Cyclophosphamide pharmacokinetics and pharmacogenetics in children with B-cell non-Hodgkin's lymphoma
Bibliographic record
Abstract
INTRODUCTION: Variation in cyclophosphamide pharmacokinetics and metabolism has been highlighted as a factor that may impact on clinical outcome in various tumour types. The current study in children with B-cell non-Hodgkin's lymphoma (NHL) was designed to corroborate previous findings in a large prospective study incorporating genotype for common polymorphisms known to influence cyclophosphamide pharmacology. METHODS: A total of 644 plasma samples collected over a 5 year period, from 49 B-cell NHL patients ≤ 18 years receiving cyclophosphamide (250 mg/m(2)), were used to characterise a population pharmacokinetic model. Polymorphisms in genes including CYP2B6 and CYP2C19 were analysed. RESULTS: A two-compartment model provided the best fit of the population analysis. The mean cyclophosphamide clearance value following dose 1 was significantly lower than following dose 5 (1.83 ± 1.07 versus 3.68 ± 1.43 L/h/m(2), respectively; mean ± standard deviation from empirical Bayes estimates; P < 0.001). The presence of at least one CYP2B6*6 variant allele was associated with a lower cyclophosphamide clearance following both dose 1 (1.54 ± 0.11 L/h/m(2) versus 2.20 ± 0.31 L/h/m(2), P = 0.033) and dose 5 (3.12 ± 0.17 L/h/m(2) versus 4.35 ± 0.37 L/h/m(2), P = 0.0028), as compared to homozygous wild-type patients. No pharmacokinetic parameters investigated were shown to have a significant influence on progression free survival. CONCLUSION: The results do not support previous findings of a link between cyclophosphamide pharmacokinetics or metabolism and disease recurrence in childhood B-cell NHL. While CYP2B6 genotype was shown to influence pharmacokinetics, there was no clear impact on clinical outcome.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".