Emerging oligonucleotide therapies for asthma and chronic obstructive pulmonary disease
Bibliographic record
Abstract
Chronic respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) are disorders of the airways largely related to the presence of persistent inflammation. The approval of inhaled corticosteroids in the early 1970s pioneered a new age of therapy in treating chronic inflammatory airway diseases. This was the first time that an anti-inflammatory product was available to reduce the characteristic lung inflammation in airways and the associated obstruction, inflammation and hyper-responsiveness. Fast forward 40 years: corticosteroids are still an important therapeutic intervention; however, they exhibit limited use in moderate to severe asthma and COPD. Oligonucleotide therapies are an emerging class which include the antisense, the RNAi (siRNA and miRNA), the immunomodulatory, the aptamer and the decoy approaches. As these approaches are rather recent in the respiratory field, most are still early in development. Nevertheless, with limitations of current small molecule therapies and the hurdles faced with biologics, the use of oligonucleotides is relevant and the door is open to the development of this category of therapeutics. This review focuses on the major classes of oligonucleotides that are currently in late stage preclinical or clinical development for the treatment of asthma and COPD, and discusses the implications for their use as therapies for respiratory diseases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".