BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.
Given the push for Quality by Design (QbD) by FDA and drug agencies worldwide, statistical methods are becoming increasingly vital for pharmaceutical manufacturers. Response surface methods for DOE provide powerful tools to manage the impact of multiple factors and their interactions.
Statistical methods are becoming increasingly vital for pharmaceutical manufacturers. Design of experiments (DOE) is a primary tool for determining the relationship between the factors that have an effect on a process and the response of that process.
Design of experiments (DOE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DOE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DOE in non-manufacturing arena.
Statistical methods are becoming increasingly important for the pharmaceutical industry. The FDA and other regulatory and standard-setting organizations are moving swiftly to establish Quality by Design (QbD) guidance relevant to the needs of pharmaceutical manufacturing. The FDA suggests the use of design of experiments (DoE) because it provides a structured, organized method for determining the relationship between factors affecting a process and the response of that process.
The statistical design of experiments is an essential ingredient of successful product development and improvement, and provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional vary one-factor-at-a-time (OFAT) experimentation, variables are changed together, permitting evaluation of interactions. Standard texts give details about the construction of specific test plans, such full and fractional factorial, and response surface designs, and the analysis of the resulting data. This article gives a brief overview. The focus here is on the fundamental elements of experimental design: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage-wise experimentation. Brief discussions dealing with the statistical analysis tools, multiple response variables, and some historical background are also provided.
To accelerate their product development, Z Corporation tooled up their engineers with the knowledge and software to do statistical design of experiments (DOE). The company developed a procedure by which every factor with a reasonable chance of affecting product performance is systematically and simultaneously evaluated via these controlled experiments.
Olive oil, an important commodity of the Mediterranean region and a main ingredient of their world-renowned diet (see sidebar), must meet stringent European guidelines to achieve the coveted status of "extra virgin." Oils made from single cultivars (a particular cultivated variety of the olive tree) will at times fall into the lower "virgin" category due to seasonal variation. Then it becomes advantageous to blend in one or more superior oils. This is a great case to become acquainted with the tools of mixture design for optimal formulation.
A statistically based design of experiments (DOE) approach developed specifically for mixtures was used to formulate a blend of rayon fibers that produced maximal tampon absorbency.
Diasorin used DOE to evaluate the robustness of its process for manufacturing an alpha-1-antitrypsin (AAT) assay. The results provided a considerable degree of confidence that existing in-process quality control criteria sufficed for being assured of meeting finished product requirements. This case study provides an excellent example of how DOE can reduce the time required to perform a latitude study while delivering statistical analysis that increases the degree of confidence in the study.