Development and In Vitro Evaluation of [64Cu]Cu-NOTA-TP-PSMA, a Novel Radiotheranostic Agent Against Prostate Cancer
Bibliographic record
Abstract
Prostate cancer (PCa), particularly in its metastatic form, remains a major clinical challenge due to limited diagnostic and therapeutic options. To address this, we developed a novel radiotheranostic agent, [64Cu]Cu-NOTA-TP-PSMA, by conjugating a prostate-specific membrane antigen (PSMA) ligand to a 64Cu-radiolabeled terpyridine-platinum (TP) compound previously shown to exert selective cytotoxicity against cancer cells. In this study, the biological performance of [64Cu]Cu-NOTA-TP-PSMA was compared with the monomeric analogs [64Cu]Cu-NOTA-PSMA and [64Cu]Cu-NOTA-TP through in vitro studies in PSMA-positive LNCaP prostate cancer cells and non-malignant HEK-293 cells. [64Cu]Cu-NOTA-TP-PSMA showed high stability, PSMA binding affinity and exhibited substantially enhanced uptake, internalization, retention, and nuclear localization in LNCaP cells relative to the monomers, whereas uptake and nuclear accumulation in HEK-293 cells were negligible. Cytotoxicity assays further demonstrated potent and selective activity in LNCaP cells, with EC50 values in the low nanomolar range, and minimal toxicity in HEK-293 cells. Collectively, these results identify [64Cu]Cu-NOTA-TP-PSMA as a promising radiotheranostic agent, warranting further in vivo evaluation for prostate cancer imaging and targeted radiotherapy.
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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".