Using ANG4043, a brain-penetrant anti-HER2 mab, to increase survival in a murine intracranial breast tumor model.
Bibliographic record
Abstract
e13013 Background: Treatments for metastatic brain tumors originating from HER2-positive breast disease are limited due to the inability of most anti-tumor agents to enter the brain. While the selectively permeable blood-brain barrier (BBB) restricts access of therapeutics such as mAbs to the brain, transcytosis of hormones, nutrients, and other homeostatic modulators is mediated by endogenous receptors such as LRP1, low density lipoprotein receptor-related protein 1. We have created a family of peptides (Angiopeps) that are recognized by LRP1 and enter the brain via transcytosis. Using these proprietary Angiopeps, we have created novel, brain-penetrant Peptide-Drug Conjugates. An iterative process of linker selection and reaction condition optimization led to the discovery of ANG4043, consisting of the Angiopep An2 attached to a trastuzimab biosimilar. This brain-penetrant peptide-mAb conjugate, which displays HER2 binding affinity and in vitro anti-proliferative properties similar to that of the native mAb, was tested in a murine model of breast tumor brain metastases. Methods: Athymic nude mice were stereotactically implanted 1.5 mm anterior and 2.5 mm lateral to bregma with 1X106 BT-474 cells twelve days prior to initiation of drug treatment. Body weight and morbidity/mortality were monitored daily. Results: Median survival in the control group was 45 days, compared with 68 days and 80 days for the 5 mg/kg (p = 0.0005) and 15 mg/kg (p = 0.0003) groups, respectively, demonstrating a 52% and 78% improvement over the control group. Conclusions: These data demonstrate that ANG4043 increases survival in a mouse HER2-positive intracranial tumor model. These results extend the validation of An2 conjugation beyond small molecules and peptides to include larger molecules such as therapeutic mAbs for development of new brain-penetrant anti-tumor therapeutics.
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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".