In the era of precision medicine, biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. Adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. In this session, I will discuss the statistical challenge of application of biomarker subgroup selection in trial design perspective and how to optimize design to support the clinical trial to maximize the overall probability of success. With a group-sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type-I-error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. The suitable designs will be discussed and compared through simulation illustration. A paper of our work has been recently published in the journal of Statistics in Biosciences.