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How to Select & Execute a Biomarker-Driven Clinical Trial Designs

At the 2016 Duke Industry Statistics Symposium, Dr. Robin Bliss, in collaboration with Dr. Jing Wang of Gilead Sciences, lead a discussion on “Biomarker-Driven Clinical Trial Designs for Precision Medicine.”  Dr. Bliss highlighted two Adaptive Enrichment Clinical Trials performed by Veristat as case studies for how to select and execute an enrichment clinical trial design.

In a world of precision medicine, biomarker-driven clinical trial designs are gaining attention in drug and biological agent research and development.  A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention (NIH).  The use of a biomarker-design can improve study efficiency by targeting specific populations and can allow for the focus of research and treatment on each individual patient rather than on an average population.

One subclass of biomarker-driven clinical trials are Enrichment Study Designs.  Enrichment is the prospective use of any patient characteristic (e.g., demographic, physiologic, historic, genetic, etc.) to obtain a study population in which the detection of a drug effect is more likely than it would be in an unselected population (FDA Guidance).  Enrichment designs can be combined with many other adaptive design techniques such as early stopping for futility and efficacy, sample size re-estimation, and focused studies on particular sub-populations of interest.

Many study designs are available for biomarker-driven research, each having different strengths.  The choice of the best study design depends, among other things, on the certainty of the biomarker as a predictive or prognostic factor, the expected difference in mechanism of action and overall effect of the test product among biomarker positive and the overall population, and the prevalence of the disease.  For example, in one case study of a rare oncology trial presented by Robin Bliss at the 2016 Duke – Industry Statistics Symposium, there was some preliminary evidence of a biomarker subpopulation that may impact the effectiveness of the novel treatment; however because the disease was so rare, recruitment in the targeted subpopulation would be difficult.  The study sponsor selected an adaptive enrichment study design which included a planned interim analysis to evaluate the treatment effect in the biomarker positive subjects and the overall population midway through the study.  At that interim analysis, predetermined decision rules were applied to select whether to continue the study with the overall population or with the biomarker positive subjects only.   In a second case study presented by Dr. Bliss, the study sponsor had strong confidence of a prognostic biomarker but was unsure of the treatment performance in the complementary population.   Here, a stratified enrichment study design was selected to allow comparisons between control and novel treatment in both subpopulations of the rare cancer.

These examples illustrate the complexities of selecting the optimal biomarker study design in order to determine a benefit to a biomarker positive or other subpopulation.


robin-blissRobin Bliss, PhD is a Manager of Biostatistics at Veristat who helps clients design and manage their clinical trials, both traditional and adaptive designs.   She is experienced working with regulatory agencies, particularly in helping them understand the advantages of applying adaptive designs for biomarker trials.

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