Kinetic Data Analysis - Laszlo Endrenyi

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Résumé :
Nonlinear Regression: Principles, Properties and Methods.- An Introduction to Nonlinear Least Squares.- Statistical Properties of Kinetic Estimates.- On Parameter Redundancy in Curve Fitting of Kinetic Data.- Tangential and Curvilinear Coordinates in Nonlinear Regression.- Use of Multiple Linear Regression in the Analysis and Interpretation of Biokinetic Data.- Multivariate Statistical Methods in Pharmacokinetics.- Tests for the Behaviour of Experimental Errors.- Robust Parameter Estimation.- Robust Estimation in Enzyme Kinetics.- Robust Alternatives to Least-Squares Curve Fitting.- Design of Experiments for Kinetic Parameter Estimation.- Design of Experiments for Estimating Enzyme and Pharmacokinetic Parameters.- Experimental Designs for the Distribution-Free Analysis of Enzyme Kinetic Data.- Kinetic Model Identification.- A Priori Identifiability Analysis in Pharmacokinetic Experiment Design.- A Frequency Response Method for Pharmacokinetic Model Identification.- Design and Analysis of Kinetic Experiments for Discrimination between Rival Models.- Combination of Experiments.- Estimation of Pooled Pharmacokinetic Parameters Describing Populations.- Analysis of Sets of Estimates from Pharmacokinetic Studies.- Some Current Problems in the Interpretation of Enzyme Kinetic Data.- Kinetic Data Analysis: Methods and Applications.- Errors Associated with the Determination of the Initial Velocities of Enzyme-Catalyzed Reactions.- Progress Curves in Enzyme Kinetics: Design and Analysis of Experiments.- Applications of Progress Curves in Enzyme Kinetics.- Data Analysis from Michaelis-Menten Kinetics: Ins and Outs.- A Generalized Numerical Deconvolution Procedure for Computing Bioavailability-Time Profiles.- Influence of Variability in Plasma Concentration on Dose PredictionAssuming Michaelis-Menten Kinetics.- Contributor Index....

Sommaire:
Kinetic models have often served as useful examples in develop? ing the methodology for the design and analysis of experiments in? volving mechanistic models. Thus, it is not surprising that these approaches have been applied quite successfully to kinetic obser? vations. Nevertheless, many ideas and methods were developed indepen? dently in various fields of science. More often than not, investi? gators working in one area have not been aware of relevant advances in others. In order to facilitate the desirable exchange of ideas, a one-day symposium was held in Toronto in conjunction with the XIth International Congress of Biochemistry. Biochemists, pharmacolo? gists,> and statisticians came together and discussed many of the topics presented in this volume. Participants in the symposium believed that it would be use? ful to publish a collection of the presentations together with some additional material. The present volume is the result. It is an attempt to convey some of the interdisciplinary concerns involv? ing mechanistic, and especially kinetic, model building. The coverage is by no means exhaustive: many principles, methods, and problems are not included. Even the applications are limited to biochemistry and pharmacology. Still, the symposium highlighted areas of current interest. These included questions of weighting, robust parameter estimation, pooled data analysis, model identification, and the design of experiments. These topics, which are of interest in many fields of science, are discus3ed also in the present volume....