Theranostic 64Cu-DOTHA2-PSMA allows low toxicity radioligand therapy in mice prostate cancer model
Bibliographic record
Abstract
Introduction We have previously shown that copper-64 (64Cu)-DOTHA2-PSMA can be used for positron emission tomography (PET) imaging of prostate cancer. Owing to the long-lasting, high tumoral uptake of 64Cu-DOTHA2-PSMA, the objective of the current study was to evaluate the therapeutic potential of 64Cu-DOTHA2-PSMA in vivo. Methods LNCaP tumor-bearing NOD-Rag1nullIL2rgnull (NRG) mice were treated with an intraveinous single-dose of 64Cu-DOTHA2-PSMA at maximal tolerated injected activity, natCu-DOTHA2-PSMA at equimolar amount (control) or lutetium-177 (177Lu)-PSMA-617 at 120 MBq to assess their impact on survival. Weight, well-being and tumor size were followed until mice reached 62 days post-injection or ethical limits. Toxicity was assessed through weight, red blood cells (RBCs) counts, pathology and dosimetry calculations. Results Survival was longer with 64Cu-DOTHA2-PSMA than with natCu-DOTHA2-PSMA (p < 0.001). Likewise, survival was also longer when compared to 177Lu-PSMA-617, although it did not reach statistical significance (p = 0.09). RBCs counts remained within normal range for the 64Cu-DOTHA2-PSMA group. 64Cu-DOTHA2-PSMA treated mice showed non-pathological fibrosis and no other signs of radiation injury. Human extrapolation of dosimetry yielded an effective dose of 3.14 × 10-2 mSv/MBq, with highest organs doses to gastrointestinal tract and liver. Discussion Collectively, our data showed that 64Cu-DOTHA2-PSMA-directed radioligand therapy was effective for the treatment of LNCaP tumor-bearing NRG mice with acceptable toxicity and dosimetry. The main potential challenge is the hepatic and gastrointestinal irradiation.
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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) | 0.002 | 0.001 |
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".