Systematic Assessment of Strategies for Lung-targeted Delivery of MicroRNA Mimics
Bibliographic record
Abstract
There is considerable interest in the use of synthetic miRNA mimics (or inhibitors) as potential therapeutic agents in pulmonary vascular disease; however, the optimal delivery method to achieve high efficiency, selective lung targeting has not been determined.Here, we sought to investigate the relative merits of different lung-targeted strategies for delivering miRNA mimics in rats.Methods: Tissue levels of a synthetic miRNA mimic, cel-miR-39-3p (0.5 nmol in 50 µL invivofectamine/PBS vehicle) were compared in male rats (n=3 rats/method) after delivery by commonly used lung-targeting strategies including intratracheal liquid instillation (IT-L), intratracheal aerosolization with (IT-A V ) or without ventilator assistance (IT-A), intranasal liquid instillation (IN-L) and intranasal aerosolization (IN-A).Intravenous (IV; via jugular vein), intraperitoneal (IP) and subcutaneous (SC) delivery served as controls.Relative levels of cel-miR-39 were quantified by RT-qPCR.Results: At 2 h post delivery, IT-L showed the highest lung mimic level, which was significantly higher than levels achieved by all other methods (from ~10-to 10,000-fold, p<0.05).Mimic levels remained detectable in the lung 24 h after delivery, but were 10-to 100-fold lower.The intrapulmonary distribution of cel-miR-39 was comparable when delivered as either a liquid or aerosol, with evidence of mimic distribution to both the left and right lung lobes and penetration to distal regions.All lung-targeted strategies showed lung-selective mimic uptake, with mimic levels 10-to 100-fold lower in heart and 100to 10,000-fold lower in liver, kidney and spleen.In contrast, IV, SC and IP routes showed comparable or higher mimic levels in non-pulmonary tissues.Conclusions: miRNA uptake in the lungs differed markedly by up to 4 orders of magnitude, demonstrating that the choice of delivery strategy could have a significant impact on potential therapeutic outcomes in preclinical investigations of miRNA-based drug candidates.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".