Influence of glutathione S-transferase gene polymorphisms on busulfan pharmacokinetics and outcome of hematopoietic stem-cell transplantation in thalassemia pediatric patients
Bibliographic record
Abstract
Hematopoietic stem-cell transplantation (HSCT) is currently the only curative therapeutic option for the treatment of thalassemia. In spite of the high cure rate, HSCT can lead to life-threatening adverse events in some patients. Busulfan (Bu) is a key component of the conditioning regimen prior to HSCT. Inter-individual differences in Bu pharmacokinetics (PK) are hypothesized to influence Bu efficacy and toxicity. Since Bu is mainly metabolized by glutathione S-transferase (GST), we investigated the relationship of GSTA1 and GSTM1 genotypes with first-dose PK and HSCT outcomes in 44 children with thalassemia intermedia and thalassemia major. All children received a myeloablative conditioning regimen with IV Bu. Association analysis revealed a relationship between GSTA169C>T (or haplotype *A/*B) and first Bu dose PK that was dependent on sex and Pesaro risk classification (PRC). Among female patients and patients with PRC I-II, homozygous individuals for the GSTA1T-69 allele defining haplotype *B, had higher Bu exposure and lower clearance (P⩽0.01). Association with HSCT outcomes showed that patients with the GSTM1 null genotypes had higher occurrence of regimen-related toxicity (P=0.01). These results suggest that GST genotypes could be useful to tailor the first Bu dose accordingly to improve HSCT 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 | 0.000 | 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) | 0.001 | 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".