Advancing Drug Repurposing for Rheumatoid Arthritis: Integrating Protein-Protein Interaction, Molecular Docking, and...

Area of Science:

• Computational drug discovery
• Network pharmacology and molecular dynamics simulations
• Drug repurposing for autoimmune diseases

Background:

• Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease causing joint destruction, morbidity, and mortality.
• Current RA treatments like NSAIDs, DMARDs, and biologics have limitations and side effects.
• There is a need for novel therapeutic agents with improved efficacy and safety profiles.

Purpose of the Study:

• To explore drug repurposing for identifying novel rheumatoid arthritis (RA) therapeutics.
• To utilize existing FDA-approved drugs as potential treatments for RA.
• To apply an integrated computational strategy for drug candidate prioritization.

Main Methods:

• Constructed a protein-protein interaction (PPI) network from 2723 RA-associated genes.
• Identified five central targets: TNF-α, IL-6, IL-1β, STAT3, and AKT1.
• Screened 2637 FDA-approved drugs, followed by molecular docking and 100 ns molecular dynamics simulations for validation.

Main Results:

• Identified Rifampicin, Telmisartan, Danazol, and Pimozide as promising drug repurposing candidates for RA.
• These candidates demonstrated strong binding affinities and stable complex formation with key RA targets (TNF-α, IL-6, IL-1β, STAT3).
• A sequential funnel approach integrating network data and simulations prioritized high-confidence drug candidates.

Conclusions:

• Repurposing FDA-approved drugs offers a cost-effective and time-efficient strategy for discovering new RA therapies.
• This study highlights the potential of targeting key RA proteins with existing medications.
• The identified drug candidates warrant further investigation for clinical efficacy in rheumatoid arthritis treatment.