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Record W7017967927

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

2012· dissertation· en· W7017967927 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typedissertation
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMcGill University
Fundersnot available
KeywordsGlioblastomaMutantEpidermal growth factor receptorMagnetic resonance imagingIron oxide nanoparticlesMutationRadiation therapyExonProtein kinase domainSingle-domain antibody
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.220
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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