Advertisement

The AAPS Journal

, 21:8 | Cite as

PBPK and its Virtual Populations: the Impact of Physiology on Pediatric Pharmacokinetic Predictions of Tramadol

  • Huybrecht T’jollyn
  • An Vermeulen
  • Jan Van Bocxlaer
Research Article Theme: Pioneering Pharmaceutical Science by Emerging Investigators
  • 113 Downloads
Part of the following topical collections:
  1. Theme: Pioneering Pharmaceutical Science by Emerging Investigators

Abstract

In pediatric PBPK models, age-related changes in the body are known to occur. Given the sparsity of and the variability associated with relevant physiological parameters, different PBPK software providers may vary in their system’s data. In this work, three commercially available PBPK software packages (PK-Sim®, Simcyp®, and Gastroplus®) were investigated regarding their differences in system-related information, possibly affecting clearance prediction. Three retrograde PBPK clearance models were set up to enable prediction of pediatric tramadol clearance. These models were qualified in terms of total, CYP2D6, and renal clearance in adults. Tramadol pediatric clearance predictions from PBPK were compared with a pooled popPK model covering clearance ranging from neonates to adults. Fold prediction errors were used to evaluate the results. Marked differences in liver clearance prediction between PBPK models were observed. In general, the prediction bias of total clearance was greatest at the youngest population and decreased with age. Regarding CYP2D6 and renal clearance, important differences exist between PBPK software tools. Interestingly, the PBPK model with the shortest CYP2D6 maturation half-life (PK-Sim) agreed best with the in vivo CYP2D6 maturation model. Marked differences in physiological data explain the observed differences in hepatic clearance prediction in early life between the various PBPK software providers tested. Consensus on the most suited pediatric data to use should harmonize and optimize pediatric clearance predictions. Moreover, the combination of bottom-up and top-down approaches, using a convenient probe substrate, has the potential to update system-related parameters in order to better represent pediatric physiology.

KEY WORDS

Pediatrics PBPK Physiology CYP2D6 Tramadol 

Notes

Supplementary material

12248_2018_277_MOESM1_ESM.docx (2.3 mb)
ESM 1 (DOCX 2352 kb)

References

  1. 1.
    Teorell T. Kinetics of distribution of substances administered to the body. I The extravascular modes of administration. Arch Int Pharmacodyn Ther. 1937;57:202–5.Google Scholar
  2. 2.
    Barrett JS, Della Casa Alberighi O, Laer S, Meibohm B. Physiologically based pharmacokinetic (PBPK) modeling in children. Clin Pharmacol Ther. 2012;92(1):40–9.CrossRefGoogle Scholar
  3. 3.
    Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol. 2009;5(2):211–23.CrossRefGoogle Scholar
  4. 4.
    Johnson TN, Rostami-Hodjegan A. Resurgence in the use of physiologically based pharmacokinetic models in pediatric clinical pharmacology: parallel shift in incorporating the knowledge of biological elements and increased applicability to drug development and clinical practice. Paediatr Anaesth. 2010;21(3):291–301.CrossRefGoogle Scholar
  5. 5.
    Bouzom F, Walther B. Pharmacokinetic predictions in children by using the physiologically based pharmacokinetic modelling. Fundam Clin Pharmacol. 2008;22(6):579–87.CrossRefGoogle Scholar
  6. 6.
    Haddad S, Restieri C, Krishnan K. Characterization of age-related changes in body weight and organ weights from birth to adolescence in humans. J Toxicol Environ Health A. 2001;64(6):453–64.CrossRefGoogle Scholar
  7. 7.
    ICRP. Basic anatomical and physiological data for use in radiological protection: reference values. A report of age- and gender-related differences in the anatomical and physiological characteristics of reference individuals. ICRP publication 89. Ann ICRP. 2002;32(3–4):5–265.Google Scholar
  8. 8.
    Johnson TN, Tucker GT, Tanner MS, Rostami-Hodjegan A. Changes in liver volume from birth to adulthood: a meta-analysis. Liver Transpl. 2005;11(12):1481–93.CrossRefGoogle Scholar
  9. 9.
    Price K, Haddad S, Krishnan K. Physiological modeling of age-specific changes in the pharmacokinetics of organic chemicals in children. J Toxicol Environ Health A. 2003;66(5):417–33.CrossRefGoogle Scholar
  10. 10.
    Edginton AN, Schmitt W, Voith B, Willmann S. A mechanistic approach for the scaling of clearance in children. Clin Pharmacokinet. 2006;45(7):683–704.CrossRefGoogle Scholar
  11. 11.
    Johnson TN, Rostami-Hodjegan A, Tucker GT. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet. 2006;45(9):931–56.CrossRefGoogle Scholar
  12. 12.
    Third National Health and Nutrition Examination Survey (NHANES III). In. National Center for Health Statistics Hyattsville MU. In: editor; 1997.Google Scholar
  13. 13.
    Willmann S, Hohn K, Edginton A, Sevestre M, Solodenko J, Weiss W, et al. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn. 2007;34(3):401–31.CrossRefGoogle Scholar
  14. 14.
    Rhodin MM, Anderson BJ, Peters AM, Coulthard MG, Wilkins B, Cole M, et al. Human renal function maturation: a quantitative description using weight and postmenstrual age. Pediatr Nephrol. 2009;24(1):67–76.CrossRefGoogle Scholar
  15. 15.
    Schwartz GJ, Feld LG, Langford DJ. A simple estimate of glomerular filtration rate in full-term infants during the first year of life. J Pediatr. 1984;104(6):849–54.CrossRefGoogle Scholar
  16. 16.
    Schwartz GJ, Haycock GB, Edelmann CM Jr, Spitzer A. A simple estimate of glomerular filtration rate in children derived from body length and plasma creatinine. Pediatrics. 1976;58(2):259–63.PubMedGoogle Scholar
  17. 17.
    Traub SL, Johnson CE. Comparison of methods of estimating creatinine clearance in children. Am J Hosp Pharm. 1980;37(2):195–201.PubMedGoogle Scholar
  18. 18.
    Murthy BV, Pandya KS, Booker PD, Murray A, Lintz W, Terlinden R. Pharmacokinetics of tramadol in children after i.v. or caudal epidural administration. Br J Anaesth. 2000;84(3):346–9.CrossRefGoogle Scholar
  19. 19.
    Garrido MJ, Habre W, Rombout F, Troconiz IF. Population pharmacokinetic/pharmacodynamic modelling of the analgesic effects of tramadol in pediatrics. Pharm Res. 2006;23(9):2014–23.CrossRefGoogle Scholar
  20. 20.
    Bressolle F, Rochette A, Khier S, Dadure C, Ouaki J, Capdevila X. Population pharmacokinetics of the two enantiomers of tramadol and O-demethyl tramadol after surgery in children. Br J Anaesth. 2009;102(3):390–9.CrossRefGoogle Scholar
  21. 21.
    Lintz W, Barth H, Becker R, Frankus E, Schmidt-Bothelt E. Pharmacokinetics of tramadol and bioavailability of enteral tramadol formulations - 2nd communication: drops with ethanol. Arzneimittelforschung. 1998;48(5):436–45.PubMedGoogle Scholar
  22. 22.
    Lintz W, Barth H, Osterloh G, Schmidt-Bothelt E. Pharmacokinetics of tramadol and bioavailability of enteral tramadol formulations - 3rd communication: suppositories. Arzneimittelforschung. 1998;48(9):889–99.PubMedGoogle Scholar
  23. 23.
    Lintz W, Becker R, Gerloff J, Terlinden R. Pharmacokinetics of tramadol and bioavailability of enteral tramadol formulations - 4th communication: drops (without ethanol). Arzneimittelforschung. 2000;50(2):99–108.PubMedGoogle Scholar
  24. 24.
    Lintz W, Erlacin S, Frankus E, Uragg H. Metabolismus von tramadol bei mensch und tier. Arzneimittelforschung. 1981;31(11):1932–43.PubMedGoogle Scholar
  25. 25.
    Allegaert K, van den Anker JN, de Hoon JN, van Schaik RH, Debeer A, Tibboel D, et al. Covariates of tramadol disposition in the first months of life. Br J Anaesth. 2008;100(4):525–32.CrossRefGoogle Scholar
  26. 26.
    Allegaert K, Anderson BJ, Verbesselt R, Debeer A, de Hoon J, Devlieger H, et al. Tramadol disposition in the very young: an attempt to assess in vivo cytochrome P-450 2D6 activity. Br J Anaesth. 2005;95(2):231–9.CrossRefGoogle Scholar
  27. 27.
    Pedersen RS, Damkier P, Brosen K. Enantioselective pharmacokinetics of tramadol in CYP2D6 extensive and poor metabolizers. Eur J Clin Pharmacol. 2006;62(7):513–21.CrossRefGoogle Scholar
  28. 28.
    Stamer UM, Musshoff F, Kobilay M, Madea B, Hoeft A, Stuber F. Concentrations of tramadol and O-desmethyltramadol enantiomers in different CYP2D6 genotypes. Clin Pharmacol Ther. 2007;82(1):41–7.CrossRefGoogle Scholar
  29. 29.
    Grond S, Sablotzki A. Clinical pharmacology of tramadol. Clin Pharmacokinet. 2004;43(13):879–923.CrossRefGoogle Scholar
  30. 30.
    Allegaert K, Holford N, Anderson BJ, Holford S, Stuber F, Rochette A, et al. Tramadol and o-desmethyl tramadol clearance maturation and disposition in humans: a pooled pharmacokinetic study. Clin Pharmacokinet. 2015;54(2):167–78.CrossRefGoogle Scholar
  31. 31.
    Dickins M, van de Waterbeemd H. Simulation models for drug disposition and drug interactions. Drug Discovery Today: BIOSILICO. 2004;2(1):38–45.CrossRefGoogle Scholar
  32. 32.
    Willmann S, Lippert J, Sevestre M, Solodenko J, Fois F, Schmitt W. PK-Sim®: a physiologically based pharmacokinetic ‘whole-body’ model. Biosilico. 2003;1(4):121–4.CrossRefGoogle Scholar
  33. 33.
    T'Jollyn H, Snoeys J, Vermeulen A, Michelet R, Cuyckens F, Mannens G, et al. Physiologically based pharmacokinetic predictions of tramadol exposure throughout pediatric life: an analysis of the different clearance contributors with emphasis on CYP2D6 maturation. AAPS J. 2015;17(6):1376–87.CrossRefGoogle Scholar
  34. 34.
    Zanger UM, Fischer J, Raimundo S, Stuven T, Evert BO, Schwab M, et al. Comprehensive analysis of the genetic factors determining expression and function of hepatic CYP2D6. Pharmacogenetics. 2001;11(7):573–85.CrossRefGoogle Scholar
  35. 35.
    Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94(6):1259–76.CrossRefGoogle Scholar
  36. 36.
    Rodrigues AD. Integrated cytochrome P450 reaction phenotyping: attempting to bridge the gap between cDNA-expressed cytochromes P450 and native human liver microsomes. Biochem Pharmacol. 1999;57(5):465–80.CrossRefGoogle Scholar
  37. 37.
    Stevens JC, Marsh SA, Zaya MJ, Regina KJ, Divakaran K, Le M, et al. Developmental changes in human liver CYP2D6 expression. Drug Metab Dispos. 2008;36(8):1587–93.CrossRefGoogle Scholar
  38. 38.
    Treluyer JM, Jacqz-Aigrain E, Alvarez F, Cresteil T. Expression of CYP2D6 in developing human liver. Eur J Biochem. 1991;202(2):583–8.CrossRefGoogle Scholar
  39. 39.
    Open Systems Pharmacology. PK-Sim Ontogeny Database Version 7.3.pdf: Github; 2018 [Document describing the ontogeny functions used for different CYP enzymes]. Available from: https://github.com/Open-Systems-Pharmacology/OSPSuite.Documentation/blob/master/PK-Sim%20Ontogeny%20Database%20Version%207.3.pdf. Accessed 31 Oct 2018.
  40. 40.
    Pearce RE, Gaedigk R, Twist GP, Dai H, Riffel AK, Leeder JS, et al. Developmental expression of CYP2B6: a comprehensive analysis of mRNA expression, protein content and bupropion hydroxylase activity and the impact of genetic variation. Drug Metab Dispos. 2016;44(7):948–58.CrossRefGoogle Scholar
  41. 41.
    Rostami-Hodjegan A, Tucker GT. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov. 2007;6(2):140–8.CrossRefGoogle Scholar
  42. 42.
    Salem F, Johnson TN, Abduljalil K, Tucker GT, Rostami-Hodjegan A. A re-evaluation and validation of ontogeny functions for cytochrome P450 1A2 and 3A4 based on in vivo data. Clin Pharmacokinet. 2014;53(7):625–36.CrossRefGoogle Scholar
  43. 43.
    Yeo KR. Abundance of cytochromes P450 in human liver: a meta-analysis. Br J Clin Pharmacol. 2004;57(5):687–8.Google Scholar
  44. 44.
    Barter ZE, Chowdry JE, Harlow JR, Snawder JE, Lipscomb JC, Rostami-Hodjegan A. Covariation of human microsomal protein per gram of liver with age: absence of influence of operator and sample storage may justify interlaboratory data pooling. Drug Metab Dispos. 2008;36(12):2405–9.CrossRefGoogle Scholar
  45. 45.
    Upreti VV, Wahlstrom JL. Meta-analysis of hepatic cytochrome P450 ontogeny to underwrite the prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling. J Clin Pharmacol. 2016;56(3):266–83.CrossRefGoogle Scholar
  46. 46.
    Russell MR, Achour B, McKenzie EA, Lopez R, Harwood MD, Rostami-Hodjegan A, et al. Alternative fusion protein strategies to express recalcitrant QconCAT proteins for quantitative proteomics of human drug metabolizing enzymes and transporters. J Proteome Res. 2013;12(12):5934–42.CrossRefGoogle Scholar
  47. 47.
    Prasad B, Gaedigk A, Vrana M, Gaedigk R, Leeder JS, Salphati L, et al. Ontogeny of hepatic drug transporters as quantified by LC-MS/MS proteomics. Clin Pharmacol Ther 2016;AOP.Google Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2018

Authors and Affiliations

  1. 1.A Division of Janssen Pharmaceutica NVQuantitative Sciences, Janssen Research and DevelopmentBeerseBelgium
  2. 2.Faculty of Pharmaceutical SciencesLaboratory of Medical Biochemistry and Clinical AnalysisGhentBelgium

Personalised recommendations