eTFC-01: a dual-labeled chelate-bridged tracer for SSTR2-positive tumors
Bibliographic record
Abstract
Abstract Background Integrating radioactive and optical imaging techniques can facilitate the prognosis and surgical guidance for cancer patients. Using a single dual-labeled tracer ensures consistency in both imaging modalities. However, developing such molecule is challenging due to the need to preserve the biochemical properties of the tracer while introducing bulky labeling moieties. In our study, we designed a trifunctional chelate that facilitates the coupling of the targeting vector and fluorescent dye at opposite sites to avoid undesired steric hindrance effects. The synthesis of the trifunctional chelate N 3 -Py-DOTAGA-(tBu) 3 ( 7 ) involved a five-step synthetic route, followed by conjugation to the linear peptidyl-resin 8 through solid-phase synthesis. After deprotection and cyclization, the near-infrared fluorescent dye sulfo-Cy.5 was introduced using copper free click chemistry, resulting in eTFC-01 . Subsequently, eTFC-01 was labeled with [ 111 In]InCl 3 . In vitro assessments of eTFC-01 binding, uptake, and internalization were conducted in SSTR2-transfected U2OS cells. Ex-vivo biodistribution and fluorescence imaging were performed in H69-tumor bearing mice. Results eTFC-01 demonstrated a two-fold higher IC50 value for SSTR2 compared to the gold standard DOTA-TATE. Labeling of eTFC-01 with [ 111 In]InCl 3 gave a high radiochemical yield and purity. The uptake of [ 111 In]In- eTFC-01 in U2OS.SSTR2 cells was two-fold lower than the uptake of [ 111 In]In-DOTA-TATE, consistent with the binding affinity. Tumor uptake in H69-xenografted mice was lower for [ 111 In]In- eTFC-01 at all-time points compared to [ 111 In]In-DOTA-TATE. Prolonged blood circulation led to increased accumulation of [ 111 In]In- eTFC-01 in highly vascularized tissues, such as lungs, skin, and heart. Fluorescence measurements in different organs correlated with the radioactive signal distribution. Conclusion The successful synthesis and coupling of the trifunctional chelate to the peptide and fluorescent dye support the potential of this synthetic approach to generate dual labeled tracers. While promising in vitro, the in vivo results obtained with [ 111 In]In- eTFC-01 suggest the need for adjustments to enhance tracer distribution.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".