Vol-PACT: Improving Volumetric CT Metrics for Precision Analysis of Clinical Trial Results

Vol-PACT: Improving Volumetric CT Metrics for Precision Analysis of Clinical Trial Results

The Biomarkers Consortium at the Foundation for the National Institutes of Health (FNIH) will provide seed funding ($250,000) to complete Aim 1 (approximately 9 months). The remaining funds to support the project ($986,000) will be raise among interested parties, such as pharmaceutical companies.

Ad hoc advisory board: Boehringer-Ingelheim; AVEO; Novartis; Genentech. Consulting: Astra-Zeneca. I have no conflicts with this project. I do serve as an independent blinded reviewer and serve on Data and Safety Monitoring Board (DSMB) through BioClinica and ICON Medical Imaging.I have no conflicts with this project. I do have research funding from Sanofi and Bayer. I am a paid consultant for Millennium. I have been an uncompensated consultant for Fujifilm and Bayer. Research funding: GSK, Genentech/Roche.

Currently drug activity in phase 2 clinical trials (typically 100-300 patients) in solid tumors is primarily assessed by computed tomography (CT) imaging-based Response Evaluation Criteria in Solid Tumors (RECIST) to determine response rate in single-arm trials where all patients are treated with the drug. Some trials compare drug activity between patients who receive the drug treatment and those who don't. In such cases, other RECIST-based tumor assessment, such as time to tumor progression (TTP) and progression-free survival (PFS), is used as the endpoint to measure drug activity. Drugs that show promise in effectiveness warrant further evaluation in phase 3 clinical trials to confirm the effectiveness and overall benefit-risk relationship in a larger number of patients. In phase 3 trials, overall survival (OS) remains the gold standard for clinical benefit. It often requires a large number of patients (e.g., 1000 or more) and long observation time to demonstrate OS advantage of the new drug over available treatment. Surrogate endpoints are accepted for drug approval in certain situations, but clinical benefit is generally required to be confirmed in subsequent or ongoing trials. In reality, up to 60% of the drugs that showed promise in phase 2 trials later proved ineffective to treat the disease when the drug is tested in phase 3 trials.

This study aims to develop new analytical methods to improve the ability of imaging in phase 2 trials in predicting the effectiveness of new drugs and their potential for success in phase 3 trials. These methods can reduce the number of patients required in clinical trials, and shorten the observation time required by currently used methods of assessing clinical benefit to patients. As a result, they improve the efficiency in bringing new drugs to the market and patients.

To develop such methods, we will generate 1000 simulated phase 2 trials by sampling patient imaging and outcomes data from completed phase 3 clinical trials that supported regulatory approval of the drug. Changes in tumor burden will be assessed by tumor volume calculated from CT imaging data. This volume assessment more closely reflects true tumor burden than the line length of a tumor lesion as seen on a cross-sectional image according to RECIST. The study also proposes to analyze tumor response as a continuous variable rather than a categorical (progression=yes or no) variable as is used in RECIST.

We will study images donated by several pharmaceutical company-sponsored phase 3 drug trials from four common measurable cancers of solid tumors. By studying multiple agents used to treat different cancers, we hope to draw broad conclusions about improved strategies for effective trial analysis.

This research has value to a wide number of parties, including academic investigators, pharmaceutical companies, regulatory representatives, and ultimately cancer patients. Our results will be published, and the tools available to the public.

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Vol-PACT: A Foundation for the NIH Public-Private Partnership That Supports Sharing of Clinical Trial Data for the Development of Improved Imaging Biomarkers in Oncology Laurent Dercle, Dana E. Connors, Ying Tang, Stacey J. Adam, et al
DOI: 10.1200/CCI.17.00137 JCO Clinical Cancer Informatics - March 2, 2018