Advances in Nasal Biopharmaceutics to Support Product Development and Therapeutic Needs
Bibliographic record
Abstract
Background/Objectives: Nasal biopharmaceutics is the scientific understanding of product and patient factors that determine the rate and extent of drug exposure following nasal administration. The authors considered whether current biopharmaceutics tools are fit for the current and future needs of nasal product development and regulation. Methods: The limitations of current methods were critically assessed, unmet needs were highlighted, and key questions were posed to guide future directions in biopharmaceutics research. Results: The emergence of physiologically based biopharmaceutics models for nasal delivery has the potential to drive the scientific understanding of nasal delivery. Simulations can guide formulation and device development, inform dose selection and generate mechanistic insights. Developments in modeling need to be complemented by advances in experimental systems, including the use of realistic or idealized nasal casts to estimate the regional deposition of nasal sprays and refined in vitro cell culture models to study nasal drug absorption and the influence of mucus. Similarly, improvements are needed to address the practicalities of using animals in non-clinical studies of nasal drug delivery, and greater clinical use of gamma scintigraphy/magnetic resonance imaging is recommended to measure the delivery and nasal retention of different formulations in humans. Conclusions: Nasal drug delivery is a rapidly growing field and requires advances in nasal biopharmaceutics to support product innovation. Key needs are (i) validated clinically relevant critical product attributes for product performance and (ii) established links between how patients administer the product and where in the nose it deposits and dissolves in order to act or be absorbed, leading to its desired clinical effect.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".