Comparative in vitro evaluation of transportability and toxicity of capecitabine and its metabolites in cells derived from normal human kidney and renal cancers
Bibliographic record
Abstract
The goal of this study was to understand roles of nucleoside and nucleobase transport processes in capecitabine pharmacology in cells derived from human renal proximal tubule cells (hRPTCs) and three human renal cell carcinoma (RCC) cell lines, A498, A704, and Caki-1. Human equilibrative nucleoside transporters 1 and 2 (hENT1 and hENT2) mediated activities and a sodium-independent nucleobase activity were present in hRPTCs. In hRPTCs, uptake of 5'-deoxy-5-fluorouridine (DFUR), a nucleoside metabolite of capecitabine, was pH dependent with highest uptake seen at pH 6.0. In RCC cell lines, hENT1 was the major nucleoside transporter. Nucleobase transport activity was variable among the three RCC cell lines, with Caki-1 showing the highest and A498 showing the lowest activities. Treatment of RCC cell lines with interferon alpha (IFN-α) increased thymidine phosphorylase levels and prior treatment of RCC cell lines with IFN-α followed by 5-FU or DFUR resulted in enhanced sensitivity of all cell lines to 5-FU and two of three cell lines to DFUR. We report for the first time a nucleobase transport activity in hRPTCs and RCC cell lines. In addition, our in vitro cytotoxicity results showed that RCC cell lines differed in their response to 5-FU and DFUR and prior treatment with IFN-α potentiated cytotoxic response to metabolites of capecitabine.
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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 | 0.000 |
| 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".