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Related Concept Videos

Drugs for Treatment of Crohn's Disease in IBD Using Biologic Agents: Anti-TNF01:24

Drugs for Treatment of Crohn's Disease in IBD Using Biologic Agents: Anti-TNF

227
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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Drugs for Treatment of Crohn's Disease in IBD Using Immunomodulatory Agents01:29

Drugs for Treatment of Crohn's Disease in IBD Using Immunomodulatory Agents

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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...
263
Drugs for Treatment of Crohn's Disease in IBD Using Glucocorticoids01:21

Drugs for Treatment of Crohn's Disease in IBD Using Glucocorticoids

199
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...
199

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Related Experiment Video

Updated: Sep 6, 2025

Mechanistic Insight into the Development of TNBS-Mediated Intestinal Fibrosis and Evaluating the Inhibitory Effects of Rapamycin
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Development of a Machine Learning Model to Predict Non-Durable Response to Anti-TNF Therapy in Crohn's Disease Using

Soo Kyung Park1,2, Yea Bean Kim3, Sangsoo Kim3

  • 1Division of Gastroenterology, Department of Internal Medicine and Inflammatory Bowel Disease Center, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University, Seoul 03181, Korea.

Journal of Personalized Medicine
|June 24, 2022
PubMed
Summary
This summary is machine-generated.

Predicting non-durable response (NDR) to anti-tumor necrosis factor (anti-TNF) therapy in inflammatory bowel disease (IBD) is crucial. Machine learning models using imputed gene expression effectively predicted NDR in Crohn

Keywords:
Crohn’s diseaseanti-TNFgenetic featuresgenotype

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Area of Science:

  • Genetics and Genomics
  • Immunology
  • Machine Learning in Medicine

Background:

  • Approximately 50% of inflammatory bowel disease (IBD) patients do not respond durably to anti-tumor necrosis factor (anti-TNF) therapies.
  • The precise mechanisms underlying non-durable response (NDR) to anti-TNF agents in IBD remain poorly understood.
  • Identifying predictive biomarkers for NDR is essential for optimizing treatment strategies in IBD.

Purpose of the Study:

  • To develop and validate machine learning models for predicting non-durable response (NDR) to anti-TNF therapy in IBD patients.
  • To investigate the utility of imputed gene expression profiles as predictive features for NDR.
  • To identify key genetic factors associated with NDR to anti-TNF treatment in Crohn's disease (CD).

Main Methods:

  • Genome-wide genotype data from 234 Crohn's disease patients receiving their first anti-TNF therapy were utilized.
  • Gene expression values were imputed from genotype data and combined with clinical parameters.
  • Logistic regression and machine learning models were trained to predict non-durable response (NDR) status.

Main Results:

  • Machine learning models incorporating imputed gene expression features demonstrated effectiveness in predicting NDR to anti-TNF agents.
  • The top predictive features for NDR were genetic, specifically the imputed expression levels of *DPY19L3*, *GSTT1*, and *NUCB1*.
  • Logistic regression analysis confirmed the association of *DPY19L3* and *GSTT1* (positive coefficients) and *NUCB1* (negative coefficient) with NDR, aligning with eQTL data.

Conclusions:

  • Imputed gene expression profiles derived from genome-wide genotype data serve as powerful predictors of non-durable response (NDR) to anti-TNF therapy in Crohn's disease.
  • Genetic factors, particularly *DPY19L3*, *GSTT1*, and *NUCB1* expression, play a significant role in determining treatment response durability.
  • This machine learning approach offers a promising strategy for personalized medicine in IBD by predicting anti-TNF treatment outcomes.