Design and Characterization of HER-2-Targeted Gold Nanoparticles for Enhanced X-radiation Treatment of Locally Advanced Breast Cancer
Bibliographic record
Abstract
Our purpose was to develop a human epidermal growth factor receptor-2 (HER-2) targeted nanotechnology-based radiosensitizer. HER-2 is overexpressed in 20-30% of all breast cancers and up to 2-fold higher in locally advanced disease (LABC). Trastuzumab was derivatized with a polyethylene glycol (OPSS-PEG-SVA) cross-linker to produce trastuzumab-PEG-OPSS. These immunoconjugates were analyzed by SDS-PAGE, and their immunoreactivity was assessed by flow cytometry using HER-2 overexpressing SK-BR-3 breast cancer cells. Reacting trastuzumab with increasing ratios of PEG resulted in an increase in molecular weight from approximately 148 kDa to 243 kDa, associated with increasing PEG substitution (0.6 to 18.9 PEG chains per trastuzumab). Attachment of approximately 7 PEG chains per trastuzumab resulted in 56% retention in immunoreactivity assessed by flow cytometry. The conjugates were then linked to 30 nm AuNPs. Using a novel (123)iodine-radiotracer based assay that overcomes the current limitations of spectrophotometric quantification of biological molecules on AuNPs we estimate 14.3 ± 2.7 antibodies were attached to each AuNP when 2 × 10(11) AuNPs were reacted with 20 μg of trastuzumab-PEG-OPSS. Specificity of trastuzumab-PEG-AuNPs for HER-2 and internalization in SK-BR-3 cells was demonstrated by comparing the uptake of trastuzumab-PEG-AuNPs or PEG-AuNPs by darkfield microscopy. The ability of trastuzumab-PEG-AuNPs in combination with 300 kVp X-rays to enhance DNA double strand breaks (DSBs) in SK-BR-3 cells was assessed by immunofluorescence using the γ-H2AX assay. γ-H2AX assay results revealed 5.1-fold higher DNA-DSBs with trastuzumab-PEG-AuNPs and X-radiation as compared to treatment with X-radiation alone. The trastuzumab-PEG-AuNPs are a promising targeted nanotechnology-based radiosensitizer for improving LABC therapy. The design and systematic approaches taken to surface modify and characterize trastuzumab-PEG-AuNPs described in this study would have application to other molecularly targeted AuNPs for cancer treatment.
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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) | 0.000 | 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 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".