Interactions of Multitargeted Kinase Inhibitors and Nucleoside Drugs: Achilles Heel of Combination Therapy?
Bibliographic record
Abstract
Multitargeted tyrosine kinase inhibitors (TKI) axitinib, pazopanib, and sunitinib are used to treat many solid tumors. Combination trials of TKIs with gemcitabine, a nucleoside anticancer drug, in pancreas, renal, lung, ovarian, and other malignancies resulted in little benefit to patients. TKI interactions with human nucleoside transporters (hNT) were studied by assessing inhibition of [(3)H]uridine uptake in yeast producing recombinant hNTs individually and in cultured human cancer cell lines. Axitinib, pazopanib, and sunitinib inhibited hENT1 at low micromolar concentrations. In A549, AsPC-1, and Caki-1 cells, [(3)H]uridine, [(3)H]thymidine, [(3)H]gemcitabine, and [(3)H]fluorothymidine (FLT) accumulation was blocked by all three TKIs. Pazopanib > axitinib ≥ sunitinib inhibited hENT1 with IC50 values of 2, 7, and 29 μmol/L, respectively, leading to reduced intracellular gemcitabine and FLT accumulation. Pretreatment or cotreatment of Caki-1 cells with TKIs reduced cellular accumulation of [(3)H]nucleosides, suggesting that TKI scheduling with nucleoside drugs would influence cytotoxicity. In combination cytotoxicity experiments that compared sequential versus simultaneous addition of drugs in Caki-1 cells, cytotoxicity was greatest when gemcitabine was added before TKIs. In clinical settings, TKI inhibitor concentrations in tumor tissues are sufficient to inhibit hENT1 activity, thereby reducing nucleoside chemotherapy drug levels in cancer cells and reducing efficacy in combination schedules. An additional unwanted interaction may be reduced FLT uptake in tumor tissues that could lead to aberrant conclusions regarding tumor 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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".