Case Studies and White Papers
Experimental design, modeling, and data analysis methods for mixture experiments provide for efficiently determining the component proportions that will yield a product with desired properties. This article presents a case study of the work performed to develop a new rubber formulation for an o-ring (a circular gasket) with requirements specified on 10 product properties. Progress in Rubber, Plastics and Recycling Technology, Vol. 29, No. 3, 2013
Note: If interested, you may contact one of the authors, Greg Piepel, Ph.D., for a copy of the paper.
DOE Improves Throughput in Manufacturing of Key Intermediate
Researchers at Codexis , a California-based, worldwide leader in protein engineering, used Design-Expert software to improve the performance of an enzyme. Their DOE rapidly developed an efficient catalytic manufacturing process for manufacturing, resulting in high throughput yield of product with excellent selectivity and purity.
Design of experiments (DoE) incorporates statistical methods and multivariate analysis into microscale chemistry. Controlled experiments help analysts evaluate processes with that involve several variables, such as temperature and osmolality in cell culture processes. Often three variables are studied together, with the results expressed in a three-dimensional response-surface graphs.
Resin manufacturer Interplastic rapidly developed a new gel coat via design of experiments using Design-Expert software. “Stat-Ease helped us to achieve an exceptionally high level of quality with this product, which has been very well received by our customers and is a resounding success in the market.”
Optimal Experimental Design with R
BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.
Framing a QbD Design Space with Tolerance Intervals
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.
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.
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.
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.
This article explores a therapeutic strategy for treating Huntington's disease.