Simultaneous Imaging and Therapy Using Epitope-Specific Anti-Epidermal Growth Factor Receptor (EGFR) Antibody Conjugates
Bibliographic record
Abstract
Matuzumab and nimotuzumab are anti-EGFR monoclonal antibodies that bind to different epitopes of domain III of EGFR. We developed 89Zr-matuzumab as a PET probe for diagnosis/monitoring of response to treatment of a noncompeting anti-EGFR nimotuzumab antibody drug conjugate (ADC) using mouse colorectal cancer (CRC) xenografts. We developed 89Zr-matuzumab and performed quality control in EGFR-positive DLD-1 cells. The KD of matuzumab, DFO-matuzumab and 89Zr-matuzumab in DLD-1 cells was 5.9, 6.2 and 3 nM, respectively. A competitive radioligand binding assay showed that 89Zr-matuzumab and nimotuzumab bound to noncompeting epitopes of EGFR. MicroPET/CT imaging and biodistribution of 89Zr-matuzumab in mice bearing EGFR-positive xenografts (HT29, DLD-1 and MDA-MB-231) showed high uptake that was blocked with pre-dosing with matuzumab but not with the noncompeting binder nimotuzumab. We evaluated nimotuzumab-PEG6-DM1 ADC in CRC cells. IC50 of nimotuzumab-PEG6-DM1 in SNU-C2B, DLD-1 and SW620 cells was dependent on EGFR density and was up to five-fold lower than that of naked nimotuzumab. Mice bearing the SNU-C2B xenograft were treated using three 15 mg/kg doses of nimotuzumab-PEG6-DM1, and 89Zr-matuzumab microPET/CT was used to monitor the response to treatment. Treatment resulted in complete remission of the SNU-C2B tumor in 2/3 mice. Matuzumab and nimotuzumab are noncompeting and can be used simultaneously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".