Characterization of novel anti-EGFR single domain antibodies and their application in active targeting of superparamagnetic iron oxide nanoparticles to glioblastoma
Notice bibliographique
Résumé
Glioblastoma multiforme is the most lethal primary brain tumor with a mean patient survival of 12 - 15 months. Efforts to treat glioblastoma with chemotherapeutics or radiation therapy have been largely ineffective, which is why the current treatment paradigm is predominantly based on surgery. Herein, it has been shown that the extent of surgical resection is correlated with patient outcome, i.e. less residual cancer cells result in a prolonged time to recurrence. In glioblastoma patients, magnetic resonance imaging (MRI) is used in diagnosis, MRI-guided surgery, and monitoring of disease progression. Superparamagnetic iron oxide nanoparticles (IONPs) are currently receiving increased attention as MRI contrast agents for brain imaging. Their reported proton relaxation properties, biocompatibility, and retention times are superior to the commonly employed gadolinium-based contrast agents. In addition, their larger surface area allows for the conjugation of targeting moieties and/or labels used for multi-modal imaging (e.g. fluorophores, radioisotopes). One of the most frequent genetic alterations in primary glioblastoma involves the epidermal growth factor receptor (EGFR). EGFR over-expression due to gene amplification is observed in 50 - 71% of the patients and among these the simultaneous expression of EGFR mutants is frequently seen. The most common mutation is the deletion of exon 2 – 7 of the extracellular domain, which results in ligand-independent, constitutive activation of the intracellular kinase domain. This mutant is named EGFRvIII and has been intensely investigated as potential therapeutic target, since it is considered a tumor-specific antigen, The objective of this project is to develop EGFR-targeted IONPs to improve the delineation of tumor outlines through targeted delivery of this MRI contrast agent to tumor cells. In addition, dual-labeling of the nanoplatform with near infrared fluorescent probes is expected to permit intra-operative optical imaging of infiltrative tumor cells, thereby decreasing the number of residual cancer cells left after surgical resection. To date antibodies have been the most successful targeting ligands and several immunoconjugates are already approved for molecular imaging in humans. However, small overall size (<100 nm) of the nanoparticle is crucial for achieving extended blood circulation times and high tumor penetration. Therefore, the use of smaller antibody fragments instead of the entire immunoglobulin molecule is preferred. In this study, I characterized novel anti-EGFR single domain antibodies (sdAbs) for their application as targeting moieties for nanoparticulate contrast agents. I determined the specificity and binding kinetics of these sdAbs for their targets EGFR and EGFRvIII using surface plasmon resonance (SPR) biosensor analysis and cell-based assays. I then conjugated the sdAbs to the surface of commercial IONPs and, after thorough investigation of the physical properties, I tested the tumor-targeting ability of these immuno-IONPs in a glioblastoma xenograft model. My findings are in agreement with published observations on the in vivo distribution of targeted superparamagnetic iron oxide nanoparticles. Modern SPR biosensors also allow the assessment of not only the binding affinity and kinetics, but also the thermodynamic parameters of protein-protein interactions. I therefore extended the use of this technology to study the interaction of a selected anti-EGFR sdAb with the extracellular domain of EGFR (EGFR-ECD), and compared this to binding of its natural ligand, the epidermal growth factor (EGF). I demonstrate that distinct thermodynamic driving forces govern sdAb and ligand binding to EGFR-ECD. My findings complement the available structural information and provide new insight into potential mechanisms of EGF-mediated receptor activation.
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) | 0,000 | 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 ».