Machine Learning Modeling for Predicting Infliximab Pharmacokinetics in Pediatric and Young Adult Patients With Crohn Disease: Leveraging Ensemble Modeling With Synthetic and Real-World Data.

ABSTRACT:

Background

Predicting infliximab pharmacokinetics (PK) is essential for optimizing individualized dosing in pediatric patients with Crohn disease (CD). Machine learning (ML) has emerged as a tool for predicting drug exposure; however, its development typically requires large datasets. This study aimed to develop an ML model for infliximab PK prediction by leveraging population PK model-based synthetic and real-world data.

Methods

An initial ML model was trained using the XGBoost algorithm with synthetic infliximab concentration data (n = 560,000) generated from an established pediatric PK model. The prediction errors were assessed using real-world data, including 292 plasma concentrations from 93 pediatric and young adult patients with CD. A second XGBoost model, incorporati

SUBMITTER: Irie K 

PROVIDER: S-EPMC12856564 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature