Book Review: Pharmacokinetic and Pharmacodynamic Data Analysis ‐ Concepts and Applications, 5th Edition, by JohanGabrielsson and Daniel Weiner Apotekarsocieteten, 20161,040 pp, hardcover ISBN: 9789198299106

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 time-delays 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 focuses on interpretation of data and modeling results, and true understanding of the behavior of biological systems. They emphasize that data analysis or model developments 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.