Erlotinib, Gefitinib, and Vandetanib Inhibit Human Nucleoside Transporters and Protect Cancer Cells from Gemcitabine Cytotoxicity
Bibliographic record
Abstract
PURPOSE: Combinations of tyrosine kinase inhibitors (TKI) with gemcitabine have been attempted with little added benefit to patients. We hypothesized that TKIs designed to bind to ATP-binding pockets of growth factor receptors also bind to transporter proteins that recognize nucleosides. EXPERIMENTAL DESIGN: TKI inhibition of uridine transport was studied with recombinant human (h) equilibrative (E) and concentrative (C) nucleoside transporters (hENT, hCNT) produced individually in yeast. TKIs effects on uridine transport, gemcitabine accumulation, regulation of hENT1 activity, and cell viability in the presence or absence of gemcitabine were evaluated in human pancreatic and lung cancer cell lines. RESULTS: Erlotinib, gefitinib and vandetanib inhibited [(3)H]uridine transport in yeast and [(3)H]uridine and [(3)H]gemcitabine uptake in the four cell lines. Treatment of cell lines with erlotinib, gefitinib, or vandetanib for 24 hours reduced hENT1 activity which was reversed by subsequent incubation in drug-free media for 24 hours. Greater cytotoxicity was observed when gemcitabine was administered before erlotinib, gefitinib, or vandetanib than when administered together and synergy, evaluated using the CalcuSyn Software, was observed in three cell lines resulting in combination indices under 0.6 at 50% reduction of cell growth. CONCLUSIONS: Vandetanib inhibited hENT1, hENT2, hCNT1, hCNT2, and hCNT3, whereas erlotinib inhibited hENT1 and hCNT3 and gefitinib inhibited hENT1 and hCNT1. The potential for reduced accumulation of nucleoside chemotherapy drugs in tumor tissues due to inhibition of hENTs and/or hCNTs by TKIs indicates that pharmacokinetic properties of these agents must be considered when scheduling TKIs and nucleoside chemotherapy in combination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".