Synthesis and Evaluation of 68Ga-Labeled (2S,4S)-4-Fluoropyrrolidine-2-Carbonitrile and (4R)-Thiazolidine-4-Carbonitrile Derivatives as Novel Fibroblast Activation Protein-Targeted PET Tracers for Cancer Imaging
Bibliographic record
Abstract
Fibroblast activation protein α (FAP-α) is a cell-surface protein overexpressed on cancer-associated fibroblasts that constitute a substantial component of tumor stroma and drive tumorigenesis. FAP is minimally expressed by most healthy tissues, including normal fibroblasts. This makes it a promising pan-cancer diagnostic and therapeutic target. In the present study, we synthesized two novel tracers, [68Ga]Ga-SB03045 and [68Ga]Ga-SB03058, bearing a (2S,4S)-4-fluoropyrrolidine-2-carbonitrile or a (4R)-thiazolidine-4-carbonitrile pharmacophore, respectively. [68Ga]Ga-SB03045 and [68Ga]Ga-SB03058 were evaluated for their FAP-targeting capabilities using substrate-based in vitro binding assays, and in PET/CT imaging and ex vivo biodistribution studies in an HEK293T:hFAP tumor xenograft mouse model. The IC50 values of natGa-SB03045 (1.59 ± 0.45 nM) and natGa-SB03058 (0.68 ± 0.09 nM) were found to be lower than those of the clinically validated natGa-FAPI-04 (4.11 ± 1.42 nM). Contrary to the results obtained in the FAP-binding assay, [68Ga]Ga-SB03058 demonstrated a ~1.5 fold lower tumor uptake than that of [68Ga]Ga-FAPI-04 (7.93 ± 1.33 vs. 11.90 ± 2.17 %ID/g), whereas [68Ga]Ga-SB03045 (11.8 ± 2.35 %ID/g) exhibited a tumor uptake comparable to that of [68Ga]Ga-FAPI-04. Thus, our data suggest that the (2S,4S)-4-fluoropyrrolidine-2-carbonitrile scaffold holds potential as a promising pharmacophore for the design of FAP-targeted radioligands for cancer diagnosis and therapy.
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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) | 0.000 | 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 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".