Abstract
Based on a frequency response approach to the sensitivity analysis of pharmacokinetic models, the concept of structural sensitivity is introduced. The core of this concept is the factorization of the system sensitivity into two multipliers. The first one, called structural sensitivity index, has an analytical form, which depends solely on the structure and connectivity of the system and does not depend on the drug administered or the factor perturbed. The second multiplier, the parameter sensitivity index, depends on the drug properties, the tissue of interest and the parameter perturbed, but is largely independent of the structure of the system. The structural and parametric sensitivity indices can be evaluated and analyzed separately. The most important feature of the proposed approach is that the conclusions drawn from the analysis of the structural sensitivity index are valid across all mammalian species, as the latter share a common anatomical and physiological structure. The concept of structural sensitivity is illustrated on the commonly used structure of the whole body physiologically based pharmacokinetic models by showing that the factorization of the sensitivity carried out arises naturally from the mechanism of the distribution of perturbations throughout the organism. The concept of structural sensitivity has interesting practical implications. It enables the formal proof of relationships and facts that have been observed previously. Moreover, the conclusions drawn introduce in fact a ranking of the tissues or subsystems with respect to their impact on the model outputs. From this ranking, direct recommendations regarding the design of experiments for whole-body physiologically based pharmacokinetic models are derived.
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REFERENCES
J. P. C. Kleijnen. Sensitivity analysis and related analyses: A review of some statistical techniques. J. Statist. Comput. Simul. 57:111–142 (1997).
R. C. H. Cheng and W. Holland. Sensitivity of computer simulation experiments to errors in input data. J. Statist. Comput. Simul. 57:219–241 (1997).
G. E. B. Archer, A. Saltelli, and I. M. Sobol. Sensitivity measures, ANOVA-like techniques and the use of bootstrap. J. Statist. Comput. Simul. 58:99–120 (1997).
I. Nestorov, L. Aarons, and M. Rowland. Physiologically based pharmacokinetic modelling of a homologous series of barbiturates in the rat. A sensitivity analysis. J. Pharmacokin. Biopharm. 25:413–447 (1997).
R. A. Yetter, F. L. Dryer, and H. Rabitz. Some interpretative aspects of elementary sensitivity gradients in combustion kinetics modeling. Combustion Flame 59:107–133 (1985).
M. V. Evans, W. D. Crank, H.-M. Yang, and J. E. Simmons. Applications of sensitivity analysis to a physiologically based pharmacokinetic model for carbon tetrachloride in rats. Toxicol. Appl. Pharmacol. 128:36–44 (1994).
D. M. Hetrick, A. M. Jarabek, and C. C. Travis. Sensitivity analysis for physiologically based pharmacokinetic models. J. Pharmacokin. Biopharm. 19:1–20 (1991).
P. Varkonyi, J. V. Bruckner, and J. M. Gallo. Effect of parameter variability on physiologically-based pharmacokinetic model predicted drug concentrations. J. Pharm. Sci. 84:381–384 (1995).
R. C. Spear, F. Y. Bois, T. Woodruff, D. Auslander, J. Parker, and S. Selvin. Modeling benzene pharmacokinetics across three sets of animal data: Parametric sensitivity and risk implications. Risk Anal. 11:641–654 (1991).
K. Thomaseth. PANSYM: A symbolic equation generator for mathematical modelling, analysis and control of metabolic and pharmacokinetic systems. Comput. Meth. Prog. Biomed. 42:99–112 (1994).
P. M. Schlosser, T. Holcomb, and J. E. Bailey. Determining metabolic sensitivity coefficient directly from experimental data. Bioechn. Bioeng. 41:1027–1038 (1993).
J. L. Gabrielsson and T. Groth. An extended physiological pharmacokinetic model of methadone disposition in the rat: Validation and sensitivity analysis. J. Pharmacokin. Biopharm. 16:183–201 (1988).
K. Godfrey. Compartmental Models and Their Application, Academic Press, 1983.
D. A. Anderson. Compartmental Modeling and Tracer Kinetics. Lecture Notes in Biomathematics, Vol. 50, Springer-Verlag, Heidelberg, 1983, p. 302.
M. Eslami. Theory of Sensitivity in Dynamic Systems. An Introduction, Springer-Verlag, Heidelberg, 1994, p. 600.
Y. Z. Tsipkin. Basics of Automatic Systems Theory [In Russian]. Nauka, Moscow, 1977, pp. 88–101.
G. J. Murphy. Basic Automatic Control Theory, van Nostrand, Princeton, NJ, 1957, p. 832.
J. M. van Rossum, J. E. G. M. de Bie, G. van Lingen, and H. W. A. Teeuwen. Pharmacokinetics from a dynamical systems point of view. J. Pharmacokin. Biopharm. 17:365–397 (1989).
L. Dedik and M. Durisova. Frequency response method in pharmacokinetics. J. Pharmacokin. Biopharm. 22:293–307 (1994).
I. A. Nestorov, L. J. Aarons, P. A. Arundel, and M. Rowland. Lumping of whole-body physiologically based pharmacokinetic models. J. Pharmacokin. Biopharm. 26:21–46 (1998).
L. E. Gerlovski and R. K. Jain. Physiologically based pharmacokinetic modeling: Principles and applications. J. Pharm. Sci. 72:1103–1129 (1983).
M. C. Kohn. The importance of anatomical realism for validation of physiological models of disposition of inhaled toxicants. Toxicol. Appl. Pharmacol. 147:448–458 (1997).
A. Bernareggi and M. Rowland. Physiological modeling of cyclosporine kinetics in rat and man. J. Pharmacokin. Biopharm. 19:21–50 (1991).
D. Krewski, Y. Wang, S. Bartlett, and K. Krishnan. Uncertainty, variability, and sensitivity analysis in physiological pharmacokinetic models. J. Biopharm. Statist. 5:245–271 (1995).
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Nestorov, I.A. Sensitivity Analysis of Pharmacokinetic and Pharmacodynamic Systems: I. A Structural Approach to Sensitivity Analysis of Physiologically Based Pharmacokinetic Models. J Pharmacokinet Pharmacodyn 27, 577–596 (1999). https://doi.org/10.1023/A:1020926525495
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DOI: https://doi.org/10.1023/A:1020926525495