Targets for obstructive sleep apnea pharmacotherapy: principles, approaches, and emerging strategies
Bibliographic record
Abstract
INTRODUCTION: Obstructive sleep apnea (OSA) is a common and serious breathing disorder. Several pathophysiological factors predispose individuals to OSA. These factors are quantifiable, and modifiable pharmacologically. AREAS COVERED: Four key pharmacotherapeutic targets are identified and mapped to the major determinants of OSA pathophysiology. PubMed and Clinicaltrials.gov were searched through April 2023. EXPERT OPINION: Target #1: Pharyngeal Motor Effectors. Increasing pharyngeal muscle activity and responsivity with noradrenergic-antimuscarinic combination is central to recent breakthrough OSA pharmacotherapy. Assumptions, knowledge gaps, future directions, and other targets are identified. #2: Upper Airway Sensory Afferents. There is translational potential of sensitizing and amplifying reflex pharyngeal dilator muscle responses to negative airway pressure via intranasal delivery of new potassium channel blockers. Rationales, advantages, findings, and potential strategies to enhance effectiveness are identified. #3: Chemosensory Afferents and Ventilatory Control. Strategies to manipulate ventilatory control system sensitivity by carbonic anhydrase inhibitors are supported in theory and initial studies. Intranasal delivery of agents to stimulate central respiratory activity are also introduced. #4: Sleep-Wake Mechanisms. Arousability is the fourth therapeutic target rationalized. Evolving automated tools to measure key pathophysiological factors predisposing to OSA will accelerate pharmacotherapy. Although not currently ready for general clinical settings, the identified targets are of future promise.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".