Multitargeted Approach Using Antisense Oligonucleotides for the Treatment of Asthma
Bibliographic record
Abstract
Asthma is characterized by inflammation and hyperresponsiveness related to the accumulation of inflammatory cells, particularly eosinophils, within the airways. We tested the hypothesis that a multitargeted approach is better than a single-targeted approach in a rat model of asthma. We simultaneously delivered oligonucleotides (ODNs) targeting the chemokine receptor CCR3 and the common beta chain subunit of the receptors for IL-3, IL-5, and GM-CSF at the time of ovalbumin challenge in sensitized Brown Norway rats. Fewer eosinophils were detected in bronchoalveolar lavage (BAL) of rats treated with both ODNs as compared to each ODN alone. Moreover, airway responsiveness to LTD(4) was significantly decreased at lower doses in the 2 ODN-treated groups compared to a single ODN. As ODN therapy has raised concerns of toxicity we therefore examined ODNs prepared with modified DNA bases, specifically 2'amino, 2'deoxyadenosine (DAP) in place of adenosine. In vivo, administration of individual DAP-ODN was efficacious in inhibiting airway hyperresponsiveness, whereas delivery of 2 DAP-ODNs (targeting CCR3 and common beta chain) reduced the influx not only of eosinophils but also lymphocytes and macrophages in the lungs of rats as compared to the unmodified ODNs. Blocking multiple inflammatory pathways simultaneously is more effective in preventing eosinophilia and airway hyperresponsiveness than inhibiting either pathway alone. The challenges associated with the development of a product containing two oligonucleotides in humans are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".