A challenge for the regenerative medicine industry is to develop cell culture processes that can be scaled up for high volume production. Finding a better way to scale up commonly used research cells like HEK293T (used for protein expression and the production of recombinant retroviruses or lentiviral vectors) would be beneficial for biologists in many fields of medicine. Dr. Franziska Bollmann, virus scientist at Sartorius Stedim Biotech in Germany, recently conducted two experiments to find out if micro bioreactor systems can help facilitate the transition from the traditional shake flask process to a more improved method optimizing process control.
One key to reducing R&D costs in the biopharmaceutical market is streamlining and speeding up process data flow for Design of Experiments (DOE). Now, a direct integration of Genedata Bioprocess® platform and Umetrics Suite MODDE® software enables seamless data flow and facilitates the design, execution and evaluation of experiments in large-molecule process development.
What’s the secret formula for creating long-lasting bubbles? Is expert knowledge of liquid dynamics needed to optimize the mixture design and develop the best bubble solution? Or can we use design of experiments (DOE) and data analytics to draw conclusions? Let’s a take a look at a fun example of how DOE can be used to optimize a mixture design in order to achieve our goal: create long-lasting bubbles.
Pressure to cut development costs and lower regulatory barriers while assuring product quality has stimulated the pharmaceutical industry to apply Quality by Design (QbD) to manage risk and gain process and product understanding. As a result, QbD is being widely promoted by regulatory authorities such as the Food and Drug Administration, and the International Conference on Harmonization.
When it comes to continuous quality improvement and removing defects from a process, Six Sigma continues to be the gold standard in manufacturing and process management. This structured, data-driven methodology for discovering problems relies on rigorous analysis of production and process data. For many companies, engaging in a Six Sigma process can be time consuming or even a bit daunting.
You may have heard the term Six Sigma used in conjunction with lean manufacturing, a Kaizen approach or continuous quality improvement. Perhaps you thought Six Sigma only applied to large-scale business operations, or that newer philosophies had overtaken Six Sigma as the most updated approach to quality management? But if you're looking for a way to improve your production processes or solve a problem you’re having with quality, Six Sigma might be the answer. Are you and your team familiar with these concepts? Here's an overview.
[This blog was a favorite last year, so we thought you'd like to see it again. Send us your comments!].
Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
At the heart of any process used to manufacture biological products is a bioreactor setup that supports a stable and reproducible biologically active environment. The bioreactor provides a controlled environment to achieve optimal growth for the particular cell cultures being used.
Biopharmaceutical companies today are challenged to develop high producing cell lines as quickly as possible. Commercially available media may fall short of performance expectations required to meet targets. The alternative —fully customized media and feed development — requires significant funding, time and in-house expertise in media development.
In life science, biopharma and other areas of research, development and production, design of experiments (DOE) provides a systematic method to determine cause and effect relationships between factors and responses affecting a process, product or analytical system. But the key to understanding your results is effective analysis of your experimental data.