Busulfan Pharmacokinetics in Adenosine Deaminase-Deficient Severe Combined Immunodeficiency Gene Therapy
Bibliographic record
Abstract
The pharmacokinetics of low-dose busulfan (BU) were investigated as a nonmyeloablative conditioning regimen for autologous gene therapy (GT) in pediatric subjects with adenosine deaminase-deficient severe combined immunodeficiency disease (ADA SCID). In 3 successive clinical trials, which included either γ-retroviral (γ-RV) or lentiviral (LV) vectors, subjects were conditioned with BU using different dosing nomograms. The first cohort received BU doses based on body surface area (BSA), the second cohort received doses based on actual body weight (ABW), and in the third cohort, therapeutic drug monitoring (TDM) was used to target a specific area under the concentration-time curve (AUC). Neither BSA-based nor ABW-based dosing achieved a consistent cumulative BU AUC; in contrast, TDM-based dosing led to more consistent AUC. BU clearance increased as subject age increased from birth to 18 months. However, weight and age alone were insufficient to accurately predict the dose that would consistently achieve a target AUC. Furthermore, various clinical, laboratory, and genetic factors (eg, genotypes for glutathione-S-transferase isozymes known to participate in BU metabolism) were analyzed, but no single finding predicted subjects with rapid versus slow clearance. Analysis of BU AUC and the postengraftment vector copy number (VCN) in granulocytes, a surrogate marker of the level of engrafted gene-modified hematopoietic stem and progenitor cells (HSPCs), demonstrated gene marking at levels sufficient for therapeutic benefit in the subjects who had achieved the target BU AUC. Although many factors determine the ultimate engraftment following GT, this work demonstrates that the BU AUC correlated with the eventual level of engrafted gene-modified HSPCs within a vector group (γ-RV versus LV), with significantly higher levels of granulocyte VCN in the recipients of LV-modified grafts compared to recipients of γ-RV-transduced grafts. Taken together, these findings provide insight into low-dose BU pharmacokinetics in the unique setting of autologous GT for ADA SCID, and these dosing principles may be applied to future GT trials using low-dose BU to open the bone marrow niche.
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 | 0.000 | 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 | 0.001 |
| 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".