A tubule cell model for ifosfamide nephrotoxicity
Bibliographic record
Abstract
Mechanisms leading to ifosfamide (IF)-induced renal damage have not been fully elucidated. Recent work suggests that localized renal tubular metabolism of IF and the production of the nephrotoxic chloroacetaldehyde may lead to nephrotoxicity. Presently no pharmacological method to reduce IF nephrotoxicity has been identified. The objectives of this study were to establish a tubule cell model for IF nephrotoxicity, to verify whether renal proximal tubular cells have the necessary cytochrome P450 (CYP) enzymes to oxidize IF, and whether they can metabolize IF to chloroacetaldehyde. CYP3A, and 2B mRNA and protein were identified in LLCPK-1 cells. The cells metabolized the R- and S-IF enantiomers to their respective 2- and 3-dechloroethylifosfamide metabolites, by-products of chloroacetaldehyde formation. Metabolite production was both time and concentration-dependent. IF did not affect cell viability. In contrast, glutathione-depleted cells showed time and dose-dependent damage. The presence of the relevant CYP enzymes in renal tubular cells along with their ability to metabolize IF to its 2- and 3-dechloroethylifosfamide metabolites suggests that nephrotoxic damage may result from the localized production of chloroacetaldehyde. Glutathione is a major defence mechanism against IF toxicity, thus pharmacological methods for replenishing intracellular glutathione may be effective in modulating IF-induced nephrotoxicity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".