Pretargeted PET of Osteodestructive Lesions in Dogs
Bibliographic record
Abstract
The last decade has witnessed the creation of a highly effective approach to in vivo pretargeting based on the inverse electron demand Diels–Alder (IEDDA) click ligation between tetrazine (Tz) and trans -cyclooctene (TCO). Despite the steady progression of this technology toward the clinic, concerns have persisted regarding whether this in vivo chemistry will work in humans given their larger size and blood volume. In this work, we describe the use of a 64 Cu-labeled Tz radioligand ([ 64 Cu]Cu-SarAr-Tz) and a TCO-bearing bisphosphonate (TCO-BP) for the pretargeted positron emission tomography (PET) imaging of osteodestructive lesions in a large animal model: companion dogs. First, in a small animal pilot study, healthy mice were injected with TCO-BP followed after 1 or 6 h by [ 64 Cu]Cu-SarAr-Tz. PET images were collected 1, 6, and 24 h after the administration of [ 64 Cu]Cu-SarAr-Tz, revealing that this approach produced high activity concentrations in the bone (>20 and >15%ID/g in the femur and humerus, respectively, at 24 h post injection) as well as high target-to-background contrast. Subsequently, companion dogs ( n = 5) presenting with osteodestructive lesions were administered TCO-BP (5 or 10 mg/kg) followed 1 h later by [ 64 Cu]Cu-SarAr-Tz (2.2–7.3 mCi; 81.4–270.1 MBq). PET scans were collected for each dog 4 h after the administration of the radioligand, and SUV values for the osteodestructive lesions, healthy bones, and kidneys were determined. In these animals, pretargeted PET clearly delineated healthy bone and produced very high activity concentrations in osteodestructive lesions. Low levels of uptake were observed in all healthy organs except for the kidneys and bladder due to the renal excretion of excess radioligand. Ultimately, this work not only illustrates that pretargeted PET with TCO-BP and [ 64 Cu]Cu-SarAr-Tz is an effective tool for the visualization of osteodestructive lesions but also demonstrates for the first time that in vivo pretargeting based on IEDDA click chemistry is feasible in large animals.
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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 | 0.000 |
| 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".