Characterization of<sup>18</sup>F-FPyKYNE-Losartan for Imaging AT<sub>1</sub>Receptors
Bibliographic record
Abstract
Most physiologic effects of the renin angiotensin system (RAS) are mediated via the angiotensin (Ang) type 1 receptor (AT1R). The 18F-FPyKYNE derivative of the clinically used AT1R blocker losartan exhibits high binding selectivity for kidney AT1R and rapid metabolism in rats. The aim of this study was to further assess the binding profile of this novel PET agent for imaging AT1R in rats and pigs. Methods: In vitro binding assays were performed with 18F-FPyKYNE-losartan in rat kidneys. Male Sprague–Dawley rats were used to assess dosimetry, antagonistic efficacy via blood pressure measurements, and presence of labeled metabolites in kidneys. Test–retest PET imaging, blocking with AT1R antagonist candesartan (10 mg/kg), and plasma metabolism analysis were performed in female Yorkshire pigs. Results:18F-FPyKYNE-losartan bound with high affinity (dissociation constant of 49.4 ± 18.0 nM and maximal binding of 348 ± 112 fmol/mm2) to rat kidney AT1R. It bound strongly to plasma proteins in rats (97%), and its labeled metabolites displayed minimal interference on renal AT1R binding. FPyKYNE-losartan fully antagonized the Ang II pressor effect, albeit with 4-fold potency reduction (the effective dose inhibiting 50% of the Ang II–induced maximal pressor response of 25.5 mg/kg) relative to losartan. PET imaging exhibited high kidney-to-blood contrast and slow renal clearance, with an SUV of 14.1 ± 6.2. Excellent reproducibility was observed in the calculated test–retest variability (7.2% ± 0.75%). Only hydrophilic-labeled metabolites were present in plasma samples, and renal retention was reduced (−60%) at 10–15 min after blockade with candesartan. Conclusion:18F-FPyKYNE-losartan has a favorable binding profile and displays high potential for translational work in humans as an AT1R PET imaging agent.
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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 | 0.001 |
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".