Enrichment strategies in oncology clinical trials have become increasingly important in the era of precision medicine. These strategies involve selecting patients with specific pre-treatment characteristics that may make them more likely to respond to a targeted therapy, thereby increasing the efficiency and effectiveness of the clinical trial.

In oncology, enrichment often involves selecting patients based on specific biomarkers or genetic mutations that are associated with the drug’s mechanism of action. For example, trials for drugs targeting HER2/neu in breast cancer or EGFR mutations in lung cancer often use enrichment strategies to include only patients whose tumors express these markers. This approach not only increases the likelihood of detecting efficacy but also helps identify the patient population most likely to benefit from the treatment.

FDA guidance on enrichment strategies for clinical trials

The FDA has issued guidance on enrichment strategies for clinical trials. This guidance defines enrichment as the prospective use of patient characteristics to select a study population more likely to demonstrate a drug effect. The guidance outlines three main categories of enrichment strategies:

1. Strategies to decrease heterogeneity, which aim to reduce variability and increase study power.
2. Prognostic enrichment strategies, which select patients with a higher likelihood of having a disease-related endpoint event or substantial worsening of condition.
3. Predictive enrichment strategies, which select patients more likely to respond to the drug based on physiological, disease characteristics, or previous response to similar drugs.

The FDA encourages the use of these strategies to enhance the understanding of the benefit-risk relationship in both the overall and the enriched population. They also emphasize the importance of properly describing study findings in drug labeling.

Benefits and trade-offs of adaptive population enrichment designs

Adaptive population enrichment designs offer sponsors additional flexibility, allowing for adjustment of eligibility criteria based on accumulating data during the trial, potentially leading to more efficient drug development and better-targeted therapies for cancer patients.

These designs start by enrolling a broad patient population but have the flexibility to restrict future recruitment after an interim analysis to patient subgroups showing greater treatment benefit. Trials designed in accordance with these principles simultaneously evaluate treatment effects in both the overall population and specific subpopulations of interest, while maintaining statistical power. By allowing for data-driven adjustments to the study population, adaptive population enrichment designs can increase trial efficiency, direct resources toward promising subgroups, and improve the likelihood of identifying effective treatments for specific patient sub-populations.

However, adaptive population enrichment designs also present several statistical challenges that require careful planning and consideration. One of the primary issues is controlling the Type I error rate, as these designs involve interim unblinded analyses and potential changes to the study population. This necessitates the use of specialized statistical methods to ensure the validity of the trial results.

Sample size determination is another critical aspect that demands thorough planning. Sponsors must consider various scenarios, including different treatment effects in subpopulations and potential adaptation decisions, to ensure adequate statistical power for detecting treatment effects in both the overall population and selected subgroups. The pre-specification of adaptation rules, hypothesis tests, and statistical methods for combining data from different stages of the trial is also essential for maintaining the integrity of the study.

Finally, there are also trade-offs and considerations regarding the timing of the interim analyses, the underlying prevalence of the sub-populations, the magnitude of the differential effects.

Final takeaways

Enrichment strategies can increase the efficiency and effectiveness of oncology trials by selecting patients more likely to respond to a targeted therapy. However, while adaptive population enrichment designs allow for adjustments based on interim data, their complexity introduces statistical challenges that require careful planning. Despite these challenges, the ability to direct resources toward subgroups showing promise holds significant potential for accelerating the development of cancer therapies.

Interested in learning more? Register today for our webinar “Oncology Clinical Trials: Design Considerations in Adaptive Population Enrichment Trials” on October 9, 2024.

The webinar will provide a comprehensive overview of statistical aspects of adaptive enrichment trials, regulatory requirements for pre-specification of design elements, and benefits and trade-offs, as well as insights from past engagements with sponsors and regulatory agencies.

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