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Tumor Necrosis Factor (TNF), a proinflammatory cytokine, contributes significantly to the inflammation seen in Crohn's disease. It exists as soluble TNF and membrane-bound TNF, with actions mediated through TNF receptors (TNFR). TNFR activation leads to the release of proinflammatory cytokines, T-cell activation, collagen production, and leukocyte migration, all contributing to inflammation in Crohn's disease. Anti-TNF monoclonal antibodies, namely infliximab (Remicade), adalimumab...
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Crohn's disease is an inflammatory bowel disorder marked by chronic inflammation of the GI tract. Various treatment strategies for Crohn's disease are employed, such as immunomodulatory agents, glucocorticoids, and biologics or anti-TNF therapy. Azathioprine (Imuran), a commonly used immunomodulatory drug for Crohn's disease, is converted in the body to mercaptopurine, which inhibits purine biosynthesis and cell proliferation. Both are utilized in severe cases of Inflammatory Bowel...
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Upon diagnosis, managing Inflammatory Bowel Disease (IBD) involves addressing several crucial aspects. The primary goals include resting the bowel, correcting malnutrition, and providing symptomatic relief. Resting the bowel may consist of medications to reduce inflammation and promote healing. Correcting malnutrition is essential, often requiring dietary adjustments and nutritional supplements. Symptomatic relief aims to ease pain, diarrhea, and other discomforts in IBD.
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Glucocorticoids, a class of anti-inflammatory drugs, are pivotal in treating moderate to severe Crohn's disease by inducing remission. They exhibit their anti-inflammatory action by inhibiting the production of inflammatory cytokines such as tumor necrosis factor (TNF)-α, interleukin (IL)-1, and chemokines like IL-8. In addition, they reduce the expression of inflammatory cell adhesion molecules and inhibit gene transcription of nitric oxide synthase, phospholipase A2, cyclooxygenase-2...
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Introduction
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Crohn's disease
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Model-informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease.

Kei Irie1, Phillip Minar2,3, Jack Reifenberg4

  • 1Division of Translational and Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Biorxiv : the Preprint Server for Biology
|August 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Q-Network (DQN) to automate personalized infliximab dosing for Crohn's disease patients, improving treatment efficiency. The AI model achieved high target attainment rates, demonstrating its potential to enhance precision dosing strategies.

Keywords:
ChildrenDeep learningInflammatory bowel diseaseModel-informed precision dosingReinforcement learning

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Area of Science:

  • Pharmacology and Therapeutics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Model-informed precision dosing (MIPD) optimizes drug therapy but often requires manual, expertise-intensive simulations.
  • Reinforcement learning (RL) presents a scalable, automated approach to complex decision-making in medicine.

Purpose of the Study:

  • To develop and evaluate a model-informed Deep Q-Network (DQN) for personalized infliximab dosing in Crohn's disease.
  • To automate and enhance the efficiency of individualized dosing regimens.

Main Methods:

  • A DQN agent was trained in a simulated environment with a population pharmacokinetic (PK) model, inter-individual variability, and assay error.
  • Virtual patients were used to explore dosing strategies, with rewards prioritizing target trough concentrations and penalizing overtreatment.
  • The DQN policy was trained for 80,000 episodes to optimize dose selection and interval timing.

Main Results:

  • The DQN achieved high target attainment probabilities (92.9% at infusion 4, 98.4% at infusion 5) in 1,000 virtual patients.
  • Optimal dosing intervals (8 weeks) were frequently selected, with high doses (11-20 mg/kg) used sparingly (0.2%).
  • Retrospective validation showed DQN-recommended doses led to trough levels closer to target ranges in real-world data.

Conclusions:

  • A DQN-based agent can effectively personalize infliximab dosing, enhancing precision and automation.
  • This approach shows feasibility for improving individualized drug therapy in pediatric populations.
  • The study highlights the potential of AI in optimizing complex treatment regimens like infliximab therapy.