P82 Is busulfan clearance different in patients with sickle cell disease (SCD) compared to patients without sickle cell disease?
Bibliographic record
Abstract
Background Haematopoietic stem cell transplantation (HSCT) is the only current curative treatment for Sickle Cell Disease (SCD), with potential life-threatening consequences. Busulfan is an alkylating agent used in HSCT conditioning regimen. Because of its narrow therapeutic window, determining the optimal first dose a priori remains a challenge. Busulfan is metabolized in the liver by conjugation with glutathione, which is catalyzed by Glutathione-S-Transferases (GSTs). GSTA1 is a known determinant of Busulfan clearance1 2 (just like age and weight) suggesting that those characteristics should be known a priori to adjust the first dose of Busulfan. Haemoglobinopathies (SCD and thalassemia) are not associated with changes in Busulfan clearance in a recent study.3 However, SCD is known to alter pharmacokinetics of other drugs.4 5 As it leads namely to liver dysfunction6 it may affect busulfan pharmacokinetics independently from genetic or anthropometric factors. Our aim is to compare the clearance of the first dose of Bu between patients with and without SCD, considering other constitutional factors. Methods Patients with SCD were paired to patients without SCD on known Busulfan clearance’s covariates including GSTA1 group, age and frequency of administration. Data were collected retrospectively from the HSCT Unit database at Sainte-Justine Hospital and also used in previous studies.1 2 Weight adjusted clearance was compared between the two paired groups using a mixed procedure on SAS software. Results Among the 129 patients included, 16 had SCD. Each patient was matched with up to 4 controls (total of 50 controls). Mean weight adjusted clearance was 3.04 ml/min/kg [SD:0,18] in patients with SCD versus 3.11 ml/min/kg [SD:0,14] in controls (difference 0.07 ml/min/kg F=0,14 p>F=0.714). Conclusions The diagnosis of SCD did not reveal to influence independently the clearance of the first dose of Bu. Consequently, no dose tailoring is needed in those patients only by the fact of being affected by SCD. References Ansari M, Curtis PH-D, Uppugunduri CRS, Rezgui MA, Nava T, Mlakar V, et al. GSTA1 diplotypes affect busulfan clearance and toxicity in children undergoing allogeneic hematopoietic stem cell transplantation: a multicenter study. Oncotarget 2017 Oct 31;8(53). Nava T, Kassir N, Rezgui MA, Uppugunduri CRS, Curtis PH-D, Duval M, et al. Incorporation of GSTA1 genetic variations into a population pharmacokinetic model for IV busulfan in paediatric hematopoietic stem cell transplantation. British Journal of Clinical Pharmacology. 84(7):1494–504. McCune JS, Baker KS, Blough DK, Gamis A, Bemer MJ, Kelton-Rehkopf MC, et al. Variation in prescribing patterns and therapeutic drug monitoring of intravenous busulfan in pediatric hematopoietic cell transplant recipients. Journal of Clinical Pharmacology. 2013 Mar;53(3):264–75. Maksoud E, Koehl B, Kaguelidou F, et al. Population Pharmacokinetics of Cefotaxime and Dosage Recommendations in Children with Sickle Cell Disease. Antimicrob Agents Chemother 2018 Apr;62(4). Dampier CD, Setty BN, Logan J, Ioli JG, Dean R. Intravenous morphine pharmacokinetics in pediatric patients with sickle cell disease. J Pediatr 1995 Mar;126(3):461–7. Gremse DA, Fillingim E, Hoff CJ, Wells DJ, Boerth RC. Hepatic function as assessed by lidocaine metabolism in sickle cell disease. J Pediatr 1998 Jun;132(6):989–93. Disclosure(s) Nothing to disclose
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".