Modulation of aryl hydrocarbon receptor activity by tyrosine kinase inhibitors (ponatinib and tofacitinib)
Bibliographic record
Abstract
Ponatinib and tofacitinib, established kinase inhibitors and FDA-approved for chronic myeloid leukemia and rheumatoid arthritis, are recently undergoing investigation in diverse clinical trials for potential repurposing. The aryl hydrocarbon receptor (AhR), a transcription factor influencing a spectrum of physiological and pathophysiological activities, stands as a therapeutic target for numerous diseases. This study employs molecular modelling tools and in vitro assays to identify ponatinib and tofacitinib as AhR ligands, elucidating their binding and molecular interactions in the AhR PAS-B domain. Molecular docking analyses revealed that ponatinib and tofacitinib occupy the central pocket within the primary cavity, similar to AhR agonists 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and (benzo[a]pyrene) B[a]P. Our simulations also showed that these compounds exhibit good stability, stabilizing many hot spots within the PAS-B domain, including the Dα-Eα loop, which serves as a regulatory element for the binding pocket. Binding energy calculations highlighted ponatinib's superior predicted affinity, revealing F295 as a crucial residue in maintaining strong interaction with the two compounds. Our in vitro data suggest that ponatinib functions as an AhR antagonist, blocking the downstream signaling of AhR pathway induced by TCDD and B[a]P. Additionally, both tofacitinib and ponatinib cause impairment in AhR-regulated CYP1A1 enzyme activity induced by potent AhR agonists. This study unveils ponatinib and tofacitinib as potential modulators of AhR, providing valuable insights into their therapeutic roles in AhR-associated diseases and enhancing our understanding of the intricate relationship between kinase inhibitors and AhR.
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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 | 0.000 |
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".