Development of a Physiology-Based Whole-Body Population Model for Assessing the Influence of Individual Variability on the Pharmacokinetics of Drugs

  • Stefan Willmann
  • Karsten Höhn
  • Andrea Edginton
  • Michael Sevestre
  • Juri Solodenko
  • Wolfgang Weiss
  • Jörg Lippert
  • Walter Schmitt
Article

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P3M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.

Keywords

population modeling PBPK simulation pharmacokinetics interindividual variability generic model 

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References

  1. 1.
    Ette E.I, Williams P.J. (2004). Population pharmacokinetics I: background, concepts, and models. Ann. Pharmacother. 38(10):1702–1706PubMedCrossRefGoogle Scholar
  2. 2.
    Willmann S., Lippert J., Sevestre M., Solodenko J., Fois F., Schmitt W. (2003). PK-Sim®: a physiologically based pharmacokinetic ‘whole-body’ model. Biosilico. 1(4):121–124CrossRefGoogle Scholar
  3. 3.
    Poulin P., Theil F.P. (2000). A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J. Pharm. Sci. 89(1):16–35PubMedCrossRefGoogle Scholar
  4. 4.
    Bjorkman S. (2005). Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br. J. Clin. Pharmacol. 59(6):691–704PubMedCrossRefGoogle Scholar
  5. 5.
    Ginsberg G., Hattis D., Sonawane B. (2004). Incorporating pharmacokinetic differences between children and adults in assessing children’s risks to environmental toxicants. Toxicol. Appl. Pharmacol. 198(2):164–183PubMedCrossRefGoogle Scholar
  6. 6.
    Theil F.P, Guentert T.W, Haddad S., Poulin P. (2003). Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection. Toxicol. Lett. 138(1–2):29–49PubMedCrossRefGoogle Scholar
  7. 7.
    Willmann S., Lippert J., Schmitt W. (2005). From physicochemistry to absorption and distribution: predictive mechanistic modelling and computational tools. Expert Opin. Drug. Meta. Toxicol. 1(1):159–168CrossRefGoogle Scholar
  8. 8.
    Schmitt W., Willmann S. (2005). Physiology-based pharmacokinetic modeling: ready to be used. Drug Discovery Today: Technologies. 2(1):125–132CrossRefGoogle Scholar
  9. 9.
    Price P.S., Conolly R.B., Chaisson C.F., Gross E.A., Young J.S., Mathis E.T., Tedder D.R. (2003). Modeling interindividual variation in physiological factors used in PBPK models of humans. Crit. Rev. Toxicol. 33(5):469–503PubMedGoogle Scholar
  10. 10.
    Edginton A.N., Willmann S., Schmitt W. (2006). Development and validation of a generic physiology-based pharmacokinetic (PBPK) model for children. Clin. Pharmacokinet. 45:1013–1034PubMedCrossRefGoogle Scholar
  11. 11.
    Haerter M.W., Keldenich J., Schmitt W. (2002). Estimation of physicochemical and ADME parameters. In: Nicolaou K.C., Hanki R., Hartwig W. (eds) Handbook of Combinatorial Chemistry: Drugs, Catalysts, Materials, Vol. 2. Wiley-VCH Verlag GmbH, Weinheim, Germany, pp 743–760Google Scholar
  12. 12.
    Keldenich J., Schmitt W., and Willmann S. A physiological/mechanistical model for predicting organ/plasma partitioning and volume of distribution. LogP2004 – The 3rd Lipophilicity Symposium, Zurich, Switzerland. Feb 29 to Mar 4, (2004).Google Scholar
  13. 13.
    Third National Health and Nutrition Examination Survey (NHANES III). 1997. National Center for Health Statistics Hyattsville, MD 20782 USA. http://www.cdc.gov/nchs/nhanes.htm. (1997).Google Scholar
  14. 14.
    International Commission on Radiological Protection (ICRP). Basic Anatomical and Physiological Data for Use in Radiological Protection: Reference Values. ICRP Publication 89. Elsevier Science, Amsterdam, The Netherlands (2002).Google Scholar
  15. 15.
    Sachs L. (2003). Angewandte Statistik, 11 Auflage. Springer Verlag GmbH, Heidelberg, DeutschlandGoogle Scholar
  16. 16.
    de la Grandmaison G.L., Clairand I., Durigon M. (2001). Organ weight in 684 adult autopsies: new tables for a Caucasoid population. Forensic. Sci. Int. 119(2):149–154Google Scholar
  17. 17.
    DuBois D., DuBois E.F. (1916). A formula to estimate the approximate surface area if height and weight be known. Arch. Int. Med. 17:863–871Google Scholar
  18. 18.
    Haycock G.B., Schwartz G.J., Wisotsky D.H. (1978). Geometric method for measuring body surface area: a height weight formula validated in infants, children and adults. J. Pediatr. 93:1:62–66CrossRefGoogle Scholar
  19. 19.
    Gehan E.A., George S.L. (1970). Estimation of human body surface area from height and weight. Cancer. Chemother. Rep. 54:225–35PubMedGoogle Scholar
  20. 20.
    Mosteller R.D. (1987). Simplified Calculation of Body Surface Area. N. Engl. J. Med. 317(17):1098(letter)PubMedGoogle Scholar
  21. 21.
    Hense H.W., Gneiting B., Muscholl M., Broeckel U., Kuch B., Doering A., G.A. Riegger, Schunkert H. (1998). The associations of body size and body composition with left ventricular mass: impacts for indexation in adults. J. Am. Coll. Cardiol. 32(2):451–457PubMedCrossRefGoogle Scholar
  22. 22.
    Lauer M.S., Anderson K.M., Larson M.G., Levy D. (1994). A new method for indexing left ventricular mass for differences in body size. Am. J. Cardiol. 74(5):487–491PubMedCrossRefGoogle Scholar
  23. 23.
    Stanforth P.R., Jackson A.S., Green J.S., Gagnon J., Rankinen T., Despres J.P., Bouchard C., Leon A.S., Rao D.C., Skinner J.S., Wilmore J.H. (2004). Generalized abdominal visceral fat prediction models for black and white adults aged 17–65 y: the HERITAGE Family Study. Int. J. Obes. Relat. Metab. Disord. 28(7):925–932PubMedCrossRefGoogle Scholar
  24. 24.
    Gimondo P., Mirk P., La B.A., Messina G., Pizzi C. (1995). Sonographic estimation of fetal liver weight: an additional biometric parameter for assessment of fetal growth. J. Ultrasound. Med. 14(5):327–333PubMedGoogle Scholar
  25. 25.
    Maroun L.L., Graem N. (2005). Autopsy standards of body parameters and fresh organ weights in nonmacerated and macerated human fetuses. Pediatr. Dev. Pathol. 8(2):204–217PubMedCrossRefGoogle Scholar
  26. 26.
    Bergmann K.E., Bergmann R.L., Von K.R., Bohm O., Richter R., Dudenhausen J.W., Wahn U. (2003). Early determinants of childhood overweight and adiposity in a birth cohort study: role of breast-feeding. Int. J. Obes. Relat. Metab. Disord. 27(2):162–172PubMedCrossRefGoogle Scholar
  27. 27.
    Urbina E.M., Gidding S.S., Bao W., Pickoff A.S., Berdusis K., Berenson G.S. (1995). Effect of body size, ponderosity, and blood pressure on left ventricular growth in children and young adults in the Bogalusa Heart Study. Circulation 91(9):2400–2406PubMedGoogle Scholar
  28. 28.
    Frankenfield D.C., Rowe W.A., Cooney R.N., Smith J.S., Becker D. (2001). Limits of body mass index to detect obesity and predict body composition. Nutrition 17(1):26–30PubMedCrossRefGoogle Scholar
  29. 29.
    Clarys J.P., Provyn S., Marfell-Jones M. J. (2005). Cadaver studies and their impact on the understanding of human adiposity. Ergonomics 48(11–14):1445–1461Google Scholar
  30. 30.
    Willmann S., Schmitt W., Keldenich J., Lippert J., Dressman J.B. (2004). A physiological model for the estimation of the fraction dose absorbed in humans. J. Med. Chem. 47(15):4022–4031PubMedCrossRefGoogle Scholar
  31. 31.
    Hardman J.G., Limbird L.E., Gilman A. (2001). Goodman and Gilman’s: The Pharmacological Basis of Therapeutics, 10th ed. McGraw Hill, New YorkGoogle Scholar
  32. 32.
    Shah A., Lettieri J., Heller A., Kaiser L., Collins S., and Birkett J. Pharmacokinetics of IV ciprofloxacin in subjects with normal renal function and with various degrees of renal impairment. Internal Report No. R6098 (1993).Google Scholar
  33. 33.
    Shah A., Lettieri J., Blum R., Millikin S., Sica D., Heller A.H. (1996). Pharmacokinetics of intravenous ciprofloxacin in normal and renally impaired subjects. J. Antimicrob. Chemother. 38(1):103–116PubMedCrossRefGoogle Scholar
  34. 34.
    Sorgel F., Naber K.G., Jaehde U., Reiter A., Seelmann R., Sigl G. (1989). Gastrointestinal secretion of ciprofloxacin. Evaluation of the charcoal model for investigations in healthy volunteers. Am. J. Med. 87(5A):62S–65SGoogle Scholar
  35. 35.
    Granfors M.T., Backman J.T., Neuvonen M., Neuvonen P.J. (2004). Ciprofloxacin greatly increases concentrations and hypotensive effect of tizanidine by inhibiting its cytochrome P450 1A2-mediated presystemic metabolism. Clin. Pharmacol. Ther. 76(6):598–606PubMedCrossRefGoogle Scholar
  36. 36.
    Dorne J.L., Walton K., Renwick A.G. (2001). Uncertainty factors for chemical risk assessment: human variability in the pharmacokinetics of CYP1A2 probe substrates. Food. Chem. Toxicol. 39(7):681–696PubMedCrossRefGoogle Scholar
  37. 37.
    Dorne J.L., Walton K., Renwick A.G. (2004). Human variability in the renal elimination of foreign compounds and renal excretion-related uncertainty factors for risk assessment. Food. Chem. Toxicol. 42:275–298PubMedCrossRefGoogle Scholar
  38. 38.
    Panday V.R., ten Bokkel Huinink W.W., Vermorken J.B., Rosing H., Koopman F.J., Swart M., Schellens J.H., Beijnen J.H. (1999). Pharmacokinetics of paclitaxel administered as a 3-hour or 96-hour infusion. Pharmacol. Res. 40(1):67–74PubMedCrossRefGoogle Scholar
  39. 39.
    van den Bongard H.J. , Kemper E.M., van T.O., Rosing H., Mathot R.A., Schellens J.H., Beijnen J.H. (2004). Development and validation of a method to determine the unbound paclitaxel fraction in human plasma. Anal. Biochem. 324(1):11–15PubMedCrossRefGoogle Scholar
  40. 40.
    Callies S., de Alwis D.P., Harris A., Vasey P., Beijnen J.H., Schellens J.H., Burgess M., Aarons L. (2003). A population pharmacokinetic model for paclitaxel in the presence of a novel P-gp modulator, Zosuquidar Trihydrochloride (LY335979). Br. J Clin. Pharmacol. 56(1):46–56PubMedCrossRefGoogle Scholar
  41. 41.
    Vaclavikova R., Horsky S., Simek P., Gut I. (2003). Paclitaxel metabolism in rat and human liver microsomes is inhibited by phenolic antioxidants. Naunyn Schmiedebergs Arch Pharmacol. 368(3):200–209PubMedCrossRefGoogle Scholar
  42. 42.
    Dorne J.L., Walton K., Renwick A.G. (2003). Human variability in CYP3A4 metabolism and CYP3A4-related uncertainty factors for risk assessment. Food. Chem. Toxicol. 41(2):201–224PubMedCrossRefGoogle Scholar
  43. 43.
    MacDonald A.J., Rostami-Hodjegan A., Tucker G.T., Linkens D.A. (2002). Analysis of solvent central nervous system toxicity and ethanol interactions using a human population physiologically based kinetic and dynamic model. Regul. Toxicol. Pharmacol. 35(2 Pt 1):165–76PubMedCrossRefGoogle Scholar
  44. 44.
    Jonsson F., Johanson G. (2002). Physiologically based modeling of the inhalation kinetics of styrene in humans using a bayesian population approach. Toxicol. Appl. Pharmacol. 15(1):35–49CrossRefGoogle Scholar
  45. 45.
    Jonsson F., Bois F.Y., Johanson G. (2001). Assessing the reliability of PBPK models using data from methyl chloride-exposed, non-conjugating human subjects. Arch. Toxicol. 75(4):189–99PubMedCrossRefGoogle Scholar
  46. 46.
    Jonsson F., Johanson G. (2001). Bayesian estimation of variability in adipose tissue blood flow in man by physiologically based pharmacokinetic modeling of inhalation exposure to toluene. Toxicology. 157(3):177–93PubMedCrossRefGoogle Scholar
  47. 47.
    Sweeney L.M., Tyler T.R., Kirman C.R., Corley R.A., Reitz R.H., Paustenbach D.J., Holson J.F., Whorton M.D., Thompson K.M., Gargas M.L. (2001). Proposed occupational exposure limits for select ethylene glycol ethers using PBPK models and Monte Carlo simulations. Toxicol. Sci. 62(1):124–39PubMedCrossRefGoogle Scholar
  48. 48.
    Isukapalli S.S., Roy A., Georgopoulos P.G. (1998). Stochastic response surface methods (SRSMs) for uncertainty propagation: application to environmental and biological systems. Risk Anal. 18(3):351–63PubMedCrossRefGoogle Scholar
  49. 49.
    Thomas R.S., Lytle W.E., Keefe T.J., Constan A.A., Yang R.S. (1996). Incorporating Monte Carlo simulation into physiologically based pharmacokinetic models using advanced continuous simulation language (ACSL): a computational method. Fundam. Appl. Toxicol. 31(1):19–28PubMedCrossRefGoogle Scholar
  50. 50.
    Clewell H.J. 3rd and Andersen M.E. Use of physiologically based pharmacokinetic modeling to investigate individual versus population risk. Toxicology 111(1–3):315–29 (1996).Google Scholar
  51. 51.
    Beck B.D., Mattuck R.L., Bowers T.S., Cohen J.T., O’Flaherty E. (2001). The development of a stochastic physiologically-based pharmacokinetic model for lead. Sci. Total Environ. 274(1–3):15–9PubMedCrossRefGoogle Scholar
  52. 52.
    Gueorguieva I., Aarons L., Rowland M. (2006). Diazepam pharmacokinetics from preclinical to phase I using a Bayesian population physiologically based pharmacokinetic model with informative prior distributions in Winbugs. J. Pharmacokin. Pharmacodyn. 33(5):571–594CrossRefGoogle Scholar
  53. 53.
    Cheymol G. (2000). Effects of obesity on pharmacokinetics. Clin Pharmacokinet. 39(3):215–231PubMedCrossRefGoogle Scholar
  54. 54.
    La S.F., Patti R., Sciarrino E., Valenza F., Costanzo G.S., Tese L., Lagalla R. (2004). Ultrasound, spleen and portal hypertension. Radiol. Med (Torino). 107(4):332–343Google Scholar
  55. 55.
    Abramson N., Melton B. (2000). Leukocytosis: basics of clinical assessment. Am. Fam. Physician 62(9):2053–2060PubMedGoogle Scholar
  56. 56.
    Crandall D.L., Hausman G.J., Kral J.G. (1997). A review of the microcirculation of adipose tissue: anatomic, metabolic, and angiogenic perspectives. Microcirculation 4(2):211–232PubMedCrossRefGoogle Scholar
  57. 57.
    Engfeldt P., Linde B. (1992). Subcutaneous adipose tissue blood flow in the abdominal and femoral regions in obese women: effect of fasting. Int. J. Obes. Relat. Metab. Disord. 16(11):875–879PubMedGoogle Scholar
  58. 58.
    Lesser G., Deutsch S. (1967). Measurement of adipose tissue blood flow and perfusion in man by uptake of 85Kr. J. Appl. Physiol. 23:621–623PubMedGoogle Scholar
  59. 59.
    Alcorn J., McNamara P.J. (2002). Ontogeny of hepatic and renal systemic clearance pathways in infants: part I. Clin. Pharmacokinet. 41(12):959–998PubMedCrossRefGoogle Scholar
  60. 60.
    Cusack B.J. (2004). Pharmacokinetics in older persons. Am. J. Geriatr. Pharmacother. 2(4):274–302PubMedCrossRefGoogle Scholar
  61. 61.
    Johnson J.A. (2000). Predictability of the effects of race or ethnicity on pharmacokinetics of drugs. Int. J. Clin. Pharmacol. Ther. 38(2):53–60PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Stefan Willmann
    • 1
  • Karsten Höhn
    • 2
  • Andrea Edginton
    • 1
  • Michael Sevestre
    • 2
  • Juri Solodenko
    • 2
  • Wolfgang Weiss
    • 2
  • Jörg Lippert
    • 1
  • Walter Schmitt
    • 1
  1. 1.Bayer Technology Services GmbHProcess Technology/Systems BiologyLeverkusenGermany
  2. 2.Bayer Technology Services GmbHProcess Technology/Computational SolutionsLeverkusenGermany

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