Role of Human Nucleoside Transporters in the Uptake and Cytotoxicity of Azacitidine and Decitabine
Bibliographic record
Abstract
The nucleoside analogs 5-azacytidine (azacitidine) and 5-aza-2'-deoxycytidine (decitabine) are active against acute myeloid leukemia and myelodysplastic syndromes. Cellular transport across membranes is crucial for uptake of these highly polar hydrophilic molecules. We assessed the ability of azacitidine, decitabine, and, for comparison, gemcitabine, to interact with human nucleoside transporters (hNTs) in Saccharomyces cerevisiae cells (hENT1/2, hCNT1/2/3) or Xenopus laevis oocytes (hENT3/4). All three drugs inhibited hCNT1/3 potently (K (i) values, 3-26 μM), hENT1/2 and hCNT2 weakly (K (i) values, 0.5-3.1 mM), and hENT3/4 poorly if at all. Rates of transport of [(3)H]gemcitabine, [(14)C]azacitidine, and [(3)H]decitabine observed in Xenopus oocytes expressing individual recombinant hNTs differed substantially. Cytotoxicity of azacitidine and decitabine was assessed in hNT-expressing or hNT-deficient cultured human cell lines in the absence or presence of transport inhibitors where available. The rank order of cytotoxic sensitivities (IC (50) values, μM) conferred by hNTs were hCNT1 (0.1) > hENT1 (0.3) ≫ hCNT2 (8.3), hENT2 (9.0) for azacitidine and hENT1 (0.3) > hCNT1 (0.8) ⋙ hENT2, hCNT2 (>100) for decitabine. Protection against cytotoxicity was observed for both drugs in the presence of inhibitors of nucleoside transport, thus suggesting the importance of hNTs in manifestation of toxicity. In summary, all seven hNTs transported azacitidine, with hCNT3 showing the highest rates, whereas hENT1 and hENT2 showed modest transport and hCNT1 and hCNT3 poor transport of decitabine. Our results show for the first time that azacitidine and decitabine exhibit different human nucleoside transportability profiles and their cytotoxicities are dependent on the presence of hNTs, which could serve as potential biomarkers of clinical response.
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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 | 0.001 | 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".