Predict PFS and OS using ORR for PD1/PDL1 combination therapy development

Oregon Convention Center 

In solid tumor therapy development, with early phase clinical efficacy objective response rate (ORR), deriving predictive models to median progression free survival (mPFS) and median overall survival (mOS) is important as it could optimize late phase trial design. In literature, there are only some ORR/mPFS/mOS association research with limited number of included trials. In this paper, we attempt to predict mPFS and mOS by ORR and optimize late phase trial design of PD1/PDL1 combination therapy. To include adequate eligible clinical trials, we derive comprehensive quantitative clinical trial landscape database by combining information from various sources. A tree-based machine learning regression model is derived to borrow strength and account for ORR/mPFS (ORR/mOS) relationship heterogeneity to ensure the predictive model is robust and has clear structure for interpretation. Over 1000 times cross validation, predictive MSE of proposed model is competitive to random forest, extreme gradient boosting, and superior to common additive and interaction regression models. Example application of proposed model on PD1/PDL1 combination therapy late phase trial POS evaluation is illustrated.

PD1 / PDL1 combination therapy development

Predict late phase clinical endpoint by early phase clinical endpoint

Quantitative clinical trial landscape data base

Tree based machine learning regression model

Borrow strength and account for heterogeneity

Clear structure for clinical interpretation and robustness 

Biopharmaceutical Section