Keywords: dynamical system; systems pharmacology; biochemical network; input-output regulation; parameter estimation; fast Fourier transform
@article{10_14736_kyb_2021_6_1005,
author = {Lynnyk, Volodymyr and Pap\'a\v{c}ek, \v{S}t\v{e}p\'an and Reh\'ak, Branislav},
title = {Biochemical network of drug-induced enzyme production: {Parameter} estimation based on the periodic dosing response measurement},
journal = {Kybernetika},
pages = {1005--1018},
year = {2021},
volume = {57},
number = {6},
doi = {10.14736/kyb-2021-6-1005},
mrnumber = {4376873},
zbl = {07478652},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2021-6-1005/}
}
TY - JOUR AU - Lynnyk, Volodymyr AU - Papáček, Štěpán AU - Rehák, Branislav TI - Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement JO - Kybernetika PY - 2021 SP - 1005 EP - 1018 VL - 57 IS - 6 UR - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2021-6-1005/ DO - 10.14736/kyb-2021-6-1005 LA - en ID - 10_14736_kyb_2021_6_1005 ER -
%0 Journal Article %A Lynnyk, Volodymyr %A Papáček, Štěpán %A Rehák, Branislav %T Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement %J Kybernetika %D 2021 %P 1005-1018 %V 57 %N 6 %U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2021-6-1005/ %R 10.14736/kyb-2021-6-1005 %G en %F 10_14736_kyb_2021_6_1005
Lynnyk, Volodymyr; Papáček, Štěpán; Rehák, Branislav. Biochemical network of drug-induced enzyme production: Parameter estimation based on the periodic dosing response measurement. Kybernetika, Tome 57 (2021) no. 6, pp. 1005-1018. doi: 10.14736/kyb-2021-6-1005
[1] Barton, H. A., Chiu, W. A., Setzer, R. W., Andersen, M. E., Bailer, A. J., Bois, F. Y., DeWoskin, R. S., Hays, S., Johanson, G., Jones, N., Loizou, G., MacPhail, R. C., Portier, C. J., Spendiff, M., Tan, Y.-M.: Characterizing uncertainty and variability in physiologically based pharmacokinetic models: State of the science and needs for research and implementation. Toxicolog. Sci. 99 (2007), 2, 395-402. | DOI
[2] Bois, F. Y.: Applications of population approaches in toxicology. Toxicology Letters 120 (2001), 1-3, 385-394. | DOI
[3] Cintrón-Arias, A., Banks, H. T., Capaldi, A., Lloyd, A. L.: A sensitivity matrix based methodology for inverse problem formulation. J. Inverse and Ill-posed Problems 17 (2009), 6. | DOI | MR
[4] Clewell, H., Andersen, M.: Risk assessment extrapolations and physiological modeling. Toxicology and Industr. Health 1 (1985), 111-–131. | DOI
[5] Clewell, H. J., Gentry, P. R., Covington, T. R., Sarangapani, R., Teeguarden, J. G.: Evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on tissue dosimetry. Toxicolog. Sci. 79 (2004), 2, :381-393. | DOI
[6] Clewell, R., Andersen, M., Barton, H.: A consistent approach for the application of pharmacokinetic modeling in cancer and noncancer risk assessment. Environmental Health Perspectives 110 (2002), 85–-93. | DOI
[7] Tebbens, J. Duintjer, Matonoha, C., Matthios, A., Papáček, Š.: On parameter estimation in an in vitro compartmental model for drug-induced enzyme production in pharmacotherapy. Appl. Math. 64 (2019), 2, 253-277. | DOI | MR
[8] Galetin, A., Burt, H., Gibbons, L., Houston, J. B.: Prediction of time-dependent CYP3A4 drug-drug interactions: impact of enzyme degradation, parallel elimination pathways, and intestinal inhibition. Drug Metabolism and Disposition 34 (2005), 1, 166-175. | DOI
[9] Michal, D. S. Gerhard: Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology. Second edition. Wiley, 2012.
[10] Gerlowski, L. E., Jain, R. K.: Physiologically based pharmacokinetic modeling: Principles and applications. J. Pharmaceut. Sci. 72 (1983), 10, 1103-1127. | DOI
[11] Huang, J.: Nonlinear output regulation: theory and applications. Advances in design and control. Society for Industrial and Applied Mathematics, Philadelphia, 2004. | MR
[12] Jia, G., Stephanopoulos, G., Gunawan, R.: Incremental parameter estimation of kinetic metabolic network models. BMC Systems Biology 6 (2012), 142, 1799–-1819. | DOI
[13] Krishnan, K.: Characterization and Application of Physiologically Based Pharmacokinetic Models in Risk Assessment. Technical Report, World Health Organization, 2010.
[14] Luke, N. S., DeVito, M. J., Shah, I., El-Masri, H. A.: Development of a quantitative model of pregnane X receptor (PXR) mediated xenobiotic metabolizing enzyme induction. Bull. Math. Biol. 72 (2010), 7, 1799–-1819. | DOI | MR
[15] MATLAB: Simulink Toolbox. Simulation and Model-Based Design. The MathWorks Inc., Natick 2020.
[16] Papáček, Š., Čelikovský, S., Rehák, B., Štys, D.: Experimental design for parameter estimation of two time-scale model of photosynthesis and photoinhibition in microalgae. Math. Computers Simul. 80 (2010), 6, 1302-1309. | DOI | MR
[17] Papáček, Š., Lynnyk, V., Rehák, B.: Regulatory network of drug-induced enzyme production: parameter estimation based on the periodic dosing response measurement. In: Programs and Algorithms of Numerical Mathematics 20, Institute of Mathematics, Czech Academy of Sciences, 2021.
[18] Rehák, B.: Alternative method of solution of the regulator equation: L2-space approach. Asian J. Control 14 (2011), 4, 1150-1154. | DOI | MR
[19] Rehák, B., Čelikovský, S.: Numerical method for the solution of the regulator equation with application to nonlinear tracking. Automatica 44 (2008), 5, 1358-1365. | DOI | MR
[20] Rehák, B., Čelikovský, S., Papáček, Š.: Model for photosynthesis and photoinhibition: Parameter identification based on the harmonic irradiation $O_{2}$ response measurement. IEEE Trans. Automat. Control 53 (Special Issue) (2008), 101-108. | DOI | MR
[21] Rehák, B., Čelikovský, S., Ruiz-León, J., Orozco-Mora, J.: A comparison of two FEM-based methods for the solution of the nonlinear output regulation problem. Kybernetika 45 (2009), 427-444. | MR
[22] Rostami-Hodjegan, A., Tucker, G.: ‘In silico’ simulations to assess the ‘in vivo’ consequences of ‘in vitro’ metabolic drug-drug interactions. Drug Discovery Today: Technologies 1 (2004), 4, 441-448. | DOI
[23] Rowland, M., Peck, C., Tucker, G.: Physiologically-based pharmacokinetics in drug development and regulatory science. Ann. Rev. Pharmacology and Toxicology 51 (2011), 1, 45-73. | DOI
[24] Sakamoto, N., Rehák, B.: Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem. In: IEEE Conference on Decision and Control and European Control Conference, 2011.
[25] Svecova, L., Vrzal, R., Burysek, L., Anzenbacherova, E., Cerveny, L., Grim, J., Trejtnar, F., Kunes, J., Pour, M., Staud, F., Anzenbacher, P., Dvorak, Z., Pavek, P.: Azole antimycotics differentially affect rifampicin-induced pregnane X receptor-mediated CYP3A4 gene expression. Drug Metabolism and Disposition 36 (2007), 2, 339-348. | DOI
[26] Zhao, P., Rowland, M., Huang, S.-M.: Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions. Clinical Pharmacology and Therapeutics 92 (2012), 17-–20. | DOI
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