<sup>212</sup>Pb-Pretargeted Theranostics for Pancreatic Cancer
Bibliographic record
Abstract
Although pancreatic ductal adenocarcinoma (PDAC) is associated with limited treatment options and poor patient outcomes, targeted α-particle therapy (TAT) represents a promising development in the field. TAT shows potential in treating metastatic cancers, including those that have become resistant to conventional treatments. Among the most auspicious radionuclides stands the in vivo α-generator 212Pb. Combined with the imaging-compatible radionuclide 203Pb, this theranostic match is a promising modality rapidly translating into the clinic. Methods: Using the pretargeting approach between a radiolabeled 1,2,4,5-tetrazine (Tz) tracer and a trans-cyclooctene (TCO) modified antibody, imaging and therapy with radiolead were performed on a PDAC tumor xenograft mouse model. For therapy, 3 cohorts received a single administration of 1.1, 2.2, or 3.7 MBq of the pretargeting agent, [212Pb]Pb-DO3A-PEG7-Tz, whereby administered activity levels were guided by dosimetric analysis. Results: The treated mice were holistically evaluated; minimal-to-mild renal tubular necrosis was observed. At the same time, median survival doubled for the highest-dose cohort (10.7 wk) compared with the control cohort (5.1 wk). Conclusion: This foundational study demonstrated the feasibility and safety of pretargeted TAT with 212Pb in PDAC while considering dose limitations and potential adverse effects.
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 | 0.000 |
| 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".