On The Ground At BPI: Takeda's Amy Shaw On Scaling Cell Therapy Manufacturing

No matter which stage your cell therapy product is in, it’s important to think about scalability in manufacturing. In strategizing how to scale manufacturing, flexibility is key. That was the focus of a session at this year’s BioProcess International (BPI) show in Boston, where Takeda head of process development Amy Shaw gave a talk titled “Scale-Out Versus Scale-Up Strategies for Maximizing Cellular Therapies.” While I wasn’t able to attend her session, I was fortunate enough to connect with Shaw afterwards to ask some questions for our audience.

The following transcript has been edited for clarity.

You gave a talk this morning on scale-up versus scale-out for cell therapies. Tell us about that talk and what you're hoping people took away from it.

Shaw: I spoke to an audience about different strategies to employ for scale-up versus scale-out in cell therapies. We did a couple case studies on different parts of the process where we looked at if it would make more sense to scale-up and increase the volumes of that process unit or to scale-out and make redundant process units throughout so that we could have a larger volume in the end, while keeping the system the same.

What I really wanted people to take away was, if you can focus on a modular process and develop each process unit individually, you can really maximize your process and go through the exercise of, does scale-up make sense for that process unit, or does scale-out? Another key takeaway I was hoping people would come away with is you don't need the largest process possible. There's always going to be confines to your process — for instance, fill-finish or visual inspection. So, don't over-maximize your process, because there may be inherent limits.

What are some factors people may not consider when deciding how to scale? What should folks pay more attention to?

Shaw: I think it's really important to meet with your clinical teams early on and define what your goals are at different stages in your clinical plan. So, when we're talking about a Phase 1 process, you're only dosing about 20 patients, but when you think about commercial processes, you want thousands of patients. So, you want to design your early process in a way that is flexible to accommodate both as much as possible. Otherwise, you're going to get buried in comparability studies and showing your CQAs from your early process are met with your later stage process, and that's not always possible when your product is a living drug, like a cell product.

What are you most excited by when it comes to using AI in cell therapy manufacturing?

Shaw: AI is definitely the hot topic. It's really interesting to come here and hear how other people are approaching AI in general. I had a really good question during the talk today about how to train models to help expedite process development and have fewer conditions to look at when scaling up. It is a difficult question for us in the field right now. We're relying on expensive processes to design, and we really have to focus on small-scale models so that we can replicate the process in a cheaper, faster, and more cost-efficient way, and so we can generate a ton of data to train our models and understand the design space we're working within. I think it's an exciting time. I would love to see everything come to fruition. It sounds like we're all hoping for best case scenario here.