HER2-Mediated Internalization of a Targeted Prodrug Cytotoxic Conjugate Is Dependent on the Valency of the Targeting Ligand
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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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 | 0.000 |
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".