A Review of the Clinical Trial Landscape in Psoriasis: An Update for Clinicians
Bibliographic record
Abstract
As our understanding of the pathogenesis of psoriasis has evolved over the past two decades, so has the number of treatment options. The introduction of biologic agents targeting specific cytokines in the interleukin (IL)-23/IL-17 pathway has proven successful in promoting skin clearance among patients. However, their use is often limited owing to cost, parenteral administration, and possible reduced efficacy over time. Topical therapies have also seen limited advancement, with agents such as corticosteroids and vitamin D derivatives remaining the mainstay of treatment, despite side effects limiting their long-term use. New therapeutic agents are needed to improve disease management for patients. In this review, we summarize pipeline and recently approved therapies undergoing clinical trials for psoriasis during a 12-month search period (30 June 2021 to 30 June 2022) using ClinicalTrials.gov. New-generation biologics and oral small molecules in phase II or III development were included, and pivotal data identified through various search modalities (PubMed, conference presentations, etc.) evaluating each drug candidate will be discussed. Topical therapies will also be discussed in line with recent US Food and Drug Administration approvals. As new therapies continue to enter the treatment landscape, long-term data and comparative trials will be needed to better understand their place among existing therapeutic agents.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".