Blog post
February 3, 2021

The Biggest Challenges of Bioprocessing - and How to Overcome Them

revised February 10, 2023

Kris Tyner, PhD, Culture’s Senior Director of Bioprocess Sciences and subject matter expert in bioprocessing, discusses what makes cultivations so difficult, and how scientists can make it easier for themselves and their teams.

Chelsea: You’ve established multiple bioprocessing programs at various companies throughout your career. What are the biggest challenges with cultivation that you’ve experienced? In other words, what makes fermentation difficult?

Kris Tyner, Fermentation Expert

Kris: I always say “biology is hard!” But seriously, bioprocessing is a complex and iterative process with many interdependent parts. It’s complex because you’re concerned with not just an organism and its genetics, but also how the physiology of that organism interacts with process you’re developing. In other words, it is the interplay between organism and cultivation process that introduces the most complexity. It would be much simpler if you just had to run a cell line through a bioreactor and that was it. However, often the people constructing cell lines are not the same people running the bioprocess and interpreting results, so it requires collaboration across different groups in a company: the bioprocessing team needs an understanding of the organism’s physiology within the process to know how to adjust the run so the cell line performs better, and cell line engineers need feedback from the bioprocessing team about the process so that they know what changes to make to improve cell line performance. To be successful, these groups need to work together closely.

Many companies often don’t have enough capacity to do all the work they need to, and to make the data driven decisions for a robust product. Even though testing in bench-scale bioreactors is precisely what will help develop a process that is robust enough to scale. You want to have a good process.

Ultimately, bioprocessing scientists need quality data to generate insights that in turn enable them to make data-driven decisions about their process - and quickly. All of the things I already said can make this difficult - the complexity of the process, involvement of multiple teams, lack of bandwidth and insufficient reactor capacity - but a major impediment to getting insights quickly is the sheer amount of data coming out of bioreactor runs, which takes time to collate and analyze. Traditionally you have to manually pull data from the fermenter, which is time-consuming, but even if you have a LIMS, compiling and overlaying data can take a while. For example, something simple like overlaying a titer plot on top of an online measurement or KPI can take much longer than it should.

The reality is, if comparing experiments is too inefficient then it won’t happen. If the data isn’t easy to access and manipulate, scientists will be less likely to do it and thus may miss useful insights and fail to make the best decisions - which may cause problems at scale.

Chelsea: When you say compiling data can take a while, how long is that?

Kris: It can take hours! People like me who are bioprocess scientists may not be experts in how to pull together large data sets, which can involve coding using Python, for example. Compiling the data and interpreting the data can be two entirely different skill sets. Because bioreactors are a bottleneck and companies always want to use their full bioreactor capacity each week, you always have to be planning for the next week and there isn’t much time to organize data. There’s only so much time you can spend looking at data before you start delaying future experiments. Plus, you’re probably focusing on other things like troubleshooting the bioprocess. At the end of the day, there’s a lot to focus on that doesn’t involve compiling data; the problem with that, as I mentioned, is getting an in-depth look at data is critical for optimizing and scaling a process. That’s why Culture’s real-time data visualization tool is so useful for analyzing data quickly and getting faster insights.

Chelsea: On the other hand, what excites you about bioprocessing? What made you want to pursue it as a career?

Kris: I actually started in genetics and microbiology as an undergrad student at Monash University in Australia, and then attended grad school at the University of Colorado Boulder where I studied MAP kinase signaling in muscle stem cells. At Boulder I pursued a graduate certificate in biotechnology, which included a rotation through chemical engineering, bioprocessing, and an internship in Amgen’s cell culture department. This is really how I found out about cell culture - where I learned you could have a career of growing cells and using them to make products. After that I continued on the path to a postdoc at National Jewish Health doing cell signaling in macrophages. During this time, I was growing a lot of primary mouse cells and I realized I enjoyed growing and cultivating cells - feeding them, keeping them happy and watching them differentiate into various kinds of cells - more than I enjoyed cell signaling.

After finishing my degree I worked at OPX Biotechnologies and then Zymergen, which was exciting because I got exposure to many different cultivation processes and organisms. I was Zymergen’s first bioprocessing hire, so I did everything there from running bioreactors in the early days to eventually building teams of scientists that managed cultivation work across different programs. I managed technical transfer of bioreactor processes, established best practices for scaling down runs into 96-well plates for high-throughput screening, and acted as a subject matter expert providing technical guidance. My favorite parts were working with a diversity of organisms, and working with people that are passionate about science, which I’m excited to do more of at Culture!

Chelsea: As someone who would have been a client of Culture’s in your previous role, how does Culture help make bioprocessing easier for scientists and their teams?

Kris: Culture provides not only the high-quality data that scientists need, but also the ability to quickly process that data into meaningful insights. As we discussed, this is so critical to companies working to  make progress on programs with aggressive timelines. Providing high-throughput bioreactor capacity with short lead times and executing experiments for clients is one part of that, but another equally important part is Culture’s real-time data visualization software, which blew me away when I first saw its capabilities! It’s a tool that’s really game-changing for bioprocess scientists. At many biotech companies, it takes too long to analyze bioprocess data; it requires downloading data from a bioreactor into a CSV which you then have to import into another program to join it with manually inputted data (e.g. OD, pH, titer), and then join those on a time series graph to interpret them over time. This makes for a lot of steps just to begin to understand your data.

With Culture’s cloud lab, you can monitor your data in real time and get insight into what your cel line is doing throughout the duration of the bioreactor run. You just log into Culture’s website, select the runs or bioreactors of interest, and instantly plot and overlay whatever measurements you want at any point in time. Our recently launched workspace feature allows you to graph your runs by color, save your graphs and share them directly with your team. It’s easy to make sense of data quickly and to collaborate with your coworkers, which is critical given the cross-functional nature of bioprocessing that I talked about earlier.

Having data easily accessible and interpretable to team members who are not as involved in bioprocessing eliminates the headache of having to spend hours reformatting data. The faster you and your team can get insights from your data, the faster you can iterate on hypotheses and get the answers you need to scale up your process.

Chelsea: Speaking of bioprocess scale-up, you mentioned earlier the importance of “de-risking” it. How can biopharma companies de-risk scale-up, and how can Culture help with that?

Kris: To scale up a bioreactor process successfully, you need to do the legwork of mitigating risks that come with manufacturing at scale. This involves understanding the constraints you have at scale and the robustness of your process - knowing what it can and can’t tolerate in terms of variation in the environment through process characterization. Because larger bioreactors have gradients in conditions, modeling and testing conditions at small scale can help alleviate challenges at larger scale, which are bound to come up. However, many companies find it difficult to do enough testing without the proper resources, such as bioreactor capacity or staff bandwidth. That’s where Culture comes in: by using Culture to run bioreactor experiments, clients can test more conditions, get answers faster, and feel confident in their process while meeting their project deadlines. Instead of outsourcing to a CRO where you lose transparency and control of the experiments or waiting for internal capacity to open up, it makes a lot more sense to let Culture execute experiments so clients can focus on designing them.

Chelsea: Besides de-risking scale-up, what are some other key factors of success for biopharmaceutical companies? Are there learnings you can share with other bioprocess scientists?

Kris: In terms of cell line development and screening, a key for success is to stay connected to your bioreactor process - and what your cells are doing in the process - and use the insights from cultivation to improve performance. Culture enables that close connection between scientists and their process by delivering high-quality data in real time. Knowing the data is of high quality - that the inputs and outputs of the bioreactor are accurate because of Culture’s precise, automated infrastructure - gives clients confidence that the process is running as intended.

I’ve also learned that one function alone will never get you to a scaled-up bioreactor process - cell line engineers, bioprocessing scientists, and downstream processing scientists and engineers are all equally necessary. As a result, close communication and collaboration across multiple teams are crucial for success. By using Culture's platform, scientists can stay connected to both their process and their teammates.

Chelsea: What are you excited to do at Culture?

Kris: So many things(!) - to work with different clients making different products. I think what I’m most excited about though is helping other companies achieve goals that they wouldn’t have been able to otherwise without a quality bioprocessing service and platform like Culture. Any success in the biopharma field is a success for all of us in helping the industry grow. I want to help make bioprocessing the standard reality, and this is a great opportunity to do that. I’m eager to help clients succeed in their mission by providing high-quality data - quickly - so they can make the best possible data-driven process decisions.

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