Characterization of novel anti-EGFR single domain antibodies and their application in active targeting of superparamagnetic iron oxide nanoparticles to glioblastoma
Bibliographic record
Abstract
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.
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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".