Differential interactions of antiretroviral agents with <scp>LXR</scp>, <scp>ER</scp> and <scp>GR</scp> nuclear receptors: potential contributing factors to adverse events
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Antiretroviral (ARV) drugs activate pregnane X receptors and constitutive androstane receptors, increasing the risk of drug interactions due to altered drug metabolism and disposition. The closely related liver X receptors (LXRα/β), oestrogen receptors (ERα/β) and glucocorticoid receptor (GR) regulate many endogenous processes such as lipid/cholesterol homeostasis, cellular differentiation and inflammation. However, ARV drug activation of these nuclear receptors has not been thoroughly investigated. EXPERIMENTAL APPROACH: The ability of an ARV drug library to activate LXRα/β, ERα/β and GR was assessed using a combined in silico and in vitro approach encompassing computational docking and molecular descriptor filtering, cell-free time-resolved fluorescence resonance energy transfer co-activator assays to assess direct binding to ligand-binding domains (LBDs), cell-based reporter assays and target gene expression. KEY RESULTS: Direct LBD interactions with LXRα and/or LXRβ were predicted in silico and confirmed in vitro for darunavir, efavirenz, flavopiridol, maraviroc and tipranavir. Likewise, efavirenz was also predicted and confirmed as a ligand of ERα-LBD. Interestingly, atazanavir and ritonavir also activated LXRα/β in reporter assays, while tipranavir enhanced transcriptional activity of ERα. Effects on ER and LXR target gene expression were confirmed for efavirenz and tipranavir. CONCLUSIONS AND IMPLICATIONS: There was good agreement between in silico predictions and in vitro results. However, some nuclear receptor interactions identified in vitro were probably due to allosteric effects or nuclear receptor cross-talk, rather than direct LBD binding. This study indicates that some of the adverse effects associated with ARV use may be mediated through 'off-target' effects involving nuclear receptor activation.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".