In vitro and in vivo analyses of eFAP: a novel FAP-targeting small molecule for radionuclide theranostics and other oncological interventions
Bibliographic record
Abstract
Abstract Background Fibroblast activation protein (FAP), a transmembrane serine protease overexpressed by cancer-associated fibroblasts in the tumor stroma, is an interesting biomarker for targeted radionuclide theranostics. FAP-targeting radiotracers have demonstrated to be superior to [18F]FDG PET/CT in various solid cancers. However, these radiotracers have suboptimal tumor retention for targeted radionuclide therapy (TRT). We aimed to develop a novel FAP-targeting pharmacophore with improved pharmacokinetics by introducing a substitution at the 8-position of (4-quinolinoyl)-glycyl-2-cyanopyrrolidine, which allows for conjugation of a chelator, dye, or other payloads. Results Here we showed the synthesis of DOTA-conjugated eFAP-6 and sulfo-Cyanine5-conjugated eFAP-7. After chemical characterization, the uptake and specificity of both tracers were determined on FAP-expressing cells. In vitro, [111In]In-eFAP-6 demonstrated a superior affinity and a more rapid, although slightly lower, peak uptake than gold standard [111In]In-FAPI-46. Confocal microscopy demonstrated a quick FAP-mediated internalization of eFAP-7. Studies with HT1080-huFAP xenografted mice confirmed a more rapid uptake of [177Lu]Lu-eFAP-6 vs. [177Lu]Lu-FAPI-46. However, tumor retention at 24 h post injection of [177Lu]Lu-eFAP-6 was lower than that of [177Lu]Lu-FAPI-46, hereby currently limiting its use for TRT. Conclusion The superior affinity and faster tumor accumulation of eFAP-6 over FAPI-46 makes it a suitable compound for radionuclide imaging. After further optimization, the eFAP series has great potential for various oncological interventions, including fluorescent-guided surgery and effective targeted radionuclide theranostics.
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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".