Development of a Limited Sampling Strategy for the Estimation of Exposure to High-Dose Etoposide After Intravenous Infusion in Pediatric Patients. Issue 2 (April 2017)

• Record Type: Journal Article
• Authors: Danielak, Dorota; Sobiak, Joanna; Wachowiak, Jacek; Główka, Franciszek; Chrzanowska, Maria
• Is Part Of: Therapeutic drug monitoring. Volume 39:Issue 2(2017:Apr.)
• Publication Date: 2017-04
• Subjects: drug monitoring of etoposide -- allogeneic hematopoietic stem cell transplantation -- multiple regression analysis -- population pharmacokinetics -- acute lymphoblastic leukemia; Pharmacokinetics -- Periodicals; Patient monitoring -- Periodicals; Drugs -- Analysis -- Periodicals; Body fluids -- Analysis -- Periodicals; Drug Therapy -- Periodicals; Monitoring, Physiologic -- Periodicals; Pharmacology -- Periodicals; 615.7
• DOI: 10.1097/FTD.0000000000000368
• Languages: English
• ISSNs: 0163-4356

Abstract

Abstract : Background: Etoposide (VP-16), a podophyllotoxin derivative, is used in conditioning regimens before allogeneic hematopoietic stem cell transplantation in children with acute lymphoblastic leukemia. The aim of this study was to develop a limited sampling strategy (LSS) suitable for the prediction of exposure to VP-16 defined as area under time–concentration curve (AUC). Methods: The study included 28 pediatric patients with acute lymphoblastic leukemia, who were administered a 4-hour infusion of 60 mg/kg VP-16. VP-16 concentrations were determined in samples collected 4–124 hours after the beginning of infusion. On obtaining the pharmacokinetic (PK) profiles, a population PK model was developed in NONMEM (ICON Development Solutions, Hanover, MD) with first-order conditional estimation with interaction algorithm. LSSs were chosen by means of a multivariate regression analysis and cross-validated with a leave-one-out approach. Predictive performance of LSSs was assessed by calculating relative prediction error (PE), mean PE, mean absolute PE, and root mean squared PE for model-predicted and observed AUC. Results: VP-16 PKs was best described by a 2-compartment first-order model, and a large variability in the PK parameters was observed. A 3-sample strategy allowed the estimation of VP-16 with highest accuracy and precision (mean relative PE = 0.18%, 95% confidence interval, 1.73%–2.09%; mean absolute relative PE = 3.47%, 95% confidence interval, 2.28%–4.66%; root mean squared PE = 4.43%). The final equation was AUC = 6.85 × C6 h + 3.88 × C12 h + 46.11 × C28 h + 282.0 (adjusted R 2 = 0.9540). Conclusions: In conclusion, developed LSS allows accurate and precise estimation of VP-16 AUC and might be useful for therapeutic drug monitoring.