Cancer is one of the most scientifically complex and dynamic diseases. Even with significant advances in our understanding of the genetic and molecular mechanisms that lead to cancer, only 10 percent of approved oncology drugs demonstrate an overall survival benefit.1 In recent years, there has been a shift in how early-phase oncology studies are conducted as adaptive trial designs have become more common. These designs seek to limit the number of patients exposed to ineffective doses or treatments while accelerating detection of efficacy signals, thereby enriching the likelihood of trial success.
The precision medicine paradigm shift
With the emergence of precision medicine, the framework of oncology clinical trials has evolved from a one-size-fits-all approach to a more personalized one. This shift has fueled the need for adaptive trial designs that allow for review of data at prespecified times during study conduct and predefined adaptations of study design elements based on observations that accrue within the trial. The key here is prespecified (or a priori), defined in the study protocol before the trial begins, in order to avoid the perception of bias. In precision oncology trials, design adaptations are often driven by two types of biomarkers:
- Predictive biomarkers, which predict the likelihood of therapeutic response
- Prognostic biomarkers, which predict the most likely prognosis of an individual patient
Predefined changes in an adaptive trial design may include refinements in sample size, dynamic adjustment of dose schedules, changes in treatment-arm allocations, enrichment of the study population with patients who are most likely to benefit from treatment, and even early-stopping decisions.
Advantages of adaptive trial designs
With increased flexibility, adaptive trial designs offer many advantages compared to conventional fixed-sample trial designs. Adaptive designs can:
- Minimize the need for amendments or new protocols, which can be costly and require site re-training.
- Enable seamless transition between study phases by eliminating the need to close a trial before opening a new protocol.
- Increase efficiency by eliminating study start-up activities for follow-on or expansion phases.
- Provide patients the opportunity to continue therapy until progression rather than re-enrolling in rollover studies or seeking expanded access programs.
- Reduce the number of patients exposed to ineffective doses or treatments.
- Allow for early efficacy readouts based on criteria established a priori for prespecified interim analyses.
Common adaptive trial designs
Adaptive design approaches that can be used to optimize early-stage oncology trials in precision medicine include:
- Seamless Phase 1/2 adaptive designs, where a dose-finding Phase 1 study transitions into a Phase 2 expansion study for determining an early efficacy signal.
- Biomarker enrichment designs, which use biomarkers to enrich the study population with patients who are more likely to demonstrate a therapeutic response.
- Biomarker stratified designs, in which molecular markers are used as stratification variables.
- Umbrella designs, which evaluate multiple targeted therapies for a single disease that is stratified into subgroups by molecular alterations.
- Basket designs, in which a single targeted therapy is evaluated on multiple diseases that have common underlying molecular alterations.
Adaptive design studies may include multiple cohorts and multiple tumor types. In addition, numerous adaptation methods may be used in a single trial.2
Challenges with adaptive designs
While adaptive trial designs offer inherent benefits in precision oncology studies, they may be challenging to execute, especially for sponsors and sites with no previous experience. Careful planning is essential for addressing logistical hurdles related to implementing the necessary data monitoring and data management processes necessary for each adaptation. Given that adaptive trial designs can be statistically and computationally complex, they require the support of an experienced biostatistics team. Appropriate training and ongoing education are also necessary for site staff and trial management personnel.
Learning more about adaptive trial designs
A recent study evaluating clinical development success rates from 2011 to 2020 found that the overall likelihood of approval (LOA) from Phase 1 for all development candidates was 7.9 percent.3 For oncology drug programs, the LOA from Phase 1 was only 5.3 percent, though 12.4 percent of immuno-oncology therapies transitioned from Phase 1 to approval. Leveraging adaptive trial designs at the early stages of oncology drug development, may help optimize success by enabling more rapid determinations of futility and early efficacy signals, thus accelerating the development of new cancer therapies.
To learn more about using adaptive trial designs in precision oncology studies, download our white paper.
[1] Chen EY, Raghunathan V, Prasad V. An overview of cancer drugs approved by the U.S. Food and Drug Administration based on the surrogate end point of response rate. JAMA Intern Med. 2019;179(7):915-921.
[2] Sverdlov O, Wong WK. Novel statistical designs for Phase I/II and Phase II clinical trials with dose-finding objectives. Ther Innov Regul Sci. 2014;48:601–612
[3] BIO, Informa Pharma Intelligence, QLS Advisors. Clinical Development Success Rates and Contributing Factors 2011–2020.
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