Improving the Utility of a Dynorphin Peptide Analogue Using Mannosylated Glycoliposomes
Bibliographic record
Abstract
Peptide therapeutics offer numerous advantages in the treatment of diseases and disorders of the central nervous system (CNS). However, they are not without limitations, especially in terms of their pharmacokinetics where their metabolic lability and low blood-brain barrier penetration hinder their application. Targeted nanoparticle delivery systems are being tapped for their ability to improve the delivery of therapeutics into the brain non-invasively. We have developed a family of mannosylated glycoliposome delivery systems for targeted drug delivery applications. Herein, we demonstrate via in vivo distribution studies the potential of these glycoliposomes to improve the utility of CNS active therapeutics using dynantin, a potent and selective dynorphin peptide analogue antagonist of the kappa opioid receptor (KOR). Glycoliposomal entrapment protected dynantin against known rapid metabolic degradation and ultimately improved brain levels of the peptide by approximately 3-3.5-fold. Moreover, we linked this improved brain delivery with improved KOR antagonist activity by way of an approximately 30-40% positive modulation of striatal dopamine levels 20 min after intranasal administration. Overall, the results clearly highlight the potential of our glycoliposomes as a targeted delivery system for therapeutic agents of the CNS.
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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".