Selective Inhibition of Human Equilibrative and Concentrative Nucleoside Transporters by BCR-ABL Kinase Inhibitors
Bibliographic record
Abstract
Human nucleoside transporters (hNTs) mediate cellular influx of anticancer nucleoside drugs, including cytarabine, cladribine, and fludarabine. BCR-ABL tyrosine kinase inhibitors (TKIs) imatinib and dasatinib inhibit fludarabine and cytarabine uptake. We assessed interactions of bosutinib, dasatinib, imatinib, nilotinib, and ponatinib with recombinant hNTs (hENT1, 2; hCNT1, -2, and -3) produced individually in yeast Saccharomyces cerevisiae Nilotinib inhibited hENT1-mediated uridine transport most potently (IC50 value, 0.7 μm) followed by ponatinib > bosutinib > dasatinib > imatinib. Imatinib inhibited hCNT2 with an IC50 value of 2.3 μm Ponatinib inhibited all five hNTs with the greatest effect seen for hENT1 (IC50 value, 9 μm). TKIs inhibited [(3)H]uridine uptake in a competitive manner. Studies in yeast with mutants at two amino acid residues of hENT1 (L442I, L442T, M33A, M33A/L442I) previously shown to be involved in uridine and dipyridamole binding, suggested that BCR-ABL TKIs interacted with Met(33) (TM1) and Leu(442) (TM11) residues of hENT1. In cultured human CEM lymphoblastoid cells, which possess a single hNT type (hENT1), accumulation of [(3)H]cytarabine, [(3)H]cladribine, or [(3)H]fludarabine was reduced by each of the five TKIs, and also caused a reduction in cell surface expression of hENT1 protein. In conclusion, BCR-ABL TKIs variously inhibit five different hNTs, cause a decrease in cell surface hENT1 expression, and decrease uridine accumulation when presented together with uridine or when given before uridine. In experiments with mutant hENT1, we showed for the first time interaction of Met(33) (involved in dipyridamole binding) with BCR-ABL inhibitors and reduced interaction with M33A mutant hENT1.
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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".