Applying interpretable machine learning workflow to evaluate exposure–response relationships for large‐molecule oncology drugs

Abstract

Abstract The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well‐established for evaluating exposure–response (E–R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E–R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree‐based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E–R relationship using clinical trial datasets. The E–R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E–R relationships for impacting key dosing decisions in drug development.