Bioequivalence Studies in Drug Development, Methods and Applications by D. Hauschke, V. Steinijans, and I. Pigeot

Shein-Chung Chow, Bioequivalence Studies in Drug Development, Methods and Applications by D. Hauschke, V. Steinijans, and I. Pigeot, Biometrics, Volume 63, Issue 3, September 2007, Pages 969–970, https://doi.org/10.1111/j.1541-0420.2007.00856_4.x

It is my pleasure to review this book. Before we introduce the book, it will be helpful to provide some background for the role of bioavailability and bioequivalence in pharmaceutical research and development. In pharmaceutical research and development, bioavailability and bioequivalence studies usually serve as surrogates for clinical studies under the fundamental assumption that if a test product is bioequivalent to a reference product in terms of drug absorption (measured by pharmacokinetic parameters such as area under the blood- or plasma-concentration time curve, AUC, and maximum concentration, C_max), then it is assumed that the two drug products are therapeutically equivalent. In 1984, the United States Food and Drug Administration (FDA) was authorized to approve generic drug products based on evidence of average bioequivalence obtained through the conduct of bioequivalence studies. As a result, the FDA published the first guidance regarding design and analysis of bioequivalence under a two-sequence, two-period (2 × 2) crossover design in 1992. As more generic drug products become available, it is a concern that the generic drug products approved based on the average bioequivalence may not be bioequivalent to one another, especially when significant subject-by-formulation (product) is observed. As a result, in early 1990, the FDA started an initiative to explore a new concept, criteria, design, and methodology for testing so-called population bioequivalence and individual bioequivalence. In 2003, the FDA published a guidance to conclude this initiative. The authors of this book actively participated in this initiative (by attending many workshops cosponsored by the FDA). Hence, it is very nice to see that the authors have developed this book along this line.

As indicated in the preface of the book, the focus of the book is to provide an up-to-date overview of available methods, via numerous examples using real data. This book consists of 10 chapters, which cover various topics regarding bioequivalence studies in drug development. As compared with other competitors, the uniqueness of this book is that it not only introduces a nonparametric approach for assessment of bioequivalence (although it is not widely used and accepted in the United States), but also provides methods for analysis of pharmacokinetic interactions. Moreover, it provides useful SAS programs for assessment of population and individual bioequivalence (Chapter 9). However, it is kind of strange to include the chapter on Therapeutic Equivalence (Chapter 10) in this book because the concept of bioequivalence is similar but different from that of therapeutic equivalence (see, e.g., Chow and Shao, 2002). For each chapter, the authors lay out basic concepts, statistical methods, and interpretations of the results well. It should, however, be noted that the US FDA's current position regarding bioequivalence assessment in drug development is that “Average bioequivalence is required; individual bioequivalence may be used. Consultation with medical/statistical reviewers prior to the adoption of average bioequivalence or population/individual bioequivalence in drug development is strongly recommended.”

While the researchers and scientists who are engaged in the research area of bioequivalence would benefit from this book, there are still some important areas that are not included in this book. For example, the authors do not cover sample size calculation for assessment of population/individual bioequivalence under a higher-order or replicated crossover design, or the assessment of in vitro bioequivalence testing for local drug delivery drug products such as nasal aerosols and nasal sprays (see, e.g., Chow, Shao, and Wang, 2003a). In addition, it may be a good idea to discuss other related topics such as dose proportionality, steady state analysis, and population pharmacokinetic for completion. Moreover, the authors may want to update references by including the following references in future editions. These references include: (i) Lee, Shao, and Chow (2004), which provided a unified approach for modified large sample confidence intervals for linear combinations of variance components following the idea proposed by Hyslop, Hsuan, and Holder (2000), (ii) a review paper by Chow (1999) whose criticisms has led to the drop of the FDA (1997) draft guidance, (iii) Chow, Shao, and Wang (2002) and Chow, Shao, and Wang (2003b), which provided correct statistical methods for assessment of individual bioequivalence under a two-sequence, three-period (2 × 3) crossover design and population bioequivalence, respectively.

Pointing out areas for improvement does not discount the value of this book. This book would be beneficial to both pharmaceutical scientists/researchers and biostatisticians who are engaged in the area of bioequivalence studies in drug development. As an individual, I would like to add this book to my book collection of pharmaceutical research and development.