Emerging concepts and challenges in the development of disease-modifying osteoarthritis drugs - a more refined

Zsuzsa Jenei-Lanzl1, Svenja Maurer2, Rolf E Brenner2

  • 1Dr Rolf M. Schwiete Research Unit for Osteoarthritis, Department of Trauma Surgery and Orthopedics, Goethe University Frankfurt, University Hospital, 60528, Frankfurt/Main, Germany.

PubMed

Insights

No disease-modifying osteoarthritis drugs (DMOADs) are approved despite research. This review analyzes why promising OA therapies fail in clinical trials, focusing on preclinical models, patient stratification, and outcome measures.

Area of Science:

  • Rheumatology
  • Pharmacology
  • Translational Medicine

Background:

  • Osteoarthritis (OA) is a prevalent degenerative joint disease with no approved disease-modifying drugs (DMOADs).
  • Numerous therapeutic strategies have failed in clinical trials, necessitating an analysis of underlying reasons.

Purpose of the Study:

  • To review and compare preclinical and clinical studies of emerging OA therapeutics.
  • To identify factors contributing to the high failure rate of DMOADs in clinical development.

Main Methods:

  • Comparative analysis of preclinical and clinical data for various therapeutic classes (antioxidants, senolytics, anti-inflammatories, Wnt inhibitors, growth factors, protease inhibitors).
  • Evaluation of limitations in common OA animal models.
  • Discussion of challenges in patient stratification and clinical outcome assessment.

Main Results:

  • Specific examples like NAC, UBX0101, Anakinra®, Lorecevivint®, and Sprifermin™ illustrate therapeutic approaches and their clinical outcomes.
  • Commonly used animal models exhibit limitations in predicting human OA progression and treatment response.
  • Challenges in defining OA endotypes/phenotypes and selecting appropriate clinical endpoints hinder trial success.

Conclusions:

  • The high failure rate of DMOADs stems from issues in preclinical models, patient selection, and outcome measurement.
  • Future DMOAD development requires improved animal models, robust patient stratification strategies, and validated clinical outcome parameters.

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