64Cu-DOTHA2-PSMA, a Novel PSMA PET Radiotracer for Prostate Cancer with a Long Imaging Time Window
Bibliographic record
Abstract
Prostate cancer imaging and late-stage management can be improved with prostate-specific membrane antigen (PSMA)-targeting radiotracers. We developed a PSMA positron emission tomography (PET) radiotracer, DOTHA2-PSMA radiolabeled with 64Cu (T1/2: 12.7 h), to leverage its large imaging time window. This preclinical study aimed to evaluate the biological and imaging properties of 64Cu-DOTHA2-PSMA. Its stability was assessed in plasma ex vivo and in mice. Cellular behavior was studied for up to 48 h in LNCaP cells. Biodistribution studies were performed in balb/c mice for up to 48 h. Dynamic (1 h) and static (4 h and 24 h) PET imaging was completed in LNCaP tumor-bearing mice. 64Cu-DOTHA2-PSMA was stable ex vivo in plasma and reached cellular internalization up to 34.1 ± 4.9% injected activity (IA)/106 cells at 48 h post-injection (p.i.). Biodistribution results showed significantly lower uptake in kidneys than 68Ga-PSMA-617, our reference PET tracer (p < 0.001), but higher liver uptake at 2 h p.i. (p < 0.001). PET images showed 64Cu-DOTHA2-PSMA’s highest tumoral uptake at 4 h p.i., with a significant difference between blocked and non-blocked groups from the time of injection to 24 h p.i. The high stability and tumor uptake with a long tumor imaging time window of 64Cu-DOTHA2-PSMA potentially contribute to the prostate cancer theranostic approach and its local recurrence detection.
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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 | 0.001 | 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".