Development of [<sup>18</sup>F]SNFT‐1, a novel tau PET tracer with little off‐target binding
Bibliographic record
Abstract
Abstract Background [18F]THK‐5351, which was originally designed to detect tau aggregates, bound to monoamine oxidase B (MAO‐B) with high affinity. Lead optimization toward selective binding profiles to tau has resulted in the development of novel selective tau PET tracer named [18F]SNFT‐1 (THK‐5562). Here we present the preclinical characteristics of this tracer. Method In vitro competitive binding assays against MAO‐A, MAO‐B, amyloid, and tau were performed. In vitro autoradiography of the human brain tissues of various neurodegenerative diseases was performed. Receptor panel screen was performed to confirm the binding selectivity of this compound. Biodistribution study was performed in mice. In addition, an acute toxicity with intravenous administration of a single dose of this compound in mice was also investigated. Result SNFT‐1 showed high affinity (K d = 0.47 nM) and selectivity for tau aggregates over other misfolding proteins as well as MAO enzymes. In vitro autoradiography demonstrated the intense laminar binding of [18F]SNFT‐1 to tau pathology in AD and less off‐target binding than currently available tau PET tracers. No remarkable binding inhibition on various receptors ion channels, and transporters was observed at 1 μM concentration. [18F]SNFT‐1 showed good initial brain uptake and rapid washout without defluorination and troublesome radiolabeled metabolites in mice. In acute toxicity study, no drug‐related changes were noted after intravenous administration of this compound in mice. Conclusion [18F]SNFT‐1 is highly selective tau PET tracer, which will enable accurate monitoring of abnormal tau pathology in AD brain.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".