Antipsoriatic Drug Development: Challenges and New Emerging Therapies
Bibliographic record
Abstract
Psoriasis is a chronic recurring skin disorder affecting up to 2% of the world's population. Psoriatic lesions are generally visible, leading to significant emotional and social disabilities for patients. In the context of psoriasis, the orchestrated interplay between activated T cells, antigen-presenting cells and keratinocytes leads to the release of proinflammatory cytokines, chemokines and chemical mediators responsible for the perpetuation of this disease. Even though some therapies are available for psoriasis treatment, there is still no cure for this skin disorder and psoriatic patients are significantly unsatisfied, as demonstrated by recent worldwide surveys. Unlike other diseases, psoriasis does not have a generally accepted animal model, which complicates the successful introduction of new antipsoriatic drugs into clinical phases of development. Moreover, psoriasis affects infants, children and elderly patients which require long-term therapies. Thus, the development of new therapeutic approaches should consider multiple factors such as efficacy, dosing frequency, route of administration, toxicity as well as co-morbidities of patients. This article analyzes current challenges for the antipsoriatic drug development and reviews recent patent applications gathered from 2000 to 2011 for psoriasis treatment. Additionally, future perspectives for antipsoriatic drug development are summarized.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".