Preclinical Evaluation of a High-Affinity <sup>18</sup>F-Trifluoroborate Octreotate Derivative for Somatostatin Receptor Imaging
Bibliographic record
Abstract
UNLABELLED: Recent studies have highlighted the high sensitivity of PET imaging with (68)Ga-labeled octreotide derivatives for the detection and staging of neuroendocrine tumors. A somatostatin receptor ligand that is easily radiolabeled with (18)F-fluoride could improve the availability of PET imaging of neuroendocrine tumors. We report an alkyltrifluoroborate-octreotate conjugate that is radiolabeled in a 1-step (18)F exchange reaction in high yield and with high specific activity. METHODS: We conjugated a new alkyltrifluoroborate to octreotate to obtain AMBF3-TATE, which was stored in 50-nmol aliquots for radiolabeling. (18)F labeling was performed by (18)F-(19)F isotope exchange with (18)F-fluoride, and the tracer was purified by C18 cartridge separation. The radiochemical yield was 20%-25%. PET imaging and biodistribution were performed on mice bearing AR42J tumor xenografts. RESULTS: AMBF3-TATE bound the somatostatin receptor subtype 2 with high affinity (inhibition constant, 0.13 ± 0.03 nM). Starting with 29.6-37 GBq (0.8-1 Ci) of (18)F-fluoride, more than 7.4 GBq (>200 mCi) of (18)F-AMBF3-TATE were obtained in 25 min (n = 5) with greater than 99% radiochemical purity at high specific activity (>111 GBq [3 Ci]/μmol). (18)F-AMBF3-TATE was stable in plasma. PET imaging and biodistribution showed rapid renal excretion with low liver activity. High tumor uptake (10.11% ± 1.67% injected dose/g, n = 5) was detected at 60 min after injection. Bone uptake was negligible. Tumor-to-liver, tumor-to-blood, tumor-to-muscle, and tumor-to-bone ratios (at 60 min) were 26.2 ± 0.8, 25.1 ± 1.0, 89.0 ± 3.1, and 21.3 ± 3.6, respectively. CONCLUSION: (18)F-AMBF3-TATE was radiolabeled in high yield and at high specific activity, did not require high-performance liquid chromatography purification, exhibited unexpectedly high binding affinity to somatostatin receptor subtype 2, and showed excellent pharmacokinetic properties in vivo, with high tumor uptake and high contrast ratios.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".