Enrichment Designs in Clinical Trials: Strategies for Precision and Efficacy

The promise of personalized medicine hinges on accurately identifying patient subgroups that will benefit most from specific therapies, particularly in the development of immunotherapies and targeted treatments. As drug development increasingly moves towards precision medicine, enrichment designs play a critical role in identifying and focusing on responder populations. This talk will explore cutting-edge adaptive enrichment designs in clinical trials that leverage advanced statistical models to dynamically target these subgroups, enhancing trial precision and efficacy. We will discuss recent advancements in enrichment strategies, including Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers (Biometrics, 2022), personalized dose-finding algorithm based on adaptive Gaussian Process regression (Pharmaceutical statistics, 2024), and new enrichment basket trial designs currently in development. These methodologies enable real-time adjustments in patient inclusion criteria based on evolving biomarker data, dynamically refining trial populations to enhance both precision and efficacy. Each example highlights how adaptive enrichment strategies can streamline decision-making, reduce costs, and accelerate timelines, ultimately making clinical trials more responsive to patient heterogeneity and advancing the field of precision medicine. 

In this presentation, we introduce a statistical consideration framework for how to effectively leverage early-phase data to select appropriate enrichment designs for confirmatory trials in the context of oncology drug development. Our approach focuses on two key metrics for evaluating different design options: overall power and overall value, of which the latter considers population size and treatment effect size. The framework quantitatively integrates available information and a variety of considerations, such as extrapolation of enrichment observed in early endpoints to clinical endpoints, strategies to enhance power by accounting for the correlation between the enriched population and the all-comer population and assessing multiplicity strategies. We will provide general guidance supported by illustrative numeric examples 

Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. With the intention to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care therapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential "comparative efficacy" of novel targeted therapies. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed to facilitate efficient implementation of randomized trials for agents that target biomarker subpopulations. This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. Only designs with type I error between 0.05 and 0.1 and power of at least 0.8 were considered when selecting an optimal efficiency design from among the candidate designs formed by different combinations of posterior and predictive threshold. A simulation study motivated by the results reported in a recent clinical trial studying atezolizumab treatment in patients with locally advanced or metastatic urothelial carcinoma is used to evaluate the operating characteristics of the various designs. Our findings suggest that this type of enrichment design can be applied to conduct smaller phase II trials than those used in practice. 

To support the expedited drug development that addresses unmet medical needs, the seamless phase 2/3 design that makes the phase switching decision based on an early surrogate endpoint is gaining more popularity in practice. For also catering to potentially more beneficial patient subgroups based on predictive biomarkers, it is appealing to incorporate the subgroup enrichment feature into the seamless phase 2/3 design. However, the sample size planning for such a complex adaptive design is challenging, as it must strike a balance among shortening development timeline, mitigating development risks, and accounting for uncertainty related to subgroup effects. To fill this gap, we propose a flexible seamless phase 2/3 design framework with population selection and sample size re-estimation using a surrogate endpoint. We elucidate the patterns of the overall type I error for the proposed adaptive design and propose an easy-to-implement approach to control the overall type I error. Extensive simulation studies are conducted to demonstrate the advantages of our proposal design compared to the fixed-sample design in terms of efficiency, power, and timeline saving.