Selective Phosphodiesterase Inhibitors for Psoriasis: Focus on Apremilast
Bibliographic record
Abstract
Phosphodiesterase (PDE) 4 participates in regulating the inflammatory response by degrading cyclic adenosine 3'5'-monophosphate (cAMP), a key second messenger. Inhibition of PDE4 increases the intracellular cAMP level, which in turn results in a reduction in inflammatory mediators and an increase in anti-inflammatory mediators. Immune-modulating effects of PDE4 inhibitors have been investigated in a number of inflammatory conditions, such as asthma, atopic dermatitis, chronic obstructive pulmonary disease, Behçet’s disease, psoriasis, and psoriatic arthritis. Apremilast, a selective PDE4 inhibitor, has been shown to block the production of pro-inflammatory cytokines (interferon-γ, tumor necrosis factor-α, interleukin [IL]-12, IL-17, and IL-23), which are the key players in the pathogenesis of psoriasis. Increased intracellular cAMP levels result in a range of anti-inflammatory effects on numerous cell lines. A decrease in proinflammatory activity has been shown to result in a reduced psoriasiform response in preclinical in vivo models of psoriasis, and reduction of biologic activity in a pilot study in humans. The efficacy and safety of apremilast in the treatment of psoriasis have been demonstrated in phase II and III clinical trials. Apremilast demonstrated efficacy in reducing the severity of moderate to severe plaque psoriasis. Treatment with apremilast was well tolerated, with generally mild gastrointestinal complaints, which occurred early in the course of the treatment and resolved over time, and there was no requirement for laboratory test monitoring. These results make apremilast an attractive therapeutic option for plaque psoriasis.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".