Receptor-mediated transcytosis for brain delivery of therapeutics: receptor classes and criteria
Bibliographic record
Abstract
The delivery of therapeutics into the brain is highly limited by the blood-brain barrier (BBB). Although this is essential to protect the brain from potentially harmful material found in the blood, it poses a great challenge for the treatment of diseases affecting the central nervous system (CNS). Substances from the periphery that are required for the function of the brain must rely on active mechanisms of entry. One such physiological pathway is called receptor-mediated transcytosis (RMT). In this process, ligands bind to specific receptors expressed at the luminal membrane of endothelial cells composing the BBB leading to the internalization of the receptor-ligand complex into intracellular vesicles, their trafficking through various intracellular compartments and finally their fusion with the abluminal membrane to release the cargo into the brain. Targeting such RMT receptors for BBB crossing represents an emerging and clinically validated strategy to increase the brain permeability of biologicals. However, the choice of an appropriate receptor is critical to achieve the best selectivity and efficacy of the delivery method. Whereas the majority of work has been focused on transferrin (Tf) receptor (TfR), the search for novel receptors expressed in brain endothelial cells (BECs) that can deliver protein or viral vector cargos across the BBB has yielded several novel targets with diverse molecular/structural properties and biological functions, and mechanisms of transcytosis. In this review, we summarize well-studied RMT pathways, and explore mechanisms engaged in BBB transport by various RMT receptors. We then discuss key criteria that would be desired for an optimal RMT target, based on lessons-learned from studies on TfR and accumulating experimental evidence on emerging RMT receptors and their ligands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".