Typical user personas/situation slides
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Job Title/Roles
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Manufacturing, Product, Quality, Process Engineer
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Upstream Scientist, R+D, Tech Transfer, Scale up
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Industry
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Pharma, Chemical, Biotech
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Critical Business Issue
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Find cause of repeated failures in production
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Improve productivity of R+D process by X%
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Improve productivity of R+D process by X%
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Problems/Reasons
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Can't find root cause to production failures, process steps are changed over time and don’t have ability to look at it in one ‘big picture’, have lots of historical data and no tool to analyze. Have sensor readings over time that may give more detail, but too much detail to look at with graphing.
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New process with no prior subject matter knowledge, attempting to run DoE’s on process factors but the sensor readings are hard to interpret. Haven’t got a good understanding of how the process looks whilst running and how that relates to production.
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Batch process gives minimal improvements in new process, need to move to fed-batch. No idea of suitable feeding parameters.
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Specific Capabilities
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Identify the ideal and non-ideal sensor readings that need to be maintained to get high levels of production.
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Identify the changes in sensor readings that are associated with productivity changes
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Identify the best feeding parameters that maximise productivity
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Business Value
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Reputation improvement, supply chain restored, reduced down-time during investigations, avoiding auditor shut-downs. Meeting deadlines/targets.
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Novel skillset for customers, faster time innovation, higher productivity levels, easier scale up and proof-of-concept
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Faster time to project completion. Improved productivity. Easier transfer to scale up.
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User Value
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Reduced 'all hands on deck' panics, quicker resolution to get back to main role, improved credibility, improved morale. More confident in approach. Short learning curve.
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Better process knowledge, better perception. Novel skillset.
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Better knowledge for scale up. Improved perception by management.
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Terms to look out for: scale up, bioreactor, microbial, mammalian, ambr, ambr250, ambr15, small-scale, process development, upstream.
Context
The work here is a continuation of this demo on using an FDOE for bioreactor production development, taking the methods applied by Clariant in this Discovery talk. In this case I’m recontextualising the process here to focus on how the vast amounts of sensor data can be gathered and used to predict the final product level. This can apply to prospects in two contexts:
- Manufacturing level – production processes will naturally vary in their sensor readings and functional control of their parameters (i.e. how well they maintain pH, stirring, temperature), which will lead to changes in the final production level – prospects can gather all of this information to investigate and identify critical process parameter settings over time or indicators of poor/good performance
- Experimental level – the application of DoE (changing the production parameters/media conditions) will naturally cause a change in the way that the bug acts over time, changing its respiration (dissolved oxygen), number of cells (biomass) and by-product production (pH) readings – knowing how these parameters change adds more process understanding on top of the information they are gaining from a standard DoE. Bioreactors always gather sensor readings, they just aren’t often in detail.
The key message/question to challenge users is ‘You have all these sensor readings being gathered whilst you run production/experiments, why don’t you use them?’.
JMP has great potential to leverage the FDE analysis for better process understanding – currently there are no other commercial softwares capable of providing the level of insight possible with FDE. Current approaches take heavy understanding of statistics and coding, which your average user (engineer/scientist) is not going to have/want to have. This is the kind of tool I would have given my left leg for back when working on bioreactor process development and would have significantly shortened our project times!
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