Kinetic modeling of CB1 PET tracer [11C]OMAR in rhesus monkeys and humans
Bibliographic record
Abstract
216 Objectives [11C]OMAR was recently developed for PET imaging of cannabinoid type 1 (CB1) receptors. Here we investigate quantification methods for [11C]OMAR in monkeys and humans. Methods Three rhesus monkeys underwent a total of 18 scans on the Focus220. Data were acquired for 120 min after 4.2±0.6 mCi [11C]OMAR. In 5 scans, unlabeled OMAR was co-injected at doses up to 1.7 mg/kg. Ten human subjects underwent a total of 18 scans. Data were collected for 120 min on the HRRT after bolus of 18±1 mCi. In all studies, arterial input functions were obtained. Regional time activity curves (TACs) were extracted from the dynamic PET data. Distribution volume (VT) was estimated by 1- and 2-tissue models with (1Tv, 2Tv) or without (1T, 2T) vascular component. Logan graphical (LGA) and multilinear analysis (MA1) were applied with a range of t* values. Binding constants ED50 and EC50 (with regard to mass dose or late plasma level, respectively) were estimated by fitting a one-site model to VT values in monkeys. Results TACs from monkeys and humans were fitted poorly by 1T and 1Tv. 2T fits were good in both species. In monkeys, 2Tv usually estimated negligible blood volume (Vb) and VT nearly identical to 2T. In humans, 2Tv improved fits slightly in some regions with Vb=3±2% and VT 1±4% lower than 2T. In both species, MA1 with t*>20 min yielded VT values highly correlated with 2T (in humans: y=0.98x+0.05, R2=0.98). LGA was reliable in humans (y=0.96x+0.06, R2=0.95) but gave many low outliers in monkeys. VT was lower in humans than monkeys (~2 vs ~10 in high binding regions), as were K1 values (~0.05 vs ~0.2 1/min). In monkeys, ED50 was ~200μg/kg and EC50 ~40 nM with small variations between methods. Conclusions 2T was the best compartmental model for analysis of [11C]OMAR data in monkeys as well as in humans, where a vascular component sometimes improved fits. Graphical methods gave VT estimates similar to 2T in humans, but MA1 performed much better than LGA in monkeys
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".