What is it about?
Traditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a neural network (NN) approach to vaccine yield classification and compares it to an existing multiple linear regression approach. As part of the Six Sigma process, this paper shows how a data mining framework can be used to extract further value and insight from the data gathered during the manufacturing process, and how insights into yield classification can be used in the quality improvement process.
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Why is it important?
Biopharmaceutical manufacturing is heavily regulated by organisations such as the FDA. There are opportunities to extract value and insights from the manufacturing data that is monitored for quality control and regulatory requirements.
Perspectives
This paper highlights our first steps in exploring which techniques are suitable to better understand biopharmaceutical manufacturing data. It gave us pause for thought to best translate the machine learning model recommendations into actionable outcomes that fit within the product regulatory requirements.
Paula Carroll
University College Dublin, Ireland
Read the Original
This page is a summary of: Improving Biopharmaceutical Manufacturing Yield Using Neural Network Classification, BioProcessing Journal, January 2016, BioProcessing Journal,
DOI: 10.12665/j144.carroll.
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