The AAPS Journal

, Volume 16, Issue 6, pp 1372–1379 | Cite as

Predicting Pediatric Age-Matched Weight and Body Mass Index

  • Sherwin K. B. Sy
  • Eduardo Asin-Prieto
  • Hartmut Derendorf
  • Emil Samara
Brief/Technical Note

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.

KEY WORDS

Age Allometry BMI Pediatric Weight 

Supplementary material

12248_2014_9657_MOESM1_ESM.pdf (169 kb)
ESM 1 (PDF 168 kb)

References

  1. 1.
    West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997;276(5309):122–6.PubMedCrossRefGoogle Scholar
  2. 2.
    Hu TM, Hayton WL. Allometric scaling of xenobiotic clearance: uncertainty versus universality. AAPS PharmSci. 2001;3(4):E29. doi: 10.1208/ps030429.PubMedCrossRefGoogle Scholar
  3. 3.
    Savage VM, Gillooly J, Woodruff W, West G, Allen A, Enquist B, et al. The predominance of quarter‐power scaling in biology. Funct Ecol. 2004;18(2):257–82.CrossRefGoogle Scholar
  4. 4.
    Nagilla R, Ward KW. A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans. J Pharm Sci. 2004;93(10):2522–34. doi: 10.1002/jps.20169.PubMedCrossRefGoogle Scholar
  5. 5.
    Mahmood I. Prediction of drug clearance in children from adults: a comparison of several allometric methods. Br J Clin Pharmacol. 2006;61(5):545–57. doi: 10.1111/j.1365-2125.2006.02622.x.PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Boxenbaum H. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J Pharmacokinet Biopharm. 1982;10(2):201–27.PubMedCrossRefGoogle Scholar
  7. 7.
    Boxenbaum H. Interspecies pharmacokinetic scaling and the evolutionary-comparative paradigm. Drug Metab Rev. 1984;15(5–6):1071–121. doi: 10.3109/03602538409033558.PubMedCrossRefGoogle Scholar
  8. 8.
    Holford NH. A size standard for pharmacokinetics. Clin Pharmacokinet. 1996;30(5):329–32.PubMedCrossRefGoogle Scholar
  9. 9.
    Mahmood I. Prediction of drug clearance in children: impact of allometric exponents, body weight, and age. Ther Drug Monit. 2007;29(3):271–8. doi: 10.1097/FTD.0b013e318042d3c4.PubMedCrossRefGoogle Scholar
  10. 10.
    Cella M, Knibbe C, Danhof M, Della Pasqua O. What is the right dose for children? Br J Clin Pharmacol. 2010;70(4):597–603.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Poggesi I, Benedetti MS, Whomsley R, Le Lamer S, Molimard M, Watelet JB. Pharmacokinetics in special populations. Drug Metab Rev. 2009;41(3):422–54. doi: 10.1080/10837450902891527.PubMedCrossRefGoogle Scholar
  12. 12.
    Tod M, Jullien V, Pons G. Facilitation of drug evaluation in children by population methods and modelling. Clin Pharmacokinet. 2008;47(4):231–43. doi: 10.2165/00003088-200847040-00002.PubMedCrossRefGoogle Scholar
  13. 13.
    Chatelut E. Population approaches in paediatrics. Fundam Clin Pharmacol. 2008;22(6):575–8. doi: 10.1111/j.1472-8206.2008.00647.x.PubMedCrossRefGoogle Scholar
  14. 14.
    Holford N. Dosing in children. Clin Pharmacol Ther. 2010;87(3):367–70. doi: 10.1038/clpt.2009.262.PubMedCrossRefGoogle Scholar
  15. 15.
    Sumpter AL, Holford NH. Predicting weight using postmenstrual age–neonates to adults. Paediatr Anaesth. 2011;21(3):309–15. doi: 10.1111/j.1460-9592.2011.03534.x.PubMedCrossRefGoogle Scholar
  16. 16.
    WHO. Working Group on Infant Growth. An evaluation of infant growth. Geneva: World Health Organization; 1994.Google Scholar
  17. 17.
    Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. CDC growth charts for the United States: methods and development. Vital Health Stat. 2002;11(246):1–190.Google Scholar
  18. 18.
    Victora CG, Morris SS, Barros FC, de Onis M, Yip R. The NCHS reference and the growth of breast- and bottle-fed infants. J Nutr. 1998;128(7):1134–8.PubMedGoogle Scholar
  19. 19.
    Dibley MJ, Goldsby JB, Staehling NW, Trowbridge FL. Development of normalized curves for the international growth reference: historical and technical considerations. Am J Clin Nutr. 1987;46(5):736–48.PubMedGoogle Scholar
  20. 20.
    Growth chart datasets from both the CDC and WHO are downloadable from http://www.cdc.gov/growthcharts/who_charts.htm
  21. 21.
    Bouazza N, Urien S, Hirt D, Frange P, Rey E, Benaboud S, et al. Population pharmacokinetics of tenofovir in HIV-1-infected pediatric patients. J Acquir Immune Defic Syndr. 2011;58(3):283–8. doi: 10.1097/QAI.0b013e3182302ea8.PubMedCrossRefGoogle Scholar
  22. 22.
    Sy SK, Innes S, Derendorf H, Cotton MF, Rosenkranz B. Estimation of intracellular concentration of stavudine-triphosphate in HIV-infected children given the reduced dose of 0.5 mg/kg twice daily. Antimicrob Agents Chemother. 2013. doi:  10.1128/AAC.01717-13.
  23. 23.
    Strougo A, Eissing T, Yassen A, Willmann S, Danhof M, Freijer J. First dose in children: physiological insights into pharmacokinetic scaling approaches and their implications in paediatric drug development. J Pharmacokinet Pharmacodyn. 2012;39(2):195–203. doi: 10.1007/s10928-012-9241-9.PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2014

Authors and Affiliations

  • Sherwin K. B. Sy
    • 1
  • Eduardo Asin-Prieto
    • 1
  • Hartmut Derendorf
    • 1
  • Emil Samara
    • 2
  1. 1.Department of Pharmaceutics, College of PharmacyUniversity of FloridaGainesvilleUSA
  2. 2.PharmaPolaris InternationalEl MaceroUSA

Personalised recommendations