Dose Optimization Design using Patient-Reported Outcomes via Bayesian Additive Regression Trees

Music City Center 

In drug development, dose optimization is crucial and challenging due to the inherent variability and exploratory nature in early phase trials. It requires careful evaluation of dose-response and toxicity to ensure that the treatment is effective and safe while maintaining an acceptable level of tolerability. The FDA's Project Optimus Initiative highlights the importance of refined dose optimization strategies. To address this challenge, we propose the Dose Optimization Design using Patient-Reported Outcomes via Bayesian Additive Regression Trees (DOD-PRO-BART). This innovative approach integrates patient-reported outcomes with clinician-reported toxicity and efficacy data, enabling a more personalized and patient-centered method in both dose escalation and dose randomization. DOD-PRO-BART provides a comprehensive assessment of treatment effects, improving our understanding of patient responses to different dosage levels. Our simulation study illustrates that the proposed method can substantially improve the optimal dose selection by integrating patient-reported data along with clinician-reported toxicity and efficacy data.

Dose optimization

Dose randomization

Patient-reported outcomes

Bayesian additive regression trees 

Biopharmaceutical Section