LAUSR

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 used to explore dosing strategies at Infusions 1, 3, and 4. Doses ranged from 1 to 10 mg/kg at Infusion 1 and from 1 to 20 mg/kg thereafter, with intervals of 4–12 weeks. The reward function prioritized achieving trough concentrations of 18–26 μg/mL before Infusion 3 and 5–10 μg/mL before Infusions 4 and 5, while penalizing overtreatment and additional infusions. The DQN policy converged after 80,000 episodes, yielding target attainment probabilities (PTAs) of 92.9% and 98.4% at Infusions 4 and 5, respectively, in 1000 virtual patients. High doses (11–20 mg/kg) were selected in only 0.2% of cases. At Infusion 4, 66.8% of patients received an 8‐week interval, and 57.3% at Infusion 5. Retrospective real‐world validation showed that patients whose actual doses matched DQN recommendations had trough levels significantly closer to target ranges. These findings support the feasibility of using DQN‐based agents to enhance and automate infliximab individualized dosing in pediatric populations.