<sup>18</sup>F-THK5351: A Novel PET Radiotracer for Imaging Neurofibrillary Pathology in Alzheimer Disease
Bibliographic record
Abstract
UNLABELLED: Imaging of neurofibrillary pathology in the brain helps in diagnosing dementia, tracking disease progression, and evaluating the therapeutic efficacy of antidementia drugs. The radiotracers used in this imaging must be highly sensitive and specific for tau protein fibrils in the human brain. We developed a novel tau PET tracer, (18)F-THK5351, through compound optimization of arylquinoline derivatives. METHODS: The in vitro binding properties, pharmacokinetics, and safety of (18)F-THK5351 were investigated, and a clinical study on Alzheimer disease (AD) patients was performed. RESULTS: (18)F-THK5351 demonstrated higher binding affinity for hippocampal homogenates from AD brains and faster dissociation from white-matter tissue than did (18)F-THK5117. The THK5351 binding amount correlated with the amount of tau deposits in human brain samples. Autoradiography of brain sections revealed that THK5351 bound to neurofibrillary tangles selectively and with a higher signal-to-background ratio than did THK5117. THK5351 exhibited favorable pharmacokinetics and no defluorination in mice. In first-in-human PET studies in AD patients, (18)F-THK5351 demonstrated faster kinetics, higher contrast, and lower retention in subcortical white matter than(18)F-THK5117. CONCLUSION: (18)F-THK5351 is a useful PET tracer for the early detection of neurofibrillary pathology in AD patients.
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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) | 0.002 | 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".