A resource of potential drug targets and strategic decision‐making for obstructive sleep apnoea pharmacotherapy
Bibliographic record
Abstract
There is currently no pharmacotherapy for obstructive sleep apnoea (OSA) but there is no principled a priori reason why there should not be one. This review identifies a rational decision-making strategy with the necessary logical underpinnings that any reasonable approach would be expected to navigate to develop a viable pharmacotherapy for OSA. The process first involves phenotyping an individual to quantify and characterize the critical predisposing factor(s) to their OSA pathogenesis and identify, a priori, if the patient is likely to benefit from a pharmacotherapy that targets those factors. We then identify rational strategies to manipulate those critical predisposing factor(s), and the barriers that have to be overcome for success of any OSA pharmacotherapy. A new analysis then identifies candidate drug targets to manipulate the upper airway motor circuitry for OSA pharmacotherapy. The first conclusion is that there are two general pharmacological approaches for OSA treatment that are of the most potential benefit and are practically realistic, one being fairly intuitive but the second perhaps less so. The second conclusion is that after identifying the critical physiological obstacles to OSA pharmacotherapy, there are current therapeutic targets of high interest for future development. The final analysis provides a tabulated resource of 'druggable' targets that are relatively restricted to the circuitry controlling the upper airway musculature, with these candidate targets being of high priority for screening and further study. We also emphasize that a pharmacotherapy may not cure OSA per se, but may still be a useful adjunct to improve the effectiveness of, and adherence to, other treatment mainstays.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".