Advancing Dose Escalation Strategies With Bayesian Modeling

A biotechnology sponsor developing a novel oncology therapy partnered with Veristat to design and execute a multicenter, open-label Phase 1/2 dose escalation and expansion study in advanced solid tumors. The primary objective was to determine the recommended Phase 2 dose (RP2D) while maintaining patient safety.

To overcome the limitations of conventional 3+3 designs, Veristat implemented a Bayesian continual reassessment method (CRM), allowing flexible cohort sizes, dynamic modeling of posterior toxicity probabilities, and real-time dose escalation, de-escalation, or re-escalation based on data from all dose-limiting toxicity–evaluable cycles. This adaptive approach reduced enrollment delays, mitigated the impact of non-evaluable participants, and supported timely safety review committee decisions. The Bayesian CRM enabled faster dose escalation with minimal toxicity risk, a more accurate multi-cycle safety profile, and reduced trial duration. This strategy advanced the study toward RP2D selection and demonstrated the advantages of Bayesian adaptive designs in early-phase oncology trials, including enhanced efficiency, improved safety oversight, and minimized patient exposure to suboptimal doses.