Skip to main content

Advertisement

SpringerLink
Log in

Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study

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

Abtract

There are situations in drug development where one may wish to reduce the dimensionality and complexity of whole body physiologically based pharmacokinetic models. A technique for formal reduction of such models, based on global sensitivity analysis, is suggested. Using this approach mean and variance of tissue(s) and/or blood concentrations are preserved in the reduced models. Extended Fourier amplitude sensitivity test (FAST), a global sensitivity technique, takes a sampling approach, acknowledging parameter variability and uncertainty, to calculate the impact of parameters on concentration variance. We used existing literature rules for formal model reduction to identify all possible smaller dimensionally models. To discriminate among those competing mechanistic models extended FAST was used, whereby we treated model structural uncertainty as another factor contributing to the overall uncertainty. A previously developed 14 compartment whole body physiologically based model for diazepam disposition in rat was reduced to three alternative reduced models, with preserved arterial mean and variance concentration profiles.

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. M. Müller, A. Pena and H. Derendorf, Issues in pharmacokinetics and pharmacodynamics of anti-infective agents: distribution in tissues. Antimicrob. Agents Chemother. 48 (2004) 1441-1453

    Article  PubMed  CAS  Google Scholar 

  2. W. Elmquist and R.J. Sawchuk, Application of microdialysis in pharmacokinetic studies. Pharm. Res. 14 (1997) 267-288

    Article  PubMed  CAS  Google Scholar 

  3. R.K. Jain, Transport of molecules in the tumour interstitium: a review. Cancer Res. 47 (1987) 3039-3051

    PubMed  CAS  Google Scholar 

  4. C.A. Presant, W. Wolf, V. Maluch, C. Wiseman, P. Kennedy, D. Blayney and R.R. Brechner, Association of intratumour pharmacokinetics of fluorouracil with clinical response. Lancet 343 (1994) 1184-1187

    Article  PubMed  CAS  Google Scholar 

  5. L.T. Baxter, F. Yuan and R.K. Jain, Pharmacokinetic analysis of the perivascular distribution of bifunctional antibodies and haptens: comparison with experimental data. Cancer Res. 52 (1992) 5838-5844

    PubMed  CAS  Google Scholar 

  6. M. Müller, R.M. Mader, B. Steiner, G. Steger, B. Jansen, M. Gnant, T. Helbich, R. Jakesz, H. Eichler and B. Blochl-Daum, 5-Flurouracil kinetics in the interstitial tumour space and clinical response in breast cancer patients. Cancer Res. 57 (1997) 2598-2601

    PubMed  Google Scholar 

  7. 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 (1989) 371-385

    Article  PubMed  CAS  Google Scholar 

  8. H. Clewell and M. Andersen, Use of physiologically based pharmacokinetic modeling to investigate individual versus population risk. Toxicology 111 (1996) 315-329

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  10. I. Gueorguieva, I. Nestorov and M. Rowland Fuzzy, simulations of pharmacokinetic models: case study of whole body physiologically based model of diazepam. J Pharmacokin. Pharmacodyn. 31 (2004) 185-211

    Article  CAS  Google Scholar 

  11. E. Kuempel, C.-L. Tran, A.J. Bailer, R. Smith, D. Dankovic and L. Stayner, Methodological issues of using observational human data in lung dosimetry models for particulates. Sci. Total. Environ. 274 (2001) 67-77

    Article  PubMed  CAS  Google Scholar 

  12. A.J. Bailer and D.A. Dankovic, An introduction to the use of physiologically based pharmacokinetic models in risk assessment. Stat. Meth. Med. Res. 6 (1997) 341-358

    Article  CAS  Google Scholar 

  13. H. Banks and L. Potter, Model predictions and comparisons for three toxicokinetics models for the systemic transport of trichloroethylene. Math. Comput. Model. 35 (2002) 1007-1032

    Article  Google Scholar 

  14. A. Collins, S. Sumner, S. Borghoff and M. Medinsky, A physiological model for tert-amyl ether and tert-amyl alcohol: hypothesis testing of model structures. Toxicol. Sci. 49 (1999) 15-28

    Article  PubMed  CAS  Google Scholar 

  15. I. Nestorov, L. Aarons, P. Arundel and M. Rowland, Lumping of whole-body physiologically based pharmacokinetic models. J. Pharmacokin. Biopharm. 26 (1998) 21-45

    CAS  Google Scholar 

  16. A. Saltelli, S. Tarantola and K.P.S. Chan, A quantitative model independent method for global sensitivity analysis of model output. Technometrics 41 (1999) 39-56

    Article  Google Scholar 

  17. J. Crawlfield, Application of first-order (FORM) and second-order (SORM) Reliability Methods: analysis and interpretation of sensitivity measures related to groundwater pressure decreases and resulting ground subsidence. In: A. Saltelli, K. Chan and E.M. Scott (eds.) SensitivityAnalysis. England: J. Wiley & Sons (2000) pp.

    Google Scholar 

  18. M. Crosetto and S. Tarantola, Uncertainty and sensitivity analysis: tools for GIS-based model implementation. Int. J. Geogr. Inf. Sci. 15 (2001) 415-437

    Article  Google Scholar 

  19. C. Planas and R. Depoutot, Sensitivity analysis for signal extraction in economic time series. In: A Saltelli, K. Chan and E.M. Scott (eds.) Sensitivity Analysis. England: J. Wiley & Sons (2000) pp.

    Google Scholar 

  20. D. Draper, A. Saltelli, S. Tarantola, and P. Prado. Scenario and parametric sensitivity and uncertainty analysis in nuclear waste disposal risk assessment: the case of GESAMAC by A. Saltelli, K. Chan, E. M. Scott (eds), J. Wiley & Sons, England, 2000.

  21. A. Saltelli, K. Chan and E.M. Scott, Sensitivity Analysis. England: J. Wiley & Sons (2000).

    Google Scholar 

  22. H.J. Clewell, T. Lee and R.L. Carpenter, Sensitivity of physiologically based pharmacokinetic models to variations in model parameters: methylene chloride. Risk Anal. 14 (1994) 521-531

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. M.D. Morris, Factorial sampling plans for preliminary computational experiments. Technometrics 33 (1991) 161-174

    Article  Google Scholar 

  25. S.C. Cotter, A screening design for factorial experiments with interactions. Biometrika 66 (1979) 317-320

    Article  Google Scholar 

  26. R.I. Cukier, C.M. Fortuin, K.E. Shuler, A.G. Petschek and J.H. Schaibly, Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I. Theory. J. Chem. Phys. 59 (1973) 3873-3878

    Article  CAS  Google Scholar 

  27. U. Klotz, K.-H. Antonin and P.R. Bieck, Pharmacokinetics and plasma binding of diazepam in man, dog, rabbit, guinea pig and rat. J. Pharmacol. Exp. Ther. 199 (1976) 67-73

    PubMed  CAS  Google Scholar 

  28. 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. Pharmacokin. Biopharm. 11 (1983) 577-593

    Article  CAS  Google Scholar 

  29. I. Kuwahira, N Gonzalez, K. Heisler and J. Piper, Regional blood flows in conscious resting rats determined by microsphere distribution. J Appl. Physiol. 74 (1993) 203-210

    PubMed  CAS  Google Scholar 

  30. A. Salteli and S. Tarantola, SimLab 1.1, User Manual. Italy: Joint Research Centre, European Commission (2001).

    Google Scholar 

  31. MATLAB. User’s Guide Version 6.1. The Mathworks, UK.

  32. M. Evans and C. Eklund, A graphical application of sensitivity analysis for gas uptake experiments using chloroform as an example. Toxicol. Method. 11 (2001) 285-297

    Article  CAS  Google Scholar 

  33. R.L. Iman and W. Conover, A distribution free approach to inducing rank correlation among input variables. Comm. Statist.-Simula. Computa 11 (1982) 311-334

    Article  Google Scholar 

  34. R.L. Iman and J.C. Helton, A comparison of uncertainty and sensitivity analysis techniques for computer models. Risk Anal. 8 (1988) 71-90

    Article  Google Scholar 

  35. A. Saltelli, T.H. Andres and T. Homma, Sensitivity analysis of model output; An investigation of new techniques. Comput. Stat. Data An. 15 (1993) 211-238

    Article  Google Scholar 

  36. A. Saltelli and J. Marivoet, Nonparametric statistics in sensitivity analysis for model output; A comparison of selected techniques. Reliab. Eng. Syst. Safe. 28 (1990) 229-253

    Article  Google Scholar 

  37. E. Dudewicz and S. Mishra, Modern Mathematical Statistics. New York: John Wiley & Sons (1988).

    Google Scholar 

  38. J.H. Schaibly and K.E. Shuler, Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients II. Applications. J. Chem. Phys. 59 (1973) 3879-3888

    CAS  Google Scholar 

  39. M. Koda, G.L. McRae and J.H. Seinfield, Automatic sensitivity analysis of kinetic mechanisms. Int. J Chem. Kinet. 11 (1979) 427-444

    Article  CAS  Google Scholar 

  40. D. Liepmann and G. Stephanopoulos, Development and sensitivity analysis of a closed ecosystem model. Ecol. Model. 30 (1985) 13-47

    Article  CAS  Google Scholar 

  41. Y. Lu and S. Mohanty, Sensitivity analysis of a complex, proposed geologic waste disposal system using the Fourier Amplitude Sensitivity Test method. Reliab. Eng. Syst. Safe. 72 (2001) 275-291

    Article  Google Scholar 

  42. G. Box and M. Muller, A note on generation of random normal deviates. Ann. Math. Stat. 28 (1953) 610-611

    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 Gueorguieva & Malcolm Rowland

  2. Zymogenetics Inc., 1201 Eastlake Avenue East, Seattle, WA, 98102, USA

    Ivan A. Nestorov

  3. School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Malcolm Rowland

Authors
  1. Ivelina 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

Corresponding author

Correspondence to Ivelina Gueorguieva.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gueorguieva, I., Nestorov, I.A. & Rowland, M. Reducing Whole Body Physiologically Based Pharmacokinetic Models Using Global Sensitivity Analysis: Diazepam Case Study. J Pharmacokinet Pharmacodyn 33, 1–27 (2006). https://doi.org/10.1007/s10928-005-0004-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-005-0004-8

Keywords

Search

Navigation