Inhalable spray-dried nano-in-microparticles encapsulating anandamide: a novel approach for the treatment of asthma
Bibliographic record
Abstract
-adrenoceptor agonists are widely established as effective therapeutics for asthma treatment, their use can be limited by systemic side effects and the potential for time-dependent desensitization. As a possible alternative, the endocannabinoid anandamide recently gathered attention due to its bronchodilatory effect. However, anandamide exhibits several unfavorable physicochemical properties, in particular strong sensitivity to temperature and light as well as poor aqueous solubility. To overcome these challenges, we developed an inhalable dry powder formulation composed of anandamide-loaded amphiphilic cyclodextrin nanoparticles, co-spray-dried with mannitol as stabilizing excipient. The resulting nano-in-microparticles exhibit a spherical, regular morphology, favorable size (1.24 ± 0.09µm) as well as aerodynamic properties suitable for targeted bronchial application (mass median aerodynamic diameter: 3.63 ± 0.03µm). Compared to pure mannitol particles, the novel carrier system encapsulates an almost three-fold higher drug load. Further physicochemical characterization proved the re-dispersibility and stability of the encapsulated nanocarriers after spray drying. Application onto primary human bronchial cells verified biocompatibility and time-dependent particle uptake of the formulation. pH-dependent intracellular release resulted in highly increased prostaglandin E2 secretion indicating a potential anti-asthmatic effect. In conclusion, the formulation unites drug protection with favorable aerosolization performance for targeted bronchial delivery. Overcoming the obstacle of anandamide degradation, the nano-in-microparticle system offers a novel approach for encapsulation of instable and sensitive drugs bearing great potential for the future treatment of lung 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".