New drug targets for the treatment of gout arthritis: what’s new?
Bibliographic record
Abstract
INTRODUCTION: Gout arthritis (GA) is an intermittent inflammatory disease affecting approximately 10% of the worldwide population. Symptomatic phases (acute flares) are timely spaced by asymptomatic periods. During an acute attack, redness, joint swelling, limited movement, and excruciating pain are common symptoms. However, the current available therapies are not fully effective in reducing symptoms and offer numerous side effects. Therefore, unveiling new drug targets and effector molecules are required in developing novel GA therapeutics. AREAS COVERED: This review discusses the pathophysiological mechanisms of GA and explores potential pharmacological targets to ameliorate disease outcome. In addition, we listed promising pre-clinical studies demonstrating effector molecules with therapeutical potential. Among those, we emphasized the importance of natural products, including traditional Chinese medicine formulas and their multitarget mechanisms of action. EXPERT OPINION: In our search, we observed that there is a massive gap between pre-clinical and clinical knowledge. Only a minority (4.4%) of clinical trials aimed to intervene by applying natural products or current hot targets described herein. In this sense, we envisage four possibilities for GA therapeutics, which include the repurposing of existing therapies, ALX/FPR2 agonism for improvement in disease outcome, the use of multitarget drugs (e.g. natural products), and targeting the neuroinflammatory component of GA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".