Modeling and Simulation of Hepatic Drug Disposition Using a Physiologically Based, Multi-agent In Silico Liver
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.
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.
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 wordsagent-based complex systems discrete event liver mechanistic modeling physiologically based predict simulation
in silico liver(s)
- S1 and S2
two classes of SSs
This research was funded in part by grants (to CAH) and Fellowships (to LY and SP) provided by the CDH Research Foundation and the Australian NHMRC (MSR). We thank G. Cosmo Haun for developing the visualizations, Teddy Lam for hepatic clearance and PK discussions, Shahab Sheikh-Bahaei for manuscript review, Pearl Johnson for the support provided to LY, and Laura Veit for manuscript assistance. We also thank the other members of the BioSystems Group for helpful discussion and commentary. The work was abstracted in part from the PhD dissertation presented by LY to the Graduate Division, University of California, Berkeley, CA.
- 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
- 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
- 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.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.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.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