Edition: 5th, revised and expanded
Publishing year: 2016
Author: Johan Gabrielsson, Weiner Daniel
File size: 166 MB
Ylva Terelius, PhD
(verified owner) – March 22, 2017
The 5th edition of “Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications” is a new, revised and expanded version of this PK/PD Bible that has been widely used for many years. It is valuable both as a text-book for beginners and as a reference book for more experienced scientists. The book contains numerous figures illustrating concepts and models.
The first 3 Chapters cover general principles and pharmacokinetic and pharmacodynamic concepts. The 4th Chapter discusses modeling strategies and ends with a checklist for assessing goodness-of-fit for models. Chapter 5 discusses elements of design for kinetic and dynamic studies and what data and design are needed to answer specific questions. Chapter 6 is entirely new and is my favorite since it deals with pattern recognition and the value of actually looking at data and curve shape as diagnostic tools for proposing mechanisms and improving data analysis and linking data to actual biological processes.
The second half of the book contains applications and case studies and the data sets are supplied on an attached USB memory stick. There are 53 PK case studies, including a few with large molecules (antibodies and enzyme), and 52 PD case studies. All exercises start with the objectives for the exercise, continue with a problem specification, describes the study design and data, the models used (with references to the concept chapters) and ends with a section on the interpretation of results and conclusions. These exercises are clearly indexed and start off with simple PK studies such as single dose bolus i.v. or p.o. studies and continue with more complex examples. There is also an excellent overview Table in Chapter 1, listing the case studies and the concepts on which they build.
I strongly recommend this book to everybody working in this field.
Elke H.J. Krekels
(verified owner) – March 22, 2017
On the cover of the fifth edition of Pharmacokinetic and Pharmacodynamic Data Analysis – Concepts and Applications, the authors, Johan Gabrielsson and Daniel Weiner, mention that this book is intended for undergraduate and graduate level teaching on pharmacokinetic and pharmacodynamic concepts. I for one, indeed find the book very useful for preparing my lectures, because it discusses fundamental pharmacological concepts by describing the underlying biological processes, visualizing the characteristic patterns in pharmacological profiles that they give rise to, and providing details on the mathematical models that can be used for the quantification of the biological system. This systematic overview of basic pharmacological principles, I believe, makes the book also a valuable work of reference for seasoned pharmacometricians working in different stages of drug development.
Like the previous version, this voluminous book is divided into two sections, one covering pharmacological concepts and the other covering practical applications of those concepts. The concepts range from simple linear plasma kinetics, to complex pharmacodynamic models with timedelays or physiological feedback mechanisms, and more. In what the authors call a ‘holistic view’, the book goes beyond numbers and mere technicalities of data analysis and focusses on interpretation of data and modeling results, and true understanding of the behavior of biological systems. They emphasize that data analysis or model development are not goals in their own right and place these activities in the broader context of drug research and development.
New in the fifth edition of this book is a chapter on Pattern Recognition at the end of the first section. Contrary to what I expected initially, this chapter does not cover the latest trends in machine learning, rather it aims at training the human data analyst in observing and interpreting data obtained in pharmacological experiments. I found this a relief in a time when professionals sometimes tend to overlook what is to be seen right in front of them, while searching for increasingly complex computational solutions for the challenges they face. The chapter consists of various examples that illustrate what conclusions can be drawn from study results and provide suggestions on for instance the design of the next study that will best inform a model or most effectively allow for differentiation between the different underlying mechanisms that could have given rise to the initial observations. However, as is the case in the rest of the book, the examples are illustrated with full pharmacological profiles that do not take any variability or uncertainty into account. One could therefore wonder for some examples, whether experimental data would reveal the subtle differences in the provided pharmacological profiles and yield the essential information needed for decision making. In that sense the examples may not go beyond mere academic exercises, but without the distraction of real-life complicating factors, I do believe they may still provide valuable insight for anybody involved in, or aspiring to be involved in, developing meaningful research strategies in drug development.
The second section of the book, contains some intentional overlap with the first section, so that the applications can also largely be understood without first reading up on the concepts. The authors have tapped into years of personal experience, to develop useful and illustrative examples that will help the reader to obtain hands-on experience with pharmacological data analysis. A complementary flash drive is provided with model files that make it even easier for the reader to go through the examples. A WinNonlin license is required to view these files, but this does not mean that all is lost for users of different software packages, because the datasets used for the examples are provided as Excel files, that can easily be adopted for use in a wide range of software packages.
In conclusion, this book will not give you the latest details on computational methods for pharmacological data analysis. However, if you want to learn what you yourself can get out of experimental data, this book is a very good starting point.
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