Skip to main content
SpringerLink
Log in

Fuzzy Simulation of Pharmacokinetic Models: Case Study of Whole Body Physiologically Based Model of Diazepam

  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro–in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.

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

Access this article

Log in via an institution

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. A. Racine-Poon and J. Wakefield. Statistical methods for population pharmacokinetics modeling. Stat. Methods Med. Res. 7:63–84 (1998).

    Article  PubMed  Google Scholar 

  2. D. Farrar, B. Allen, K. Crump, and A. Shipp. Evaluation of uncertainty in input parameters to pharmacokinetic models and the resulting uncertainty in output. Toxicol. Lett. 49:371–385 (1989).

    Article  PubMed  Google Scholar 

  3. H. J. Clewell and M. Andersen. Use of physiologically based pharmacokinetic modeling to investigate individual versus population risk. Toxicology 111:315–329 (1996).

    Article  PubMed  Google Scholar 

  4. E. Binaghi, A. Dellaventura, A. Rampini, and R. Schettini. Fuzzy reasoning approach to similarity evaluation in image analysis. Int. J. Intell. Syst. 8:749–769 (1993).

    Google Scholar 

  5. K. Sugeno and T. Yasukawa. A fuzzy-logic-based approach to qualitative modeling. IEEE T Fuzzy Syst. 1:7–31 (1993).

    Google Scholar 

  6. M. Hadiprino and T. Ross. A rule based fuzzy logic deduction technique for damage assessment for protective structures. Fuzzy Set. Syst. 44:459–468 (1991).

    Article  Google Scholar 

  7. B. L. Sproule, M. Bazoon, K. I. Shulman, B. Turksen, and C. A. Naranjo. Fuzzy logic pharmacokinetic modeling: Application to lithium concentration prediction. Clin. Pharmacol. Ther. 62:29–40 (1997).

    PubMed  Google Scholar 

  8. C. A. Naranjo, K. Bremner, M. Bazoon, and B. Turksen. Using fuzzy logic to predict response to citalopram in alcohol dependence. Clin. Pharmacol. Ther. 62:209–224 (1997).

    PubMed  Google Scholar 

  9. F. Bois. Analysis of PBPK models for risk characterization. Ann NY Acad Sci. 895:317–337 (1999).

    PubMed  Google Scholar 

  10. D. Hattis, P. White, L. Marmostein, and P. Koch. Uncertainties in pharmacokinetic modeling of perchloroethylene. I. Comparison of model structure, parameters, and predictions for low dose metabolism rates for models derived by different authors. Risk Anal. 10:449–458 (1990).

    PubMed  Google Scholar 

  11. D. Krewski, Y. Wang, S. Barlett, and K. Krishman. Uncertainty, variability and sensitivity analysis in physiological pharmacokinetic models. J. Biopharm. Statist. 5:245–271 (1995).

    Google Scholar 

  12. J. M. Gearhart, D. A. Mahle, R. J. Greene, C. S. Seckel, C. D. Flemming, J. W. Fisher, and H. J. Clewell. Variability of physiologically based pharmacokinetic (PBPK) model parameters and their effects on PBPK model predictions in a risk assessment for perchlorethylene (PCE). Toxicol. Lett. 68:131–144 (1993).

    Article  PubMed  Google Scholar 

  13. D. Dubois and H. Prade. Fuzzy Sets and Systems: Theory and Applications. Academic Press: New York (1980).

    Google Scholar 

  14. T. Ross. Fuzzy Logic with Engineering Applications. McGraw Hill: New York (1995).

    Google Scholar 

  15. H. J. Zimmermann. Uncertainty modelling and fuzzy sets. In: Uncertainty Models and Measures (H.G., Natke and Y., Ben-Haim eds), Akademie-Verlag Broschiert (1997), pp. 84–100.

    Google Scholar 

  16. R. Berkan and S. Trubatch. Fuzzy Systems Design principles. IEEE Press: New York (1997).

    Google Scholar 

  17. E. Hüllermeier. An approach to modeling and simulation of uncertain dynamical systems. Int. J. Uncertainty, Fuzziness and Knowledge-based Systems 5:117–137 (1997).

    Google Scholar 

  18. L. A. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Set. Syst. 1:3–28 (1978).

    Article  Google Scholar 

  19. M. Civanlar and H. J. Trussell. Constructing membership functions using statistical data. Fuzzy Set. Syst. 18:1–13 (1986).

    Article  Google Scholar 

  20. M. Easterling, M. Evans, and E. Kenyon. Comparative analysis of software for physiologically based pharmacokinetic modeling: simulation, optimization, and sensitivity analysis.Toxicol. Method 10:203–229 (2000).

    Article  Google Scholar 

  21. M. D. McKay, R. J. Beckman, and W. J. Conover. A comparison of three methods of selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239–245 (1979).

    Google Scholar 

  22. P. Varkonyi, J. Bruckner, and J. Gallo. Effect of parameter variability on physiologically-based pharmacokinetic model predicted drug concentrations. J. Pharm. Sci. 84:381–384 (1995).

    PubMed  Google Scholar 

  23. T. Inaba, E. Tsutsumi, W. Mahon, and W. Kalow. Biliary excretion of diazepam in the rat. Drug Metab. Dispos. 2:429–432 (1974).

    PubMed  Google Scholar 

  24. E. Arnold. A simple method for determining diazepam and its major metabolites in biological fluids: application in bioavailability studies. Acta Pharmacol. Toxicol. 36:335–352 (1975).

    Google Scholar 

  25. I. Nestorov, I. Gueorguieva, H. M. Jones, B. Houston, and M. Rowland. Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. Drug Metab. Dispos. 30:276–282 (2002).

    Article  PubMed  Google Scholar 

  26. Y. Igari, Y. Sugiyama, Y. Sawada, T. Iga, and M. Hanano. Prediction of diazepam disposition in the rat and man by a physiologically based pharmacokinetic model. J. Pharmacokinet. Phar. 11:577–593 (1983).

    Google Scholar 

  27. I. Kuwahira, N. Gonzalez, N. Heisler, and J. Piiper. Regional blood flows in conscious resting rats determined by microsphere distribution. J. Appl. Physiol. 74: 203–210 (1993).

    PubMed  Google Scholar 

  28. R. J. Carroll and D. Ruppert. Transformations and Weighting in Regression. Chapman and Hall: London (1988).

    Google Scholar 

  29. T. Iwatsubo, N. Hirota, T. Ooie, H. Suzuki, and N. Shimada. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol. Ther. 73:147–171 (1997).

    Article  PubMed  Google Scholar 

  30. T. Lave, S. Dupin, C. Schmitt, B. Valles, G. Ubeaud, R. C. Chou, D. Jaeck, and P. Coassolo. The use of human hepatocytes to select compounds based on their expected extraction ratios in humans. Pharm. Res. 14:152–155 (1996).

    Article  Google Scholar 

  31. R. S. Obach, J. G. Baxter, T. E. Liston, B. M. Silber, B. C. Jones, F. MacIntyre, D. J. Rance, and P. Wastall. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J. Pharmaco. Exp. Ther. 283:46–58 (1997).

    Google Scholar 

  32. R. S. Obach. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab. Dispos. 27:1350–1359 (1999).

    PubMed  Google Scholar 

  33. K. Zomorodi, D. J. Carlile, and J. B. Houston. Kinetics of diazepam metabolism in rat hepatic microsomes and hepatocytes and their use in predicting in vivo hepatic clearance. Xenobiotica 25:907–916 (1995).

    PubMed  Google Scholar 

  34. T. Woodruff and F. Bois. Optimization issues in physiological toxicokinetic modeling: A case study with benzene. Toxicol. Lett. 69:181–196 (1992).

    Article  Google Scholar 

  35. F. Bois, L. Zeise, and T. Tozer. Precision and sensitivity of pharmacokinetic models for cancer risk assessment: Tetrachlorethylene in mice, rats and humans. Toxicol. Appl. Pharmacol. 102:300–315 (1990).

    Article  PubMed  Google Scholar 

  36. I. Nestorov, A. Aarons, and M. Rowland. Physiologically based pharmacokinetic modelling of a homologous series of barbiturates in the rat: A sensitivity analysis. J. Pharmacokinet. Biopharm. 25:413–447 (1997).

    Article  PubMed  Google Scholar 

  37. G. Klir and B. Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall: London (1995).

    Google Scholar 

  38. M. Laviolette and J. Seaman. Unity and diversity of fuzziness-from a probability viewpoint.IEEE T Fuzzy Syst. 2:38–41 (1994).

    Article  Google Scholar 

  39. G. Klir. On the alleged superiority of probabilistic representation of uncertainty. IEEE T Fuzzy Syst. 2:27–31 (1994).

    Article  Google Scholar 

  40. D. Dubois and H. Prade. Fuzzy sets and probability: Misunderstandings, bridges and gaps. Proc 2nd IEEE Int. Conf. Fuzzy Systems 1059–1068 (1993).

  41. G. Klir and B. Parviz. Probability-possibility transformations: a comparison. Int. J. Gen. Syst. 21:291–310 (1992).

    Google Scholar 

  42. G. Klir. A principle of uncertainty and information invariance. Int. J. Gen. Syst. 17:249–275 (1990).

    Google Scholar 

  43. D. Dubois and H. Prade. Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms. Fuzzy Set. Syst. 10:15–20 (1983).

    Google Scholar 

  44. A. Gelman, F. Bois, and J. Jiang. Physiological pharmacokinetic analysis using population modeling and informative prior distributions. J. Am. Stat. Assoc. 91:1400–1412 (1996).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK;

    Ivelina I. Gueorguieva

  2. Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA, 91320-1799, USA

    Ivan A. Nestorov

  3. Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Malcolm Rowland

Authors
  1. Ivelina I. Gueorguieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan A. Nestorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malcolm Rowland
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gueorguieva, I.I., Nestorov, I.A. & Rowland, M. Fuzzy Simulation of Pharmacokinetic Models: Case Study of Whole Body Physiologically Based Model of Diazepam. J Pharmacokinet Pharmacodyn 31, 185–213 (2004). https://doi.org/10.1023/B:JOPA.0000039564.35602.78

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:JOPA.0000039564.35602.78

Search

Navigation