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8 chemicals from [11] were chosen as our test drugs to be cleared in silico by simulated rat hepatocytes. In vitro, each compound (at a concentration of 1000 µM) had been incubated for various time periods at 37°C with freshly isolated rat hepatocytes. The cell density in the reaction mixture is reported 0.5´106 (cells/ml) for FK1052 and Diltiazem, and 1´106 (cells/ml) for Acetaminophen. Calculation of parameter. Because the Hepatocytes and the test drug are in the same volume of mixture: P1/P2=C1/C2 For example consider FK1052: P1/P2=1000(pmol/ml)/0.5e6(cells/ml)=1000(fmol)/500(cells) To encompass 500 cells we need 0.001ml of the mixture (V = cells/cell-density = 500/.5´106 = 0.001ml). Assuming each spot in the WanderSpace corresponds to 350´10-9 ml of the mixture, for 0.001ml we will need 2857 spots. So we chose the WanderSpace to be 53 by 54 (=2862). Other parameters were chosen as follows: HepDensity = 500/2857 = 0.175, TotalSoluteMass = 1000 fmol. Other parameters (SoluteBindingProb,SoluteBinding- Cycles, BindersPerCe-llMin, BindersPerCellMax and MetabolizationProb) were iteratively searched to optimize the output –an acceptable similarity score was attained. We used the Nedler and Mead simplex method [16] to optimize the parameters. This method has been frequently used for optimization of stochastic simulation models, where one tries to estimate the model parameters that optimize some specific output of the simulation model [16]. The parameter values are summarized in Table 1. Figure 5 shows the output of the IS-HIC ArtModel using above parameterization along with in vitro clearance profiles of 8 drugs. The simulation results are in a good agreement with both the mathematical model and the in vitro data.
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Table 1- The parameter values
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Acetaminophen Diazepam Diltiazem FK079 FK480 FK1052 Quinotolast Zidovudine Figure 5 |
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[1] Hunt, C.A., G.E.P. Ropella, M.S. Roberts, and L. Yan, 2004, “Biomimetic In Silico Devices. Computational Methods in Systems Biology,” Second International Workshop, CMSB 2004 (Paris, France, May 26-28, 2004) Proceedings. Lecture Notes in Bioinformatics, Springer (in press); available at http://biosystems.ucsf.edu/Researc/RecentPapers/HuntCMSB04b.pdf. [2] Leahy, D.E., 2004, “Drug Discovery Information Integration: Virtual Humans for Pharmacokinetics,” DDT: Biosilico. 2, no. 2: 78-84. [3] Lipscomb, J.C., M. Meek, K. Krishnan, G.L. Kedderis, H. Clewell, and L. Haber, 2004, “Incorporation of Pharmacokinetic and Pharmacodynamic Data Into Risk Assessments,” Toxicology Mechanisms and Methods, 14, no. 3: 145-158. [4] Gunaratna, C., 2001, “Drug Metabolism and Pharmacokinetics in Drug Discovery: A Primer for Bioanalytical Chemists, Part II,” Current Separations, 19, no. 3 (www.currentseparations.com/issues/19-3/19-3e.pdf. [5] Venkatakrishnan, K., L.L. von Moltke, and D.J. Greenblatt, 2001, “Human Drug Metabolism and the Cytochromes P450: Application and Relevance of In Vitro Models,” Journal of Clinical Pharmacology, 41, no. 11: 1149-1179. [6] Takahashi, K., K. Kaizu, B. Hu, and M. Tomita, 2004, “A Multi-algorithm, Multi-timescale Method for Cell Simulation,” Bioinformatics, 20, no. 4: 538-46. [7] Meng,T.C., S. Somani, and P. Dhar, 2004, “Modeling and Simulation of Biological Systems with Stochasticity,” In Silico Biology, 4, 0024. [8] S. Eklins, 2003, “In Silico Approaches to Predicting Drug Metabolism, Toxicology and Beyond,” Biochemical Society Transactions, 31,(Pt 3): 611-4. [9] Ropella, G.E.P., and C.A. Hunt, 2003, “Prerequisites for Effective Experimentation in Computational Biology,” 25th Annual Conference of the IEEE Engineering in Medicine and Biology Society (Cancun, September 17-21, 2003); available at http://128.218.188.153:8080/~gepr/furm/docs/EMBC03Paper1.pdf. [10] Daniels, M., 1999, “Integrating Simulation Technologies with Swarm,” Agent Simulation: Applications, Models and Tools Conference (University of Chicago, October 1999); available at http://www.santafe.edu/~mgd/anl/anlchicago.html .[1] [11] Naritomi,Y., S. Terashita, A. Kagayama, and Y. Sugiyama, 2003, “Utility of Hepatocytes in Predicting Drug Metabolism: Comparison of Hepatic Intrinsic Clearance in Rats and Humans In Vivo and In Vitro,” Drug Metabolism and Disposition, 31, no. 5: 580-588. [12] Shibata, Y., H. Takahashi, and Y. Ishii, 2000, “A Convenient In Vitro Screening Method for Predicting In Vivo Drug Metabolic Clearance Using Isolated Hepatocytes Suspended in Serum,” Drug Metabolism and Disposition, 28, no. 12: 1518-1523. [13] Ropella, G.E.P., D.A. Nag, and C.A. Hunt, 2000, “Similarity Measures for Automated Comparison of In Silico and In Vitro Experimental Results,” ibid; available at http://128.218.188.153:8080/~gepr/furm/docs/EMBC03Paper2.pdf . [14] Treijtel, N., A. Barendregt, A.P. Freidig, B.J. Blaauboer, and J.C.H. van Eijkeren, 2004, “Modeling the In Vitro Intrinsic Clearance of the Slowly Metabolized Compound Tolbutamide Determined in Sandwich-Cultured Rat Hepatocytes,” Drug Metabolism and Disposition, 32, no. 8: 884-891. [15] Haenen, B., C. Rompelberg, K. Van Twillert, M. Hamzink, J. Dormans, and J. Van Eijkeren, 2002, “Utility of Rat Liver Slices to Estimate Hepatic Clearance for Application in Physiologically Based Pharmacokinetic Modeling: A Study With Tolbutamide, a Compound with Low Extraction Efficiency,” Drug Metabolism and Disposition, 30, no. 3: 307-313. [16] H.G. Neddermeijer & G.J. van Oortmarssen & N. Piersma & R. Dekker, 2000, "Adaptive extensions of the Nelder and Mead Simplex Method for optimization of stochastic simulation models," Econometric Institute Report 199, Erasmus University Rotterdam, Econometric Institute.
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