Identification and in vivo characterization of a brain-penetrating nanobody
Bibliographic record
Abstract
BACKGROUND: Preclinical models to determine blood to brain transport ability of therapeutics are often ambiguous. In this study a method is developed that relies on CNS target-engagement and is able to rank brain-penetrating capacities. This method led to the discovery of an anti-transferrin receptor nanobody that is able to deliver a biologically active peptide to the brain via receptor-mediated transcytosis. METHODS: Various nanobodies against the mouse transferrin receptor were fused to neurotensin and injected peripherally in mice. Neurotensin is a neuropeptide that causes hypothermia when present in the brain but is unable to reach the brain from the periphery. Continuous body temperature measurements were used as a readout for brain penetration of nanobody-neurotensin fusions after its peripheral administration. Full temperature curves were analyzed using two-way ANOVA with Dunnett multiple comparisons tests. RESULTS: One anti-transferrin receptor nanobody coupled to neurotensin elicited a drop in body temperature following intravenous injection. Epitope binning indicated that this nanobody bound a distinct transferrin receptor epitope compared to the non-crossing nanobodies. This brain-penetrating nanobody was used to characterize the in vivo hypothermia model. The hypothermic effect caused by neurotensin is dose-dependent and could be used to directly compare peripheral administration routes and various nanobodies in terms of brain exposure. CONCLUSION: This method led to the discovery of an anti-transferrin receptor nanobody that can reach the brain via receptor-mediated transcytosis after peripheral administration. This method could be used to assess novel proteins for brain-penetrating capabilities using a target-engaging readout.
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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 | 0.000 |
| 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".