Thursday 4 July 2019 at 14:30
Lorenzo Trippa, Harvard University and Dana-Farber Cancer Institute, Department of Statistics and Computational Biology
CNR IMATI, Via A. Corti, 12, Milano, Aula B
*Adaptive trial design in glioblastoma*
There have been few treatment advances for patients with glioblastoma (GBM) despite increasing scientific understanding of the disease. While factors such as intrinsic tumor biology and drug delivery are challenges to developing efficacious therapies, it is unclear whether the current clinical trial landscape is optimally evaluating new therapies and biomarkers. We queried ClinicalTrials.gov for interventional clinical trials for patients with GBM initiated between January 2005 and December 2016 and abstracted data regarding phase, status, start and end dates, testing locations, endpoints, experimental interventions, sample size, clinical presentation/indication, and design to better understand the clinical trials landscape. Only approximately 8%–11% of patients with newly diagnosed GBM enroll on clinical trials with a similar estimate for all patients with GBM. Trial duration was similar across phases with median time to completion between 3 and 4 years. While 93% of clinical trials were in phases I–II, 26% of the overall clinical trial patient population was enrolled on phase III studies. We use the results of this meta analysis to discuss pros and cons of trial designs in GBM. This analysis includes platforms and the use of external controls. Platform design allow to add new experimental arms to the clinical study when they become available, while the aim of an external controls is to leverage external data that were not generated from the clinical study.