Nogo-A neutralization in the central nervous system with a blood-brain barrier-penetrating antibody
Bibliographic record
Abstract
The poor penetration of monoclonal antibodies (mAb) across the blood-brain barrier (BBB) impedes the development of regenerative therapies for neurological diseases. For example, Nogo-A is a myelin-associated protein highly expressed in the central nervous system (CNS) whose inhibitory effects on neuronal plasticity can be neutralized with direct administration of 11C7 mAb in CNS tissues/fluids, but not with peripheral administrations such as intravenous injections. Therefore, in the present study, we engineered a CNS-penetrating antibody against Nogo-A by combining 11C7 mAb and the single-chain variable fragment (scFv) of 8D3, a rat antibody binding transferrin receptor 1 (TfR) and mediating BBB transcytosis (11C7-scFv8D3). The binding of 11C7-scFv8D3 to Nogo-A and to TfR/CD71 was validated by capture ELISA and Biolayer Interferometry. After intravenous injection in mice, capture ELISA measurements revealed fast plasma clearance of 11C7-scFv8D3 concomitantly with brain and spinal cord accumulation at levels up to 19 fold as high as those of original 11C7 mAb. 11C7-scFv8D3 detection in the parenchyma indicated effective blood-to-CNS transfer. A single dose of 11C7-scFv8D3 induced stronger activation of the growth-promoting AkT/mTOR/S6 signaling pathway than 11C7 mAb or control antibody. Taken together, our results show that BBB-crossing 11C7-scFv8D3 engages Nogo-A in the mouse CNS and stimulates neuronal growth mechanisms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".