Quantitative validation of an intracerebral β‐sensitive microprobe system to determine in vivo drug‐induced receptor occupancy using [<sup>11</sup>C]raclopride in rats
Bibliographic record
Abstract
In this study, we evaluated the potential of using a new beta-sensitive microprobe system for in vivo quantification of [11C]raclopride binding and for in vivo determination of drug-induced receptor occupancy in the rat striatum. To validate this system, an ex vivo tissue dissection method was used to corroborate in vivo beta-microprobe measurements. Our data showed that the beta-microprobe-derived [11C]raclopride binding kinetics in striatum could be quantified using a tissue compartmental model with a cerebellar reference region. Haloperidol (0.001-0.1 mg/kg; i.v.) induced a dose-dependent decrease in [11C]raclopride binding in striatum as measured using the beta-microprobe with an ED50 value of 0.013 mg/kg. Highly significant relationships (P < 0.0001) were observed, within the same animals, between in vivo and ex vivo measures of haloperidol-induced D2-receptor occupancy (r = 0.98) as well as between in vivo and ex vivo measures of [11C]raclopride binding potentials (r = 0.99). Results from pretreatment and displacement studies with unlabeled raclopride and amphetamine conformed to the effect of these drugs as observed in humans using [11C]raclopride and PET and allowed estimation of the in vivo k(off) value of raclopride to 0.025 +/- 0.004 min(-1). However, allowing the system to stabilize before measurements and shielding the photomultiplier tubes were critical for obtaining these consistent results. This study demonstrates that the beta-microprobe provides reliable measurements of [11C]raclopride binding kinetics in rodents, allows for quantitative in vivo measurements of antipsychotic drug action in brain, and represents a valid and cost-effective alternative to positron emission tomography imaging in small animals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".