Novel 68Ga-Labeled Pyridine-Based Fibroblast Activation Protein-Targeted Tracers with High Tumor-to-Background Contrast
Bibliographic record
Abstract
Compared to quinoline-based fibroblast activation protein (FAP)-targeted radiotracers, pyridine-based FAP-targeted tracers are expected to have faster pharmacokinetics due to their smaller molecular size and higher hydrophilicity, which we hypothesize would improve the tumor-to-background image contrast. We aim to develop 68Ga-labeled pyridine-based FAP-targeted tracers for cancer imaging with positron emission tomography (PET), and compare their imaging potential with the clinically validated [68Ga]Ga-FAPI-04. Two DOTA-conjugated pyridine-based AV02053 and AV02070 were synthesized through multi-step organic synthesis. IC50(FAP) values of Ga-AV02053 and Ga-AV02070 were determined by an enzymatic assay to be 187 ± 52.0 and 17.1 ± 4.60 nM, respectively. PET imaging and biodistribution studies were conducted in HEK293T:hFAP tumor-bearing mice at 1 h post-injection. The HEK293T:hFAP tumor xenografts were clearly visualized with good contrast on PET images by [68Ga]Ga-AV02053 and [68Ga]Ga-AV02070, and both tracers were excreted mainly through the renal pathway. The tumor uptake values of [68Ga]Ga-AV02070 (7.93 ± 1.88%ID/g) and [68Ga]Ga-AV02053 (5.6 ± 1.12%ID/g) were lower than that of previously reported [68Ga]Ga-FAPI-04 (12.5 ± 2.00%ID/g). However, both [68Ga]Ga-AV02070 and [68Ga]Ga-AV02053 showed higher tumor-to-background (blood, muscle, and bone) uptake ratios than [68Ga]Ga-FAPI-04. Our data suggests that pyridine-based pharmacophores are promising for the design of FAP-targeted tracers. Future optimization on the selection of a linker will be explored to increase tumor uptake while maintaining or even further improving the high tumor-to-background contrast.
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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 | 0.001 | 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".