Effects of gefitinib and vandetanib on human equilibrative nucleoside transporter 1 and on gemcitabine cytotoxicity.
Bibliographic record
Abstract
2546 Background: Combination chemotherapy with tyrosine kinase inhibitors (TKIs) and gemcitabine has been attempted with little added benefit to patients. We hypothesized that TKIs that were designed to bind to ATP pockets of growth factor tyrosine kinases also bind to proteins that recognize nucleosides, thereby potentially interfering with gemcitabine pharmacology. Methods: Interaction of TKIs with human nucleoside transporters (NTs) was studied using recombinant NTs produced in yeast. Effects of TKIs on uridine transport, gemcitabine transport and accumulation, regulation of NT activity and cytotoxicity with and without gemcitabine were evaluated in human A549 lung cancer cells. Results: In yeast, vandetanib inhibited two equilibrative NTs (hENT1, hENT2) and three concentrative NTs (hCNT1, hCNT2, hCNT3) with the greatest inhibition seen with hENT1 whereas gefitinib strongly inhibited hENT1 and hCNT1 only. In A549 cells, which possess major hENT1 and minor hENT2 activities, [3H]uridine uptake was inhibited by vandetanib and gefitinib with IC50 values of 16 ± 4 and 5 ± 0.3µM, respectively. Both TKIs also inhibited [3H]gemcitabine transport and accumulation in A549 cells. hENT1 protein levels were decreased during exposures to vandetanib or gefitinib for 24 hours, and cytotoxicity was greatest when gemcitabine was given prior to vandetanib or gefitinib. Conclusions: Vandetanib and gefitinib inhibited human NTs, especially hENT1, resulting in reduced intracellular gemcitabine accumulation. Gefitinib or vandetanib levels achieved in plasma and tumor tissues are sufficient to inhibit hENT1 activity. Because TKIs can block uptake of nucleoside chemotherapy drugs in cultured cancer cells, attention must be paid to TKIs and nucleoside pharmacokinetic properties when scheduling TKIs and nucleoside chemotherapy.
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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".