Evaluation of N-Succinimidyl S-Acetylthioacetate Ligand for Radiolabeling of Humanized Antibodies with <sup>188</sup> Rhenium
Bibliographic record
Abstract
Radioimmunotherapy offers an effective way to direct ionizing radiation to cancer cells through attachment of radionuclides to antibodies while limiting negative effects of off-target irradiation. This, however, requires effective facile methods for attachment of therapeutic radionuclides onto antibodies. Herein, the authors report their efforts in evaluating N-succinimidyl S-acetylthioacetate (SATA), a commercially available reagent, for use as a bifunctional chelating agent (BCA) to attach 188Rhenium (188Re) onto h8C3, a humanized IgG antibody that can effectively target extracellular melanin present in malignant melanoma. Micro single photon emission computer tomography/computer tomography was used to determine an effective timeline for antibody uptake in B16-F10 tumor bearing C57BL6 mice guiding the selection of 188Re with its 16.9 h physical half-life. Radio instant thin layer chromatography coupled with radio high-performance liquid chromatography was used to assess radioisotope incorporation, as well as stability during the labeling process for SATA conjugated h8C3. It was determined that despite the relatively mild conditions used, incorporation of the SATA conjugate resulted in antibody instability during labeling requiring a different BCA to facilitate rhenium incorporation onto the antibodies.
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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) | 0.000 | 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".