Pharmacokinetic-Targeting and Dose-Adjustment of I.V. Busulfan for Myeloablative Conditioning in Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Introduction: Pharmacokinetically (PK)-targeting intravenous busulfan improves outcomes following allogeneic hematopoietic cell transplantation (HCT). However, the optimal busulfan area under the curve (AUC) likely differs based on the specific conditioning regimen, and the target busulfan exposure is unknown in combination with fludarabine and low-dose total body irradiation (TBI). Methods: This study included 1019 adult HCT recipients that received myeloablative conditioning including fludarabine, busulfan, anti-thymocyte globulin, and low-dose (4cGy) TBI. Busulfan was administered as a total dose of ~3.2mg/kg given equally from days -5 to -2 pre-transplant. Total AUC was estimated using measurements of serial serum samples. Multivariate Cox and Fine-Gray regression were used for comparison of AUC subgroups. The primary outcomes of interest were relapse-free survival (RFS) and overall survival (OS). Results: Median AUC was 62.3 mg∙hr/L (range: 39.4-128.0 mg∙hr/L). Total AUC exposure of 49.3-57.5 mg∙hr/L was associated with greater RFS (67% vs. 47%, HR=1.82, P=0.014) and OS (71% vs. 46%, HR=1.99, P=0.008) compared to patients with higher AUCs of 57.5-73.9 mg∙hr/L. Although very low (<49.3 mg∙hr/L) or very high (>73.9 mg∙hr/L) AUCs trended towards worse RFS and OS compared to 49.3-57.5 mg∙hr/L, this analysis was limited by the small number of patients with extreme AUCs and did not reach statistical significance. Except potentially for patients with a high/very high HCT disease risk index, 49.3-57.5 mg∙hr/L appeared to be the optimal AUC regardless of patient sex, age, or primary disease. Conclusion: Within the evaluated AUC range, 49.3-57.5 mg∙h/L appeared to be associated with the most favourable survival. Pharmacokinetic-targeting to this range may improve outcomes.
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 | 0.000 |
| 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) | 0.000 | 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".