Risankizumab for the treatment of active psoriatic arthritis in adults
Bibliographic record
Abstract
INTRODUCTION: Psoriatic arthritis (PsA) is a chronic rheumatic disease that displays a variety of clinical manifestations. Although new treatments have emerged over the last 2 decades, challenges remain in controlling inflammation in multiple PsA clinical domains. AREAS COVERED: Risankizumab, one of the biologic disease modification anti-rheumatic drugs (bDMARDs) that target the interleukin (IL)-23 p19 subunit, was recently approved for PsA worldwide. This review primarily highlights the recent clinical trials of risankizumab covering its physiological evaluation, patient-reported outcomes, and safety profiles in patients with PsA. We also provide evidence for anti-IL-23 therapies against extra-articular manifestations and axial symptoms of PsA. Furthermore, potential distinct efficacies and mechanisms of action in anti-IL-23 therapies are discussed. Overall, risankizumab is effective in a variety of clinical signs and symptoms of PsA regardless of prior bDMARDs experience. EXPERT OPINION: Accumulating evidence shows that anti-IL-23 drugs, including risankizumab, are promising treatments that can be used as first- or second-line therapies for PsA. However, multiple challenges remain, including confirming efficacy for axial symptoms and identifying the phenotype of specific patients who respond better to risankizumab than other drugs. Lastly, future data focusing on the long-term efficacy and safety of risankizumab beyond the 1-year observation period are also needed.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".