Comparative Radioimmunotherapy of Experimental Melanoma with Novel Humanized Antibody to Melanin Labeled with 213Bismuth and 177Lutetium
Bibliographic record
Abstract
Melanoma is a cancer with increasing incidence and there is a need for alternatives to immunotherapy within effective approaches to treatment of metastatic melanoma. We performed comparative radioimmunotherapy (RIT) of experimental B16-F10 melanoma with novel humanized IgG to melanin h8C3 labeled with a beta emitter, 177Lu, and an alpha-emitter, 213Bi, as well as biodistribution, microSPECT/CT imaging, and mouse and human dosimetry calculations. microSPECT/CT imaging showed that a humanized antibody that targets “free” melanin in the tumor microenvironment had high tumor uptake in B16F10 murine melanoma in C57Bl/6 mice, with little to no uptake in naturally melanized tissues. Extrapolation of the mouse dosimetry data to an adult human demonstrated that doses delivered to major organs and the whole body by 177Lu-h8C3 would be approximately two times higher than those delivered by 213Bi-h8C3, while the doses to the tumor would be almost similar. RIT results indicated that 213Bi-h8C3 was more effective in slowing down the tumor growth than 177Lu-h8C3, while both radiolabeled antibodies did not produce significant hematologic or systemic side effects. We concluded that h8C3 antibody labeled with 213Bi is a promising reagent for translation into a clinical trial in patients with metastatic melanoma.
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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 | 0.001 |
| 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".