Prediction of infliximab and anti-drug antibody concentrations in patients with inflammatory bowel disease using machine learning models with real-world data from a prospective cohort study

Abstract

Background: Although population pharmacokinetic models are the standard approach for identifying inter-individual variability and optimizing infliximab concentration, their development and validation are complex and time-consuming. Therefore, this study aimed to develop and evaluate machine learning (ML) models to predict infliximab and anti-drug antibody (ADA) concentrations in patients with inflammatory bowel disease (IBD) receiving maintenance infliximab therapy. Methods: A total of 1,806 infliximab and ADA concentration measurements were prospectively collected from 149 IBD patients. Recurrent neural networks (RNN)-based models, including long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, as well as regression-based models such as Elastic Net, Support Vector Regression, Random Forest (RF), and extreme gradient boosting (XGBoost), were developed. Recursive multi-step prediction was applied to evaluate short-term forecasting performance. Results: RF outperformed in predicting infliximab concentrations, and XGBoost yielded the best performance in predicting ADA levels (2-fold accuracy, 86.67% and 96.67%, respectively). The infliximab prediction model maintained acceptable accuracy up to two recursive predictions steps but exhibited a notable performance decline at the third step. In contrast, the ADA model showed robust performance across all three recursive steps, maintaining 2-fold accuracy exceeding 96%. Conclusion: ML models were developed to predict infliximab and ADA concentrations, with RF and XGBoost showing the best performance for infliximab and ADA prediction, respectively. The ADA model demonstrated stable multi-step forecasting capability. These models may support individualized dosing strategies and reduce the need for frequent therapeutic drug monitoring in clinical practice. Copyright © 2026 Kim, Song, Hong, Kim, Kim, Chang and Kim.