Strategies To Deliver Peptide Drugs to the Brain
Bibliographic record
Abstract
Neurological diseases such as neurodegeneration, pain, psychiatric disorders, stroke, and brain cancers would greatly benefit from the use of highly potent and specific peptide pharmaceuticals. Peptides are especially desirable because of their low inherent toxicity. The presence of the blood brain barrier (BBB), their short duration of action, and their need for parenteral administration limits their clinical use. However, over the past decade there have been significant advances in delivering peptides to the central nervous system. Angiopep peptides developed by Angiochem (Montreal, Canada), transferrin antibodies developed by ArmaGen (Santa Monica, USA), and cell penetrating peptides have all shown promise in delivering therapeutic peptides across the BBB after intravenous administration. Noninvasive methods of delivering peptides to the brain include the use of chitosan amphiphile nanoparticles for oral delivery and nose to brain strategies. The uptake of the chitosan amphiphile nanoparticles by the gastrointestinal epithelium is important for oral peptide delivery. Finally protecting peptides from plasma degradation is integral to the success of most of these peptide delivery strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.003 | 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".