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)
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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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".