Meclizine is not an inverse agonist or antagonist of human constitutive androstane receptor
Bibliographic record
Abstract
Certain tissues express both pregnane X receptor (PXR) and constitutive androstane receptor (CAR), and various target genes are cross‐regulated by these receptors. Meclizine was reported to be an inverse agonist and antagonist of human CAR (hCAR) and an activator of human PXR (hPXR) in cell‐based reporter gene assays. Therefore, activation of hPXR by meclizine may attenuate its apparent hCAR inverse agonistic effects. To test this hypothesis, we investigated whether meclizine would still be capable of suppressing hCAR target gene expression ( CYP2B6 ) in cultured human hepatocytes, which are known to express hCAR and hPXR. Meclizine (0.03–60 μM) did not decrease constitutive CYP2B6 mRNA expression or attenuate hCAR agonist‐mediated increase in CYP2B6 mRNA and CYP2B6‐catalyzed bupropion hydroxylation levels. These findings reflect hPXR agonism and the lack of hCAR inverse agonism and antagonism by meclizine, which were assessed by a reporter gene assay and a mammalian two‐hybrid assay in transfected HepG2 cells. Control experiment indicated that PK11195, which is a hCAR inverse agonist and antagonist, decreased constitutive and hCAR agonist‐mediated hCAR activity. In conclusion, meclizine does not act as a hCAR inverse agonist or antagonist in human hepatocytes. Therefore, it is not appropriate to use this drug as a pharmacological tool to study hCAR function in this cell type.[Supported by CIHR and MSFHR]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".