A Kinome-Wide Selective Radiolabeled TrkB/C Inhibitor for in Vitro and in Vivo Neuroimaging: Synthesis, Preclinical Evaluation, and First-in-Human
Bibliographic record
Abstract
The proto-oncogenes NTRK1/2/3 encode the tropomyosin receptor kinases TrkA/B/C which play pivotal roles in neurobiology and cancer. We describe herein the discovery of [ 11 C]-( R )- 3 ([ 11 C]-( R )-IPMICF16), a first-in-class positron emission tomography (PET) TrkB/C-targeting radiolabeled kinase inhibitor lead. Relying on extensive human kinome vetting, we show that ( R )- 3 is the most potent and most selective TrkB/C inhibitor characterized to date. It is demonstrated that [ 11 C]-( R )- 3 readily crosses the blood–brain barrier (BBB) in rodents and selectively binds to TrkB/C receptors in vivo, as evidenced by entrectinib blocking studies. Substantial TrkB/C-specific binding in human brain tissue is observed in vitro, with specific reduction in the hippocampus of Alzheimer’s disease (AD) versus healthy brains. We additionally provide preliminary translational data regarding the brain disposition of [ 11 C]-( R )- 3 in primates including first-in-human assessment. These results illustrate for the first time the use of a kinome-wide selective radioactive chemical probe for endogenous kinase PET neuroimaging in human.
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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 | 0.001 |
| Open science | 0.000 | 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".