HER2-Mediated Internalization of a Targeted Prodrug Cytotoxic Conjugate Is Dependent on the Valency of the Targeting Ligand
Why this work is in the frame
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Bibliographic record
Abstract
HER2 is a validated therapeutic target for cancer. There are no natural ligands, but monoclonal antibodies and peptides that bind HER2 act as artificial ligands, selectively affecting HER2-overexpressing tumors. One reported mechanism for this effect is receptor downregulation, but the expected correlation of ligand-dependent HER2 internalization and tumor inhibition remain poorly characterized. Moreover, HER2 ligands have limited therapeutic efficacy and often they require adjuvant treatment with the chemotherapeutic Taxol. Here, we generated a series of HER2 ligands (Anti-HER2/neu peptide ligands, AHNPmonovalent and AHNPbivalent) with different valency and correlated their internalization-promoting ability to biological potency. Since AHNPbivalent (but not AHNPmonovalent) induces rapid receptor internalization, we exploited this feature to deliver cytotoxic conjugates coupling AHNPbivalent and Taxol (Taxol . AHNPbivalent). The prodrug conjugate releases Taxol after receptor-mediated internalization, and cytotoxicity can be used as a marker of internalization. Taxol . AHNPbivalent is significantly more cytotoxic than free Taxol + free AHNPbivalent. Hence, the Taxol x AHNP(bivalent) prodrug binds to HER2, induces receptor internalization and downregulation, and the subsequent release of free Taxol inside the targeted cell results in synergistic toxicity, The effect is selective towards HER2- expressing cells. This work links HER2 receptor internalization and growth arrest, and the chemical conjugation strategy may yield improved and HER2 selective therapeutics.
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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 | 0.000 | 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) | 0.000 | 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