Phosphodiesterase 7A: A New Therapeutic Target for Alleviating Chronic Inflammation?
Bibliographic record
Abstract
Over the last fifteen years there has been much excitement in the idea that targeting phosphodiesterase (PDE) 4 with small molecule inhibitors could lead to the discovery of novel, steroid-sparing compounds with utility in treating a multitude of diseases associated with chronic inflammation. However, dose-limiting side effects, of which nausea and vomiting are the most common are worrisome, have hampered their clinical development. Indeed, a fundamental obstacle that still is to be overcome by the pharmaceutical industry is to make compounds that dissociate beneficial from the adverse events. Unfortunately, both of these activities of PDE4 inhibitors represents an extension of their pharmacology and improving the therapeutic ratio has proved to be a major challenge. Several strategies have been considered, with some degree of success, but compounds with an optimal pharmacophore still have not been reported. An alternative approach to targeting PDE4 is to inhibit other cAMP PDE families that are also expressed in immune and pro-inflammatory cells in the hope that the beneficial activity can be retained at the expense of side effects. One such candidate is PDE7A. In this article we review the literature on PDE7A and explore the possibility that selective small molecule inhibitors of this enzyme family could provide a novel approach to alleviate the inflammation that is associated with many inflammatory diseases including asthma, chronic obstructive pulmonary disease, atopic dermatitis, psoriasis, lupus, rheumatoid arthritis and multiple sclerosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".