Emerging Technologies for Delivery of Biotherapeutics and Gene Therapy Across the Blood–Brain Barrier
Bibliographic record
Abstract
Antibody, immuno- and gene therapies developed for neurological indications face a delivery challenge posed by various anatomical and physiological barriers within the central nervous system (CNS); most notably, the blood-brain barrier (BBB). Emerging delivery technologies for biotherapeutics have focused on trans-cellular pathways across the BBB utilizing receptor-mediated transcytosis (RMT). 'Traditionally' targeted RMT receptors, transferrin receptor (TfR) and insulin receptor (IR), are ubiquitously expressed and pose numerous translational challenges during development, including species differences and safety risks. Recent advances in antibody engineering technologies and discoveries of RMT targets and BBB-crossing antibodies that are more BBB-selective have combined to create a new preclinical pipeline of BBB-crossing biotherapeutics with improved efficacy and safety. Novel BBB-selective RMT targets and carrier antibodies have exposed additional opportunities for re-targeting gene delivery vectors or nanocarriers for more efficient brain delivery. Emergence and refinement of core technologies of genetic engineering and editing as well as biomanufacturing of viral vectors and cell-derived products have de-risked the path to the development of systemic gene therapy approaches for the CNS. In particular, brain-tropic viral vectors and extracellular vesicles have recently expanded the repertoire of brain delivery strategies for biotherapeutics. Whereas protein biotherapeutics and bispecific antibodies enabled for BBB transcytosis are rapidly heading towards clinical trials, systemic gene therapy approaches for CNS will likely remain in research phase for the foreseeable future. The promise and limitations of these emerging cross-BBB delivery technologies are further discussed in this article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".