Apoptotic Epidermal Growth Factor (EGF)-Conjugated Block Copolymer Micelles as a Nanotechnology Platform for Targeted Combination Therapy
Bibliographic record
Abstract
The overexpression of epidermal growth factor receptor (EGFR) in human epithelial cancers has been associated with aggressive disease, poor patient prognosis, and a high incidence of metastases. In the present study, block copolymer micelles are conjugated with epidermal growth factor (EGF), which acts as both a targeting ligand for the drug carrier and an apoptotic factor against EGFR-overexpressing cancers. Drug-free EGF-conjugated micelles are shown to result in cell-cycle arrest at the G 1 phase and subsequent induction of cell-type-specific apoptosis in EGFR-overexpressing breast cancer cells as demonstrated by flow cytometric analysis. EGF delivered as EGF-conjugated micelles was found to be 13-fold more potent than free EGF; the IC 50 was decreased from 0.98 +/- 0.1 nM for free EGF to 0.076 +/- 0.01 nM for EGF micelles. The apoptotic micelles, however, are non-antiproliferative to cells expressing a low level of EGFR, suggesting that the apoptotic micelles have minimal or no toxicity against normal healthy tissues. Ellipticine, a chemotherapeutic agent, was loaded into the EGF-micelles after it had been shown, using the combination index-isobologram equation, to act synergistically with EGF. A 10-fold increase in EGF content in the ellipticine-loaded micelles lowered the IC 50 of ellipticine in EGFR-overexpressing breast cancer cells by more than 18-fold. The EGF-micelles have the potential to be further pursued as a versatile nanotechnology platform for targeted delivery of a wide range of chemotherapeutic agents as a combination therapy for the treatment of EGFR-overexpressing cancers.
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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".