Kinetic Modeling of the Monoamine Oxidase B Radioligand [ <sup>11</sup> C]SL25.1188 in Human Brain with High-Resolution Positron Emission Tomography
Bibliographic record
Abstract
This article describes the kinetic modeling of [(11)C]SL25.1188 ([(S)-5-methoxymethyl-3-[6-(4,4,4-trifluorobutoxy)-benzo[d]isoxazol-3-yl]-oxazolidin-2-[(11)C]one]) binding to monoamine oxidase B (MAO-B) in the human brain using high-resolution positron emission tomography (PET). Seven healthy subjects underwent two separate 90- minute PET scans after an intravenous injection of [(11)C]SL25.1188. Complementary arterial blood sampling was acquired. Radioactivity was quickly eliminated from plasma with 80% of parent compound remaining at 90 minutes. Metabolites were more polar than the parent compound. Time-activity curves showed high brain uptake, early peak and washout rate consistent with known regional MAO-B concentration. A two-tissue compartment model (2-TCM) provided better fits to the data than a 1-TCM. Measurement of total distribution volume (VT) showed very good identifiability (based on coefficient of variation (COV)) for all regions of interest (ROIs) (COV(VT)<8%), low between-subject variability (∼20%), and quick temporal convergence (within 5% of final value at 45 minutes). Logan graphical method produces very good estimation of VT. Regional VT highly correlated with previous postmortem report of MAO-B level (r(2)= ≥ 0.9). Specific binding would account from 70% to 90% of VT. Hence, VT measurement of [(11)C]SL25.1(1)88 PET is an excellent estimation of MAO-B concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".