Unexpectedly high affinity of a novel histamine<scp>H<sub>3</sub></scp>receptor antagonist,<scp>GSK239512</scp>,<i>in vivo</i>in human brain, determined using<scp>PET</scp>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study aimed to investigate the relationship between the plasma concentration (PK) of the novel histamine H3 receptor antagonist, GSK239512, and the brain occupancy of H(3) receptors (RO) in healthy human volunteers. EXPERIMENTAL APPROACH: PET scans were obtained after i.v. administration of the H(3) -specific radioligand [(11) C]GSK189254. Each subject was scanned before and after single oral doses of GSK239512, at 4 and 24 h after dose. PET data were analysed by compartmental analysis, and regional RO estimates were obtained by graphical analysis of changes in the total volumes of distribution of the radioligand, followed by a correction for occupancy by the high affinity radioligand. The PK/RO relationship was analysed by a population-modelling approach, using the average PK of GSK239512 during each scan. KEY RESULTS: Following administration of GSK239512, there was a reduction in the brain uptake of [(11) C]GSK189254 in all regions, including cerebellum. RO at 4 h was higher than at 24 h, and the PK/RO model estimated a PK associated with 50% of RO of 0.0068 ng·mL(-1) . This corresponds to a free concentration of 4.50 × 10(-12 ) M (pK = 11.3). CONCLUSIONS AND IMPLICATIONS: The affinity of GSK239512 for brain H3 receptors in humans in vivo is much higher than that expected from studies in vitro, and higher than that observed in PET studies in pigs. The study illustrates the utility of carrying out PET studies in humans early in drug development, providing accurate quantification of GSK239512 RO in vivo as a function of time and dose.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".