Development of a 99mTc-labeled tetrazine for pretargeted SPECT imaging using an alendronic acid-based bone targeting model
Bibliographic record
Abstract
Pretargeting, which is the separation of target accumulation and the administration of a secondary imaging agent into two sequential steps, offers the potential to improve image contrast and reduce radiation burden for nuclear imaging. In recent years, the tetrazine ligation has emerged as a promising approach to facilitate covalent pretargeted imaging due to its unprecedented kinetics and bioorthogonality. Pretargeted bone imaging with TCO-modified alendronic acid (Aln-TCO) is an attractive model that allows the evaluation of tetrazines in healthy animals without the need for complex disease models or targeting regimens. Recent structure-activity relationship studies of tetrazines evaluated important parameters for the design of potent tetrazine-radiotracers for pretargeted imaging. However, limited information is available for 99mTc-labeled tetrazines. In this study, four tetrazines intended for labeling with fac-[99mTc(OH2)3 (CO)3]+ were synthesized and evaluated using an Aln-TCO mouse model. 3,6-bis(2-pyridyl)-1,2,4,5-Tz without additional linker showed higher pretargeted bone uptake and less background activity compared to the same scaffold with a PEG8 linker or 3-phenyl-1,2,4,5-Tz-based compounds. Additionally, improved bone/blood ratios were observed in pretargeted animals compared to animals receiving directly labeled Aln-TCO. The results of this study implicate 3,6-bis(2-pyridyl)-1,2,4,5-Tz as a promising scaffold for potential 99mTc-labeled tetrazines.
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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 | 0.001 |
| 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".