Disease Modeling and External Model Evaluation Through Clinical Data Sharing Platform for HR+/HER2- Breast Cancer.

Predictive models for disease progression are valuable for clinical trial design and interpretation; however, suitable data are needed for the development of such models. This study aimed to develop a Tumor Growth Inhibition-Overall Survival (TGI-OS) model for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2 negative (HER2-) breast cancer using clinical trial data available through Vivli, a clinical trial data sharing platform. The CONFIRM study (Phase 3 study comparing fulvestrant 250 vs. 500 mg) was used for model development, and the PALOMA-3 and SANDPIPER Phase 3 studies (palbociclib and taselisib) were used for external model qualifications. Longitudinal tumor size profiles were first analyzed with the TGI model. The TGI-OS model, a parametric model linking TGI metrics and baseline predictors of survival outcomes, was then developed using data from the CONFIRM study and showed successful internal qualification, including the prediction of the survival difference between two dose groups. The TGI-OS model showed large underestimation for the OS for PALOMA-3; nevertheless, the predicted treatment effect (hazard ratio of OS) was in good agreement with the observation for both studies, suggesting its potential as a tool to support drug development decisions. While integrating shared clinical trial data from multiple sources, facilitated by platforms like Vivli, is crucial for advancing predictive modeling efforts, caution should be exercised when such models are applied for new studies, especially when there are breakthroughs in the treatment landscape.