Drug-independent models for predicting overall survival
*Rene Bruno, Pharsight Consulting Services Laurent Claret, Pharsight Consulting Services Francois Mercier , Pharsight Consulting Services
Keywords: tumor growth inhibition, overall survival prediction
Phase II clinical trial designs and endpoints are generally poorly informative in early oncology trials and end-of-phase II decisions often remain subjectives. Model-based approaches are particularly advocated when the main objective is to learn prior to conducting large confirmatory (phase III) trials. Model-based estimates of tumor growth inhibition (TGI) metrics have the potential to enhance learning in early (phase II) clinical studies. They could be used as endpoints and biomarkers to predict treatment effect on clinical outcome measures (e.g. overall survival (OS)) and support phase II study design, end-of-phase II decisions and phase III planning and execution.
Drug-independent models linking TGI metrics to OS have been proposed in a number of solid and hematologic tumors (1-6) and some of these models have been prospectively used to simulate independent studies (6, 7). Methodological approaches to develop and assess predictive performance of the models will be reviewed and examples of clinical trial simulations will be given.
It is needed to further assess these models in simulating independent studies (external assessment) with treatments of varying mechanism of action. These efforts are made difficult due to limited availability of patient-level clinical trial data and cross-institution collaborations are warranted.
1 - Claret L, Girard P, O'Shaughnessy J, et al. Model-based predictions of expected anti-tumor response and survival in phase III studies based on phase II data of an investigational agent. J. Clin. Oncol., 24, 307s (suppl, abstract 2530), 2006. 2 - Claret L, Girard P, Hoff PM, et al. Model-based prediction of Phase III overall survival in colorectal cancer based on Phase II tumor dynamics. J. Clin. Oncol., 27, 4103-4108, 2009. 4 - Wang Y, Sung C, Dartois C, et al. Elucidation of relationship between tumor size and survival in non-small cell lung cancer patients can aid early decision making in clinical drug development. Clin. Pharmacol. Ther., 86, 167-174, 2009. 5 - Claret L, Gupta M, Joshi A, et al. Evaluation of tumor size response metrics to predict survival and progression free survival in first line metastatic colorectal cancer. J. Clin. Oncol., 31, 2110-2114, 2013. 6 - Bruno R, Jonsson F, Zaki M et al. Simulation of clinical outcome for pomalidomide plus low-dose dexamethasone in patients with refractory multiple myeloma based on week 8 M-protein response. Blood 118 (21),1881 (abstract) 2011. 7 - Claret L, Lu JF, Bruno R, at al. Simulations using a public domain drug-disease modeling framework and Phase II data predict Phase III survival outcome in first-line non-small-cell lung cancer (NSCLC). Clin. Pharmacol. Ther. 92, 631-634, 2012.
April 30 - May 22, 2013
Invited Abstract Submission Open
June 4, 2013
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August 9 - August 23, 2013
Invited Abstract Editing
August 23, 2013
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August 26, 2013
September 9, 2013
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September 16 - September 18, 2013
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