Pharmacokinetic analysis for model-supported therapeutic drug monitoring of busulfan in Japanese pediatric hematopoietic stem cell transplantation recipients.
Yuichi ImanakaDaiichiro HasegawaKei IrieAkira OkadaSayaka NakamuraAkihiro TamuraNobuyuki YamamotoAiko KozakiAtsuro SaitoToshiaki IshidaShoji FukushimaYoshiyuki KosakaPublished in: Pediatric transplantation (2020)
This prospective observational study analyzed the pharmacokinetics of busulfan in Japanese children and evaluated the predicting accuracy of previous pediatric PPK models of busulfan. This study enrolled five patients (aged 2-12 years, BW 14-48 kg) receiving a busulfan-based conditioning regimen for hematopoietic stem cell transplantation at our hospital between January 2017 and December 2018. All patients received a 2-hour intravenous busulfan infusion four times daily for a total of 16 doses. After the infusions, 51 plasma samples were collected with the plasma busulfan concentration measured by liquid chromatography-tandem mass spectrometry. PPK model fitting was analyzed using the (%MPE) and the (%MAPE). Limited sampling strategies for estimating busulfan AUC were also evaluated. High interpatient variability was observed in the PK parameters. The most suitable PPK model that reflected our data was McCune's two-compartment model (%MPE -8.7, %MAPE 19.3). A combination sampling method using the busulfan concentration at 2 and 6 hours after the start of the first busulfan dose was found to be able to estimate AUC4 day . These results provide useful information on busulfan therapeutic drug monitoring in the Japanese pediatric population.
Keyphrases
- allogeneic hematopoietic stem cell transplantation
- end stage renal disease
- liquid chromatography tandem mass spectrometry
- ejection fraction
- newly diagnosed
- acute myeloid leukemia
- chronic kidney disease
- prognostic factors
- healthcare
- young adults
- physical activity
- peritoneal dialysis
- emergency department
- simultaneous determination
- mass spectrometry
- low dose
- machine learning
- patient reported
- artificial intelligence
- liquid chromatography