"The use of statistics in process development is increasing throughout the biotechnology industry, due in part to an FDA mandate demanding the increased use of statistical concepts, the recognition of the importance of statistics, and the availability of powerful and user-friendly software. This book gives an overview of statistical methods for biotechnology process development, with an emphasis on application and minimal theoretical details. Examples using real data and case studies with JMP software are used throughout to illustrate topics."--Provided by publisher.

Written specifically for biotechnology scientists, engineers, and quality professionals, this book describes and demonstrates the proper application of statistical methods throughout Chemistry, Manufacturing, and Controls (CMC). Filled with case studies, examples, and easy-to-follow explanations of how to perform statistics in modern software, it is the first book on CMC statistics written primarily for practitioners. While statisticians will also benefit from this book, it is written particularly for industry professionals who don't have access to a CMC statistician or who want to be more independent in the design and analysis of their experiments. Provides an introduction to the statistical concepts important in the biotechnology industry Focuses on concepts with theoretical details kept to a minimum Includes lots of real examples and case studies to illustrate the methods Uses JMP software for implementation of the methods Offers a text suitable for scientists in the industry with some quantitative training Written and edited by seasoned veterans of the biotechnology industry, this book will prove useful to a wide variety of biotechnology professionals. The book brings together individual chapters that showcase the use of statistics in the most salient areas of CMC.

Written specifically for biotechnology scientists, engineers, and quality professionals, this book describes and demonstrates the proper application of statistical methods throughout Chemistry, Manufacturing, and Controls (CMC). Filled with case studies, examples, and easy-to-follow explanations of how to perform statistics in modern software, it is the first book on CMC statistics written primarily for practitioners. While statisticians will also benefit from this book, it is written particularly for industry professionals who don’t have access to a CMC statistician or who want to be more independent in the design and analysis of their experiments. Provides an introduction to the statistical concepts important in the biotechnology industry Focuses on concepts with theoretical details kept to a minimum Includes lots of real examples and case studies to illustrate the methods Uses JMP software for implementation of the methods Offers a text suitable for scientists in the industry with some quantitative training Written and edited by seasoned veterans of the biotechnology industry, this book will prove useful to a wide variety of biotechnology professionals. The book brings together individual chapters that showcase the use of statistics in the most salient areas of CMC.

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right. This text brings together for the first time in a single volume a comprehensive survey of methods important to the nonclinical science areas within the pharmaceutical and biotechnology industries. Specifically the Discovery and Translational sciences, the Safety/Toxiology sciences, and the Chemistry, Manufacturing and Controls sciences. Drug discovery and development is a long and costly process. Most decisions in the drug development process are made with incomplete information. The data is rife with uncertainties and hence risky by nature. This is therefore the purview of Statistics. As such, this book aims to introduce readers to important statistical thinking and its application in these nonclinical areas. The chapters provide as appropriate, a scientific background to the topic, relevant regulatory guidance, current statistical practice, and further research directions.

Traditional pharmaceutical development is an unwieldy process requiring extensive experimentation and long lead times before process scientists can fully understand the effect that process parameters such as pH, temperature, cell viability, or process yield may have on the product acceptability. Implementation of quality by design is a science-based approach that allows the operating ranges and the acceptance criteria to be established based on the impact on product quality attributes. During manufacturing, process monitoring becomes part of a continuous verification effort and statistical control limits can be used to signal potential trends or drifts in the process. Single manufacturing batches that are aberrant are readily identified. The melding of scientific understanding, information systems architecture, instrumentation, software, and personnel training provides a large return on investment by ensuring that the manufacturing process produces a consistent pharmaceutical product that meets acceptable release standards for human use. Table of Contents: Abbreviations / Introduction / From the Traditional Development Path to Quality by Design / Continuous Process Verification and Process Monitoring / Process Monitoring and Statistical Control Limits / Multivariate Analysis: A Mature State of Statistical Process Monitoring / Conclusion / Bibliography

The proper use of statistical methods is essential in food processing and biotechnology, and very few publications have focused on the practical application of these methods. This book utilizes case studies to explain the corresponding basics with a focus on understanding how and when to use the appropriate statistical methods. The text is structured according to practical applications, starting from data collection and data treatment, moving to product development, process control and analytical methods. Practical Examples of Using Statistics in Food and Biotechnology begins with a detailed introduction to statistics and their use in food processing. The first main section focuses on statistics for data collection and treatment featuring scales of measurement, sampling and summarizing and presenting data. Another section looks at statistics for food product and process development, examining mixture experiments for different food groups from meat and fish to vegetables, juices, rice and olive oils. Further sections focus on statistical process control, statistics for sensory science and a wide range of analytical methods including LC-MS/MS and other cutting edge methods. In providing practical, hands-on information on the use of statistics in food processing, this work will be of major importance for food professionals, industry staff and researchers in food and biotechnology. .

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development. Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.