Development of New Peptide Vectors for the Transport of Therapeutic Across the blood–brain Barrier
Bibliographic record
Abstract
The blood-brain barrier (BBB) is formed by the special nature of the endothelial cells of the brain capillaries characterized by tight junctions between cells and a high expression of efflux pumps only allowing the brain access to nutrients necessary for cell survival and function. These properties of the BBB result in the incapacity of small and large therapeutic compounds to reach the brain at therapeutic concentrations. Various strategies are now being developed to enhance the amount and concentration of these compounds in the brain parenchyma. The development of new technologies such as peptide vectors has the potential to achieve the delivery of active agents in therapeutic concentrations across the BBB to treat brain diseases such as brain primary and metastatic cancers and neurodegenerative disorders. In this review, the design of new active peptides and development of new peptide vectors for drug brain delivery using physiological approaches will be addressed. A new chemical entity incorporating angiopep peptide in a small anticancer agent (paclitaxel) is now in clinical trials. It is the first of such designed agents to be validated for the treatment of human brain cancers and opens the door for such approaches.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".