Model-Informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease.

ABSTRACT: Model-informed precision dosing (MIPD) utilizes pharmacokinetic/pharmacodynamic (PK/PD) models to optimize drug therapy. However, conventional MIPD often requires manual simulation and regimen selection, which are time-consuming and demand specialized expertise. Reinforcement learning (RL), in which an agent learns optimal decisions through iterative interactions with an environment, offers a scalable and automated alternative. In this study, we developed a model-informed Deep Q-Network (DQN) to personalize infliximab dosing for patients with Crohn's disease. The DQN was trained in a simulation environment incorporating a population PK model, inter-individual variability, and assay error. Virtual patients with randomly and independently sampled covariates from log-normal distributions were

SUBMITTER: Irie K 

PROVIDER: S-EPMC12816428 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature