Given the push for Quality by Design by FDA and agencies worldwide, statistical methods are becoming increasingly vital for pharmaceutical manufacturers. DOE is used to determine the impact of multiple factors and their interaction.
Researchers at Codexis Laboratories Singapore performed a full-factorial designed experiment with 20 runs to determine the impact of four independent variables on product selectivity during a silylation reaction. The result was a process that delivered 95% selectivity along with an 88% yield.
Researchers aiming to upgrade a fed-batch process observed that basal and feed media have interrelated impacts on process outcomes (a pairing effect). They did a DOE enabled by Design-Expert software for fed-batch cell culture process optimization.
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.
This article explores a therapeutic strategy for treating Huntington's disease.
This study was conducted to examine the chemical and sensory characteristics of fermented worts and consumer acceptability according to added flavorings.
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 are gathered.
A consultant assists a student in Malaysia by using Design-Expert's diagnostic features.
The TRW team used a combination of DOE (response surface methodology RSM) and Monte Carlo analysis to optimize a braking system where one of the objectives was to quickly generate pressure when demanded by the vehicle stability control system.