In vivo PET Imaging of [11C]CIMBI-5, a 5-HT2AR Agonist Radiotracer in Nonhuman Primates
Bibliographic record
Abstract
PURPOSE: 5-HT2AR exists in high and low affinity states. Agonist PET tracers measure binding to the active high affinity site and thus provide a functionally relevant measure of the receptor. Limited in vivo data have been reported so far for a comparison of agonist versus antagonist tracers for 5-HT2AR used as a proof of principle for measurement of high and low affinity states of this receptor. We compared the in vivo binding of [11C]CIMBI-5, a 5-HT2AR agonist, and of the antagonist [11C]M100907, in monkeys and baboons. METHODS: [11C]CIMBI-5 and [11C]M100907 baseline PET scans were performed in anesthetized male baboons (n=2) and male vervet monkeys (n=2) with an ECAT EXACT HR+ and GE 64-slice PET/CT Discovery VCT scanners. Blocking studies were performed in vervet monkeys by pretreatment with MDL100907 (0.5 mg/kg, i.v.) 60 minutes prior to the scan. Regional distribution volumes and binding potentials were calculated for each ROI using the likelihood estimation in graphical analysis and Logan plot, with either plasma input function or reference region as input, and simplified reference tissue model approaches. RESULTS: PET imaging of [11C]CIMBI-5 in baboons and monkeys showed the highest binding in 5-HT2AR-rich cortical regions, while the lowest binding was observed in cerebellum, consistent with the expected distribution of 5-HT2AR. Very low free fractions and rapid metabolism were observed for [11C]CIMBI-5 in baboon plasma. Binding potential values for [11C]CIMBI-5 were 25-33% lower than those for [11C]MDL100907 in the considered brain regions. CONCLUSION: The lower binding potential of [11C]CIMBI-5 in comparison to [11C]MDL100907 is likely due to the preferential binding of the former to the high affinity site in vivo in contrast to the antagonist, [11C]MDL100907, which binds to both high and low affinity sites.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".