Emerging Oral Therapies for the Treatment of Psoriasis: A Review of Pipeline Agents
Bibliographic record
Abstract
The introduction of biologic agents for the treatment of psoriasis has revolutionized the current treatment landscape, targeting cytokines in the interleukin (IL)-23/IL-17 pathway and demonstrating strong efficacy and safety profiles in clinical trials. These agents however are costly, are associated with a risk of immunogenicity, and require administration by intravenous or subcutaneous injection, limiting their use among patients. Oral therapies, specifically small molecule and microbiome therapeutics, have the potential to be more convenient and cost-effective agents for patients and have been a focus of development in recent years, with few targeted oral medications available for the disease. In this manuscript, we review pipeline oral therapies for psoriasis identified through a search of ClinicalTrials.gov (30 June 2022-1 October 2023). Available preclinical and clinical trial data on each therapeutic agent are discussed. Small molecules under development include tumor necrosis factor inhibitors, IL-23 inhibitors, IL-17 inhibitors, phosphodiesterase-4 inhibitors, Janus kinase inhibitors, A3 adenosine receptor agonists, and sphingosine-1-phosphate receptor 1 agonists, several of which are entering phase III trials. Oral microbials have also demonstrated success in early phase studies. As new oral therapies emerge for the treatment of psoriasis, real-world data and comparative trials are needed to better inform their use among patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.004 | 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".