Novel pharmacotherapies and breakthroughs in psoriatic arthritis treatment
Bibliographic record
Abstract
INTRODUCTION: Psoriatic arthritis (PsA) is a systemic inflammatory disease affecting joints, entheses, skin, nails, and spine, often accompanied by comorbidities such as obesity, metabolic syndrome, and cardiovascular disease. Despite therapeutic advances, progressive joint damage, functional impairment, and reduced quality of life remain major concerns. AREAS COVERED: This review highlights recent and emerging therapies in PsA, including newer biologic DMARDs (risankizumab and bimekizumab), tyrosine kinase 2 (TYK2) inhibitors, dual Janus Kinase (JAK) 1/TYK2 inhibitors, interleukin-23 receptor (IL-23 R)-targeted peptides, Affibody® molecules, and IL-17A/IL-17F-inhibiting nanobodies. Metabolic-targeted approaches, particularly glucagon-like peptide-1 receptor agonists (GLP-1RAs), offer potential benefits in obesity-driven disease. Evidence from randomized controlled trials (RCTs) and observational studies is summarized, encompassing efficacy across disease domains, safety profiles, and durability of response. Dual-pathway targeting for refractory cases is also discussed. EXPERT OPINION: Advances in mechanism-based and metabolic-targeted therapies enable more individualized PsA management, improving the likelihood of achieving treatment targets. Ongoing challenges include early recognition, treatment of refractory disease, and long-term safety assessment. Progress in patient-centered therapy selection and precision medicine is expected to enhance outcomes, reduce disease burden, and optimize healthcare resources. Emerging pharmacotherapies continue to expand the treatment landscape, supporting a shift toward more effective, durable, and personalized management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".