Modelling and simulation of variability and uncertainty in toxicokinetics and pharmacokinetics.

Two important methodological issues within the framework of the variability and uncertainty analysis of toxicokinetic and pharmacokinetic systems are discussed: (i) modelling and simulation of the existing physiologic variability in a population; and (ii) modelling and simulation of variability and uncertainty when there is insufficient or not well defined (e.g. small sample, semiquantitative, qualitative and vague) information available. Physiologically based pharmacokinetic models are especially suited for separating and characterising the physiologic variability from the overall variability and uncertainty in the system. Monte Carlo sampling should draw from multivariate distributions, which reflect all levels of existing dependencies in the intact organism. The population characteristics should be taken into account. A fuzzy simulation approach is proposed to model variability and uncertainty when there is semiquantitative, qualitative and vague information about the model parameters and their statistical distributions cannot be defined reliably.

[1]  Eyke Hüllermeier,et al.  An Approach to Modelling and Simulation of Uncertain Dynamical Systems , 1997, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[3]  W. Calder.  Size, Function, and Life History , 1988 .

[4]  R C Spear,et al.  Comparison of three physiologically based pharmacokinetic models of benzene disposition. , 1991, Toxicology and applied pharmacology.

[5]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[6]  B. Allen,et al.  Evaluation of uncertainty in input parameters to pharmacokinetic models and the resulting uncertainty in output. , 1989, Toxicology letters.

[7]  R. Armstrong,et al.  Adrenoreceptor effects on rat muscle blood flow during treadmill exercise. , 1987, Journal of applied physiology.

[8]  R. Leggett,et al.  Reference values for resting blood flow to organs of man. , 1989, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[9]  D Krewski,et al.  Uncertainty, variability, and sensitivity analysis in physiological pharmacokinetic models. , 1995, Journal of biopharmaceutical statistics.

[10]  Malcolm Rowland,et al.  Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. , 2002, Drug metabolism and disposition: the biological fate of chemicals.

[11]  R C Spear,et al.  Structure and parameterization of pharmacokinetic models: their impact on model predictions. , 1992, Risk analysis : an official publication of the Society for Risk Analysis.

[12]  S. Harapat,et al.  Computer Simulation of the Effects of Alterations in Blood Flows and Body Composition on Thiopental Pharmacokinetics in Humans , 1997, Anesthesiology.

[13]  R. Armstrong,et al.  Distribution of cardiac output during diurnal changes of activity in rats. , 1991, The American journal of physiology.

[14]  H. Wagner,et al.  Measurement of the distribution of cardiac output in unanesthetized rats. , 1971, Journal of applied physiology.

[15]  R. Leggett,et al.  Suggested reference values for regional blood volumes in humans. , 1991, Health physics.

[16]  W. R. Stahl,et al.  Scaling of respiratory variables in mammals. , 1967, Journal of applied physiology.

[17]  Daniel Wartenberg,et al.  Correlated Inputs in Quantitative Risk Assessment: The Effects of Distributional Shape , 1995 .

[18]  D. Randall,et al.  Roles of cardiac output and peripheral resistance in mediating blood pressure response to stress in rats. , 1998, The American journal of physiology.

[19]  T. Adams,et al.  Cardiac output related to body weight in small mammals , 1968 .

[20]  J. P. Holt,et al.  Ventricular volumes and body weight in mammals. , 1968, The American journal of physiology.

[21]  D Hattis,et al.  Uncertainties in pharmacokinetic modeling for perchloroethylene. I. Comparison of model structure, parameters, and predictions for low-dose metabolism rates for models derived by different authors. , 1990, Risk analysis : an official publication of the Society for Risk Analysis.

[22]  F Y Bois,et al.  Modeling human interindividual variability in metabolism and risk: the example of 4-aminobiphenyl. , 1995, Risk analysis : an official publication of the Society for Risk Analysis.

[23]  F Y Bois,et al.  Precision and sensitivity of pharmacokinetic models for cancer risk assessment: tetrachloroethylene in mice, rats, and humans. , 1990, Toxicology and applied pharmacology.

[24]  N Heisler,et al.  Regional blood flow in conscious resting rats determined by microsphere distribution. , 1993, Journal of applied physiology.

[25]  F Y Bois,et al.  Interspecies extrapolation of physiological pharmacokinetic parameter distributions. , 1996, Risk analysis : an official publication of the Society for Risk Analysis.

[26]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[27]  H. Trussell,et al.  Constructing membership functions using statistical data , 1986 .

[28]  F Y Bois,et al.  Analysis of PBPK Models for Risk Characterization , 1999, Annals of the New York Academy of Sciences.