Differential activation of human constitutive androstane receptor and its<scp>SV23</scp>and<scp>SV24</scp>splice variants by rilpivirine and etravirine
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Rilpivirine and etravirine are second-generation non-nucleoside reverse transcriptase inhibitors (NNRTIs) indicated for the treatment of HIV/AIDS. The constitutive androstane receptor (CAR) regulates the expression of genes involved in various biological processes, including the transport and biotransformation of drugs. We investigated the effect of rilpivirine and etravirine on the activity of the wild-type human CAR (hCAR-WT) and its hCAR-SV23 and hCAR-SV24 splice variants, and compared it with first-generation NNRTIs (efavirenz, nevirapine, and delavirdine). EXPERIMENTAL APPROACH: Receptor activation, ligand-binding domain (LBD) transactivation, and co-activator recruitment were investigated in transiently transfected, NNRTI-treated HepG2 cells. Nuclear translocation of green fluorescent protein-tagged hCAR-WT and CYP2B6 gene expression were assessed in NNRTI-treated human hepatocytes. KEY RESULTS: Rilpivirine and etravirine activated hCAR-WT, but not hCAR-SV23 or hCAR-SV24, and without transactivating the LBD or recruiting steroid receptor coactivators SRC-1, SRC-2, or SRC-3. Among the first-generation NNRTIs investigated, only efavirenz activated hCAR-WT, hCAR-SV23, and hCAR-SV24, but none of them transactivated the LBD of these receptors or substantively recruited SRC-1, SRC-2, or SRC-3. Rilpivirine, etravirine, and efavirenz triggered nuclear translocation of hCAR-WT and increased hCAR target gene (CYP2B6) expression. CONCLUSION AND IMPLICATIONS: NNRTIs activate hCAR-WT, hCAR-SV23, and hCAR-SV24 in a drug-specific and isoform-selective manner. The activation occurs by a mechanism that does not appear to involve binding to the LBD or recruitment of SRC-1, SRC-2, or SRC-3.
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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) | 0.001 | 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".