Off‑target effect of imatinib and nilotinib on human vitamin D3 metabolism
Bibliographic record
Abstract
Prolonged treatment with tyrosine kinase inhibitors (TKI) including imatinib (IMA) or nilotinib (NIL), induces severe disturbances of bone metabolism in patients with chronic myeloid leukaemia. As vitamin D3 (VD3) is involved in the complex cycle of bone remodelling, the present study investigated in vitro, the influence of IMA and NIL on VD3 metabolism i) in HaCaT cells and ii) in cultured outer root sheath keratinocytes (ORS‑KC) from hair follicles of IMA treated children. Cells were incubated in the presence of IMA or NIL. Concomitantly, specific inhibitors were applied to analyze the inhibition of the VD3 processing cytochrome P450 isoenzyme family by TKIs. In vitro, IMA and NIL significantly impaired the production of calcitriol in HaCaT and cultured ORS‑KC cells from hair follicles of IMA treated children. For NIL, this inhibitory effect demonstrated a 4‑fold increase. In HaCaT and ORS‑KC, application of specific CYP450 inhibitors revealed that CYP27B1 was impaired by IMA and NIL leading to an intracellular accumulation of calcidiol. However, during TKI treatment, KC of IMA treated children revealed no differences in calcidiol and calcitriol levels. In conclusion, IMA and NIL interfere with the vitamin D3 cascade due to their metabolism by CYP27B1.
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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".