Explorers Series

Unearth the possibilities in your data

JMP GUEST LECTURE

Quality by Design Focus for Statistical Modelling

Date: 07 Februray 2019
Time: 10:00 -13:30 CET
Location: SAS Institute, Copenhagen
Registration: Free

In biopharmaceutical development, the importance of a methodical, risk-based approach to efficiently achieving desired product attributes cannot be overstated.

Quality by Design involves defining clear requirements and then striving to better understand how input materials and process conditions must be controlled or changed to deliver what’s required. As part of this approach, some companies integrate statistical modeling throughout the product lifecycle to better manage risk, and learn more quickly and reliably. Such companies often derive significant competitive advantage from this.

In this seminar, you will see firsthand how the integration of statistical modeling throughout biopharmaceutical development can achieve these results, leveraging designed experiments and observational data to create a coherent narrative. Additionally, the seminar will show real-world examples from pioneering biopharmaceutical cases to illustrate the accelerating power of statistical modeling in Quality by Design.

Register now to attend seminar.

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ABOUT THE SPEAKERS
Julia O'Neill
Julia O'Neill

Julia O’Neill has over 30 years of experience bridging statistics and chemical engineering. Her current focus includes statistical, validation and regulatory strategy support for a broad range of novel accelerated products. Previously, O’Neill worked as Director of Engineering at Merck and Co., where her team integrated Continued Process Verification for all vaccines and biologics. O’Neill earned an MS in statistics from the University of Wisconsin-Madison and a BS in chemical engineering from the University of Maine.

Ian Cox
Ian Cox

Ian Cox, PhD, is Marketing Manager for JMP in Europe. A Six Sigma Black Belt and Fellow of the Royal Statistical Society, Cox is a co-author of ‘Visual Six Sigma’ and ‘Discovering Partial Least Squares with JMP’.

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