Skip to main content
Log in

Prediction of Drug Clearance in Children: an Evaluation of the Predictive Performance of Several Models

  • Research Article
  • Published:
The AAPS Journal Aims and scope Submit manuscript

Abstract

The objective of this study is to evaluate the predictive performance of several models to predict drug clearance in children ≤5 years of age. Six models (allometric model (data-dependent exponent), fixed exponent of 0.75 model, maturation model, body weight-dependent model, segmented allometric model, and age-dependent exponent model) were evaluated in this study. From the literature, the clearance values for six drugs from neonates to adults were obtained. External data were used to evaluate the predictive performance of these models in children ≤5 years of age. With the exception of a fixed exponent of 0.75, the mean predicted clearance in most of the age groups was within ≤50% prediction error. Individual clearance prediction was erratic by all models and cannot be used reliably to predict individual clearance. Maturation, body weight-dependent, and segmented allometric models to predict clearances of drugs in children ≤5 years of age are of limited practical value during drug development due to the lack of availability of data. Age-dependent exponent model can be used for the selection of first-in-children dose during drug development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE. Developmental pharmacology—drug disposition, action, and therapy in infants and children. N Engl J Med. 2003;349:1157–67.

    Article  PubMed  CAS  Google Scholar 

  2. Gibaldi M. Gastrointestinal absorption: Physicochemical considerations. In: Biopharmaceutics and clinical pharmacokinetics. 3rd ed. Philadelphia: Lea and Febiger; 1984.

    Google Scholar 

  3. McNammara PJ, Alcorn J. Protein binding predictions in infants. AAPS PharmaSci. 2002;4:1–8.

    Google Scholar 

  4. Blanco JG, Harrison PL, Evans WE, et al. Human cytochrome P450 maximal activities in pediatric versus adult liver. Drug Metab Disp. 2000;28:379–82.

    CAS  Google Scholar 

  5. Cresteil T. Onset of xenobiotic metabolism in children: toxicological implications. Food Addit Contam. 1998;15:45–51.

    Article  PubMed  CAS  Google Scholar 

  6. Loebstein R, Koren G. Clinical pharmacology and therapeutic drug monitoring in neonates and children. Pediatr Rev. 1998;19:423–8.

    PubMed  CAS  Google Scholar 

  7. Mahmood I. Dose selection in children: allometry and other methods. In: Pediatric pharmacology and pharmacokinetics. Rockville: Pine House Publishers; 2008. p. 184–216.

    Google Scholar 

  8. Alcorn J, McNamara PJ. Ontogeny of hepatic and renal systemic clearance pathways in infants: part I. Clin Pharmacokinet. 2002;41:959–98.

    Article  PubMed  CAS  Google Scholar 

  9. Alcorn J, McNamara PJ. Ontogeny of hepatic and renal systemic clearance pathways in infants: part II. Clin Pharmacokinet. 2002;41:1077–94.

    Article  PubMed  CAS  Google Scholar 

  10. Hayton WL. Maturation and growth of renal function: dosing renally cleared drugs in children. AAPS PharmSci. 2000;2:article 3 (1-7).

  11. Hayton WL, Kneer J, de Groot R, Stoeckel K. Influence of maturation and growth on cefetamet pivoxil pharmacokinetics: rational dosing for infants. Antimicrob Agents Chemother. 1996;40:567–74.

    PubMed  CAS  PubMed Central  Google Scholar 

  12. Boxenbaum H. Interspecies pharmacokinetic scaling and the evolutionary-comparative paradigm. Drug Metab Rev. 1984;15:1071–121.

    Article  PubMed  CAS  Google Scholar 

  13. Mahmood I. Introduction to allometry. In: Interspecies pharmacokinetic scaling: principles and application of allometric scaling, pp:23-38; Pine House Publishers, Rockville, MD; 2005.

  14. Cella M, Zhao W, Jacqz-Aigrain E, Burger D, Danhof M, Della PO. Paediatric drug development: are population models predictive of pharmacokinetics across paediatric populations? Br J Clin Pharmacol. 2011;72:454–64.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  15. Santen G, Horrigan J, Danhof M, Della Pasqua O. From trial and error to trial simulation. Part 2: an appraisal of current beliefs in the design and analysis of clinical trials for antidepressant drugs. Clin Pharmacol Ther. 2009;86:255–62.

    Article  PubMed  CAS  Google Scholar 

  16. Anand KJ, Anderson BJ, Holford NH, NEOPAIN Trial Investigators Group, et al. Morphine pharmacokinetics and pharmacodynamics in preterm and term neonates: secondary results from the NEOPAIN trial. Br J Anaesth. 2008;101:680–9.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  17. Anderson BJ. Pediatric models for adult target-controlled infusion pumps. Paediatr Anaesth. 2010;20:223–32.

    Article  PubMed  Google Scholar 

  18. Anderson BJ, Larsson P. A maturation model for midazolam clearance. Paediatr Anaesth. 2011;21(3):302–8.

    Article  PubMed  Google Scholar 

  19. Wang C, Sadhavisvam S, Krekels EH, Dahan A, Tibboel D, Danhof M, et al. Developmental changes in morphine clearance across the entire paediatric age range are best described by a bodyweight-dependent exponent model. Clin Drug Investig. 2013;33:523–34.

    Article  PubMed  Google Scholar 

  20. Wang C, Peeters MY, Allegaert K, van Blussé van Oud-Alblas HJ, Krekels EH, Tibboel D, et al. A bodyweight-dependent allometric exponent for scaling clearance across the human life-span. Pharm Res. 2012;29:1570–81.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  21. Wang C, Allegaert K, Peeters MY, Tibboel D, Danhof M, Knibbe CA. The allometric exponent for scaling clearance varies with age: a study on seven propofol datasets ranging from preterm neonates to adults. Br J Clin Pharmacol. 2014;77:149–59.

    Article  PubMed  CAS  Google Scholar 

  22. Bartelink IH, Boelens JJ, Bredius RG, Egberts AC, Wang C, Bierings MB, et al. Body weight-dependent pharmacokinetics of busulfan in paediatric haematopoietic stem cell transplantation patients: towards individualized dosing. Clin Pharmacokinet. 2012;51:331–45.

    Article  PubMed  CAS  Google Scholar 

  23. Guidance for Industry: Clinical Investigation of Medicinal Products in the Pediatric Population. U.S. Department of Health and Human Services and ICH, 2000.

  24. Mahmood I. Allometric exponents and population pharmacokinetics: a single or body weight dependent exponents. In: Pharmacokinetic allometric scaling in pediatric drug development. Rockville, MD, USA; Pine House Publishers, 2013; pp:121-137.

  25. Mahmood I. Prediction of drug clearance in preterm and term neonates: different exponents for different age groups. In: Pharmacokinetic allometric scaling in pediatric drug development. Rockville, MD, USA; Pine House Publishers, 2013; pp:88-109.

  26. Mahmood I. Prediction of clearance in children from adult clearance: allometric scaling versus exponent 0.75. In: Pharmacokinetic allometric scaling in pediatric drug development, pp41-55, 2013; Pine House Publishers, Rockville, MD.

  27. Mahmood I. Prediction of drug clearance in children (≤5 years) by Boxenbaum coefficient methods. In: Pharmacokinetic allometric scaling in pediatric drug development. Rockville, MD, USA; Pine House Publishers, 2013; pp:64-77

  28. Brody S. Bioenergetics and growth, with special reference to the efficiency complex in domestic animals. 1945; London: Hafner Press, New York, and MacMillan Publishers.

  29. Hayssen V, Lacy RC. Basal metabolic rates in mammals: taxonomic differences in the allometry of BMR and body mass. Comp Biochem Physiol. 1985;81A:741–54.

    Article  Google Scholar 

  30. West GB, Brown JH, Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997;276:122–6.

    Article  PubMed  CAS  Google Scholar 

  31. Kozłowski J, Konarzewski M. Is West, Brown and Enquist’s model of allometric scaling mathematically correct and biologically relevant? Funct Ecol. 2004;18:283–9.

    Article  Google Scholar 

  32. Kozłowski J, Konarzewski M. West, Brown and Enquist’s model of allometric scaling again: the same questions remain. Funct Ecol. 2005;19:739–43.

    Article  Google Scholar 

  33. Painter PR. The fractal geometry of nutrient exchange surfaces does not provide an explanation for 3/4-power metabolic scaling. Theor Biol Med Model. 2005;2:30.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Petit G, Anfodillo T. Plant physiology in theory and practice: an analysis of the WBE model for vascular plants. J Theor Biol. 2009;259:1–4.

    Article  PubMed  Google Scholar 

  35. Glazier DS. Beyond the ‘3/4-power law’: variation in the intra- and interspecific scaling of metabolic rate in animals. Biol Rev Camb Philos Soc. 2005;80:611–62.

    Article  PubMed  Google Scholar 

  36. White CR, Cassey P, Blackburn TM. Allometric exponents do not support a universal metabolic allometry. Ecology. 2007;88:315–23.

    Article  PubMed  Google Scholar 

  37. Packard GC, Birchard GF. Traditional allometric analysis fails to provide a valid predictive model for mammalian metabolic rates. J Exp Biol. 2008;211(Pt 22):3581–7.

    Article  PubMed  Google Scholar 

  38. Mahmood I. Theoretical versus empirical allometry: facts behind theories and application to pharmacokinetics. J Pharm Sci. 2010;99:2927–33.

    PubMed  CAS  Google Scholar 

  39. Mahmood I. Application of fixed exponent 0.75 to the prediction of human drug clearance: an inaccurate and misleading concept. Drug Metab Drug Interact. 2009;24:57–81.

    Article  CAS  Google Scholar 

  40. West D, West BJ. Physiologic time: a hypothesis. Phys Life Rev. 2013;10:210–24.

    Article  PubMed  Google Scholar 

  41. Bentley LP, Stegen JC, Savage VM, et al. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol Lett. 2013;16:1069–78.

    Article  PubMed  Google Scholar 

  42. Mahmood I. Prediction of drug clearance in children: impact of allometric exponents, body weight and age. Ther Drug Monitor. 2007;29:271–8.

    Article  Google Scholar 

  43. Mahmood I. Prediction of drug clearance in children from adults: a comparison of several allometric methods. Br J Clin Pharmacol. 2006;61:545–57.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  44. Peeters MY, Allegaert K, Blussé van Oud-Alblas HJ, et al. Prediction of propofol clearance in children from an allometric model developed in rats, children and adults versus a 0.75 fixed-exponent allometric model. Clin Pharmacokinet. 2010;49:269–75.

    Article  PubMed  CAS  Google Scholar 

  45. Björkman S. Prediction of cytochrome p450-mediated hepatic drug clearance in neonates, infants and children: how accurate are available scaling methods? Clin Pharmacokinet. 2006;45:1–11.

    Article  PubMed  Google Scholar 

  46. Edginton AN, Shah B, Sevestre M, Momper JD. The integration of allometry and virtual populations to predict clearance and clearance variability in pediatric populations over the age of 6 years. Clin Pharmacokinet. 2013;52:693–703.

    Article  PubMed  Google Scholar 

  47. Momper JD, Mulugeta Y, Green DJ, et al. Adolescent dosing and labeling since the Food and Drug Administration Amendments Act of 2007. JAMA Pediatr. 2013;167:926–32.

    Article  PubMed  Google Scholar 

  48. Mahmood I. Prediction of drug clearance in children 3 months and younger: an allometric approach. Drug Metabol Drug Interact. 2010;25:25–34.

    Article  PubMed  CAS  Google Scholar 

  49. Mahmood I. Evaluation of a morphine maturation model for the prediction of morphine clearance in children: how accurate is the predictive performance of the model? Br J Clin Pharmacol. 2011;71:88–94.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Mahmood I. Response to the comments of Professors Anderson & Holford. Br J Clin Pharmacol. 2011;72(3):521–3.

    Article  CAS  PubMed Central  Google Scholar 

  51. Mahmood I. Evaluation of sigmoidal maturation and allometric models: prediction of propofol clearance in neonates and infants. Am J Ther. 2013;20:21–8.

    PubMed  Google Scholar 

  52. Chappell WR, Mordenti J. Extrapolation of toxicological and pharmacological data from animals to humans. Adv Drug Res. 1991;20:1–116.

    Article  CAS  Google Scholar 

  53. Wieser W. A distinction must be made between the ontogeny and the phylogeny of metabolism in order to understand the mass exponent of energy metabolism. Respir Physiol. 1984;55:1–9.

    Article  PubMed  CAS  Google Scholar 

  54. McMohan TA, Bonner JT. Proportions and size. In: On size and life. New York: Scientific American Library; 1983. p. 25–67.

    Google Scholar 

  55. Box GEP, Draper NR. Empirical model building and response surfaces. New York: John Wiley & Sons; 1987. p. 24.

    Google Scholar 

Download references

Conflict of Interest

The authors do not have any financial or conflict of interest. The views expressed in this article are those of the authors and do not reflect the official policy of the FDA or any private enterprise. No official support or endorsement by the FDA or any private enterprise is intended or should be inferred.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Iftekhar Mahmood.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

ESM 1

(DOC 1890 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmood, I., Staschen, CM. & Goteti, K. Prediction of Drug Clearance in Children: an Evaluation of the Predictive Performance of Several Models. AAPS J 16, 1334–1343 (2014). https://doi.org/10.1208/s12248-014-9667-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1208/s12248-014-9667-7

KEY WORDS

Navigation