Literature DB >> 25155824

Predicting pediatric age-matched weight and body mass index.

Sherwin K B Sy1, Eduardo Asin-Prieto, Hartmut Derendorf, Emil Samara.   

Abstract

The empirical scaling from adult to pediatric using allometric size adjustments based on body weight continued to be the mainstream method for pediatric dose selection. Due to the flexibility of a polynomial function to conform to the data trend, an empirical function for simulating age-matched weight and body mass index by gender in the pediatric population is developed by using a polynomial function and a constant coefficient to describe the interindividual variability in weight. A polynomial of up to fifth order sufficiently described the pediatric data from the Center for Disease Control (CDC) and the World Health Organization (WHO). The coefficients of variation to describe the variability were within 17%. The percentages of the CDC simulated weights for pediatrics between 0 and 5 years that fell outside the WHO 90% and 95% confidence boundaries were well within the expected percentage values, indicating that the CDC dataset can be used to substitute for the WHO dataset for the purpose of pediatric drug development. To illustrate the utility of this empirical function, the CDC-based age-matched weights were simulated and were used in the prediction of the concentration-time profiles of tenofovir in children based on a population pharmacokinetic model whose parameters were allometrically scaled. We have shown that the resulting 95% prediction interval of tenofovir in newborn to 5 years of age was almost identical whether the weights were simulated based on WHO or CDC dataset. The approach is simple and is broadly applicable in adjusting for pediatric dosages using allometry.

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Year:  2014        PMID: 25155824      PMCID: PMC4389747          DOI: 10.1208/s12248-014-9657-9

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  20 in total

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Journal:  Clin Pharmacokinet       Date:  1996-05       Impact factor: 6.447

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Authors:  Iftekhar Mahmood
Journal:  Br J Clin Pharmacol       Date:  2006-05       Impact factor: 4.335

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Journal:  Drug Metab Rev       Date:  1984       Impact factor: 4.518

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7.  2000 CDC Growth Charts for the United States: methods and development.

Authors:  Robert J Kuczmarski; Cynthia L Ogden; Shumei S Guo; Laurence M Grummer-Strawn; Katherine M Flegal; Zuguo Mei; Rong Wei; Lester R Curtin; Alex F Roche; Clifford L Johnson
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8.  Estimation of intracellular concentration of stavudine triphosphate in HIV-infected children given a reduced dose of 0.5 milligrams per kilogram twice daily.

Authors:  Sherwin K B Sy; Steve Innes; Hartmut Derendorf; Mark F Cotton; Bernd Rosenkranz
Journal:  Antimicrob Agents Chemother       Date:  2013-12-02       Impact factor: 5.191

9.  A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans.

Authors:  Rakesh Nagilla; Keith W Ward
Journal:  J Pharm Sci       Date:  2004-10       Impact factor: 3.534

10.  The NCHS reference and the growth of breast- and bottle-fed infants.

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Journal:  J Nutr       Date:  1998-07       Impact factor: 4.798

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3.  Physiologically based pharmacokinetic modeling of candesartan related to CYP2C9 genetic polymorphism in adult and pediatric patients.

Authors:  Eui Hyun Jung; Chang-Keun Cho; Pureum Kang; Hye-Jung Park; Yun Jeong Lee; Jung-Woo Bae; Chang-Ik Choi; Choon-Gon Jang; Seok-Yong Lee
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4.  Prediction of Tissue Exposures of Meropenem, Colistin, and Sulbactam in Pediatrics Using Physiologically Based Pharmacokinetic Modeling.

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Journal:  Clin Pharmacokinet       Date:  2022-08-10       Impact factor: 5.577

5.  Pharmacodynamic Evaluation of the Potential Clinical Utility of Fosfomycin and Meropenem in Combination Therapy against KPC-2-Producing Klebsiella pneumoniae.

Authors:  James Albiero; Sherwin K B Sy; Josmar Mazucheli; Silvana Martins Caparroz-Assef; Bruno Buranello Costa; Janio Leal Borges Alves; Ana Cristina Gales; Maria Cristina Bronharo Tognim
Journal:  Antimicrob Agents Chemother       Date:  2016-06-20       Impact factor: 5.191

6.  Reduction of quantitative systems pharmacology models using artificial neural networks.

Authors:  Abdallah Derbalah; Hesham S Al-Sallami; Stephen B Duffull
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  6 in total

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