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Record W2023282269 · doi:10.1002/chir.20268

Enantioselective metabolism of ifosfamide by the kidney

2006· article· en· W2023282269 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChirality · 2006
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsIfosfamideChemistryMetaboliteNephrotoxicityEnantiomerEnantioselective synthesisKidneyMetabolismPharmacologyRacemic mixtureChirality (physics)BiochemistryChemotherapyStereochemistryCisplatinToxicityInternal medicineOrganic chemistryMedicineCatalysis

Abstract

fetched live from OpenAlex

Ifosfamide (IF), a potent chemotherapeutic agent for solid tumors, is known to cause high rates of nephrotoxicity, which is most likely due to the renal production of the metabolite chloroacetaldehyde. Enantioselective oxidation of IF has been shown in the liver but has never been reported in the kidney. Using porcine and human kidney samples, as well as the renal porcine cell line LLCPK-1, we document enantioselective metabolism of IF with prevalent production of the N-dechloroethylifosfamide (DCEIF) metabolites from the (S)-IF enantiomer compared to the amount of N-DCEIF metabolites produced from the (R)-IF enantiomers. Since IF enantiomers appear to be equally effective in chemotherapy, these results suggest that replacing the clinically standard racemic mixture of IF with (R)-IF may decrease renal metabolism of the drug and hence may decrease nephrotoxicity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.240
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it