Strategies for designing novel positron emission tomography (PET) radiotracers to cross the blood–brain barrier
Bibliographic record
Abstract
Positron emission tomography (PET) is a powerful tool for imaging biological processes in the central nervous system (CNS). Designing PET radiotracers capable of crossing the blood–brain barrier (BBB) remains a major challenge. In addition to being brain‐penetrant, a quantifiable CNS PET radiotracer must have high target affinity and selectivity, appropriate pharmacokinetics, minimal non‐specific binding, negligible radiometabolites in the brain, and generally must be amenable to labeling with carbon‐11 (11C) or fluorine‐18 (18F). This review aims to give an overview of some of the critical physicochemical and biochemical contributors specific for CNS PET radiotracer design and how they can differ from pharmaceutical drug development, including in vitro assays, in silico predictions, and in vivo studies, with examples for how such methods can be implemented to optimize brain uptake of radiotracers based on experiences from our neuroimaging program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".