Abstract P4-15-04: Synthesis and characterization of EGFR antibody-mediated tumor targeted “gold nanobombs” for treatment of locally advanced breast cancer
Bibliographic record
Abstract
Abstract Purpose: Epidermal growth factor receptor (EGFR) represents an important target for treatment of locally advanced breast cancer (LABC), since about 30% of patients with LABC have triple negative breast tumor, and these tumors are often EGFR-positive. Our aim was to synthesize and characterize a novel form of “gold nanobomb” (GNB) for localized targeted radioimmunotherapy of LABC. GNB uses gold nanoparticles (AuNPs) as a carrier to conjugate both EGFR targeting monoclonal antibody (panitumumab) and the β-particle emitting radionuclide 177Lu. Ultimately, these GNB would be administered locally in LABC for tumor treatment while minimizing normal tissue exposure. Methods: The basic idea for construction of this new-targeted agent is to use different length of poly(ethylene glycol) (PEG) spacers to conjugate antibody and DOTA metal chelators for 177Lu. First, OPSS-PEG-NH2 was used to synthesize OPSS-PEG-DOTA for complexing 177Lu and OPSS-PEG-SVA was used to link antibody. Then these PEG spacers were linked to 30 nm AuNPs to form “GNB”. OPSS-PEG-antibody was analyzed by SDS-PAGE and trinitrobenzenesulfonic acid (TNBSA) assay to determine the degree of amine substitution after PEGylation. A radiometric assay was used to determine the number of PEGylated antibody and DOTA-PEG-OPSS per AuNPs. Specificity of GNB for EGFR and internalization into MBA-MD-468 (1×106 EGFR/cell), MBA-MD-231 (1×105 EGFR/cell) and MCF-7 (1×104 EGFR/cell) was confirmed by confocal and darkfield fluorescence microscopy. Binding assay and cell fractionation study were used to determine the binding affinity to EGFR and the total amount of GNB internalized. Results: Reacting panitumumab with increasing ratios of PEG resulted in an increase in molecular weight from 147 to 250 kDa. This result was in accordance with TNBSA assay where increasing ratio of PEG was associated with increasing lysine substitution (1.4 to 50.9 PEGylated lysine per antibody). PEGylation of 1.4 ± 1.7 lysine per antibody resulted in retention of immunoreactivity and when the immunoconjugates were linked to AuNPs, 8.09 ± 0.38 antibodies were attached to one AuNP. The total number of OPSS-PEG-DOTA per AuNP was estimated to be 812 ± 53. The total size of GNB was 67.3 ± 0.6 nm and there was no particle precipitation or aggregation after incubation in PBS. Competitive binding assay revealed that GNB was targeted strongly to EGFR (Kd antibody: 2.7 ± 0.3 nM; Kd GNB: 5.0 ± 4.9 nM) and darkfield microscopy showed that low PEG to antibody ratio increased the targeting efficiency when compared to high PEG to antibody ratio. Confocal microscopy revealed specific GNB uptake in MDA-MB-468 cells and cell fractionation study demonstrated receptor-mediated internalization of GNB, where the total amount of GNB internalized was about 3.1 and 22.2 fold higher in MDA-MB-468 cells than in MDA-MB-231 or MCF-7 cells. Conclusion: This GNB is very effective in targeting and in internalizing into EGFR, thus the GNB labeled with 177Lu emitting β-particles may be a promising new localized treatment for LABC. We plan to now examine the cytotoxicity of GNB both in vitro and in vivo will be explored for treatment of EGFR positive tumors. Supported by a grant from the Canadian Breast Cancer Foundation. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-15-04.
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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".