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Adaptive clinical trials in oncology
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, 4-5062 Pickens Academic Tower, Houston, TX 77030-1402, USA. dberry@mdanderson.org
Abstract:
Modern oncology drug development faces challenges very different from those of the past and it must adapt accordingly. The size and expense of phase III clinical trials continue to increase, but the success rate remains unacceptably low. Adaptive trial designs can make development more informative, addressing whether a drug is safe and effective while showing how it should be delivered and to whom. An adaptive design is one in which the accumulating data are used to modify the trial's course. Adaptive designs are ideal for addressing many questions at once. For example, a single trial might identify the appropriate patient population, dose and regimen, and therapeutic combinations, and then switch seamlessly into a phase III confirmatory trial. Adaptive designs rely on information, including from patients who have not achieved the trial's primary end point. Longitudinal models of biomarkers (including tumor burden assessed via imaging) enable predictions of primary end points. Taking a Bayesian perspective facilitates building an efficient and accurate trial, including using longitudinal information. A wholly new paradigm for drug development exemplifying personalized medicine is evinced by an adaptive trial called I-SPY2, in which drugs from many companies are evaluated in the same trial--a phase II screening process.
Insights
Adaptive trial designs improve oncology drug development by using accumulating data to modify trials. This approach enhances efficiency and personalization, leading to better drug delivery and patient selection for improved outcomes.
Area of Science:
- Oncology Drug Development
- Clinical Trial Design
- Personalized Medicine
Background:
- Traditional oncology drug development faces escalating costs and low success rates in Phase III trials.
- Existing trial structures are often inefficient for addressing multiple research questions simultaneously.
- There is a need for more informative and adaptable clinical trial methodologies.
Purpose of the Study:
- To explore the utility of adaptive trial designs in modern oncology.
- To demonstrate how adaptive designs can optimize drug development processes.
- To highlight the role of adaptive trials in advancing personalized medicine.
Main Methods:
- Utilizing accumulating trial data to dynamically modify trial parameters.
- Employing longitudinal models of biomarkers, including imaging-based tumor burden, for endpoint prediction.
- Adopting a Bayesian perspective to enhance trial efficiency and accuracy.
Main Results:
- Adaptive designs allow for simultaneous evaluation of patient populations, dosages, regimens, and combinations.
- Longitudinal biomarker data enables prediction of primary endpoints, even in patients not achieving them.
- The I-SPY2 trial exemplifies a novel adaptive approach for evaluating multiple drugs within a single screening process.
Conclusions:
- Adaptive trial designs offer a more informative and efficient paradigm for oncology drug development.
- These designs facilitate personalized medicine by optimizing drug delivery and patient selection.
- Adaptive trials represent a significant advancement, exemplified by platforms like I-SPY2.
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