Cyclic nucleotide phosphodiesterases as drug targets
Bibliographic record
Abstract
Cyclic nucleotides are synthesized by adenylyl and/or guanylyl cyclase, and downstream of this synthesis, the cyclic nucleotide phosphodiesterase families (PDEs) specifically hydrolyze cyclic nucleotides. PDEs control cyclic adenosine-3',5'monophosphate (cAMP) and cyclic guanosine-3',5'-monophosphate (cGMP) intracellular levels by mediating their quick return to the basal steady state levels. This often takes place in subcellular nanodomains. Thus, PDEs govern short-term protein phosphorylation, long-term protein expression, and even epigenetic mechanisms by modulating cyclic nucleotide levels. Consequently, their involvement in both health and disease is extensively investigated. PDE inhibition has emerged as a promising clinical intervention method, with ongoing developments aiming to enhance its efficacy and applicability. In this comprehensive review, we extensively look into the intricate landscape of PDEs biochemistry, exploring their diverse roles in various tissues. Furthermore, we outline the underlying mechanisms of PDEs in different pathophysiological conditions. Additionally, we review the application of PDE inhibition in related diseases, shedding light on current advancements and future prospects for clinical intervention. SIGNIFICANCE STATEMENT: Regulating PDEs is a critical checkpoint for numerous (patho)physiological conditions. However, despite the development of several PDE inhibitors aimed at controlling overactivated PDEs, their applicability in clinical settings poses challenges. In this context, our focus is on pharmacodynamics and the structure activity of PDEs, aiming to illustrate how selectivity and efficacy can be optimized. Additionally, this review points to current preclinical and clinical evidence that depicts various optimization efforts and indications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".