Effect of Tyrosine Kinase Inhibitors on 6‐Mercaptopurine Cytotoxicity: Interactions with Equilibrative Nucleobase Transporter 1
Bibliographic record
Abstract
Background Acute lymphoblastic leukemia (ALL) is the most prevalent malignancy in children and adolescents in the developed world. The nucleobase analogue 6‐mercaptopurine (6‐MP) is a key component in the treatment of pediatric ALL, particularly for the maintenance of remission. While most patients tolerate 6‐MP well, intra‐individual variability between patients continues to complicate attainment of target drug efficacy with low off‐target toxicities at standardized doses of 6‐MP. Drug transporter expression is a known factor for patient variability in drug response/toxicity. We have recently established that the SLC43A3‐encoded transporter, equilibrative nucleobase transporter 1 (ENBT1), is the primary mechanism by which 6‐MP enters cells. ENBT1 expression levels in human leukemia cell lines directly correlate with the rate of 6‐MP uptake and resulting cytotoxicity. Tyrosine kinase inhibitors (TKI) are used in combination with 6‐MP for treating some subtypes of ALL, i.e., Philadelphia chromosome‐positive ALL. Previous studies have indicated that TKI can interfere with nucleobase uptake by cells, and thus may compete for ENBT1. Therefore, it is important to establish the existence of potential drug‐drug interactions between 6‐MP and TKIs that may affect therapeutic efficacy. Hypothesis We hypothesized that TKI can inhibit ENBT1‐mediated 6‐MP transport and thereby reduce the therapeutic efficacy of 6‐MP in ALL cell lines. Methods [ 3 H]Adenine was used in an oil‐stop centrifugation assay to assess ENBT1‐mediated uptake activity in a SLC43A3‐transfected human embryonic kidney 293 (HEK293) cell line in the presence and absence of various TKI. To determine impact on 6‐MP toxicity, a well‐established human ALL (MOLT‐4) cell line was incubated for 48 hours with a range of 6‐MP concentrations (78 nM – 1.28 mM), plus or minus a defined concentration of TKI and assessed for their cell viability via the MTT assay. Results An initial screen showed significant inhibition of ENBT1‐mediated [ 3 H]adenine uptake by various TKI at 10 μM: gefitinib >> imatinib = dasatinib (one‐way ANOVA, F 4,26 =22, p<0.0001, n=6). Subsequent concentration‐inhibition analyses showed a gefitinib IC 50 of 5.5 ± 0.8 μM for inhibiting [ 3 H]adenine uptake in MOLT‐4 cells (n=5). Gefitinib alone also decreased MOLT‐4 cell viability with an EC 50 of 90 ± 68 μM (n=5). From these data, 3 μM gefitinib was selected as a concentration that did not affect MOLT‐4 cell viability but did inhibit ENBT1‐mediated adenine uptake by approximately 30%. Co‐administration of 6‐MP and 3 μM gefitinib in MOLT‐4 cells resulted in a significant 2‐fold increase in the cytotoxicity of 6‐MP: Control EC 50 =1.5 ± 0.6 μM vs Gefitinib EC 50 = 0.72 ± 0.19 μM (unpaired T‐test, t 8 =2.763, p=0.0245. n=5). Conclusion Our results show that TKI can significantly inhibit ENBT1‐mediated transport of nucleobases by ENBT1. However, contrary to expectations, gefitinib increased the cytotoxicity of 6‐MP. We hypothesize gefitinib inhibits xanthine oxidase, an enzyme that metabolizes 6‐MP into an inactive and readily excreted metabolite. Additional work is needed to confirm the mechanism(s) underlying this synergistic effect of gefitinib on 6‐MP cytotoxicity.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".