Bibliographic record
Abstract
Molecular imaging (MI) agents use biomarker-targeted delivery vehicles complexed to radionuclides that emit gamma (γ)-photons or positrons for visualizing tumours by SPECT or PET, respectively. These MI agents can be functionalized with radionuclides that emit cytotoxic radiation such as beta (β) particles and structurally modified for radioimmunotherapy (RIT), effectively transforming into a theranostic agent. This thesis aimed to develop a MI agent to visualize tumours that overexpress the biomarkers of interest and demonstrate the feasibility of a theranostic approach against these tumours. Epidermal growth factor receptors (EGFR) are overexpressed in triple-negative breast cancer (TNBC), head and neck squamous cell carcinoma (HNSCC) and pancreatic ductal adenocarcinoma (PDCa). Panitumumab is an antibody that targets EGFR and can be modified and functionalized for MI and RIT. The first study demonstrated a novel technique to conjugate synthetic His6-peptides to panitumumab Fab for complexing [99mTc(H2O)3(CO)3]+, rendering it the ability to visualize EGFR-positive cell-line or patient-derived tumour xenografts in mouse models of TNBC, HNSCC and PDCa. The next study showed the synthesis of the theranostic agent, DOTA-panitumumab F(ab')2, that can be complexed with 64-copper (64Cu) for PET imaging or 177-lutetium (177Lu) for RIT. This work showed that 64Cu-DOTA-panitumumab F(ab')2 imaged EGFR-positive HNSCC PDX mouse models on PET and importantly, allowed for prediction of radiation doses to normal organs and tumours from RIT with the 177Lu-labelled analogue, demonstrating a theranostic strategy towards HNSCC. The third study was an attempt to visualize PDCa tumours overexpressing FZD#5 in mouse models using a recombinant anti-FZD#5 Fab that harbored an endogenous His6-tag for complexing [99mTc(H2O)3(CO)3] + or an immunoglobulin (IgG) analogue modified with desferrioxamine (DFO) for labelling with 89-zirconium (89Zr). Overexpression of FZD#5 in PDCa is mediated by RNF-43 mutation and has shown tremendous therapeutic potential in recent studies. This work showed an insufficient density of FZD#5 for imaging PDCa with the 99mTc-labelled Fab on SPECT and non-specific uptake of the 89Zr-labelled IgG on PET due to enhanced permeation and retention effects. In conclusion, panitumumab can be functionalized as a MI agent or a theranostic agent for visualizing and treating EGFR-positive tumours. FZD#5 is not a viable biomarker for MI despite its therapeutic potential.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".