New therapies under development for psoriasis treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: An improved understanding of the psoriasis pathogenesis has provided new insights into potential new therapeutic targets, which has positively influenced the development of novel therapies. This monograph reviews recent clinical trials concerning new small molecules and biotech products under investigation for plaque psoriasis treatment. Emphasis is placed on mechanism of action, efficacy and adverse effects of these new agents. RECENT FINDINGS: Recent literature has shown that there are several new drugs under development for psoriasis treatment including new A3 adenosine receptor agonists, biologics like anti-tumor necrosis factor, anti-interleukin-17, anti-interleukin-12/23 and anti-interleukin-17 receptor agents, as well as Janus kinase and phosphodiesterase 4 inhibitors, among others. Although clinical trials were too short for predicting the real long-term safety of these treatments, other studies longer than those presently available are expected in the future. SUMMARY: On the basis of novel advances in psoriasis therapy, treatment paradigms could change in the following years. However, the real contribution of these new drugs to the antipsoriatic therapeutic armamentarium still needs to be established.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".