Concentration monitoring and dose optimization for infliximab in Crohn’s disease patients: a machine learning-based covariate ensemble model

Background: Trough concentration of Infliximab (IFX) was associated with its efficacy and toxicity. However, traditional therapeutic drug monitoring often results in suboptimal outcomes because dose adjustments are delayed. We aimed to develop and validate a machine learning (ML) framework to enable real-time trough concentration prediction (pre-infusion point-of-care prediction) and individualized dosing for Crohn's disease (CD) patients. Methods: Leveraging data from a retrospective cohort of 274 Chinese CD patients (460 samples), we dichotomized outcomes based on an IFX trough concentration threshold (≥3 μg/mL). After a systematic evaluation of nine nonlinear ML algorithms, we identified four optimal predictive models. These were subsequently integrated into a soft-voting ensemble classifier to improve predictive performance for individualized IFX monitoring. SHAP analysis was employed to identify key predictors, followed by prospective external validation of dose adjustment strategies. Results: The ensemble model showed optimal discrimination on the test set (AUC = 0.829, accuracy=0.826, sensitivity=0.778, specificity=0.846, F1 score=0.724) and maintained robust clinical net benefits within a threshold range of 0.48 to 0.62. Five-fold cross-validation confirmed model stability (AUC = 0.850 ± 0.049), and the external validation further demonstrated strong generalizability (AUC = 0.800). SHAP analysis revealed anti-drug antibodies (ADA, 22.8%) and fibrinogen (Fg, 21.4%) as dominant covariates, followed by IFX dose (8.2%). Compared to traditional empirical dosing regimens, the model recommends a more cautious strategy that prioritizes the minimum effective dose to ensure concentrations within the therapeutic window. Conclusion: We developed and validated an interpretable ensemble model that can dynamically monitor drug concentrations and optimize personalized dosing of IFX therapy in CD patients, demonstrating the potential of an ML-based approach to enhance treatment efficacy and safety.