Uridine glucuronosyltransferase 2B7 pharmacogenetics predicts epirubicin clearance and myelosuppression
Bibliographic record
Abstract
2504 Background: Epirubicin (EPI) is widely used to treat breast cancer. EPI is predominantly metabolized by uridine glucuronosyltransferase (UGT) 2B7 to inactive glucuronides. We previously showed that a UGT enhancer single nucleotide polymorphism (SNP) at position -161 T to C correlated with rates of morphine glucuronidation. Methods: We performed a prospective pharmacogenetic study of effects of this SNP on EPI metabolism in M0 breast cancer patients (PTS) receiving adjuvant or neoadjuvant FEC100 (5-fluorouracil 500 mg/m2, EPI 100 mg/m2 and cyclophosphamide 500 mg/m2) given every 3 wks. PTS with ALT and AST ≤ upper limit of normal (ULN), a total bilirubin ≤ ULN, and normal renal and cardiac function were eligible. EPI levels were drawn at approximately 1 and 24 hrs. Cycle 1 toxicities were assessed using NCIC CTG toxicity criteria. Results: 123 PTS entered this study, mean (range): age 51 (28 - 74), sex 122 F/ 1 M, baseline AST 24 U/L (13–66), ALT 22 U/L (5–90), bilirubin 8 μmol/L (2–26), creatinine 74 μmol/L (50 - 126). PTS were genotyped using Pyrosequencing; 26 PTS were TT homozygotes, 59 were CT heterozygotes, and 33 were CC homozygotes. 5 PTS could not be genotyped. A three compartment population pharmacokinetic model in NONMEM V 1.1 for EPI was used incorporating all PTS. The baseline objective function was 1817, and inclusion of genotype significantly improved the objective function to 1764; CC genotype PTS had decreased EPI clearance 88.9 L/hr compared to CT/TT genotype PTS 129 L/hr, p<0.001. Rates of first cycle grade 3/4 leucopenia were 78% in CC PTS and 48% in CT/TT PTS; consistent with the pharmacokinetic analysis. Conclusions: A SNP in UGT 2B7 is common and appears to predicts EPI clearance and myelosuppresion in non-metastatic breast cancer PTS and may form the basis for a method to individualize EPI treatment. No significant financial relationships to disclose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".