Molecular Imaging and Radionuclide Therapy of Melanoma Targeting the Melanocortin 1 Receptor
Bibliographic record
Abstract
Melanoma is a deadly disease at late metastatic stage, and early diagnosis and accurate staging remain the key aspects for managing melanoma. The melanocortin 1 receptor (MC1 R) is overexpressed in primary and metastatic melanomas, and its endogenous ligand, the α-melanocyte-stimulating hormone (αMSH), has been extensively studied for the development of MC1 R-targeted molecular imaging and therapy of melanoma. Natural αMSH is not well suited for this purpose due to low stability in vivo. Unnatural amino acid substitutions substantially stabilized the peptide, while cyclization via lactam bridge and metal coordination further improved binding affinity and stability. In this study, we summarized the development and the in vitro and in vivo characteristics of the radiolabeled αMSH analogues, including 99m Tc-, 111 In-, 67 Ga-, or 125 I-labeled αMSH analogues for imaging with single-photon emission computed tomography; 68 Ga-, 64 Cu-, or 18 F-labeled αMSH analogues for imaging with positron emission tomography; and 188 Re-, 177 Lu-, 90 Y-, or 212 Pb-labeled αMSH analogues for radionuclide therapy. These radiolabeled αMSH analogues showed promising results with high tumor uptake and rapid normal tissue activity clearance in the preclinical model of B16F1 and B16F10 mouse melanomas. These results highlight the potential of using radiolabeled αMSH analogues in clinical applications for molecular imaging and radionuclide therapy of 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 | 0.000 |
| 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".