Bibliographic record
Abstract
Positron emission tomography (PET) is a highly sensitive, quantitative imaging technique that can track sub-nanomolar quantities of positron-emitting radionuclides throughout the body. By incorporating such radionuclides into molecules of interest, we can directly assess their pharmacokinetic and pharmacodynamic (PK/PD) characteristics in vivo without changing their physicochemical characteristics or eliciting a pharmacological response. As such, PET imaging has long been used as a tool to aid drug discovery programs from preclinical biomarker validation all the way through to clinical trials. In this perspective we discuss the use of PET radioligands in central nervous system (CNS) drug discovery and development, with a focus on recent applications in psychiatry (e.g. 5-HT 2 A, 11β-HSD1), neuro-oncology (e.g. KRAS G12C , ATM, ALK2), and neurodegeneration (e.g. amyloid beta plaques, MAPK p38), while exploring the intricacies associated with developing novel radiotracers for CNS targets. Examples highlight the preclinical and clinical uses of PET for studying biomarker function, drug candidate PK/PD, target occupancy/engagement, dosing regimen determination, clinical trial patient selection, and quantifying biomarker changes in response to treatments.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".