Engineering nano-embedded microparticles as formulation platform for site-specific drug delivery to the respiratory tract
Bibliographic record
Abstract
Effective drug delivery to the lung often implies selective spatial deposition of the active molecule in a specific lung region, such as the bronchi for asthma therapy or the alveoli for combating infections of the deep lung. Depending on the site of action, platform formulations adaptable to spatio-selective delivery are valuable. A Design of Experiments approach was employed to systematically analyze the influence of selected spray drying parameters on mannitol particle characteristics. Additionally, one candidate was formulated with varying concentrations of leucine as an aerosolization enhancer. Based on their physicochemical characteristics, three different lead formulations – tailored for tracheobronchial, bronchial, and alveolar deposition – were identified for further nanoparticle encapsulation to develop a pulmonary delivery platform based on spray-dried amphiphilic cyclodextrin nano-in-microparticles. The formulations exhibited mean geometric particle diameters of 1.31 to 1.71 μm and favorable aerosolization performance, with mass median aerodynamic diameters of 4.69 ± 0.22, 3.79 ± 0.17 and 2.18 ± 0.55 μm, respectively. Physicochemical characterization of the incorporated nanoparticles revealed hydrodynamic diameters below 200 nm, narrow size distributions, and stable negative surface charges. Moreover, biocompatibility and time-dependent intracellular uptake of the formulations were verified using human in vitro models of bronchi and deep lung. The novel formulations can serve as an adaptive platform enabling targeted delivery of challenging drug candidates and advanced pulmonary therapeutics.
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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".