(190c) Real-Time Batch Process Monitoring in Biopharmaceutical Manufacturing: Big Data Analytics for Small Data
AIChE Spring Meeting and Global Congress on Process Safety
Wednesday, March 29, 2017 - 4:30pm to 5:00pm
To allow for an efficient monitoring and control of biopharmaceutical processes, multivariate statistical techniques are routinely deployed. Despite over two decades of research and advancements in industrial batch process monitoring and control, biopharmaceutical process monitoring faces a unique challenge due to scarcity of historical data. In fact, it is common in biopharmaceutical manufacturing to have only a few engineering batches for a product with no commercial production history at a manufacturing facility before the actual good manufacturing practice (GMP) campaign. While limiting the number of engineering runs improves speed to market and reduces manufacturing costs, it also limits the amount of representative data available for establishing a monitoring baseline, which presents several problems. First, with small data sets it is nontrivial to capture the inherent variability under the normal operating conditions (NOC) in their entirety. Second, most of the multivariate monitoring methods, such as principal component analysis (PCA) and partial-least squares (PLS) are big-data technologies that are effective only when there are large historical data sets.
In this talk we will discuss several machine-learning methods to deal with the data scarcity problem in biopharmaceutical process monitoring. We propose a non-parametric modeling approach to generate virtual campaign data under normal operations on-demand. Using virtual campaign data, standard batch monitoring techniques are used to effectively monitor a biopharmaceutical manufacturing process. Encouraging results are reported from practice.
. Hamilton G. The Biotechnology Market Outlook: Growth Opportunities and Effective Strategies for Licensing and Collaboration. Dublin: Research and Markets. 2005.