Widespread brain distribution and activity following i.c.v. infusion of anti‐β‐secretase (BACE1) in nonhuman primates
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The potential for therapeutic antibody treatment of neurological diseases is limited by poor penetration across the blood-brain barrier. I.c.v. delivery is a promising route to the brain; however, it is unclear how efficiently antibodies delivered i.c.v. penetrate the cerebrospinal spinal fluid (CSF)-brain barrier and distribute throughout the brain parenchyma. EXPERIMENTAL APPROACH: We evaluated the pharmacokinetics and pharmacodynamics of an inhibitory monoclonal antibody against β-secretase 1 (anti-BACE1) following continuous infusion into the left lateral ventricle of healthy adult cynomolgus monkeys. KEY RESULTS: Animals infused with anti-BACE1 i.c.v. showed a robust and sustained reduction (~70%) of CSF amyloid-β (Aβ) peptides. Antibody distribution was near uniform across the brain parenchyma, ranging from 20 to 40 nM, resulting in a ~50% reduction of Aβ in the cortical parenchyma. In contrast, animals administered anti-BACE1 i.v. showed no significant change in CSF or cortical Aβ levels and had a low (~0.6 nM) antibody concentration in the brain. CONCLUSION AND IMPLICATIONS: I.c.v. administration of anti-BACE1 resulted in enhanced BACE1 target engagement and inhibition, with a corresponding dramatic reduction in CNS Aβ concentrations, due to enhanced brain exposure to antibody.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".