[<sup>18</sup>F]Fluoropyridine‐losartan: A new approach toward human Positron Emission Tomography imaging of Angiotensin II Type 1 receptors
Bibliographic record
Abstract
Angiotensin II type 1 receptors (AT1R) blocker losartan is used in patients with renal and cardiovascular diseases. [18F]fluoropyridine‐losartan has shown favorable binding profile for quantitative renal PET imaging of AT1R with selective binding in rats and pigs, low interference of radiometabolites and appropriate dosimetry for clinical translation. A new approach was developed to produce [18F]fluoropyridine‐losartan in very high molar activity. Automated radiosynthesis was performed in a three‐step, two‐pot, and two‐HPLC‐purification procedure within 2 h. Pure [18F]FPyKYNE was obtained by radiofluorination of NO2PyKYNE and silica‐gel‐HPLC purification (40 ± 9%), preventing the formation of nitropyridine‐losartan in the second step. Conjugation with trityl‐losartan azide via click chemistry, followed by acid hydrolysis, C18‐HPLC purification and reformulation provided [18F]fluoropyridine‐losartan in 11 ± 2% (decay‐corrected from [18F]fluoride, EOB). Using tris[(1‐(3‐hydroxypropyl)‐1H‐1,2,3‐triazol‐4‐yl)methyl]‐amine (THPTA) as a Cu(I)‐stabilizing agent for coupling [18F]FPyKYNE to the unprotected losartan azide afforded [18F]fluoropyridine‐losartan in similar yields (11 ± 3%, decay‐corrected from [18F]fluoride, EOB). Reverse‐phase HPLC was optimized by reducing the pH of the mobile phase to achieve complete purification and high molar activities (467 ± 60 GBq/μmol). The use of radioprotectants prevented tracer radiolysis for 10 h (RCP > 99%). The product passed the quality control testing. This reproducible automated radiosynthesis process will allow in vivo PET imaging of AT1R expression in several diseases.
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 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) | 0.003 | 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".