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 thesenonclinical areas. The chapters provide as appropriate, a scientific background to the topic, relevant regulatory guidance, current statistical practice, and further research directions.

Provides the first volume dedicated to nonclinical statistics Brings together relevant statistical methods for nonclinical research in pharma/biotech industries with a mission promoting nonclinical statistics as a single discipline as clinical biostatistics Serves as an excellent resource for academic researchers and graduate students who want to pursue their research in these areas Includes supplementary material: sn.pub/extras

Autorentext

Lanju Zhang is Director in Statistics and Head of Nonclinical Statistics Group in the department of Data and Statistical Sciences at AbbVie. He leads a group providing statistical support to preclinical studies and CMC areas. Prior to moving to AbbVie, he was in MedImmune with increasing responsibilities to support all nonclinical areas. He is active in research and has published many papers and book chapters in nonclinical and clinical areas. He received his PhD in Statistics in 2005 from University of Maryland Baltimore County.

Max Kuhn is a Senior Director in Research Statistics in Pfizer R&D in Groton CT. He has over 15 years of experience in the pharmaceutical and diagnostic industries. His interests are in the application of machine learning models and estimation problems in general. He is the co-author of the best selling text Applied Predictive Modeling and the author of eight R packages.

Ian Peers is a****senior Leader in Global Medicines Development at AstraZeneca. He received his BSc from the University of Wales, Bangor and his Masters and Ph.D degrees from the University of Manchester. He has over 26 years experience working as an academic and industrial Statistician in Research and Development with 14 years experience in Biopharmaceuticals research including 11 years as Global Head of Statistics at AstraZeneca. Ian has collaborated with academic researchers globally, has supervised doctoral and postdoctoral students, worked in several therapeutic areas and has published and presented on pre-clinical, translational and clinical research. Ian is a professional Charted Statistician (CStat)of the Royal Statistical Society and holds an Honorary Professorship in the Faculty of Natural Sciences at the University of Stirling.

Stan Altan is Senior Director and Research Fellow of Nonclinical Statistics at Janssen Research & Development, LLC . Stan received his Ph.D. from Temple University in Biometrics. Stan is a Fellow of the American Statistical Association. Over the past 30+ years, Stan has supported drug discovery, toxicology, pharmaceutical and chemical development, biologics, and all Phases of clinical studies. Stan is a founding member of the Nonclinical Biostatistics Leaders' Forum, the IQ Consortium Statistical Leadership Group and the AAPS CMC Statistics Focus Group. Stan is on the editorial board of the Statistics in Biopharmaceutical Research journal, an ASA publication.

Inhalt

Introduction to Nonclinical Statistics for Pharmaceutical and Biotechnology Industries.- Regulatory Nonclinical Statistics.- How to be a good nonclinical statistician.- Statistical Methods for Drug Discovery.- High-throughput Screening Data Analysis.- Quantitative-Structure Activity Relationship Modeling and Cheminformatics.- GWAS for Drug Discovery.- Statistical applications in Design and Analysis of In-Vitro Safety Screening Assays.- Nonclinical safety assessment: an introduction for statisticians.- General Toxicology, Safety Pharmacology, Reproductive Toxicology and Juvenile Toxicology Studies.- Clinical Assays for Biological Macromolecules.- Recent Research Projects by FDA's Pharmacology and Toxicology Statistics Team.- Design and evaluation of drug combination studies.- Biomarkers.- Overview of Drug Development and Statistical Tools for Manufacturing and Testing.- Assay Validation.- Lifecycle Approach to Bioassay.- Quality by Design: Building Quality into Products and Processes.- Process Validation.- Acceptance Sampling.- Process Capability and Statistical Process Control.- Statistical Considerations for Stability and the Estimation of Shelf Life.- In Vitro Dissolution Testing: Statistical Approaches and Issues.- Assessing Content Uniformity.- Chemometrics and Predictive Modelling.- Statistical Methods for Comparability Studies.