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The clinical trials landscape for glioblastoma: is it adequate to develop new treatments?

Alyssa M Vanderbeek1,2, Rifaquat Rahman1,2, Geoffrey Fell3,4,2

  • 1Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts.

Neuro-Oncology
|March 9, 2018
PubMed
Abstract

Insights

Glioblastoma clinical trials show slow progress and low patient enrollment. Few phase III trials yield positive results, indicating a need for improved trial design and data sharing for glioblastoma (GBM) therapies.

Area of Science:

  • Neuro-oncology
  • Clinical trial design
  • Biomedical research

Background:

  • Glioblastoma (GBM) has seen limited treatment advances despite increased scientific understanding.
  • Challenges in GBM therapy development include tumor biology and drug delivery.
  • The effectiveness of current clinical trials in evaluating new GBM therapies and biomarkers is uncertain.

Purpose of the Study:

  • To analyze the landscape of interventional clinical trials for glioblastoma (GBM).
  • To identify trends and challenges in GBM clinical trial design and execution.
  • To assess patient enrollment rates and trial outcomes in GBM research.

Main Methods:

  • Data were extracted from ClinicalTrials.gov for GBM interventional trials initiated between January 2005 and December 2016.
  • Key data points included trial phase, status, duration, interventions, endpoints, and sample size.
  • Analysis focused on understanding the structure and progression of GBM clinical trials.

Main Results:

  • Patient enrollment in GBM clinical trials remains low, estimated at 8%-11%.
  • Phase III trials had a low success rate, with only 1 of 8 completed trials reporting positive results.
  • A significant portion of phase III trials lacked robust supporting data from randomized phase II studies.

Conclusions:

  • The glioblastoma clinical trial landscape is marked by lengthy development timelines.
  • Inadequate information dissemination and suboptimal decision-making processes hinder progress.
  • Low patient participation rates represent a significant challenge in advancing GBM research.

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