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Record W2766645178 · doi:10.3892/mmr.2017.7952

Off‑target effect of imatinib and nilotinib on human vitamin D3 metabolism

2017· article· en· W2766645178 on OpenAlexaff
Lysann Kroschwald, Meinolf Suttorp, Josephine T. Tauer, Nick Zimmermann, Claudia Gï¿ ⁄ nther, Andrea Bauer

Bibliographic record

VenueMolecular Medicine Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsShriners Hospitals for Children - Canada
FundersNovartis Pharma
KeywordsHaCaTCalcitriolNilotinibImatinibVitamin D and neurologyBone remodelingInternal medicineEndocrinologyMedicinePharmacologyChemistryIn vitroBiochemistryMyeloid leukemia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.327
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

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