Identifying novel aryl hydrocarbon receptor (AhR) modulators from clinically approved drugs: In silico screening and In vitro validation
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AhR) functions as a vital ligand-activated transcription factor, governing both physiological and pathophysiological processes. Notably, it responds to xenobiotics, leading to a diverse array of outcomes. In the context of drug repurposing, we present here a combined approach of utilizing structure-based virtual screening and molecular dynamics simulations. This approach aims to identify potential AhR modulators from Drugbank repository of clinically approved drugs. By focusing on the AhR PAS-B binding pocket, our screening protocol included binding affinities calculations, complex stability, and interactions within the binding site as a filtering method. Comprehensive evaluations of all DrugBank small molecule database revealed ten promising hits. This included flibanserin, butoconazole, luliconazole, naftifine, triclabendazole, rosiglitazone, empagliflozin, benperidol, nebivolol, and zucapsaicin. Each exhibiting diverse binding behaviors and remarkably very low binding free energy. Experimental studies further illuminated their modulation of AhR signaling, and showing that they are consistently reducing AhR activity, except for luliconazole, which intriguingly enhances the AhR activity. This work demonstrates the possibility of using computational modelling as a quick screening tool to predict new AhR modulators from extensive drug libraries. Importantly, these findings hold immense therapeutic potential for addressing AhR-associated disorders. Consequently, it offers compelling prospects for innovative interventions through drug repurposing.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".