Labelling chemistry and characterization of [90Y/177Lu]-DOTA-ZHER2:342-3 Affibody molecule, a candidate agent for locoregional treatment of urinary bladder carcinoma
Bibliographic record
Abstract
The direct instillation of radiolabelled conjugates in the urinary bladder is a promising path for the treatment of bladder carcinoma. The targeting of HER2/neu receptors expressed on the surface of many bladder carcinoma cells shows potential to be developed as a therapeutic strategy, and patients identified with a high risk of progression may benefit from adjuvant targeted radionuclide therapy. A phage-display selected Affibody molecule (Z(HER2:342)) which binds to HER2/neu with picomolar affinity, can be used for targeting HER2/neu-expressing bladder carcinomas. A DOTA-derivative of Z(HER2:342), designated as DOTA-Z(HER2:342)-3, is considered as a suitable targeting agent for therapy. The DOTA chelator provides stable labelling with radiometals, and the low molecular weight (7.2 kDa) of the DOTA-Z(HER2:342)-3 compound is expected to enable efficient tumor penetration. DOTA-Z(HER2:342)-3 was radiolabelled with 90Y and 177Lu in 1 M ammonium acetate buffer, at pH 5.5, and in the presence of ascorbic acid. Nearly quantitative labelling yields were achieved for both nuclides after 15 min of incubation at 60 degrees C. After chelation, the conjugates retained their capacity to specifically bind to HER2/neu-expressing SKOV-3 cells. The radiolabelled affibody conjugate (DOTA-Z(HER2:342)-3) demonstrated high antigen-binding capacity and good cellular retention. Biodistribution in normal mice demonstrated low uptake in all organs and tissues except for kidneys.
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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) | 0.001 | 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".