Doxorubicin‐Conjugated Immuno‐Nanoparticles for Intracellular Anticancer Drug Delivery
Bibliographic record
Abstract
Abstract A polymeric nanoparticle comprised of surface furan groups is used to bind, by Diels–Alder (DA) coupling chemistry, both targeting anti‐human epidermal growth factor receptor 2 (anti‐HER2) antibodies and chemotherapeutic doxorubicin (DOX) for targeted, intracellular delivery of DOX. In this new approach for delivery, where both chemotherapeutic and targeting ligand are attached, for the first time, to the surface of the delivery vehicle, the nuclear localization of DOX in HER2‐overexpressing breast cancer SKBR‐3 cells is demonstrated, as determined by confocal laser scanning microscopy. Flow cytometric analysis shows that the conjugated DOX maintains its biological function and induces similar apoptotic progression in SKBR‐3 cells as free DOX. The viable cell counts of SKBR‐3 cancer cells following incubation with different nanoparticle formulations demonstrates that the combined DOX and anti‐HER2 nanoparticle is more efficacious than the nanoparticle formulation with either DOX or anti‐HER2 alone. While free DOX shows similar cytotoxicity against both cancerous SKBR‐3 cells and healthy HMEC‐1 cells, the combined DOX‐anti‐HER2 nanoparticle is significantly more cytotoxic against SKBR‐3 cells than HMEC‐1 cells, suggesting the benefit of nanoparticle‐conjugated DOX for cell type‐specific targeting. The DOX‐conjugated immuno‐nanoparticle represents an entirely new method for localized co‐delivery of chemotherapeutics and antibodies.
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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 | 0.001 |
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".