Unlocking the potential of RNA-based therapeutics in the lung: current status and future directions
Bibliographic record
Abstract
Awareness of RNA-based therapies has increased after the widespread adoption of mRNA vaccines against SARS-CoV-2 during the COVID-19 pandemic. These mRNA vaccines had a significant impact on reducing lung disease and mortality. They highlighted the potential for rapid development of RNA-based therapies and advances in nanoparticle delivery systems. Along with the rapid advancement in RNA biology, including the description of noncoding RNAs as major products of the genome, this success presents an opportunity to highlight the potential of RNA as a therapeutic modality. Here, we review the expanding compendium of RNA-based therapies, their mechanisms of action and examples of application in the lung. The airways provide a convenient conduit for drug delivery to the lungs with decreased systemic exposure. This review will also describe other delivery methods, including local delivery to the pleura and delivery vehicles that can target the lung after systemic administration, each providing access options that are advantageous for a specific application. We present clinical trials of RNA-based therapy in lung disease and potential areas for future directions. This review aims to provide an overview that will bring together researchers and clinicians to advance this burgeoning field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".