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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 | 0.001 |
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 teacher head, 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".