By Jim E. Riviere
Now in a revised version, Comparative Pharmacokinetics: rules, suggestions, and functions offers the rules and strategies of comparative and veterinary pharmacokinetics in an in depth but functional demeanour. constructed as a device for making sure that pharmacokinetics experiences are safely designed and properly interpreted, the booklet offers whole assurance of the conceptual foundation of pharmacokinetics as used for quantifying organic approaches from the views of body structure and medicine. New chapters were additional on quantitative constitution permeability relationships and bioequivalence, and a few present chapters were considerably revised and improved to supply a present source for veterinary and comparative pharmacokinetics.
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Extra info for Comparative Pharmacokinetics: Principles, Techniques and Applications
QSAR Combinatorial Science. 26:694–701. , and Papa, E. 2007. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction. Journal of Molecular Graphing and Modelling. 25:755–766. J. 1972. Linear relationships between lipophilic character and biological activity of drugs. Journal of Pharmaceutical Sciences. 61:1–19. M. 1990. Concepts and Applications of Molecular Similarity. New York: John Wiley & Sons. , and Langer, R. 1995. Permeation of steroids through human skin.
Although the relationship between a molecular descriptor and biological activity may be linear or nonlinear, it remains common practice to use linear approaches such as multiple linear regression (MLR) or partial least squares (PLS) regression analysis to construct the QSPeR model. , 2000). The selection of a statistical method is dependent on the complexity of the transport mechanisms, data availability, and molecular descriptor selection. These decisions would ultimately determine the application range of the established model and prediction robustness of the model.
10 Training Fig. 35 Validation Williams plot showing applicability domain for QSPeR model. compounds and the validation set has 31 compounds. The Williams plot defined by the data set is given in Fig. 2. 102 (one in the training set and one in the validation set). These outliers and high-leverage compounds can be removed from the data set for model development, resulting in a training set of 86 and validation set of 29 compounds used in the previous analysis. 3 OTHER MODELS AND APPLICATIONS The focus of this chapter was to briefly introduce the reader to QSPeR analysis as an example of how pharmacokinetic parameters that will be developed in future chapters can be correlated to physical chemical properties.