Antitumour activity of ANG1005, a conjugate between paclitaxel and the new brain delivery vector Angiopep‐2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Paclitaxel is highly efficacious in the treatment of breast, head and neck, non-small cell lung cancers and ovarian carcinoma. For malignant gliomas, paclitaxel is prevented from reaching its target by the presence of the efflux pump P-glycoprotein (P-gp) at the blood-brain barrier. We investigated the utilization of a new drug delivery system to increase brain delivery of paclitaxel. EXPERIMENTAL APPROACH: Paclitaxel molecules were conjugated to a brain peptide vector, Angiopep-2, to provide a paclitaxel-Angiopep-2 conjugate named ANG1005. We determined the brain uptake capacity, intracellular effects and antitumour properties of ANG1005 in vitro against human tumour cell lines and in vivo in human xenografts. We then determined ANG1005 activity on brain tumours with intracerebral human tumour models in nude mice. KEY RESULTS: We show by in situ brain perfusion that ANG1005 enters the brain to a greater extent than paclitaxel and bypasses the P-gp. ANG1005 has an antineoplastic potency similar to that of paclitaxel against human cancer cell lines. We also demonstrate that ANG1005 caused a more potent inhibition of human tumour xenografts than paclitaxel. Finally, ANG1005 administration led to a significant increase in the survival of mice with intracerebral implantation of U87 MG glioblastoma cells or NCI-H460 lung carcinoma cells. CONCLUSIONS AND IMPLICATIONS: These results demonstrate the antitumour potential of a new drug, ANG1005, and establish that conjugation of anticancer agents with the Angiopep-2 peptide vector could increase their efficacy in the treatment of brain cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it