ANG4043, a Novel Brain-Penetrant Peptide–mAb Conjugate, Is Efficacious against HER2-Positive Intracranial Tumors in Mice
Bibliographic record
Abstract
Anti-HER2 monoclonal antibodies (mAb) have been shown to reduce tumor size and increase survival in patients with breast cancer, but they are ineffective against brain metastases due to poor brain penetration. In previous studies, we identified a peptide, known as Angiopep-2 (An2), which crosses the blood-brain barrier (BBB) efficiently via receptor-mediated transcytosis, and, when conjugated, endows small molecules and peptides with this property. Extending this strategy to higher molecular weight biologics, we now demonstrate that a conjugate between An2 and an anti-HER2 mAb results in a new chemical entity, ANG4043, which retains in vitro binding affinity for the HER2 receptor and antiproliferative potency against HER2-positive BT-474 breast ductal carcinoma cells. Unlike the native mAb, ANG4043 binds LRP1 clusters and is taken up by LRP1-expressing cells. Measuring brain exposure after intracarotid delivery, we demonstrate that the new An2-mAb conjugate penetrates the BBB with a rate of brain entry (Kin) of 1.6 × 10(-3) mL/g/s. Finally, in mice with intracranially implanted BT-474 xenografts, systemically administered ANG4043 increases survival. Overall, this study demonstrates that the incorporation of An2 to the anti-HER2 mAb confers properties of increased uptake in brain endothelial cells as well as BBB permeability. These characteristics of ANG4043 result in higher exposure levels in BT-474 brain tumors and prolonged survival following systemic treatment. Moreover, the data further validate the An2-drug conjugation strategy as a way to create brain-penetrant biologics for neuro-oncology and other CNS indications.
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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) | 0.000 | 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".