A Theranostic Approach to Imaging and Treating Melanoma with 203Pb/212Pb-Labeled Antibody Targeting Melanin
Bibliographic record
Abstract
Metastatic melanoma is a deadly disease that claims thousands of lives each year despite the introduction of several immunotherapeutic agents into the clinic over the past decade, inspiring the development of novel therapeutics and the exploration of combination therapies. Our investigations target melanin pigment with melanin-specific radiolabeled antibodies as a strategy to treat metastatic melanoma. In this study, a theranostic approach was applied by first labeling a chimeric antibody targeting melanin, c8C3, with the SPECT radionuclide 203Pb for microSPECT/CT imaging of C57Bl6 mice bearing B16-F10 melanoma tumors. Imaging was followed by radioimmunotherapy (RIT), whereby the c8C3 antibody is radiolabeled with a 212Pb/212Bi “in vivo generator”, which emits cytotoxic alpha particles. Using microSPECT/CT, we collected sequential images of B16-F10 murine tumors to investigate antibody biodistribution. Treatment with the 212Pb/212Bi-labeled c8C3 antibody demonstrated a dose-response in tumor growth rate in the 5–10 µCi dose range when compared to the untreated and radiolabeled control antibody and a significant prolongation in survival. No hematologic or systemic toxicity of the treatment was observed. However, administration of higher doses resulted in a biphasic tumor dose response, with the efficacy of treatment decreasing when the administered doses exceeded 10 µCi. These results underline the need for more pre-clinical investigation of targeting melanin with 212Pb-labeled antibodies before the clinical utility of such an approach can be assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".