Multi-target drug discovery for rheumatoid arthritis: a comprehensive computational approach using bioactive compounds
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is one of the most common inflammatory diseases in the world. Due to this importance, several drugs have been produced against effective targets in the pathogenesis of the disease. Considering various inflammatory pathways involved in RA, targeting each of these pathways alone cannot achieve sufficient improvement of the patients. Therefore, this study aimed to introduce safe natural product drug candidates screened in silico against several important protein targets (multi-targets) in the pathogenesis of RA. Tyrosine kinase 2 is a member of the Janus Kinase family, and due to its role in the signaling of numerous cytokines, its inhibition is considered an effective treatment option in inflammatory diseases. IL-6 is one of the most important innate immune cytokines that is secreted from activated macrophages in RA, and its level is directly related to the severity of joint destruction. Therefore, its inhibition plays an important role in improving the symptoms of RA. Also, the severity of the disease is directly related to the level of ACPA in the serum and synovial fluid of patients. Therefore, inhibiting its production by precluding the differentiation of B lymphocytes into plasma cells can improve the disease. In this study, after Virtual Screening, Molecular Docking, and Molecular Dynamics, we introduced Rutaecarpine, Hecogenin, Angustine, and Vomicine as new drug candidates for the treatment of RA. Rutaecarpine, Hecogenin, and Angustine inhibit all three targets with high affinity and stability, while Vomicine inhibits both TYK2 and IL-6 but not CD20. Future experimental studies can verify these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".