H<sub>4</sub>octapa-Trastuzumab: Versatile Acyclic Chelate System for <sup>111</sup>In and <sup>177</sup>Lu Imaging and Therapy
Bibliographic record
Abstract
A bifunctional derivative of the versatile acyclic chelator H4octapa, p-SCN-Bn-H4octapa, has been synthesized for the first time. The chelator was conjugated to the HER2/neu-targeting antibody trastuzumab and labeled in high radiochemical purity and specific activity with the radioisotopes (111)In and (177)Lu. The in vivo behavior of the resulting radioimmunoconjugates was investigated in mice bearing ovarian cancer xenografts and compared to analogous radioimmunoconjugates employing the ubiquitous chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA). The H4octapa-trastuzumab conjugates displayed faster radiolabeling kinetics with more reproducible yields under milder conditions (15 min, RT, ~94-95%) than those based on DOTA-trastuzumab (60 min, 37 °C, ~50-88%). Further, antibody integrity was better preserved in the (111)In- and (177)Lu-octapa-trastuzumab constructs, with immunoreactive fractions of 0.99 for each compared to 0.93-0.95 for (111)In- and (177)Lu-DOTA-trastuzumab. These results translated to improved in vivo biodistribution profiles and SPECT imaging results for (111)In- and (177)Lu-octapa-trastuzumab compared to (111)In- and (177)Lu-DOTA-trastuzumab, with increased tumor uptake and higher tumor-to-tissue activity ratios.
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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 | 0.001 |
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".