Pharmaceutical Research

, Volume 25, Issue 5, pp 1023–1036

Modeling and Simulation of Hepatic Drug Disposition Using a Physiologically Based, Multi-agent In Silico Liver

  • Li Yan
  • Glen E. P. Ropella
  • Sunwoo Park
  • Michael S. Roberts
  • C. Anthony Hunt
Research Paper

Abstract

Purpose

Validate a physiologically based, mechanistic, in silico liver (ISL) for studying the hepatic disposition and metabolism of antipyrine, atenolol, labetalol, diltiazem, and sucrose administered alone or in combination.

Materials and Methods

Autonomous software objects representing hepatic components such as metabolic enzymes, cells, and microarchitectural details were plugged together to form a functioning liver analogue. Microarchitecture features were represented separately from drug metabolizing functions. Each ISL component interacts uniquely with mobile objects. Outflow profiles were recorded and compared to wet-lab data. A single ISL structure was selected, parameterized, and held constant for all compounds. Parameters sensitive to drug-specific physicochemical properties were tuned so that ISL outflow profiles matched in situ outflow profiles.

Results

ISL simulations were validated separately and together against in situ data and prior physiologically based pharmacokinetic (PBPK) predictions. The consequences of ISL parameter changes on outflow profiles were explored. Selected changes altered outflow profiles in ways consistent with knowledge of hepatic anatomy and physiology and drug physicochemical properties.

Conclusions

A synthetic, agent-oriented in silico liver has been developed and successfully validated, enabling us to posit that static and dynamic ISL mechanistic details, although abstract, map realistically to hepatic mechanistic details in PBPK simulations.

Key words

agent-based complex systems discrete event liver mechanistic modeling physiologically based predict simulation 

Abbreviations

CV

central vein

ISL(s)

in silico liver(s)

MW

molecular weight

PB

physiologically based

PCPs

physicochemical properties

PK

pharmacokinetic

PV

portal vein

S1 and S2

two classes of SSs

SD

standard deviation

SS(s)

sinusoidal segment(s)

Supplementary material

11095_2007_9494_MOESM1_ESM.doc (1.5 mb)
ESM 1(DOC 1.51 MB)

References

  1. 1.
    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).PubMedCrossRefGoogle Scholar
  2. 2.
    C. A. Hunt, G. E. Ropella, L. Yan, D. Y. Hung, and M. S. Roberts. Physiologically based synthetic models of hepatic disposition. J. Pharmacokinet. Pharmacodyn 33:737–772 (2006).PubMedCrossRefGoogle Scholar
  3. 3.
    Y. G. Anissimov, and M. S. Roberts. A compartmental model of hepatic disposition kinetics: 1. Model development and application to linear kinetics. J. Pharmacokinet. Pharmacodyn. 29:131–156 (2002).PubMedCrossRefGoogle Scholar
  4. 4.
    D. Y. Hung, P. Chang, K. Cheung, B. McWhinney, P. P. Masci, M. Weiss, and M. S. Roberts. Cationic drug pharmacokinetics in diseased livers determined by fibrosis index, hepatic protein content, microsomal activity, and nature of drug. J. Pharmacol. Exp. Ther 301:1079–1087 (2002).PubMedCrossRefGoogle Scholar
  5. 5.
    D. Y. Hung, P. Chang, M. Weiss, and M. S. Roberts. Structure-hepatic disposition relationships for cationic drugs in isolated perfused rat livers: transmembrane exchange and cytoplasmic binding process. J. Pharmacol. Exp. Ther 297:780–789 (2001).PubMedGoogle Scholar
  6. 6.
    L. Liu, and K. S. Pang. An integrated approach to model hepatic drug clearance. Eur. J. Pharm. Sci 29:215–230 (2006).PubMedCrossRefGoogle Scholar
  7. 7.
    M. S. Roberts, and Y. G. Anissimov. Modeling of hepatic elimination and organ distribution kinetics with the extended convection-dispersion model. J. Pharmacokinet. Biopharm 27:343–382 (1999).PubMedCrossRefGoogle Scholar
  8. 8.
    G. A. Siebert, D. Y. Hung, P. Chang, and M. S. Roberts. Ion-trapping, microsomal binding, and unbound drug distribution in the hepatic retention of basic drugs. J. Pharmacol. Exp. Ther 308:228–235 (2004).PubMedCrossRefGoogle Scholar
  9. 9.
    Y. Liu, and C. A. Hunt. Studies of intestinal drug transport using an in silico epithelio-mimetic device. Biosystems 82:154–167 (2005).PubMedCrossRefGoogle Scholar
  10. 10.
    Y. Liu, and C. A. Hunt. Mechanistic study of the cellular interplay of transport and metabolism using the synthetic modeling method. Pharm. Res 23:493–505 (2006).PubMedCrossRefGoogle Scholar
  11. 11.
    S. Sheikh-Bahaei and C. A. Hunt. Prediction of in vitro hepatic biliary excretion using stochastic agent-based modeling and fuzzy clustering. In L. F. Perrone and et al. (eds.), Proceedings of the 37th conference on Winter simulation, Monterey, CA, 2006, pp. 1617–1624.Google Scholar
  12. 12.
    H. Steen, R. Oosting, and D. K. Meijer. Mechanisms for the uptake of cationic drugs by the liver: a study with tributylmethylammonium (TBuMA). J. Pharmacol. Exp. Ther 258:537–543 (1991).PubMedGoogle Scholar
  13. 13.
    K. Cheung, P. E. Hickman, J. M. Potter, N. I. Walker, M. Jericho, R. Haslam, and M. S. Roberts. An optimized model for rat liver perfusion studies. J. Surg. Res 66:81–89 (1996).PubMedCrossRefGoogle Scholar
  14. 14.
    H. F. Teutsch, D. Schuerfeld, and E. Groezinger. Three-dimensional reconstruction of parenchymal units in the liver of the rat. Hepatology 29:494–505 (1999).PubMedCrossRefGoogle Scholar
  15. 15.
    D. L. Miller, C. S. Zanolli, and J. J. Gumucio. Quantitative morphology of the sinusoids of the hepatic acinus. Quantimet analysis of rat liver. Gastroenterology 76:965–969 (1979).PubMedGoogle Scholar
  16. 16.
    J. J. Gumucio, and D. L. Miller. Zonal hepatic function: solute-hepatocyte interactions within the liver acinus. Prog. Liver Dis 7:17–30 (1982).PubMedGoogle Scholar
  17. 17.
    L. X. Garmire, D. G. Garmire, and C. A. Hunt. An in silico transwell device for the study of drug transport and drug–drug interactions. Pharm Res 24:2171–2186.Google Scholar
  18. 18.
    S. Sheikh-Bahaei, G. E. P. Ropella, and C. A. Hunt. In silico hepatocyte: agent-based modeling of the biliary excretion of drugs in vitro. In L. Yilmaz et al (eds.), Proceedings of the Agent-Directed Simulation Symposium of the Spring Simulation Multiconference (SMC'06), SCS Press, San Diego, CA, 2006, pp. 157–163.Google Scholar
  19. 19.
    G. E. Ropella, C. A. Hunt, and D. A. Nag. Using heuristic models to bridge the gap between analytic and experimental models in biology. In L. Yilmaz (ed), Proc 2005 Agent-Direc Simul Symp (ADS'05), Simulation Series, Vol. 37, SCS Press, San Diego, CA, 2005, pp. 182–190.Google Scholar
  20. 20.
    G. E. Ropella, C. A. Hunt, and S. Sheikh-Bahaei. Methodological Considerations of Heuristic Modeling of Biological Systems, Proc 9th World Multi-Conf Systemics, Cybernetics and Informatics, Orlando, Florida, 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Li Yan
    • 1
  • Glen E. P. Ropella
    • 2
  • Sunwoo Park
    • 2
  • Michael S. Roberts
    • 3
  • C. Anthony Hunt
    • 1
    • 2
  1. 1.The UCSF/UCB Joint Graduate Group in BioengineeringUniversity of CaliforniaBerkeleyUSA
  2. 2.Department of Biopharmaceutical SciencesUniversity of CaliforniaSan FranciscoUSA
  3. 3.School of Medicine, Princess Alexandra HospitalUniversity of QueenslandQueenslandAustralia

Personalised recommendations