Bristol-Myers Squibb Improves Chromatography and Batch Comparisons Using Data and Analytics – Part 2

By Janice Abel

ARC Report Abstract


With all the hype surrounding the Industrial Internet of Things, cloud, and bms21.JPGdigital transformation, it still comes down to data; connecting the sensors, systems, and applications that generate, store, find, and use the data to obtain operational intelligence. Data volumes are increasing rapidly and this will continue. The ability to find and make sense of the data to obtain intelligence to improve process outcomes is more important than ever.

For clinical or commercial scale manufacturing in the pharmaceutical and biotech industries, just finding the right data to analyze can consume significantly more time than it does to perform the analysis. Having the right applications to store and contextualize the data is imperative, but being able to obtain business value by accessing and interpreting it quickly and effectively can have a huge impact on decision-making and, ultimately, the business’s bottom line.

This insight is the second part of a two-part series on how Bristol-Myers Squibb (BMS) leverages the OSIsoft PI System data infrastructure and Seeq to find, compare, and analyze data for batch processes and scale up to improve the company’s production efficiencies.

Our previous report discussed identifying the best temperatures and times for product drying. This ARC Insight, based on discussions with Dr. Robert Forest, Development Engineer at BMS, will focus on improving chromatography column packaging and comparing unit operations performance across batches.

PI System Infrastructure at Bristol-Myers Squibb bms22.JPGBMS stores its data in multiple OSIsoft PI systems and various other systems. The company can be challenged in its ability to find the data corresponding to specific process conditions, events, and time periods. As a global biopharmaceutical company, one of the company’s goals is to apply scientific rigor to produce clinical and economic benefits through medicines that improve patient lives. To enable this, BMS needs to provide its scientists and engineers with better technologies to find and analyze the data faster and for easier tech transfer from clinical trials to production-scale processes.

Scale-up and Tech Transfer Challenges

The goal of BMS’s scale-up group is to develop robust and efficient processes for molecules in the company’s development pipeline. If the molecule proves successful in clinical trials, the group must then transfer these processes to commercial manufacturing. A big part of developing these processes involves capturing and analyzing data to generate adequate process knowledge to support both technology transfer and regulatory filings.

Data is captured at a wide variety of scales. These range from experiments executed on bench top lab reactors (at the gram scale) to generating hundreds of kilos of product in large-scale equipment for clinical supplies. Due to the nature of the work, the company has unique challenges around data collection. The group works with a large number of molecules from within its portfolio. The process employed to make each molecule can vary quite a bit, with many different unit operations employed to make different molecules.

Seeq Analysis Finds Conductivity Peak from Raw Data bms23.JPGBMS continuously improves the processes used to make these molecules. As a result, whenever scale-up is initiated for a particular molecule, the group may have only limited on-scale experience for that process. The company wants to make sure it has as much data as possible to generate a complete understanding of the scale-up process. Large amounts of data are generated during scale up, but assembling the data is time consuming. This often hampers data sharing, reuse, and collaboration.

In its pilot plants, the company’s Emerson DeltaV control systems execute the batch recipes. These systems provide batch data and S88-related context to the OSIsoft PI System. BMS users can access and visualize the needed information using its various PI tools. These include PI Vision for process visualization, ProcessBook for batch views, and Excel for DataLink for spreadsheet information.

Automating Chromatography Column Characterization

In many biopharmaceutical processes, chromatography is an important unit operation. Chromatography column packing needs to be done uniformly because it impacts product throughput, purity, and yield. BMS performs a conductivity test to help ensure that a column is packed properly and uses Seeq and the PI System to automate characterization of the homogeneity of its chromatography column packing.

Asymmetry Ratio Calculated for Chromatography Column bms24.JPGPrior to applying Seeq, characterization was performed manually, using a printout to calculate symmetry. This tedious process involved measuring conductivity input, creating a step change, and then measuring conductivity output of the column. If the column is packed uniformly the peak will be uniform. If not, the peak will be asymmetric. An asymmetry ratio was calculated to determine if the column would affect the product negatively and should be repacked.


Steps to Calculate Asymmetry Ratio

When BMS performs conductivity tests, it uses a specific set of flowrates. The company sets up a search in Seeq to automatically find the conductivity peaks. The data is smoothed, filtered, and a derivative calculation performed. Using the first derivative, the peaks are split into a left and right half; implemented by setting up a search for derivatives greater than zero for the left half and less than zero for the right half. The 10 percent rt threshold value was found by calculating the range of the peak; from the maximum peak value to a baseline average before the peak. Then, the peak is trimmed between the periods of time when the peak is greater than the 10 percent threshold value for each half. The right and left half durations are used to calculate the asymmetry ratio automatically – all done visually and interactively in Seeq. Next, this value is plotted on a reference curve in PI Vision to enable visualization of the curve symmetry.


Using modern technologies, BMS captures the specialized knowledge PI Vision Visualizes Different Conductivity Test Peak Data from Seeq  bms25.JPGneeded to test the uniformity of the column packing. PI AF and Seeq are used to quickly find the data of interest for conductivity testing to calculate asymmetry, summarize data, and to plot the curves for verification by the experts. Seeq enables BMS to operationalize its analytics by calculating a critical value (such as the asymmetry ratios) and distributing it across its enterprise. This enables BMS to know when a column was packed correctly, preventing product losses, product quality issues, and even complete loss of a batch.


Based on ARC research and analysis, we recommend the following actions to help pharmaceutical, biotech, and other data-intensive manufacturing organizations transform data into knowledge:

  • Use analytics to discover patterns, identify performance bottlenecks, prevent abnormal behavior, and enable near-real-time manufacturing simulations.

  • Open architectures can help users connect, store, explore, discover, and find the right data quickly.

  • Use technologies that can easily and quickly store, access, and discover industrial real-time data intelligence.

  • Tools that enable easier and better visualization of data can provide immense value to companies.

  • Apply analytics to enable conditions for faster scale-up from clinical to manufacturing.

  • Utilize technologies that have a proven track record and then roll out to other appropriate applications to improve product quality, throughput, and production efficiencies.

ARC has written many reports and articles on advanced analytics and data infrastructure. These topics will also be covered in depth at the upcoming ARC Industry Forum, February 12-15, 2018 in Orlando, Florida.

ARC Advisory Group clients can view the complete report at ARC Main Client Portal or at ARC Office 365 Client Portal

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Keywords: Bristol-Myers Squibb, Self-service Analytics, Data Platform, OSIsoft, Seeq, Chromatography, Batch, Scale-up, GMP Environment, ARC Advisory Group.


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