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Updated: Nov 16, 2025

Determination of the Relative Potency of an Anti-TNF Monoclonal Antibody mAb by Neutralizing TNF Using an In Vitro Bioanalytical Method
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Determination of the Relative Potency of an Anti-TNF Monoclonal Antibody mAb by Neutralizing TNF Using an In Vitro Bioanalytical Method

Published on: September 16, 2017

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Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Ikram Mezghiche1,2, Hanane Yahia-Cherbal1,3, Lars Rogge1,4

  • 1Department of Immunology, Immunoregulation Unit, Institut Pasteur, Paris, France.

Expert Review of Clinical Immunology
|February 24, 2021
PubMed

Insights

Predicting patient response to biologic therapies for chronic inflammatory diseases (CIDs) remains challenging. Developing new bioinformatic approaches to combine multiple biomarkers may improve treatment predictions.

Area of Science:

  • Immunology
  • Pharmacology
  • Biomarker Discovery

Background:

  • Chronic inflammatory diseases (CIDs) significantly impact patient quality of life.
  • Biological therapies, like tumor-necrosis-factor (TNF) blockers, have advanced CID treatment.
  • Over a third of patients do not respond to TNF blockers, necessitating predictive tools.

Purpose of the Study:

  • To review recent studies on biomarkers for disease assessment and predicting therapeutic response to TNF blockers.
  • To focus on immune responses in spondyloarthritis (SpA), rheumatoid arthritis, and inflammatory bowel disease.
  • To explore the development of predictive biomarkers for treatment response in CIDs.

Main Methods:

  • PubMed literature search for recent studies on biomarkers and therapeutic response prediction.
  • Focus on TNF blockers' effects on immune responses in SpA, rheumatoid arthritis, and inflammatory bowel disease.
  • Analysis of current literature to draw conclusions on predictive biomarker development.

Main Results:

  • No validated biomarker currently exists for predicting treatment response in CIDs.
  • TNF blockers are effective for many, but a significant portion of patients do not benefit.
  • Understanding immune responses is crucial for improving treatment efficacy.

Conclusions:

  • Validated biomarkers for predicting treatment response in CIDs are currently unavailable.
  • Multidimensional biomarkers, integrating genetic, immunological, and environmental factors, are needed.
  • Bioinformatic modeling offers a promising approach to combine these biomarkers for improved prediction.

Keywords:
Spondyloarthritisankylosing spondylitisanti-TNF therapybiomarkerschronic inflammatory diseaseimmune responsesinflammatory bowel diseasepredictionrheumatoid arthritistranscriptometreatment response

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