Uncertain pharmacokinetic/pharmacodynamic systems: Design, estimation and control

Abstract In the study of many biological systems, measurement of some process variables occurs only infrequently and at irregular intervals relative to system time constants, while others are completely unobservable. The quantitative study of such sparse data systems (common in the fields of biochemistry, endocrinology, immunology, metabolism, pharmacology, pharmaceutical sciences, toxicology, and other areas), requires modeling methodologies developed expressly to handle the challenges of modeling and data analysis under the constraints of limited data. This presentation reviews current methods for design, estimation and control of sparse data systems, focusing on methods that formally incorporate important sources of uncertainty (both biological and experimental) into the modeling and analysis processes. The methods are illustrated using examples from pharmacokinetics and pharmacodynamics .

[1]  D. Reinhardt,et al.  Methods for evaluating optimal dosage regimens and their application to theophylline. , 1982, International journal of clinical pharmacology, therapy, and toxicology.

[2]  Alf Isaksson,et al.  Identification of ARX-models subject to missing data , 1993, IEEE Trans. Autom. Control..

[3]  Charles DeLisi,et al.  Mathematics and Computers in Biomedical Applications , 1985 .

[4]  Jean-Louis Steimer,et al.  Feedback Control Methods for Drug Dosage Optimisation , 1985, Clinical pharmacokinetics.

[5]  F. W. Harpley,et al.  Pharmacokinetics during Drug Development: Data Analysis and Evaluation Techniques. , 1983 .

[6]  David Z. D'Argenio,et al.  Discrete approximation of multivariate densities with application to Bayesian estimation , 1984 .

[7]  E. Walter,et al.  Robust experiment design via maximin optimization , 1988 .

[8]  R. Maine,et al.  Formulation and implementation of a practical algorithm for parameter estimation with process and measurement noise , 1980 .

[9]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[10]  David Z. D'Argenio,et al.  Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis , 2004, Springer US.

[11]  D Z D'Argenio,et al.  Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments. , 1990, Mathematical biosciences.

[12]  Pharmacokinetic Parameter Estimation with Stochastic Dynamic Models , 1991 .

[13]  E. Walter,et al.  Robust experiment design via stochastic approximation , 1985 .

[14]  D. Lindley.  On a Measure of the Information Provided by an Experiment , 1956 .

[15]  L Pronzato,et al.  Optimal experiment design for nonlinear models subject to large prior uncertainties. , 1987, The American journal of physiology.

[16]  David Z. D'Argenio,et al.  Parameter Estimation for Uncertain Dynamic Systems , 1994 .

[17]  H. L. Lucas,et al.  DESIGN OF EXPERIMENTS IN NON-LINEAR SITUATIONS , 1959 .

[18]  J. L. Steimer,et al.  Variability in drug therapy : description, estimation, and control : a Sandoz workshop , 1985 .

[19]  Eric Walter,et al.  Qualitative and quantitative experiment design for phenomenological models - A survey , 1990, Autom..

[20]  E. Krüger-Thiemer,et al.  Formal theory of drug dosage regimens. I , 1966 .

[21]  James D. Hamilton.  Time Series Analysis , 1994 .

[22]  Eric Walter,et al.  A general-purpose global optimizer: implementation and applications , 1984 .