Abstract P4-15-04: Synthesis and characterization of EGFR antibody-mediated tumor targeted “gold nanobombs” for treatment of locally advanced breast cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».