Identifying Compound Effect of Drugs on Rheumatoid Arthritis Treatment Based on the Association Rule and a Random...

Area of Science:

• Rheumatology
• Computational Biology
• Pharmacology

Background:

• Rheumatoid arthritis (RA) is a chronic autoimmune disease with unclear etiology.
• Current diagnostic methods rely on clinical signs, symptoms, and lab markers.
• There's a need for dynamic methods to evaluate RA medication efficacy.

Purpose of the Study:

• To apply a random walk model for evaluating traditional Chinese medicine (TCM) constituent compatibility and therapeutic efficacy in RA.
• To identify effective drug combinations for RA treatment using computational analysis.
• To develop a novel method for dynamically assessing treatment effectiveness.

Main Methods:

• Cluster analysis and association rule analysis were used to investigate commonly used RA drugs and identify constituent compatibilities.
• A random walk model was developed utilizing a database of 9,408 RA patient clinical records.
• Frequently administered medicines were grouped into three correlated sets for analysis.

Main Results:

• The random walk model demonstrated that specific drug combinations improved erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), C3, C4, and immunoglobulin A (IgA) levels more effectively than individual drugs.
• Analysis revealed complementary interactions among TCM constituents.
• Different combinations of TCM constituents yielded varied therapeutic effects on RA.

Conclusions:

• TCM constituents exhibit complementary actions in RA treatment.
• The random walk model provides a robust method for evaluating TCM efficacy and optimizing drug combinations for RA.
• This approach offers a dynamic way to assess and improve therapeutic strategies for rheumatoid arthritis.