Faster Research and Development of Pharmaceutical Production Lines
Use Case Business Improvement

Challenge

In order to maximize return, it is critical to find a way to reduce the time and expense it takes to introduce a new pharmaceutical product to the marketplace. Right now, however, the length of time it takes to compare data from various experiments and pilot batches is so large that it is faster and easier to simply conduct new experiments. 

 

Solution

Seeq empowers users by connecting to all relevant data sources to visually represent batches and perform analytics with process data. Users are able to calculate scale-up metrics, batch KPIs, and performance measurements for each experiment conducted with Seeq Workbench. They can quickly compare the results of various measurements and experiments, and document all findings in electronic journals or Seeq Organizer for easy collaboration to refine results,better design future experiments, and compare results during scale-up. 

Benefits

Operators using Seeq can benefit from batch-based data contextualization. This allows for clear and thorough comparisons of collected data, including data from multiple sources. Seeq empowers researchers to reconfigure their process rapidly to meet clinical timelines, worth approximately $1.5 million. There is an overall process development time reduction, $250 per hour for 2 months or $87k. This allows for engineering manpower improvement, 1 scientist for 6 months or $70k.   

 

Data Sources

  • Process Data Historian: DeltaV OPC-HDA server 
  • Text files: Vi-CELL XR

Before using Seeq software, a thorough analysis required scientists and engineers to combine their data manually in a spreadsheet and spend hours/days formatting and aligning timestamps to perform analytics. Not only was this incredibly time consuming, but it allowed significant room for human error. Seeq addresses this issue with easy to use data connectors and a wide array of analytics possibilities.

 

Data Cleansing

Those using Seeq have access to a wide variety of data cleansing options including an integral function which provides the total oxygen uptake trend and smooths the noisy signal for dissolved oxygen (DO).

 

Calculations and Capsules

With Seeq, a capsule was created for each batch or experiment run, and the integral was calculated for dissolved oxygen over the capsule. The cell-specific oxygen uptake rate was calculated and plotted for comparison between experimental and pilot scales. A “golden batch” of the cell growth rate was produced to track differences in future experiments and scale-up batches. 

Summarizing Results

Seeq operators can improve their processes and greatly benefit from more effective analysis of data. The cell-specific oxygen uptake rates were overlaid for the different scales of bioreactor. The different growth curve for the 100L bioreactor was clearly visible.