of 0.49-0.69 compared to the observed HR of 0.54 [1]). The model was also qualified in simulating PFS distribution and HR in the independent study [6]. The simulations suggest that if a new investigational treatment could further reduce ETS at week 8 by 30% compared with sunitinib, an expected HR [95% PI] of the new treatment vs. sunitinib would be 0.59 [0.46,0.79].
Conclusions: A model that uses early change in tumor size to predict the HR for PFS between treatments for first-line mRCC has been developed. Such a model can be used to support early decisions for investigational agents in development for this indication.
References:
[1] Motzer RJ, Hutson TE, Tomczak P et al (2007) Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med 356:115-124.
[2] Hutson TE, Lesovoy V, Al-Shukri S et al (2013). Axitinib versus sorafenib as first-line therapy in patients with metastatic renal-cell carcinoma: a randomised open-label phase 3 trial. Lancet Oncol 14:1287-1294.
[3] Claret L, Gupta M, Han K, et al (2013) Evaluation of tumor size response metrics to predict overall survival in western and Chinese patients with first line metastatic colorectal cancer. J Clin Oncol 31:2110-2114.
[4] Bruno R, Mercier F, Claret L (2014) Evaluation of tumor-size response metrics to predict survival in oncology clinical trials. Clin Pharmacol Ther 95:386-393.
[5] Beal SL, Sheiner LB, Boeckmann AJ & Bauer RJ (Eds.) NONMEM Users Guides. 1989-2011. Icon Development Solutions, Ellicott City, Maryland, USA.
[6] Rini BI, Melichar B, Ueda T et al (2013). Axitinib with or without dose titration for first-line metastatic renal-cell carcinoma: a randomized double-blind phase 2 trial. Lancet Oncol 14:1233-1242
Reference: PAGE 25 (2016) Abstr 5727 [www.page-meeting.org/?abstract=5727]
Poster: Drug/Disease modeling - Oncology