Preclinical Evaluation of [155/161Tb]Tb-Crown-TATE—A Novel SPECT Imaging Theranostic Agent Targeting Neuroendocrine Tumours
Bibliographic record
Abstract
Terbium radioisotopes (149Tb, 152Tb, 155Tb, 161Tb) offer a unique class of radionuclides which encompass all four medicinally relevant nuclear decay modalities (α, β+, γ, β−/e−), and show high potential for the development of element-matched theranostic radiopharmaceuticals. The goal of this study was to design, synthesise, and evaluate the suitability of crown-TATE as a new peptide-conjugate for radiolabelling of [155Tb]Tb3+ and [161Tb]Tb3+, and to assess the imaging and pharmacokinetic properties of each radiotracer in tumour-bearing mice. [155Tb]Tb-crown-TATE and [161Tb]Tb-crown-TATE were prepared efficiently under mild conditions, and exhibited excellent stability in human serum (>99.5% RCP over 7 days). Longitudinal SPECT/CT images were acquired for 155Tb- and 161Tb- labelled crown-TATE in male NRG mice bearing AR42J tumours. The radiotracers, [155Tb]Tb-crown-TATE and [161Tb]Tb-crown-TATE, showed high tumour targeting (32.6 and 30.0 %ID/g, respectively) and minimal retention in non-target organs at 2.5 h post-administration. Biodistribution studies confirmed the SPECT/CT results, showing high tumour uptake (38.7 ± 8.0 %ID/g and 38.5 ± 3.5 %ID/g, respectively) and favourable tumour-to-background ratios. Blocking studies further confirmed SSTR2-specific tumour accumulation. Overall, these findings suggest that crown-TATE has great potential for element-matched molecular imaging and radionuclide therapy using 155Tb and 161Tb.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".