Skip to main content

Modelling and PBPK Simulation in Drug Discovery

The AAPS Journal volume 11, pages 155–166 (2009)Cite this article

Abstract

Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and “first in human” stages for the prediction of human PK.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. H. Boxenbaum. Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics. J. Pharmacokinet. Biopharm. 10:201–227 (1982).

    PubMed  Article  CAS  Google Scholar 

  2. I. Mahmood, and J. D. Balian. Interspecies scaling: Predicting pharmacokinetic parameters of antiepileptic drugs in humans from animals with special emphasis on clearance. J. Pharm. Sci. 85:411–414 (1996).

    PubMed  Article  CAS  Google Scholar 

  3. 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. Pharmacol. Exp. Ther. 283:46–58 (1997).

    PubMed  CAS  Google Scholar 

  4. T. Lave, P. Coassolo, and B. Reigner. Prediction of hepatic metabolic clearance based on interspecies allometric scaling techniques and in vitro–in vivo correlations. Clin. Pharmacokinet. 36:211–231 (1999a).

    PubMed  Article  CAS  Google Scholar 

  5. C. Tanaka, R. Kawai, and M. Rowland. Dose-dependent pharmacokinetics of cyclosporin A in rats: Events in tissues. Drug Metab. Dispos. 28:582–589 (2000).

    PubMed  CAS  Google Scholar 

  6. I. Nestorov. Whole body pharmacokinetic models. Clin. Pharmacokinet. 42:883–908 (2003).

    PubMed  Article  CAS  Google Scholar 

  7. J. B. Houston. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem. Pharmacol. 47:1469–1479 (1994).

    PubMed  Article  CAS  Google Scholar 

  8. T. Iwatsubo, N. Hirota, T. Ooie, H. Suzuki, N. Shimada, K. Chiba, T. Ishizaki, C. E. Green, C. A. Tyson, and Y. Sugiyama. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol. Ther. 73:147–171 (1997).

    PubMed  Article  CAS  Google Scholar 

  9. 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 hepatic extraction ratios in humans. Pharm. Res. 14:152–155 (1997).

    PubMed  Article  CAS  Google Scholar 

  10. 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  CAS  Google Scholar 

  11. P. Poulin, and F. P. Theil. 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. 89:16–35 (2000).

    PubMed  Article  CAS  Google Scholar 

  12. P. Poulin, K. Schoenlein, and F. P. Theil. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J. Pharm. Sci. 90:436–447 (2001).

    PubMed  Article  CAS  Google Scholar 

  13. P. Poulin, and F. P. Theil. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J. Pharm. Sci. 91:129–156 (2002a).

    PubMed  Article  CAS  Google Scholar 

  14. L. M. Berezhkovskiy. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J. Pharm. Sci. 93(6):1628–40 (2004).

    PubMed  Article  CAS  Google Scholar 

  15. T. Rodgers, D. Leahy, and M. Rowland. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J. Pharm. Sci. 94:1259–1276 (2005).

    PubMed  Article  CAS  Google Scholar 

  16. T. Rodgers, and M. Rowland. Physiologically based pharmacokinetic modeling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J. Pharm. Sci. 95(6):1238–57 (2006).

    PubMed  Article  CAS  Google Scholar 

  17. T. Rodgers, and M. Rowland. Mechanistic approaches to volume of distribution predictions: Understanding the processes. Pharm. Res. 24(5):918–33 (2007).

    PubMed  Article  CAS  Google Scholar 

  18. P. Poulin, and F. P. Theil. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J. Pharm. Sci. 91:1358–1370 (2002b).

    PubMed  Article  CAS  Google Scholar 

  19. F. P. Theil, T. W. Guentert, S. Haddad, and P. Poulin. Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection. Toxicol. Lett. 138:29–49 (2003).

    PubMed  Article  CAS  Google Scholar 

  20. N. Parrott, N. Paquereau, P. Coassolo, and T. Lave. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery. J. Pharm. Sci. 94:2327–2343 (2005).

    PubMed  Article  CAS  Google Scholar 

  21. F. A. Brightman, D. E. Leahy, G. E. Searle, and S. Thomas. Application of a generic physiologically-based pharmacokinetic model to the estimation of xenobiotic levels in rat plasma. Drug Metab. Dispos. 34:84–93 (2006a).

    PubMed  Article  CAS  Google Scholar 

  22. F. A. Brightman, D. E. Leahy, G. E. Searle, and S. Thomas. Application of a generic physiologically-based pharmacokinetic model to the estimation of xenobiotic levels in human plasma. Drug Metab. Dispos. 34:94–101 (2006b).

    PubMed  Article  CAS  Google Scholar 

  23. H. M. Jones, N. Parrott, K. Jorga, and T. Lave. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin. Pharmacokinet. 45(5):511–542 (2006).

    PubMed  Article  CAS  Google Scholar 

  24. S. S. De Buck, V. K. Sinha, L. A. Fenu, M. J. Nijsen, C. E. Mackie, and R. A. H. J. Gilissen. Prediction of human pharmacokinetics using physiologically based modeling: A retrospective analysis of 26 clinically tested drugs. Drug Metab. Dispos. 35(10):1766–80 (2007a).

    PubMed  Article  Google Scholar 

  25. S. A. Peters. Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin. Pharmacokinet. 47(4):261–75 (2008).

    PubMed  Article  CAS  Google Scholar 

  26. M. Rowland, L. Balant, and C. Peck. Physiologically based pharmacokinetics in drug development and regulatory science: A workshop report (Georgetown University, Washington, DC, May 29–30, 2002). AAPS PharmSci. 6:E6 (2004).

    PubMed  Article  Google Scholar 

  27. B. Davies, and T. Morris. Physiological parameters in laboratory animals and humans. Pharm. Res. 10:1093–1095 (1993).

    PubMed  Article  CAS  Google Scholar 

  28. R. P. Brown, M. D. Delp, S. L. Lindstedt, L. R. Rhomberg, and R. P. Beliles. Physiological parameter values for physiologically based pharmacokinetic models. Toxicol. Ind. Health. 13:407–484 (1997).

    PubMed  CAS  Google Scholar 

  29. R. L. Dedrick. Animal scale-up. J. Pharmacokinet. Biopharm. 1:435–461 (1973).

    PubMed  Article  CAS  Google Scholar 

  30. J. Mordenti. Man versus beast: Pharmacokinetic scaling in mammals. J. Pharm. Sci. 75:1028–1040 (1986).

    PubMed  Article  CAS  Google Scholar 

  31. T. Lave, R. Portmann, G. Schenker, A. Gianni, A. Guenzi, M. A. Girometta, and M. Schmitt. Interspecies pharmacokinetic comparisons and allometric scaling of napsagatran, a low molecular weight thrombin inhibitor. J. Pharm. Pharmacol. 51:85–91 (1999b).

    PubMed  Article  CAS  Google Scholar 

  32. H. Tang, and M. Mayersohn. A novel model for prediction of human drug clearance by allometric scaling. Drug Metab. Dispos. 33:1297–1303 (2005).

    PubMed  Article  CAS  Google Scholar 

  33. H. Tang, and M. Mayersohn. A global examination of allometric scaling for predicting tested drugs. Drug Metab. Dispos. 35:1766–1780 (2007).

    Article  Google Scholar 

  34. I. Mahmood. Prediction of human drug clearance from animal data: Application of the rule of exponents and ‘fu corrected intercept method’ (FCIM). J. Pharm. Sci. 95(8):1810–1821 (2006).

    PubMed  Article  CAS  Google Scholar 

  35. V. K. Sinha, S. S. De Buck, L. A. Fenu, J. W. Smit, M. Nijsen, R. A. H. J. Gilissen, A. Van Peer, K. Lavrijsen, and C. E. Mackie. Predicting oral clearance in humans how close can we get with allometry? Clin. Pharmacokinet. 47(1):35–45 (2008).

    PubMed  Article  CAS  Google Scholar 

  36. W. L. Chiou, G. Robbie, S. M. Chung, T. C. Wu, and C. Ma. Correlation of plasma clearance of 54 extensively metabolized drugs between humans and rats: mean allometric coefficient of 0.66. Pharm. Res. 15(9):1474–9 (1998).

    PubMed  Article  CAS  Google Scholar 

  37. J. B. Houston, and D. J. Carlile. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab. Rev. 29:891–922 (1997).

    PubMed  Article  CAS  Google Scholar 

  38. K. Ito, and J. B. Houston. Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm. Res. 21:785–792 (2004).

    PubMed  Article  CAS  Google Scholar 

  39. H. M. Jones, and J. B. Houston. Substrate depletion approach for determining in vitro metabolic clearance: Time dependencies in hepatocyte and microsomal incubations. Drug Metab. Dispos. 32:973–982 (2004).

    PubMed  Article  CAS  Google Scholar 

  40. R. P. Austin, P. Barton, S. L. Cockroft, M. C. Wenlock, and R. J. Riley. The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metab. Dispos. 30:1497–1503 (2002).

    PubMed  Article  CAS  Google Scholar 

  41. R. P. Austin, P. Barton, S. Mohmed, and R. J. Riley. The binding of drugs to hepatocytes and its relationship to physicochemical properties. Drug Metab. Dispos. 33:419–425 (2005).

    PubMed  Article  CAS  Google Scholar 

  42. D. Hallifax, and J. B. Houston. Binding of drugs to hepatic microsomes: Comment and assessment of current prediction methodology with recommendation for improvement. Drug Metab. Dispos. 34(4):724–26 (2006).

    PubMed  Article  CAS  Google Scholar 

  43. D. B. Turner, A. Rostami-Hodjegan, G. T. Tucker, and K. Rowland-Yeo. Prediction of non-specific hepatic microsomal binding from readily available physicochemical properties. 9th European ISSX Meeting June 4th–7th, Manchester, UK. (2006).

  44. D. J. Carlile, K. Zomorodi, and J. B. Houston. Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes, and the intact liver: studies with induced livers involving diazepam. Drug Metab. Dispos. 25:903–911 (1997).

    PubMed  CAS  Google Scholar 

  45. Z. E. Wilson, A. Rostami-Hodjegan, J. L. Burn, A. Tooley, J. Boyle, S. W. Ellis, and G. T. Tucker. Inter-individual variability in levels of human microsomal protein and hepatocellularity per gram of liver. Br. J. Clin. Pharmacol. 56:433–440 (2003).

    PubMed  Article  CAS  Google Scholar 

  46. A. E. Barter, P. H. Beaune, A. R. Boobis, D. J. Carlile, R. J. Edwards, J. B. Houston, J. B. Lipscomb, O. R. Pelkonen, G. T. Tucker, and A. Rostami Hodjegan. Scaling factors for the extrapolation of in vivo microsomal protein and hepatocellularity per gram of liver. Curr. Drug. Metab. 8(1):33–45 (2007).

    PubMed  Article  CAS  Google Scholar 

  47. Y. Sawada, M. Hanano, Y. Sugiyama, H. Harashima, and T. Iga. Prediction of the volumes of distribution of basic drugs in humans based on data from animals. J. Pharmacokinet. Biopharm. 12:587–596 (1984).

    PubMed  Article  CAS  Google Scholar 

  48. L. X. Yu, and G. L. Amidon. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharm. 186:119–125 (1999).

    PubMed  Article  CAS  Google Scholar 

  49. B. Agoram, W. S. Woltosz, and M. B. Bolger. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Deliv. Rev. 50(Suppl 1):S41–67 (2001).

    PubMed  Article  CAS  Google Scholar 

  50. D. B. Turner, M. Jamei, G. T. Tucker, and A. Rostami-Hodjegan. Supersaturation properties of poorly soluble weak bases are key factors in determining drug absorption: A simulation study of nifedipine using the ADAM model (Simcyp v7.1). “EUFEPS & COST B25 Conference on Bioavailability and Bioequivalence: Focus on physiological factors and variability. October 1–2, 2007, Athens, Greece. (2007).

  51. S. Willmann, W. Schmitt, J. Keldenich, and J. B. Dressman. A physiologic model for simulating gastrointestinal flow and drug absorption in rats. Pharm. Res. 20:1766–1771 (2003).

    PubMed  Article  CAS  Google Scholar 

  52. S. Willmann, W. Schmitt, J. Keldenich, J. Lippert, and J. B. Dressman. A physiological model for the estimation of the fraction dose absorbed in humans. J. Med. Chem. 47:4022–4031 (2004).

    PubMed  Article  CAS  Google Scholar 

  53. M. Germani, P. Crivori, M. Rocchetti, P. S. Burton, A. G. E. Wilson, M. E. Smith, and I. Poggesi. Evaluation of a basic physiologically based pharmacokinetic model for simulating the first-time-in-animal study. Eur. J. Pharm. Sci. 31:190–201 (2007).

    PubMed  Article  CAS  Google Scholar 

  54. S. S. De Buck, V. K. Sinha, L. A. Fenu, R. A. H. J. Gilissen, C. E. Mackie, and M. J. Nijsen. The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools. Drug Metab. Dispos. 35:649–659 (2007b).

    PubMed  Article  Google Scholar 

  55. K. Ito, and J. B. Houston. Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches. Pharm. Res. 22:103–112 (2005).

    PubMed  Article  CAS  Google Scholar 

  56. Y. Naritomi, S. Terashita, A. Kagayama, and Y. Sugiyama. Utility of hepatocytes in predicting drug metabolism: Comparison of hepatic intrinsic clearance in rats and humans in vivo and in vitro. Drug Metab. Dispos. 31:580–588 (2003).

    PubMed  Article  CAS  Google Scholar 

  57. H. S. Brown, M. Griffin, and J. B. Houston. Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance. Drug Metab. Dispos. 35:293–301 (2007).

    PubMed  Article  CAS  Google Scholar 

  58. D. F. McGinnity, M. G. Soars, R. A. Urbanowicz, and R. J. Riley. Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metab. Dispos. 32:1247–1253 (2004).

    PubMed  Article  CAS  Google Scholar 

  59. Y. Shibata, H. Takahashi, and Y. Ishii. A convenient in vitro screening method for predicting in vivo drug metabolic clearance using isolated hepatocytes suspended in serum. Drug Metab. Dispos. 28:1518–1523 (2000).

    PubMed  CAS  Google Scholar 

  60. R. J. Riley, D. F. McGinnity, and R. P. Austin. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab. Dispos. 33:1304–1311 (2005).

    PubMed  Article  CAS  Google Scholar 

  61. S. E. Clarke, and P. Jeffrey. Utility of metabolic stability screening: Comparison of in vitro and in vivo clearance. Xenobiotica. 31:591–598 (2001).

    PubMed  Article  CAS  Google Scholar 

  62. K. Grime, and R. J. Riley. The impact of in vitro binding on in vitro–in vivo extrapolations, projections of metabolic clearance and clinical drug–drug interactions. Curr. Drug Metab. 7:251–264 (2006).

    PubMed  Article  CAS  Google Scholar 

  63. Y. Y. Lau, E. Sapidou, X. Cu, R. E. White, and K. C. Cheng. Development of a novel in vitro model to predict hepatic clearance using fresh, cryopreserved and sandwich-cultured hepatocytes. Drug. Metab. Dispos. 30:1446–1454 (2002).

    PubMed  Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank Sue Cole, Rhys Jones, and Anne Heatherington for their input to this work.

Author information

Affiliations

  1. Pfizer Global R&D, Department of Pharmacokinetics, Dynamics and Metabolism, IPC 654, Ramsgate Road, Sandwich, Kent, CT13 9NJ, UK

    Hannah M. Jones, Iain B. Gardner & Kenny J. Watson

Authors
  1. Hannah M. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iain B. Gardner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenny J. Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hannah M. Jones.

Additional information

Guest Editors: Lawrence X. Yu, Steven C. Sutton, and Michael B. Bolger

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jones, H.M., Gardner, I.B. & Watson, K.J. Modelling and PBPK Simulation in Drug Discovery. AAPS J 11, 155–166 (2009). https://doi.org/10.1208/s12248-009-9088-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1208/s12248-009-9088-1

Key words

  • absorption
  • clearance
  • distribution
  • PBPK
  • pharmacokinetics