Elizabeth Marek1, Jeremiah D Momper2, Ronald N Hines3, Cheryl M Takao4, Joan C Gill3, Vera Pravica5, Andrea Gaedigk6, Gilbert J Burckart7, Kathleen A Neville8. 1. Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, Pennsylvania. 2. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California. 3. Department of Pediatrics, Medical College of Wisconsin, City, Milwaukee, Wisconsin. 4. Division of Cardiology, Children's Hospital of Los Angeles, Los Angeles, California. 5. Institute of Microbiology and Immunology, School of Medicine, University of Belgrade, Belgrade, Serbia. 6. Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, and School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri. 7. Pediatric Clinical Pharmacology Staff, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland. 8. Section of Pharmacology & Toxicology, University of Arkansas for Medical Sciences/Arkansas Children's Hospital, Little Rock, Arkansas.
Abstract
OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R(2)), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose. RESULTS: Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from -2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R(2) = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses. CONCLUSIONS: Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed.
OBJECTIVES: The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial. METHODS: Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R(2)), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose. RESULTS: Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from -2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R(2) = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses. CONCLUSIONS: Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed.
Authors: Tina T Biss; Peter J Avery; Leonardo R Brandão; Elizabeth A Chalmers; Michael D Williams; John D Grainger; Julian B S Leathart; John P Hanley; Ann K Daly; Farhad Kamali Journal: Blood Date: 2011-10-18 Impact factor: 22.113
Authors: Nita A Limdi; Donna K Arnett; Joyce A Goldstein; T Mark Beasley; Gerald McGwin; Brian K Adler; Ronald T Acton Journal: Pharmacogenomics Date: 2008-05 Impact factor: 2.533
Authors: Mitchell K Higashi; David L Veenstra; L Midori Kondo; Ann K Wittkowsky; Sengkeo L Srinouanprachanh; Fred M Farin; Allan E Rettie Journal: JAMA Date: 2002-04-03 Impact factor: 56.272
Authors: Alan H B Wu; Ping Wang; Andrew Smith; Christine Haller; Katherine Drake; Mark Linder; Roland Valdes Journal: Pharmacogenomics Date: 2008-02 Impact factor: 2.533
Authors: Anna-Karin Hamberg; Lena E Friberg; Katarina Hanséus; Britt-Marie Ekman-Joelsson; Jan Sunnegårdh; Anders Jonzon; Bo Lundell; E Niclas Jonsson; Mia Wadelius Journal: Eur J Clin Pharmacol Date: 2013-01-11 Impact factor: 2.953
Authors: Nguyenvu Nguyen; Peter Anley; Margaret Y Yu; Gang Zhang; Alexis A Thompson; Larry J Jennings Journal: Pediatr Cardiol Date: 2012-11-25 Impact factor: 1.655