Preparation and Evaluation of <sup>99m</sup>Tc-labeled Tridentate Chelates for Pre-targeting Using Bioorthogonal Chemistry
Bibliographic record
Abstract
Pre-targeting combined with bioorthogonal chemistry is emerging as an effective way to create new radiopharmaceuticals. Of the methods available, the inverse electron demand Diels-Alder (IEDDA) cycloaddition between a radiolabeled tetrazines and trans-cyclooctene (TCO) linked to a biomolecule has proven to be a highly effective bioorthogonal approach to imaging specific biological targets. Despite the fact that technetium-99m remains the most widely used isotope in diagnostic nuclear medicine, there is a scarcity of methods for preparing 99mTc-labeled tetrazines. Herein we report the preparation of a family of tridentate-chelate-tetrazine derivatives and their Tc(I) complexes. These hitherto unknown compounds were radiolabeled with 99mTc using a microwave-assisted method in 31% to 83% radiochemical yield. The products are stable in saline and PBS and react rapidly with TCO derivatives in vitro. Their in vivo pre-targeting abilities were demonstrated using a TCO-bisphosphonate (TCO-BP) derivative that localizes to regions of active bone metabolism or injury. In murine studies, the 99mTc-tetrazines showed high activity concentrations in knees and shoulder joints, which was not observed when experiments were performed in the absence of TCO-BP. The overall uptake in non-target organs and pharmacokinetics varied greatly depending on the nature of the linker and polarity of the chelate.
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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 | 0.001 | 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".