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 - Kim, Minjung; Song, Joo Hye; Hong, Sung Noh; Kim, Myeong Gyu; Kim, Eun Ran; Chang, Dong Kyung; Kim, Young-Ho (Frontiers in Pharmacology, 2026)
BackgroundAlthough 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.MethodsA 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.ResultsRF 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%.ConclusionML 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.