GSTA1 Genetic Variants and Conditioning Regimen: Missing Key Factors in Dosing Guidelines of Busulfan in Pediatric Hematopoietic Stem Cell Transplantation
Bibliographic record
Abstract
Busulfan (Bu) is a key component of conditioning regimens used before hematopoietic stem cell transplantation (SCT) in children. Different predictive methods have been used to calculate the first dose of Bu. To evaluate the necessity of further improvements, we retrospectively analyzed the currently available weight- and age-based guidelines to calculate the first doses in 101 children who underwent allogenic SCT in CHU Sainte-Justine, Montreal, after an intravenous Bu-containing conditioning regimen according to genetic and clinical factors. The measured areas under the curve (AUCs) were within target (900 to 1500 µM/min) in 38.7% of patients after the administration of the first dose calculated based on age and weight, as locally recommended. GSTA1 diplotypes linked to poor Bu metabolism (G3) and fludarabine-containing regimens were the only factors associated with AUC within target (OR, 4.7 [95% CI, 1.1 to 19.8, P = .04]; and OR, 9.9 [95% CI, 1.6 to 61.7, P = .01], respectively). From the 11 methods selected for dose calculation, the percentage of AUCs within the target varied between 16% and 74%. In some models G3 was associated with AUCs within the therapeutic and the toxic range, whereas rapid metabolizers (G1) were correlated with subtherapeutic AUCs when different methods were used. These associations were confirmed by clearance-prediction analysis, in which GSTA1 diplotypes consistently influenced the prediction errors of the methods. These findings suggest that these factors should be considered in Bu dose prediction in addition to the anthropometric data from patients. Furthermore, our data indicated that GSTA1 diplotypes was a factor that should be included in future population pharmacokinetic models, including similar conditioning regiments, to improve the prediction of Bu exposure after its initial dose.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".