Genetic Determinants of Busulfan Clearance and Outcomes in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation- Result of a Multicentric Prospective Study on Behalf of the Pediatric Disease Working Party of the European Blood and Marrow Transplantation Group
Bibliographic record
Abstract
Abstract Busulfan (BU) dose adjustment following therapeutic drug concentration monitoring improves outcomes of hematopoietic stem cell transplantation (HSCT). Further improvement could be achieved through genotype-based BU dose adjustments. Studies with large and homogeneous samples are essential to establish the utility of genetic factors as a predictor of BU dose and outcomes of HSCT. In this multicentric study, we included 139 children aged between 0.1 to 19.9 years (65 females) receiving BU based myeloablative conditioning regimen prior to allogeneic HSCT. Genetic predictors of BU plasma levels, dose adjustment, and clinical outcomes post HSCT were prospectively evaluated. Patients received BU in a conventional 6 h dosing schedule as a two hours infusion for four days in combination with cyclophosphamide (CY; n=106), or CY and etoposide (15) or melphalan and CY (15).The remaining patients received BU with melphalan (2) or fludarabine (1). BU first dose pharmacokinetic parameters (PKs) in patients were estimated and were adjusted to steady state concentrations (Css) of 600-900 ng/mL. All the patients were genotyped for defining GSTA1*A1,*A2, *B1 and *B2 haplotypes, GSTM1, GSTT1 null alleles, CYP2C9*2,*3, CYP2C19*2,*17, CYP2B6*5, *9 and GSTP1 1578A>G, GSTP1 2293 C>T polymorphisms. Standard criteria were followed for defining clinical outcomes of HSCT such as neutrophil recovery, platelet recovery, acute graft versus host disease (aGvHD), sinusoidal obstruction syndrome (SOS), event-free survival (EFS), non-relapse mortality (NRM), hemorrhagic cystitis (HC), and graft failure (Ther Drug Monit. 2014;36:93-9; Bone Marrow Transplant. 2013; 48:939-46; Pharmacogenomics J. 2014; 14:263-71). PKs such as BU clearance (CL), area under the curve concentration (AUC), and Css were compared among genotype groups using non-parametric tests. The PKs and genetic variants or haplotypes were also associated with the clinical outcomes individually or in combinations in both univariate and multivariate analysis. Statistical significance was set at p<0.05. Higher BU CL, and lower AUC and Css levels were seen in GSTA1*A2 carriers compared to the other patients (p<0.05). The effect was more apparent in patients with malignancies (n=86; p<0.02) and in females (n=65; p<0.01). Patients with two GSTA1*A2 copies needed a higher BU dose adjustment than the other patients (p=0.01). Higher incidences of NRM (26.1 vs 5.7 %, p<0.0001) and lower EFS in patients with BU Css above median 631.0 ng/mL (21.4 vs 55.1%, p<0.0001, 70 patients had first dose Css above 631 ng/mL) has also been documented. Higher frequency of SOS was seen in individuals with two copies of GSTA1*B haplotype (28.6 Vs 8.6%; p=0.02). Higher incidences of aGVHD grade I-IV was seen in homozygous carriers of GSTA1*B haplotype (61.5 vs 30.4 %; p=0.008) and seems to be further increased in patients having also GSTP1 1578 GG genotype (75% Vs 32.2 %; p=0.006). Patients with malignancies carrying both GSTM1 and T1 null genotype had lower EFS compared to the remaining patients (n=86; 11.1 vs 59.7 %, p<0.0001). Higher incidences of HC before day 30 was seen in individuals with GSTM1 functional alleles compared to null allele carriers (n=138; 23.4 Vs 8.2 %; p=0.01) and was further potentiated through interaction with CYP2C9 (n=104; 25.7 Vs 5.8 %; p=0.004). In pediatric patients receiving conditioning regimen based on two alkylating agents with BU as one of the components, dosing algorithms for BU based on demographics and GSTA1 haplotype may improve the outcomes of HSCT. These observations also indicate that therapeutic window of BU in children might not be similar to that of adult patients especially in GSTM1 and T1 null allele carriers. This data also highlight that presence of both GSTM1 and T1 null genotypes are the independent risk factors for the occurrence of events in malignant patients receiving HSCT. Disclosures No relevant conflicts of interest to declare.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".