Phase 1 Oncology Clinical Trial Designs: Adaptive Strategies That Accelerate Milestones And De-Risk Regulatory Approval

By Fred Snikeris, Kurt Preugschat, and Paul Steven

Phase 1 oncology trials are undergoing a major transformation. Traditional 3+3 dose-escalation designs, long considered the standard, often fall short in today’s landscape of targeted therapies and immunotherapies. These older methods lack flexibility, slow timelines, and provide limited insight into optimal dosing. Regulatory initiatives like the FDA’s Project Optimus now demand robust dose optimization data, challenging sponsors to move beyond identifying the maximum tolerated dose (MTD) toward determining the optimal biological dose (OBD) that balances efficacy and safety.

Modern adaptive designs, such as Bayesian Optimal Interval (BOIN), Continuous Reassessment Method (CRM), and Bayesian Logistic Regression Model (BLRM), offer dynamic, data-driven approaches that improve patient allocation, accelerate dose escalation, and generate richer datasets for regulatory submissions. These designs enable real-time decision-making, integrate pharmacokinetic and biomarker data, and reduce the risk of under- or overdosing. While operational complexity and statistical expertise are required, the benefits include faster timelines, better patient outcomes, and stronger regulatory alignment.

For sponsors, selecting the right design is a strategic decision that impacts trial efficiency, data quality, and long-term success. Adaptive methodologies represent the future of oncology drug development, delivering speed, precision, and compliance in an increasingly competitive and regulated environment.