Modeling Downstream Disruptive Technologies to Improve Downstream Bioprocessing

In this podcast and accompanying article we interviewed Dr. Yuyi Shen, Principal Scientist, Grifols about the benefits of modeling downstream disruptive technologies to improve downstream bioprocessing and evaluating the financial impact of implementing these technologies.

Dr. Shen is the principal scientist in the technical development department at Grifols,  She has also served different scientific roles in process development and manufacturing sciences  at various biotech companies including  XOMA, Bayer and BioMarin. She holds a PhD degree in Chemical Engineering from the University of California, Davis.

Yuyi provides scientific leadership with specialties in process improvement, scale up, and technical transfer to bioprocess manufacturing in cGMP environment. She is a strong subject matter expert in designing robust processes for protein purification of monoclonal antibodies, antigens, conjugated mAbs, recombinant proteins,  and viral like particles etc., always ensuring Critical Quality Attributes are achieved. She has solid trouble shooting and problem solving skills and encourages incorporating innovative technologies to achieve high efficiency and better quality.


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Modeling Downstream Disruptive Technologies Show Notes:

We were fortunate to be able to interview Yuyi prior to her talk, “Evolve Manufacturing Processes by Implementing Disruptive Technologies,” to be presented at CBI’s  Downstream Disruptive Technologies conference in San Diego, June 21-22. This conference promises to highlight exciting new technologies for addressing key pain points in downstream bioprocessing. Platforms and strategies to de-risk innovation, accelerate technology adoption and increase operations efficiency will be discussed.

I began my interview with Dr. Shen by asking her if she could give me some examples of disruptive technologies in downstream processing. She described a couple different types of disruptive approaches. The first would be a localized change, for example, a chromatography matrix change. Another type would be one involving changing industry norms, for example implementing single-use technologies throughout the process or a continuous bioprocessing approach. She went on to say that some disruptive technologies need to be established from scratch in newer fields like cell and gene therapy. Also novel technologies can be more disruptive the closer the process is to commercialization.

Next I asked Yuyi how you can model these technologies to see if they make sense for a particular process. She said that before implementation you must conduct a very careful risk assessment. If the technology improves product quality, efficiency or cost savings, it must be validated very carefully to weigh the risk vs. benefits. If there is risk, it can also be explored how to minimize those risks.
It is also important to select a high throughput scale down model for your experiments and utilize a Quality by Design (QbD) approach to validate all aspects of the process. A data driven model will allow you to define Critical Quality Attributes (CQA), Critical Process Parameters (CPP), and process controls. A good model should be able to predict process space, economic factors, and analytical requirements to avoid process variation. After this you should have enough information to weigh all the pros and cons to determine whether it is worth implementing.
I then asked what is the best way to assess the financial impact? Yuyi described how the financial impact and cost depends on the stage of manufacturing process that the new technology will be implemented in. For example, at commercial scale you would need to have a valid scale down process model,  redo validation, change out control, and approval. This means lots of resources and time, so the threshold for risk vs. benefit will be different than if it was implemented in an earlier stage. This is why it is critical to have a well established scale down model with a 360 degree view of the technology change. To achieve this you must have discussions and support from all affected stakeholders.
Last, I asked if she had any advice for teams that were wanting to model different downstream disruptive technologies for their process? She said it takes a lot to implement a novel technology. You must be able to show good justification and a data driven risk assessment based on very reliable scale down model is the best way to get this justification. I would also recommend a high throughput approach to save time and cost of modeling these technologies. There is a large gap between recognizing the value of a new technology and taking the time and effort to implement it. You need the cooperation of all stakeholders and good partnerships. It takes persistence and a belief in the innovation.
Don’t miss Yuyi’s talk on Thursday, June 21st at 10:15 am at Disruptive Downstream Technologies, June 21-22, 2018 in San Diego. See the agenda, for other exciting talks on transformative technologies to address many challenges facing downstream bioprocessing today.