Abstract
The majority of drugs currently used to treat rheumatoid arthritis (RA) act on a small number of immunomodulatory targets. We applied an integrative biomedical-informatics-based approach and in vivo testing to identify new drug candidates and potential therapeutic targets that could form the basis for future drug development in RA. A computational model of RA was constructed by integrating patient gene expression data, molecular interactions, and clinical drug-disease associations. Drug candidates were scored based on their predicted efficacy across these data types. Ten high-scoring candidates were subsequently screened in a collagen-induced arthritis model of RA. Treatment with exenatide, olopatadine, and TXR-112 significantly improved multiple preclinical endpoints, including animal mobility which was measured using a novel digital platform. These three drug candidates do not act on common RA therapeutic targets; however, links between known candidate pharmacology and pathological processes involved in RA suggest hypothetical mechanisms contributing to the observed efficacy.
Footnotes
↵* Email: laura{at}vium.com
** Email: aradin{at}twoXAR.com
- Acronyms and abbreviations
- RA
- Rheumatoid Arthritis
- FDA
- Food and Drug Administration
- DMARDs
- Disease-Modifying Anti-Rheumatic Drugs
- FDR
- False Discovery Rate
- FGFR
- Fibroblast Growth Factor Receptor
- PI3K
- Phosphoinositide 3-Kinase
- ECM
- Extracellular Matrix
- TTD
- Therapeutic Target Database
- FAERS
- FDA Adverse Event Reporting System
- CIA
- Collagen-Induced Arthritis
- DAI
- Digital Arthritis Index
- T2D
- Type 2 Diabetes
- TNF-α
- Tumor Necrosis Factor-alpha
- IL-6
- Interleukin-6
- GLP-1
- Glucagon-Like Peptide-1
- S100A12
- S100 calcium-binding protein A12
- GSEA
- Gene Set Enrichment Analysis
- IFA
- Incomplete Freud’s Adjuvant
- Dex.
- Dexamethasone