Machine Learning-based Quantification of Patient Factors Impacting Remission in Patients with Ulcerative Colitis: Insights from Etrolizumab Phase III Clinical Trials.

Etrolizumab, an investigational anti-β7 integrin monoclonal antibody, has undergone evaluation for safety and efficacy in phase 3 clinical trials on patients with moderate to severe ulcerative colitis (UC). Etrolizumab was terminated since mixed efficacy results were shown in the induction and maintenance phase in UC patients. In this post-hoc analysis, we characterized the impact of explanatory variables on the probability of remission using XGBoost machine learning (ML) models alongside with the SHapley Additive exPlanations (SHAP) framework for explainability. We employed patient-level data encompassing demographics, physiology, disease history, clinical questionnaires, histology, serum biomarkers, and etrolizumab drug exposure to develop ML models aimed at predicting remission. Baseline covariates and early etrolizumab exposure at week 4 in the induction phase were utilized to develop an induction ML model, while covariates from the end of the induction phase and early etrolizumab exposure at week 4 in the maintenance phase were used to develop a maintenance ML model. Both the induction and maintenance ML models exhibited good predictive performance, achieving an AUROC of 0.74±0.03 and 0.75±0.06 (mean±std), respectively. Compared to placebo, the highest tertile of etrolizumab exposure contributed to 15.0% (95% CI: [9.7, 19.9]) and 17.0% (95% CI: [8.1, 26.4]) increases in remission probability in the induction and maintenance phases, respectively. Additionally, the key covariates that predicted remission were CRP, MAdCAM-1 and stool frequency for the induction phase and WBCs, fecal calprotectin and age for the maintenance phase. These findings hold significant implications for establishing stratification factors in the design of future clinical trials.