An overview of in vitro and in vivo techniques for characterization of intranasal protein and peptide formulations for brain targeting
Bibliographic record
Abstract
The surge in neurological disorders necessitates innovative strategies for delivering active pharmaceutical ingredients to the brain. The non-invasive intranasal route has emerged as a promising approach to optimize drug delivery to the central nervous system by circumventing the blood-brain barrier. While the intranasal approach offers numerous advantages, the lack of a standardized protocol for drug testing poses challenges to both in vitro and in vivo studies, limiting the accurate interpretation of nasal drug delivery and pharmacokinetic data. This review explores the in vitro experimental assays employed by the pharmaceutical industry to test intranasal formulation. The focus lies on understanding the diverse techniques used to characterize the intranasal delivery of drugs targeting the brain. Parameters such as drug release, droplet size measurement, plume geometry, deposition in the nasal cavity, aerodynamic performance and mucoadhesiveness are scrutinized for their role in evaluating the performance of nasal drug products. The review further discusses the methodology for in vivo characterization in detail, which is essential in evaluating and refining drug efficacy through the nose-to-brain pathway. Animal models are indispensable for pre-clinical drug testing, offering valuable insights into absorption efficacy and potential variables affecting formulation safety. The insights presented aim to guide future research in intranasal drug delivery for neurological disorders, ensuring more accurate predictions of therapeutic efficacy in clinical contexts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".