One-Step<sup>18</sup>F-Labeling of Carbohydrate-Conjugated Octreotate-Derivatives Containing a Silicon-Fluoride-Acceptor (SiFA): In Vitro and in Vivo Evaluation as Tumor Imaging Agents for Positron Emission Tomography (PET)
Why this work is in the frame
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Bibliographic record
Abstract
The synthesis, radiolabeling, and initial evaluation of new silicon-fluoride acceptor (SiFA) derivatized octreotate derivatives is reported. So far, the main drawback of the SiFA technology for the synthesis of PET-radiotracers is the high lipophilicity of the resulting radiopharmaceutical. Consequently, we synthesized new SiFA-octreotate analogues derivatized with Fmoc-NH-PEG-COOH, Fmoc-Asn(Ac₃AcNH-β-Glc)-OH, and SiFA-aldehyde (SIFA-A). The substances could be labeled in high yields (38 ± 4%) and specific activities between 29 and 56 GBq/μmol in short synthesis times of less than 30 min (e.o.b.). The in vitro evaluation of the synthesized conjugates displayed a sst2 receptor affinity (IC₅₀ = 3.3 ± 0.3 nM) comparable to that of somatostatin-28. As a measure of lipophilicity of the conjugates, the log P(ow) was determined and found to be 0.96 for SiFA-Asn(AcNH-β-Glc)-PEG-Tyr³-octreotate and 1.23 for SiFA-Asn(AcNH-β-Glc)-Tyr³-octreotate, which is considerably lower than for SiFA-Tyr³-octreotate (log P(ow) = 1.59). The initial in vivo evaluation of [¹⁸F]SiFA-Asn(AcNH-β-Glc)-PEG-Tyr³-octreotate revealed a significant uptake of radiotracer in the tumor tissue of AR42J tumor-bearing nude mice of 7.7% ID/g tissue weight. These results show that the high lipophilicity of the SiFA moiety can be compensated by applying hydrophilic moieties. Using this approach, a tumor-affine SiFA-containing peptide could successfully be used for receptor imaging for the first time in this proof of concept study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it