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Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions
Jian Wang1, Binbing Yu2, Yannan Nancy Dou1
1Oncology Regulatory Science, Strategy & Excellence, AstraZeneca, Gaithersburg, MD.
Abstract:
In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.
Insights
Biomarker-guided oncology trials can improve drug development efficiency. Analyzing past approvals reveals key factors for regulatory benefit-risk assessments in biomarker-defined subgroups versus all-comers.
Area of Science:
- Oncology
- Clinical Trial Design
- Biomarker Development
Background:
- Biomarkers are crucial for selecting patient populations likely to benefit from cancer therapies, enhancing clinical trial efficiency.
- Many oncology drugs approved by the US Food and Drug Administration (FDA) were initially tested in broad patient groups but later restricted to biomarker-defined subgroups.
Purpose of the Study:
- To analyze FDA-approved oncology therapeutics tested in "all-comers" populations but approved for biomarker-defined subgroups.
- To identify critical factors for regulatory benefit-risk assessments comparing biomarker-defined subgroups to all-comers approvals.
- To propose a decision tree for optimizing clinical trial design using patient enrichment and stratification strategies.
Main Methods:
- Review of pivotal trials for oncology therapeutics with "all-comers" designs that resulted in biomarker-defined subgroup approvals.
- Analysis of key considerations for regulatory benefit-risk assessments: biological/clinical rationale, biomarker prevalence, safety, trial design, and subgroup efficacy.
- Development of a decision tree for clinical trial design, incorporating marker type, prevalence, and assay logistics.
- Recommendation for prespecifying clinically meaningful improvement in biomarker-negative subgroups and using Bayesian methods for evidence integration.
Main Results:
- Favorable benefit-risk assessments were often established in biomarker-positive subgroups, even when trials included all-comers.
- Key factors influencing regulatory decisions include biological rationale, biomarker prevalence, safety data, and efficacy within subgroups.
- A decision tree is proposed to guide the choice between patient enrichment and stratification based on various factors.
Conclusions:
- Optimizing biomarker-driven drug development in oncology requires innovative clinical trial designs, robust statistical methodologies, and careful regulatory considerations.
- Strategic use of biomarkers can enhance the efficiency and success rate of oncology drug development.
- The analysis provides a framework for regulatory benefit-risk assessments and clinical trial design in the era of precision oncology.
