Phosphodiesterase-4: Selective and Dual-Specificity Inhibitors for the Therapy of Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Phosphodiesterase-4 isoenzymes have absolute specificity for cyclic adenosine-3',5'-monophosphate and are considered potential therapeutic targets for the treatment of chronic inflammatory disorders, such as chronic obstructive pulmonary disease, with small-molecule inhibitors. Several selective phosphodiesterase-4 inhibitors are in clinical trials of chronic obstructive pulmonary disease, including cilomilast and roflumilast. Despite some encouraging data from phase III clinical trials, the current generation of phosphodiesterase-4 inhibitors is hampered by a low therapeutic ratio. Indeed, a major obstacle is their propensity to evoke non-steroid-like side effects, of which nausea, diarrhea, abdominal pain, vomiting, and dyspepsia are the most common. In addition, a particularly worrying potential toxicity of phosphodiesterase-4 inhibitors, also shared by phosphodiesterase-3 inhibitors and other vasodilators, is arteritis/periarteritis. One potential means of improving the therapeutic ratio and safety of phosphodiesterase-4 inhibitors may lie in the development of compounds that have broader phosphodiesterase specificity. Of the 11 phosphodiesterase families that have been unequivocally identified, dual-specificity compounds that inhibit phosphodiesterase-4 and phosphodiesterase-1, phosphodiesterase-3, or phosphodiesterase-7 may offer the best opportunities to enhance clinical efficacy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".