Statistical Design and Analysis of Stability Studies
The US Food and Drug Administration's Report to the Nation in 2004 and 2005 indicated that one of the top reasons for drug recall was that stability data did not support existing expiration dates. Pharmaceutical companies conduct stability studies to characterize the degradation of drug products and to estimate drug shelf life. Illustrating how stability studies play an important role in drug safety and quality assurance, Statistical Design and Analysis of Stability Studies presents the principles and methodologies in the design and analysis of stability studies.
After introducing the basic concepts of stability testing, the book focuses on short-term stability studies and reviews several methods for estimating drug expiration dating periods. It then compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. Following a chapter on the statistical methods for stability analysis under a linear mixed effects model, the book examines stability analyses with discrete responses, multiple components, and frozen drug products. In addition, the author provides statistical methods for dissolution testing and explores current issues and recent developments in stability studies.
To ensure the safety of consumers, professionals in the field must carry out stability studies to determine the reliability of drug products during their expiration period. This book provides the material necessary for you to perform stability designs and analyses in pharmaceutical research and development.
Chapter 2 Accelerated Testing
Chapter 3 Expiration Dating Period
Chapter 4 Stability Designs
Chapter 5 Stability Analysis with Fixed Batches
Chapter 6 Stability Analysis with Random Batches
Chapter 7 Stability Analysis with a Mixed Effects Model
Chapter 8 Stability Analysis with Discrete Responses
Chapter 9 Stability Analysis with Mulitiple Components
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1987 FDA stability active ingredient application Arrhenius equation assay asymptotic batch-to-batch variation Biopharmaceutical Chapter Chow and Shao common slope components confidence interval considered covariance covariance matrix defined degradation line degradation rate degrees of freedom design factors dissolution profiles dissolution testing dosage forms drug characteristic drug product drug shelf-life drug substance effects model estimated shelf-life expiration dating period factorial design FDA stability guideline fractional factorial design given guideline for stability intercepts label claim least squares estimates level of significance levothyroxine sodium linear regression long-term stability studies lower confidence bound manufacturing matrixing design mean degradation mean squared error null hypothesis p-value package type pharmaceutical production batches proposed quantile random effects model requirements retest period Ruberg sampling time points Section shelf-life estimation stability analysis stability data stability design stability testing strength sum of squares TABLE three batches Uniform uniform matrix USP-NF variability variance
Page 2 - Active ingredient means any component that is intended to furnish pharmacological activity or other direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease, or to affect the structure or any function of the body of man or other animals.
Page xvii - This book series will provide comprehensive and unified presentations of statistical designs and analyses of important applications in biostatistics, such as those in biopharmaceuticals. A well-balanced summary will be given of current and recently developed statistical methods and interpretations for both statisticians and researchers/scientists with minimal statistical knowledge who are engaged in the field of applied biostatistics.
Page iii - Dalene K. Stangl and Donald A. Berry 5. Generalized Linear Models: A Bayesian Perspective, Dipak K. Dey, Sujit K. Ghosh, and Bani K. Mallick 6. Difference Equations with Public Health Applications, Lemuel A. Moye and Asha Seth Kapadia 7. Medical Biostatistics, Abhaya Indrayan and Sanjeev B. Sarmukaddam 8. Statistical Methods for Clinical Trials, Mark X. Norleans 9. Causal Analysis in Biomedicine and Epidemiology: Based on Minimal Sufficient Causation, Mikel Aickin 10. Statistics in Drug Research:...
Page 320 - ... changes: chemistry, manufacturing and controls, in vitro dissolution testing and in vivo bioequivalence documentation...
Page 2 - ... direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease, or to affect the structure or any function of the body of man or other animals. The term includes those com-ponents that may undergo chemical change in the manufacture of the drug product and be present in the drug product in a modified form intended to furnish the specified activity or effect. (8) Inactive ingredient means any component other than an active ingredient.
Page xvii - Biostatistics series are to provide useful reference books for researchers and scientists in academia, industry, and government, and to offer textbooks for undergraduate and/or graduate courses in the area of biostatistics. This book series will provide comprehensive and unified presentations of statistical designs and analyses of important applications in biostatistics, such as those in biopharmaceuticals. A wellbalanced summary...
Statistical Design and Analysis of Stability Studies
Limited preview - 2007
Statistical Design and Analysis in Pharmaceutical Science: Validation ...
Shein-Chung Chow,Jen-pei Liu
No preview available - 1995