Our food systems face a monumental challenge: by 2050, the global population could reach 10 billion, demanding at least 60% more food than we produce today. This stark reality is one of the main drivers for innovation in agri-biotech. Traditional agriculture alone is unlikely to shoulder the burden, especially under the shadow of climate change, deforestation, and depleted resources.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann steps into the lab with Steven Lang, Chief Technology Officer of California Culture. A veteran of both biopharma (and a former leader at Upside Foods), Steven is at the forefront of scaling plant cell culture for real-world impact.
Our future food systems have to incorporate new technologies because our population is growing. We're going to be 9 to 10 billion people on this globe relatively soon. And there are projections that we need to produce 60% more food than we're producing today by 2050 to support that population.
So what I like to say is that we need to continue with our conventional agriculture and supplement and add to it new technologies like what we're doing with plant cell culture and cellular agriculture, because there have to be multiple shots on goal to be able to feed our population. The alternative is malnutrition and starvation. To me, that's unacceptable.
David Brühlmann [00:00:46]:
Welcome back to Part Two of our conversation with Steven Lang from California Cultured. In Part One, we explored how plant cell culture works and the bioprocessing fundamentals behind cultured cacao. Now we're tackling the hard questions: Can this actually scale? What's the economic reality? And how do we move cellular agriculture from laboratory curiosity to commercial production? Whether you're in biopharma, considering a career pivot, or simply curious about the future of sustainable food, this conversation will change how you think about biomanufacturing.
On the taste part, because that's an important part for me. As a Swiss, I must say I'm pretty critical when it comes to chocolate. Producing cocoa that tastes well is quite a complex process because it's not just a plant. There is a complex fermentation process going on. How do you reproduce that in the lab to get as close as possible to natural cacao?
Steven Lang [00:03:03]:
That is a really important question. And I’d like to set the stage by basically giving a synopsis of how chocolate is made. The cacao pods are opened up on the forest floor at these small farms that grow cacao, and the beans are removed from the pod and then spread out on the floor to ferment. That develops some of the flavors. Then they dry those beans and go into the roasting process, followed by all the processing that goes into creating the cocoa nibs and extracting the cocoa butter to then recreate chocolate.
We’re starting with a different starting material because our cells are not analogous to the cocoa bean that's extracted from the pod, fermented, and roasted. So we have to take a food science approach and really think about how we can ferment our cocoa powder as well as roast it to achieve those flavors. The nice thing is that we can do that. In that process, we degrade some of the flavanols, so the astringent taste is decreased, and you can bring out some of the chocolate flavors.
Let me say to that developing great-tasting chocolate isn’t our primary goal right now, but it will be in the future—that’s where we’re heading toward the commodity market. Right now, we’re focused on scaling and commercializing, aiming to get onto the market in 2026 with our high-flavanol cocoa. Once we get there, we’ll have more time and bandwidth to build processes that allow us to pull levers to improve the sensory attributes of the cocoa powder to create really fantastic chocolate.
Once you understand the biology of those sensory attributes in the cell culture process, you can start building different varietals of tastes and flavors in chocolate. That’s really the exciting biology I’m looking forward to. And I think that will require, based on what we know about plant cell culture media optimization as well as improvements in bioreactors,…
David Brühlmann [00:05:05]:
What are the technologies you need to produce such high-quality cocoa products. Because you have the cell culture, and then you have the whole sensory area, which is highly complex—what do you need, and what kind of equipment do you need to use to make sure you get this high-quality product?
Steven Lang [00:05:24]:
That’s where I like to think that for cellular agriculture to be successful, we need to change people’s minds about it, not just overcome the technical challenges. We can take simpler processes like coffee or cacao cell culture to help explain the processes to people, which will create more consumer demand and actually pull the products from us.
With plant cell culture, we don’t use a lot of process analytical technologies. So we don’t control pH, there is no temperature control, and that’s essentially it. What we really need is macronutrient profiling at the end, flavanol concentrations at the end, and that’s essentially it.
Anything else relates to safety—microbial testing, for example. Speaking of safety, many people know there’s a heavy metal concern with most chocolates. Consumer Reports released a report showing that about one-third of chocolate products contain heavy metals above safe limits. The great thing about our cocoa powder is that because we can control all raw materials, we can produce products with essentially zero heavy metals. That’s a huge step forward when thinking about food safety and convincing consumers to try these products. Not only are they nutritious and healthy, but they’re also safer than conventional products.
David Brühlmann [00:06:50]:
This leads me to the bigger picture, because when we talk about cultivated meat or lab-grown coffee or chocolate, we hear a lot about climate change, deforestation, and environmental challenges, and you and other companies are trying to solve or at least alleviate these problems. How realistic is it that, maybe in five to ten years, we can produce enough in labs to reduce this burden? Or is this not realistic at all?
Steven Lang [00:07:22]:
No, I think this has to be the future. Our future food systems have to incorporate new technologies because our population is growing. We're going to be 9 to 10 billion people on this globe relatively soon. There are projections that we need to produce 60% more food than we produce today by 2050 to support that population.
What I like to say is that we need to continue with conventional farming and agriculture, and supplement it with new technologies like what we’re doing with plant cell culture and cellular agriculture. There have to be multiple shots on goal to feed our population. The alternative is malnutrition and starvation, which to me is unacceptable.
I am very motivated by using my cell culture background and everything I’ve learned in biopharma and at upstream biomanufacturing to really push the envelope. We need to ensure we’re improving human health, food security, and sustainability with the foods we eat in the future, rather than assuming that conventional industrialized farming will solve all our issues. Because frankly, that assumption distracts from the negative aspects of conventional agriculture.
David Brühlmann [00:08:40]:
And if we keep this future-focused lens, zooming out beyond coffee and cacao, what other lab-grown products do you see emerging? What are the hot technologies coming into this space?
Steven Lang [00:08:53]:
I think a lot of supplements as well as other food products could benefit. For example, one of my colleagues just started a saffron company using plant cell culture. Saffron is hugely expensive and difficult to produce, so if we can produce it in cell culture, it really reduces demand on the industry and also produces a higher-quality product. Other interesting products include ginseng, echinacea, and there’s even a startup trying to make cell-cultured wood. That one just blows my mind—it must be premium.
David Brühlmann [00:09:27]:
Wood must be very complex to make. I would imagine—you probably produce some cells in a bioreactor, and then you would need to assemble them to get your finished product at the end, right?
Steven Lang [00:09:43]:
Exactly. Very much like cultured meat. Wood probably has a lot of different components you need to recreate the structure. That’s where the complexity comes in. That’s why we need to start with simpler processes like cocoa or even cell-cultured wood—to build a foundation from which we can later be successful with cultivated meat and allow that technology to mature.
David Brühlmann [00:10:12]:
If one of our listeners thinks, “Well, this is interesting—cellular agriculture is great, you’re solving a big problem,” what advice would you give them as they transition into this exciting field?
Steven Lang [00:10:27]:
First and foremost, look for cell culture work outside of cultivated meat and biopharma. There are startups and companies using this technology—start thinking about where you can get involved. If you’re an entrepreneur, look for products that can be produced from plant cells, because those probably have the fastest route to market and are likely the most consumer-accepted.
The other thing to think about—and we discussed this back in 2023 for people coming from cell culture and biopharmaceutical backgrounds—is that you really need a safety and efficacy mindset. All the products we produce must be safe, and that’s paramount.
Next is efficacy. In drugs, we understand what an efficacious drug is. In foodstuffs or other products, it comes down to things scientists and engineers often overlook: texture, mouthfeel, aftertaste, packaging, and many other factors that are critical to consumers. If you build these considerations into your product intelligently, you can significantly increase your chances of success.
David Brühlmann [00:11:35]:
Before we wrap up, Steven, what burning question haven’t I asked that you’re eager to share with our biotech community?
Steven Lang [00:11:43]:
I’d like to highlight the collaborative aspect. One question I haven’t heard yet is: What does a team look like that can make this successful? I want to call out my technical team. They’ve done amazing work not only with cocoa, but also coffee and cocoa butter. With a small startup over a short period, they’ve developed cell lines capable of producing high-quality products.
We also recently brought in someone to help set up our data foundation. That’s near and dear to my heart. As a startup, setting up a data foundation and capturing all data in a way that can be used in the future is paramount. Many pharmaceutical or mature companies have siloed data systems, which prevents the use of machine learning or AI.
I really want to recognize my technical team, not only for their technical achievements, but also for how we’re building a foundation to implement advanced analytics—the kind of AI-driven insights that are rapidly transforming the industry.
David Brühlmann [00:13:02]:
What is the most important takeaway from all that we’ve discussed today?
Steven Lang [00:13:06]:
David, the most important takeaway is that cellular agriculture is much more than cultivated meat, and we need to start thinking about other processes using plant cell culture to produce products. I really encourage all of your listeners to explore opportunities where you’re doing non-traditional work and don’t be afraid, because cells and these technologies can be evolved to suit our purposes as we develop new products.
I really think about pressure-testing your systems and your assumptions about cells and what they’re capable of. I’ve been surprised throughout my 20-plus years working in cell culture at how far you can push a cell, and it’s amazing what they can do. So I encourage people to continue pushing and look for additional opportunities to help food security, human health, and sustainability.
David Brühlmann [00:14:02]:
This has been great, Steven. Thank you so much for helping us expand our horizons beyond traditional cell culture. It’s exciting what’s happening in that space. Where can people get a hold of you and also potentially taste this high-flavanol product or your coffee?
Steven Lang [00:14:22]:
Certainly reach out through LinkedIn. We’re hoping to be selling our product by the middle to late part of next year. We’re a B2B company, so we’ll be partnering with chocolatiers and hopefully co-branding, but that’s yet to be determined. I’d say definitely look for us in end-2026 or early 2027 in some chocolate products.
David Brühlmann [00:14:46]:
Fantastic. I’m also looking forward to that.
Steven Lang [00:14:51]:
As a Swiss, I’m sure you are.
David Brühlmann [00:14:52]:
Absolutely. Well, thank you, Steven, for these great insights, for expanding our vision, and thank you for being on the show today.
Steven Lang [00:15:02]:
David, I really appreciate what you do here on this podcast. It’s all about scientific communication and bringing people along with what we’re doing, which is very exciting and impactful. We just need more people to understand and get involved, so I truly appreciate the opportunity. Always good talking with you.
David Brühlmann [00:15:21]:
Steven Lang has given us a compelling vision of cellular agriculture’s potential to reshape food production while addressing real sustainability challenges. The bioprocessing fundamentals we discussed today apply far beyond chocolate—they’re principles you can use in your own CMC development work. If this conversation sparked new ideas, share it with a colleague and leave a review on Apple Podcasts or wherever you listen. Until next time, thank you so much for tuning in today and keep doing biotech the smart way. For additional bioprocessing tips, visit us at smartbiotechscientist.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Steven Lang
Steven Lang is a biopharmaceutical executive with more than 20 years of experience across large pharma, CROs, and startups. As Head of R&D, Bioprocess, and Analytics at California Cultured, he leads CMC-driven development of plant-cell-derived cocoa and coffee products.
His expertise spans cell line development, process optimization, analytics, and regulatory strategy, with prior leadership roles at Genentech and Johnson & Johnson.
Connect with Steven Lang on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
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What if your daily chocolate or coffee could be brewed without farms—or deforestation—but straight from a bioreactor? It sounds like a technological fantasy, but plant cell culture is quietly remaking some of the world’s most beloved food staples. This episode cracks open the world of cellular agriculture, moving beyond the hype of lab-grown meat to explore how plant cells are ushering in a new era of sustainable food manufacturing.
Joining Smart Biotech Scientist Podcast host David Brühlmann is Steven Lang, Head of R&D at California Cultured. Steven is no stranger to ambitious challenges. After nearly two decades in biopharma with industry giants like J&J and Genentech, he pivoted toward cultivated foods, determined to go beyond the narrow focus of animal cell-derived products.
Cellular agriculture is using cell culture to produce agricultural products. And the frustration that I've had over the last three or four years is that everything that is considered cellular agriculture is actually synonymous with cultivated meat. And there's so much more to cellular agriculture than just meat.
I really want, through this venue as well as other venues, to drive home the message that cellular agriculture includes any type of cell culture product that can be derived from animal cells like cultivated meat, plant cells like cacao, or precision fermentation with microbial cells. And that broader definition of cellular agriculture is important to me because I see there is a resistance from consumers to adopt or try cultivated meat.
David Brühlmann [00:00:55]:
Imagine a world where your favorite chocolate bar doesn't require a single cacao tree, where coffee comes from a bioreactor, not a plantation. Sounds like science fiction. It's happening right now in California. Today we're joined by Steven Lang, who is the head of R&D at California Cultured, who left two decades in biopharma to revolutionize how we produce food. He's cultivating plant cells to create real cacao and coffee. No farms, no deforestation, no compromise on flavor. Let's explore the future of what we eat.
Welcome back, Steven, to the Smart Biotech Scientist. It's a pleasure to have you on today.
Steven Lang [00:02:52]:
It's great to see you again, David. I'm really pleased to have this conversation again and catch up.
David Brühlmann [00:02:57]:
Sure, Steven. Share something that you believe about bioprocess development that most people disagree with.
Steven Lang [00:03:06]:
Interesting. Well, since we talked last, the thing that I've really come to learn is that we need to walk before we can run. And that is not necessarily around bioprocess development per se, but it's more on the biotech industry and how we have to have stage-appropriate models to really kind of push this industry forward. We can get into more of those details, but that's kind of a nutshell of what I think—that we need to really look at simpler models that can help us answer some of these fundamental questions, not only on the technical side, but also on the consumer acceptance side.
David Brühlmann [00:03:39]:
I like that. Learn to walk before you run.
Steven Lang [00:03:43]:
Exactly. Yeah.
David Brühlmann [00:03:45]:
And I'm excited to have this conversation today on cellular agriculture. Let's start with you, because not everyone listened to our first conversation. So I'd love to hear your origin story—what sparked your interest in biotech and what were some pivotal moments, because a lot of things happened actually since we last spoke.
Steven Lang [00:04:05]:
So my origin story we kind of went into in the last podcast, so I'd recommend listeners go back and listen to that one. But just as a synopsis, I've got almost 20 years of experience in biopharmaceuticals with large, mature pharmaceutical companies—Johnson & Johnson and Genentech—as well as small CROs.
About four years ago, I decided that I wanted to do something a little bit different outside of the pharmaceutical arena. The opportunity came along to work on cultivated meat, which blew my mind, because using these expensive bioprocessing technologies to produce a commodity just doesn't make any sense.
As we spoke about back in 2023, that's really exciting for intelligent people—to have huge, audacious challenges to take on—and certainly cultivated meat presented that to me. That's what got me interested in it.
Throughout the biotech industry, there's been a lot of retraction and consolidation, especially in the food sector, which is more cost-conscious than the pharmaceutical sector. I was caught up in that and was laid off from UPSIDE Foods as part of a reduction in force, as they were consolidating their resources to extend their runway. I thought that was a little bit shortsighted, but I wasn't making those decisions.
I took about eight months off and did some introspection on what I wanted to do with the rest of my career. That brought me to wanting to have a larger impact on the world beyond just human health. That's where the opportunity came along to join California Cultured.
As we talk further, I hope to elaborate on how this cell culture technology can really have an impact on human health, food security, and sustainability—all of which are near and dear to my heart. Having the opportunity to work on something with that type of global impact was really compelling for me.
So I made the jump without much hesitation into California Cultured, where we're producing cultured chocolate (cacao powder) to begin with, as well as coffee. Those are the first products we're planning to get out the door.
This is a relatively small startup—about 18–19 people—founded in 2020, and we're building out not only plant cell culture products, but also the supporting biomanufacturing processes. We're actually developing new bioreactors for plant cells, which is fascinating work as well.
David Brühlmann[00:06:37]:
Yeah, I'd love to dive into more. It's fascinating, and I must say I'm a bit biased. As a Swiss, I love, love, love chocolate. And I ask some critical questions.
Steven Lang [00:06:49]:
Perfect, I'm ready for them.
David Brühlmann [00:06:57]:
Let's unpack it from the start because we have heard, or some of you listening have heard, the term cellular agriculture. But even to me, sometimes it's not clear what exactly is in that field. It's also evolving. So I would love to hear your definition. What is inside? Is even cultivated meat inside of that, or is it outside? Tell us more.
Steven Lang [00:07:16]:
No, that's great. Really good question. Because I think this definition has been evolving, and it's important for us as an industry to consolidate around a single definition. And I think that definition should be that cellular agriculture is using cell culture to produce agricultural products.
The frustration that I've had over the last three or four years is that everything that is considered cellular agriculture is actually synonymous with cultivated meat. And there's so much more to cellular agriculture than just meat. I'd also like to argue that the focus on cultivated meat has done us a disservice, because we have had some opposition to cultivated meat—political arenas, political grandstanding—where states and even countries have banned lab-grown meat even before it's available on the market.
So I really want, through this venue as well as other venues, to drive home the message that cellular agriculture includes any type of cell culture–derived products from animal cells like cultivated meat, plant cells like cacao, or precision fermentation with microbial cells. And that broader definition of cellular agriculture is important to me because I see resistance from consumers to adopt or try cultivated meat. So we need to get away from the singular focus on cultivated meat and move more toward these other products that people understand better and adopt more easily.
David Brühlmann [00:08:50]:
When you say precision fermentation is part of cellular agriculture, what comes to my mind—and this is a question—is beer production part of cellular agriculture or not?
Steven Lang [00:09:01]:
You could argue that, but it's traditionally been called fermentation, which is a stretch. You could call that precision fermentation.
David Brühlmann [00:09:08]:
Okay, fair enough.
Steven Lang [00:09:10]:
I think of precision fermentation as really using genetically modified microbes to produce a specific product, whereas beer and wine fermentation are essentially producing alcohol—which is important—but it's not as complicated as, say, producing lactoferrin from yeast.
David Brühlmann [00:09:26]:
Let's walk through the fundamentals of what you're doing. You're now growing cacao cells in a bioreactor. Tell us more. How does this work? Is it similar to a CHO cell culture? Is it very different, and more?
Steven Lang [00:09:40]:
I like to say cell culture is cell culture. What we do in plant cell culture is very analogous to a CHO cell line in cell line development or any type of cultivated meat, where you take a biopsy from the agricultural plant or animal, and you immortalize those cells and then expand them to produce biomass that is the product. So there are no differences there.
The differences are really about the terminology. With plants, what we do is take an explant instead of a biopsy. And plants are interesting because all of their cells are totipotent, so they can be differentiated into a full plant from any individual cell.
What we've done here at California Cultured, with our cell line development group, is take explants from a cacao pod. We aseptically open the pod, expose the beans inside, and take explants from those beans. Those explants are then put on solid agar media and induced to dedifferentiate. Those are called callus cells.
This technology has actually been around longer than a lot of animal cell culture, because it was historically used to propagate plants for the field. We can take those callus cells on the plate and use them as a cell bank, then screen and select the cell lines we want and adapt them to suspension culture, much like you would with animal cells, and then scale them up.
From there, once we go into scaled production, it's a very simple process—very much like cell culture. We're controlling the environment and the culture, running for a certain number of days, and then harvesting the bioreactor.
One big difference with the plant cell culture products we're working on is that there's relatively little downstream processing. Our downstream processing is dewatering, drying, milling, and packaging. Compare that to cultivated meat, where you take the biomass from the bioreactor and then have extensive downstream processing—such as scaffolding, maturation, or formulation with animal or plant-based proteins to create a meat analog.
With our plant cell culture products, downstream processing is very simple. And I think that alleviates some consumer concerns about cellular agriculture products because it's easier to understand. The products coming out of the bioreactor also really look like conventional cocoa powder or coffee. So it's easier for consumers to connect what we're producing with what they already think of as cocoa powder and coffee.
David Brühlmann [00:12:33]:
Regarding consumer acceptance, I think this cocoa product is much easier to sell because it's not a genetically modified cell, correct?
Steven Lang [00:12:43]:
Correct. We have not done any genetic modification to our cell lines or products. We're relying on the natural diversity in the genetics and epigenetics of cacao to select the cell lines we're interested in—not only based on growth parameters, but also the phenotypes we care most about, which are the sensory attributes and higher levels of bioactives that are important in cacao and cocoa powder.
David Brühlmann [00:13:11]:
Do you clone the cell or your cell lines as you would do in cell culture?
Steven Lang [00:13:16]:
Not necessarily. Since we're not doing any genetic modification, we don't need to ensure that our product originates from a single cell, like what the FDA requires for biopharmaceuticals. Essentially, we do perform clonal selection, but these cells tend to aggregate, so it's difficult to say whether the production unit is a single cell or an aggregate.
That said, we have strict quality control processes and specifications to ensure we're producing a safe, high-quality product.
David Brühlmann [00:13:46]:
And how do you manage the variability that comes from different cells? I imagine the taste will change, maybe the texture.
Steven Lang [00:13:55]:
There's not a lot of that. Right now, what we're finding is that if you control the environment very carefully for plant cells, you can control the consistency and quality of the product coming out. So we’ve been sticking with a very straightforward media and process, and we’ll talk more about that. The bioreactors themselves are pretty consistent, and that gives us stable production over time.
What we’ve demonstrated so far is stability over about six months at the lab scale. When we move to full scale, we’re hoping to translate that stability into a continuous manufacturing process.
David Brühlmann [00:14:30]:
I'm curious about the media cost, because that's a big challenge in the cultivated meat space. How does that work in cacao?
Steven Lang [00:14:38]:
Plants in the field just need fertilizer. And since we're growing them without sunlight, they also need a carbon source. Our media is relatively simple—about 18 components, compared to animal cell culture media, which typically has 40 to 60 components, including some very expensive growth factors.
Our components are mostly fertilizers—phosphorus, nitrogen—plus a carbon source and some relatively inexpensive growth factors that are already found in conventional food we eat today.
David Brühlmann [00:15:13]:
All of the components you're using are chemically defined, correct?
Steven Lang [00:15:16]:
Correct—chemically defined media. I should also add that all the components we’re using have gone through a GRAS (Generally Recognized As Safe) process and are totally acceptable for food production.
In addition to having fewer raw materials, the costs are dramatically different. Back when I was doing cell line development at Johnson & Johnson, we were paying around $40 per liter for media. I don’t know how much that’s changed, but in plant cell culture, we’re well below $10 per liter, and we expect to get below $1 per liter.
David Brühlmann [00:15:48]:
Wow. And what about the economics? Is it relatively easy to reach a point where the product is profitable?
Steven Lang [00:15:58]:
Yes, absolutely. And I should back up and describe our first product coming out the door, which is a high-flavanol cocoa powder. We’ve selected a cell line that produces higher levels of flavanols, which are bioactive compounds responsible for many of the health benefits of dark chocolate.
There was a very large clinical study called the COSMOS Trial, which included more than 21,000 participants, mostly elderly, and more than half had pre-existing conditions such as cardiovascular disease or diabetes. This long-term study supplemented diets with 500 mg of flavanols per day and found a 27% reduction in cardiovascular events. Additional analyses are still coming out of that study.
So we’re interested in improving not only flavor and taste, but also the health benefits of cocoa powder. Through our cell line selection, we believe we can do that. This allows us to enter the market with a premium product at a higher price point.
While we scale that product and generate revenue, we’ll continuously work on improving COGS and processes to reduce cost. That will allow us to move into commodity cocoa and eventually other products like coffee. So we start premium and then move toward commodity products.
David Brühlmann [00:17:38]:
That's an excellent strategy.
Steven Lang [00:17:40]:
Yeah. Other companies are doing this as well. I like to point out Gourmey, which has been very successful in France producing cultivated foie gras as a premium product. It really is the way to go—it’s more of the Tesla model, where you launch with a premium product first.
David Brühlmann [00:17:57]:
Yes, exactly. It’s a Tesla model.
Steven Lang [00:17:59]:
Exactly.
David Brühlmann [00:18:00]:
Before we go on to flavor, I just want to dive into the bioreactor side for a minute, because not everyone listening is familiar with plant cell culture. Can you describe how the bioreactor looks? What parameters are you controlling? How does a run typically work? Is it batch, fed-batch, or—you mentioned continuous—how does it work?
Steven Lang [00:18:23]:
With these cacao cell lines and our cocoa powder product, we’ve run the cells in both stirred-tank bioreactors and airlift bioreactors. They’re very robust and grow under a wide range of conditions, so we don’t have to control very much.
In one of your previous podcasts, you talked about Process Analytical Technology (PAT). For us, the main parameter we control is dissolved oxygen. These plant cells grow at ambient temperature, relatively low pH, and all they really need is oxygen and nutrients.
That’s not to say the processes are trivial, but they’re very flexible, and we can use different reactor modalities. Right now, we’re running batch processes, not fed-batch. A typical run starts with inoculation, and about seven days later, we harvest.
We use a higher inoculum than typical animal cell culture, but our cells are also much larger. We usually start with about 5–10% packed cell volume (PCV), and by harvest we reach 30–40% PCV. That yield is dramatically higher than what you typically see in even highly optimized animal cell culture processes.
So right out of the gate, the amount of biomass we can generate from plant cell culture is significantly higher than what animal cells can produce.
David Brühlmann [00:19:50]:
And how long does a run typically last?
Steven Lang [00:19:52]:
For seven days. And the seed train running up to that is your typical four- to five-day split ratio. Depending on the scale you're going up to, you'll need that time to get the inoculum ready for the final production vessel.
David Brühlmann [00:20:07]:
And what vessels are you using in the seed train? Are these shake flasks, spin tubes, the usual sub-specs, all that?
Steven Lang [00:20:14]:
Standard stuff—shakers and shake flasks, Fernbach flasks, the larger shakers—until we get into the bioreactors. At very small scale, starting at 5 and 10 liters, we keep them in a controlled environment, then scale from 5 to 10 liters to 100 liters, 500 liters, and then 2,000 liters.
David Brühlmann [00:20:34]:
How critical are these cultures with respect to contamination?
Steven Lang [00:20:39]:
The major problem with any scaled cell culture is contamination—how do you maintain a sterile envelope for the cells to really take off? The nice thing about plant cells, as I mentioned earlier, is that the culture media has a relatively low pH, which is not conducive to microbial growth. In addition, plant cells themselves produce antimicrobial compounds that help fend off contamination.
That said, we still deal with contamination, and that’s something we’re actively working through. As we talk about some of the bioreactor innovations we’re developing to create low-CAPEX, low-OPEX bioreactors, the sterilization strategy is a big part of that.
David Brühlmann [00:21:24]:
And you're using stainless steel tanks for the cell culture—it's not single-use, right?
Steven Lang [00:21:29]:
We’re not single-use, and we’re also not stainless steel, because we’re going after commodity products. One of the things that really attracted me to this company is that they’d clearly thought through the business model and understood they’re targeting commodities, so everything has to be interrogated for cost reduction.
Right now, we’re working with plastic bioreactors. The reason is simple: we can buy a 2,000-liter plastic tank for about USD 3,000. Try buying a 2,000-liter stainless steel bioreactor for less than USD 1 million. Right there, you can see we’re taking a very different approach—maybe even recreating the wheel in some cases.
But our cells are well suited to these low-cost bioreactors, which we can then scale out very easily. Another important part of the business model is that instead of scaling up beyond 2,000 liters, we scale out. That allows us to convert underutilized office space into laboratories where we can produce cocoa and coffee.
This is fantastic, because here in West Sacramento, where my labs are located, we’re using underutilized office space with very low cost per square foot, compared to what you’d pay for a GMP facility or a site designed for large-scale mammalian cell culture.
David Brühlmann [00:22:51]:
Yeah, that's interesting, because this model could also be very compelling for emerging markets. You could put this somewhere in Africa, Asia, or Southeast Asia. That works, and people there have the competencies to run it.
Steven Lang [00:23:10]:
Absolutely. And I’m really glad you brought that up. Not only can we co-localize these bioreactors because they’re so inexpensive, but we can also onshore production that historically hasn’t happened in Europe or the United States—particularly coffee and cocoa, which are restricted to regions around the equator.
In the U.S., the only places we can really grow coffee or cocoa are Puerto Rico and Hawaii, and that’s not enough. Our vision is to build not just the cell lines, but also the processes and infrastructure that allow us to deploy these production units anywhere in the world—co-localized with chocolatiers or manufacturers, right next to their facilities.
That also enables the creation of more biomanufacturing jobs. All of these are what I’d call positive externalities that come from focusing on low-CAPEX, low-OPEX biomanufacturing for products people genuinely want—and frankly, people are addicted to chocolate and coffee.
David Brühlmann [00:24:16]:
Yeah. And I think this is just the beginning, because you can produce all kinds of products in these cell culture systems.
Steven Lang [00:24:23]:
Yeah, absolutely. There are already plenty of precedents. Plant cell culture has been around for a long time. There are drugs being produced—at least four that I know of—from plant cell culture. There are also animal vaccines, as well as a wide range of nutraceuticals, cosmetic ingredients, and specialty compounds produced using plant cell culture.
David Brühlmann [00:24:44]:
That’s just the beginning of our conversation with Steven Lang. We've explored the foundations of cellular agriculture and the bioprocessing challenges of growing cacao in bioreactors. In part two, we'll dive into the economics, the scalability, and what this means for the future of food production.
If you’re finding value in this episode, please leave us a review on Apple Podcasts or your favorite platform. It helps other biotech scientists like you discover these conversations. We’ll see you in part two.
All right, smart scientists—that’s all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you.
For additional bioprocessing tips, visit us at smartbiotechscientist.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Steven Lang
Steven Lang is the Head of R&D, Bioprocess, and Analytics at California Cultured, where he leads the development of sustainable cocoa and coffee produced directly from plant cells. He brings over 20 years of experience in cell culture and biomanufacturing from the biopharmaceutical industry, including roles at Genentech and Johnson & Johnson.
Steven is passionate about applying scientific rigor and scalable bioprocessing to build more resilient and sustainable food systems.
Connect with Steven Lang on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
For decades, biotechnology development relied on 2D cultures—cells grown as flat layers in petri dishes or flasks. While useful for many experiments, these models don't reflect the true complexity of living tissues.
In this episode of the Smart Biotech Scientist Podcast, host David Brühlmann speaks with Catarina Brito, Principal Investigator at ITQB NOVA and Head of the Advanced Cell Models Laboratory at iBET and ITQB NOVA in Portugal, about how 3D cell models are reshaping preclinical research and driving a fundamental shift in the field.
We have a breast cancer microenvironment model in which we include breast cancer cells alongside immune cell populations. We also incorporate stromal cells, namely fibroblasts, and we were studying antibody–drug conjugates (ADCs). These ADCs are very potent and can kill cancer cells quite quickly.
However, when we use a 3D model in which we reconstitute the tumor microenvironment, the response changes. The stromal cells—these fibroblasts—contribute to resistance to ADCs, and we can observe and study this effect directly in the 3D model. This is something you cannot—and would not—see in a flat, 2D culture flask.
David Brühlmann [00:00:46]:
Welcome back. In part one, Catarina Brito introduced us to the power of advanced 3D cell models that mirror the complexity of human tissues farbeyond traditional culture systems.
Now we’re going deeper into the immune microenvironment, the hidden factors that can make or break therapeutic success. From immunotherapy resistance in tumors to unexpected responses to gene therapy vectors, Catarina reveals how understanding innate immunity is reshaping preclinical strategy. Plus, she’ll share her vision for the future. Let’s continue.
Can you give us a concrete example of how modeling the tumor microenvironment in your system has revealed insights about specific interactions or mechanisms? How does that work?
Catarina Brito [00:02:53]:
There are different types of examples. One practical example comes from work we have published, where we developed a breast cancer microenvironment model that includes breast cancer cells, immune cell populations, and stromal cells, namely fibroblasts. In this study, we were evaluating antibody–drug conjugates (ADCs).
Typically, in 2D models, we culture HER2-positive breast cancer cells and test an anti-HER2 ADC. These ADCs are very potent and can kill cancer cells quite quickly. However, when we move to a 3D model, in which we reconstitute the tumor microenvironment, the response changes. The stromal cells—specifically fibroblasts—contribute to resistance to these ADCs. This effect can be observed and studied in the 3D model, and it is something you cannot—and would not—see in a flat 2D culture flask.
Another example comes from our work with neural models, which we use to study the early steps of immunogenicity to gene therapy vectors. In these models, we include neurons, glial cells, and microglia, which are the resident macrophages of the brain. Using this system, we were able to identify very transient but intense immune responses to viral vectors. These responses had not been detected in other preclinical models but were observed in patients. This illustrates how better reproducing human physiology can help identify factors that can later be used to improve therapies.
David Brühlmann [00:04:44]:
Where do you see these models evolving in the next few years? There’s a lot happening in the bioprocessing field—how we culture cells, how we maintain conditions, advances in analytical technologies, and now the integration of AI and machine learning. How will these technological advances affect the way you conduct your experiments?
Catarina Brito [00:05:16]:
I think advanced cell models combined with multi-omics data—multiple layers of omics information—and spatial information will be key. This is particularly relevant when we consider three-dimensionality and the multiple components of these environments.
This type of data will feed AI-based models, allowing a more accurate representation of human biology and pathophysiology. Until now, we have mainly been making observations and comparing them with human data. But to start making predictions, the integration with AI will be essential.
One starting point is thinking about digital twins of the models themselves, benchmarked against patient-derived data. From there, many things become possible: better tailoring treatments to biological patient profiles, optimizing dosing strategies, and designing drug combinations. Overall, this will be a major step forward for precision medicine and its meaningful implementation.
David Brühlmann [00:06:28]:
What is your advice to a smart scientist listening and wondering how they could implement these advanced models? We have many powerful tools available, but it can also feel overwhelming. Where do you start, and how simple can you keep these models while still gaining value from them?
Catarina Brito [00:06:53]:
I think the answer lies in focusing on the biological question you need to answer. I completely agree with what you suggested—we should aim to be as simple as possible to answer that question. The technology should not be the driving force; the question should lead us to define the biological endpoint, whether that is mechanism of action, potency, or safety.
From there, we decide on the level of complexity required. One risk today is overengineering systems too early, before fully understanding the cells and the question. That’s why I advocate for modular systems: starting simply with tumor or organ-specific spheroids, and then adding stromal or immune components only when the project demands it.
At the same time, we should avoid oversimplifying by treating these systems like regular 2D cultures. Factors such as mass transfer, cell source selection, and process control are critical. These are processes, and they require proper control to ensure reproducibility and standardization, which is often what feels daunting with these models.
David Brühlmann [00:08:17]:
At iBET, you collaborate extensively with pharmaceutical companies. I imagine you also guide them in how to use the models effectively. Can you describe the types of collaborations you have, how you run them, and how they shape model design and application?
Catarina Brito [00:08:42]:
That’s true—we collaborate extensively with industry. Over the years, we’ve been fortunate to work with several pharmaceutical partners who have helped shape how we operate. Pharma partners bring questions driven by translational needs: how to make models more predictive, more scalable, and more compatible with their development workflows.
These applied questions strongly influence model design and help us prioritize our research directions. They help ground the models in real-world use. A key factor for successful collaboration is alignment and transparency—being very clear about the biological question, the performance metrics, and the limitations of the system.
Open data sharing in both directions has been essential. This approach has helped move several of our platforms from proof-of-concept to tools that genuinely contribute to accelerating translation within companies.
David Brühlmann [00:09:49]:
What kind of companies do you generally work with?
Catarina Brito [00:09:53]:
I’ve worked mainly with pharmaceutical companies. Among our collaborations, we’ve worked with Boehringer Ingelheim, Merck Global Health—which is the social business of Merck KGaA, Germany—and AbbVie in the United States. These are large companies developing advanced and novel therapies.
David Brühlmann [00:10:14]:
Do you also work with smaller companies or startups?
Catarina Brito [00:10:18]:
Yes, we do. The collaborations I mentioned were long-term partnerships, often spanning several years, where we developed models from scratch to answer questions that were not addressable with existing systems. We’ve also worked with smaller companies and startups, typically on more focused questions related to their compounds, using models we already have established.
David Brühlmann [00:10:44]:
I have a question about potential pitfalls. As we mentioned earlier, there are many technologies available—so where do you start? What do you leverage? What is hype? What really makes a difference? Can you point out two or three pitfalls to watch out for?
Catarina Brito [00:11:03]:
As I mentioned earlier, one major pitfall is overengineering systems in a technology-driven way before truly understanding the cellular system, what you need the cells to do, and which cell types are actually required.
Another critical aspect is the cell source. Depending on your question, you may never get the answer you’re looking for from the cells you’re using—no matter how much you engineer them.
A third pitfall is insufficient validation and characterization. Moving too quickly to application without properly characterizing the system can be problematic. Thorough validation is essential before drawing conclusions or translating results.
David Brühlmann [00:11:45]:
Catarina, what burning question haven’t I asked that you’re eager to share with our biotech community?
Catarina Brito [00:11:52]:
I think we covered things quite thoroughly. It was a really good conversation—and you can probably tell how passionate I am about this topic.
David Brühlmann [00:12:05]:
Yes, absolutely.
Catarina Brito [00:12:07]:
And this also gives context to my work. My academic appointment is with NOVA University Lisbon, but my lab is also affiliated with iBET. It is through iBET that I collaborate with industry, because iBET is a partnering research organization that bridges engineering and biology to accelerate biopharmaceutical development.
This creates an ecosystem where we combine expertise ranging from cell line development, cell bioprocessing, and advanced analytics, and then close the loop with translational models under the same organizational framework. This setup gives us access to a highly collaborative environment and makes it easier to work with advanced therapeutics, from antibodies to gene and cell therapies.
David Brühlmann [00:12:59]:
This has been great. Catarina, what is the most important takeaway you want our listeners to walk away with from our conversation?
Catarina Brito [00:13:08]:
I think the key takeaway is that advanced cell models are here—and they are here to stay. They will play a major role in shaping translational research in the future, while at the same time driving transformation across the entire industry.
David Brühlmann [00:13:24]:
This was great. Catarina, thank you so much for letting us into the world of 3D cell models. Where can people get in touch with you?
Catarina Brito [00:13:33]:
I’m on LinkedIn, like most people, and also reachable through the iBET website and ITQB NOVA’s website. But I would say LinkedIn is the best way to contact me.
David Brühlmann [00:13:42]:
Excellent, smart biotech scientists—you’ll find all the links in the show notes. Please take this opportunity to reach out to Catarina. And Catarina, once again, thank you so much for being on the show today.
Catarina Brito [00:13:56]:
Thank you, David. It’s been a pleasure. Bye-bye.
David Brühlmann [00:13:59]:
That wraps up our conversation with Catarina Brito. Her work exemplifies the future of preclinical research, where complexity becomes predictability and where patient-specific insights move from aspiration to reality.
If this episode sparked new ideas for your own development programs, I’d love to hear about it. And please take a moment to leave a review on Apple Podcasts or wherever you listen—your support helps us bring more innovators like Catarina to the biotech community.
Thank you so much for tuning in today. Until next time.
For additional bioprocessing insights, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech conversations in our next episode. Until then—let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Catarina Brito
Catarina Brito is Principal Investigator of the Advanced Cell Models Laboratory within the Animal Cell Technology Unit at iBET and ITQB-NOVA. Her research centers on the development of human cell models using induced pluripotent stem cells, patient-derived cells, and established cell lines, and on applying these models to investigate disease-associated alterations of the cellular microenvironment and their impact on therapeutic response.
Her key research interests include the innate immune microenvironment in cancer and neurological disorders, as well as gene and cell therapies and immunotherapies. Her laboratory is supported by funding from FCT (Portugal), the Innovative Medicines Initiative Joint Undertaking (IMI; EU & EFPIA), and the international pharmaceutical industry.
Connect with Catarina Brito on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
Ever wondered what actually happens to human cells when you scale up bioprocessing from a petri dish to a bioreactor? Most scientists see it as a matter of bigger equipment and higher volume. But that shift isn’t just technical—it's biological. The rules that govern cell behavior change, sometimes dramatically, reshaping everything we thought we knew about cell therapy development.
Smart Biotech ScientistPodcast host David Brühlmann is joined by Catarina Brito, Principal Investigator at ITQB NOVA and Head of the Advanced Cell Models Laboratory at iBET and ITQB NOVA , Portugal, to talk about how 3D cell models are driving a paradigm shift in preclinical research.
I really believe bioprocess development starts with understanding cell biology at scale. Cells are social entities, and they sense each other. They remodel their microenvironment, they rewire their signaling cascades when things such as cell density, mass transfer, and mechanical cues change. When we are moving from the T-flask to the bioreactor, I think we are not just increasing volume; we are changing the entire context in which the biology of the cells operates. That’s why I think we really need to respect biology as a primary design input.
David Brühlmann [00:00:39]:
Imagine a world where we could predict how your body would respond to a therapy before it ever enters clinical trials. Today’s guest, Catarina Brito from ITQB NOVA and iBET in Portugal, is making that vision tangible. She’s pioneering 3D human cell models that recreate the complex microenvironment of human tissues—from brain and liver to tumors—revolutionizing how we test biologics and gene therapies.
If you’re tired of the limitations of Petri dishes and animal models, this conversation will reshape how you think about preclinical research.
Catarina, welcome to the Smart Biotech Scientist. It’s great to have you on.
Catarina Brito [00:02:35]:
Hi David. Thanks for having me here. I’m glad to be here.
David Brühlmann [00:02:39]:
It’s a pleasure. Catarina, share something you believe about bioprocess development that most people disagree with.
Catarina Brito [00:02:47]:
I think people are starting to believe it in the bioprocess industry—maybe not as strongly as I do, or not from the beginning. It’s a strong conviction of mine because I truly believe bioprocess development starts with understanding cell biology at scale.
Cells are social entities, and they sense each other. They remodel their microenvironment, they rewire their signaling cascades when things such as cell density, mass transfer, and mechanical cues change. When we move from the T-flask to the bioreactor, we are not just increasing volume—we are changing the entire context in which the biology of the cells occurs.
That’s why I believe we really need to respect biology as a primary (PR) input—media composition, oxygenation, and nutrient delivery—to keep biology within the right operating window so that we can successfully scale up. This means thinking about more than just equipment and volume, but really starting with biology as the foundation.
David Brühlmann [00:03:49]:
Scale-up is much more than “scaling up.” And you said it very well—there are so many different parameters you have to consider when scaling a process.
Before we dive deeper into today’s topic, let’s talk about you. Catarina, draw us into your story. What sparked your interest in biotechnology, and how did you arrive at the exciting field you’re working in today?
Catarina Brito [00:04:19]:
It all started during my PhD and was driven by the questions I was trying to answer. I was studying mechanisms driven by glycan–protein interactions, and these mechanisms are very different between murine and human cells. That was an early wake-up call—it made me really think about human biology and the accuracy of the models we were using, as well as the need for models that truly reflect human physiology.
The cellular processes I was studying involved neural cells and axonal outgrowth—processes that are highly dependent on context, particularly the extracellular matrix. Yet we were growing murine neurons on plastic surfaces, which are extremely artificial compared to what actually happens in the brain.
This reinforced the need for better models of the physical context and how it shapes signaling, morphology, and neuronal connectivity. And then there’s the cellular context—neurons are not isolated; many critical cues come from neighboring cells, particularly glial cells. The models we were using were monocultures, which was quite frustrating.
All of this led me to look for a postdoc where I could tackle these questions. My motivation truly crystallized when I had the opportunity to join iBET for my postdoc. I worked under the supervision of Dr. Paula Alves, who was already doing pioneering work on 3D culture systems and demonstrating that biology can—and should—be a primary design input for experimental models.
It was also a very exciting time: it was the first time human pluripotent stem cells were being used in Portugal. That experience was incredibly important and really set the direction for my career—building models that integrate human, multicellular complexity.
David Brühlmann [00:06:24]:
That’s fascinating. Let’s unpack this a bit, because not everyone listening today is familiar with animal models, 2D culture, or 3D culture systems—and there’s a lot happening in this space.
Let’s start with the traditional tools: 2D cell culture and animal models, which have dominated preclinical research for decades. What are the critical limitations of these systems, and what advantages do newer models offer?
Catarina Brito [00:06:53]:
Both. I should start by saying that, because we are often on the defensive. I’m always advocating for advanced models and 3D models, but all models have value, right?
2D cultures and animal models have taught us a lot about biological questions and pathological aspects as well. But of course, they also have limitations. And I think that understanding the limitations of each model is extremely important, especially when we’re trying to develop advanced therapies.
2D cultures are cells grown on a flat surface. Typically, they involve immortalized cell lines that are easy to culture. They offer a lot of control and high throughput, but they lack the structural and functional reality of tissues. Tissues are not flat. Some tissues are layered, but most tissues are three-dimensional.
Cell polarity is altered; diffusion of nutrients—and also of compounds with therapeutic potential—is different. There is no physical confinement of cells and no proper neighborhood effects. As a result, cell–cell interactions change significantly.
If we think about cells under these conditions, their receptor localization, metabolism, and overall phenotype change when moving from 2D to 3D, or from 2D to native tissue. So if we’re thinking about the development of biologics, for example, these molecules depend on receptor engagement, transport, and access to the microenvironment. There is therefore a major difference in both biology and therapeutic response.
When we think about animal models, they are still invaluable because they provide systemic biology—they represent a complete organism. However, they miss many human-specific aspects, particularly in the immune system, glycosylation patterns (as I mentioned before), genetic variability, and even disease etiology.
This can result in false positives and false negatives due to interspecies differences that can distort the readout. Of course, animal models remain useful and are part of a toolbox that should be as comprehensive as possible—but they do have limitations.
David Brühlmann [00:09:09]:
There’s been quite a push from regulatory agencies—especially the FDA—to reduce the use of animal models lately. What’s your take on this? Do you think we’ll ever reach a point where animal models are no longer used at all, or is that unrealistic? Will we always need a hybrid approach?
Catarina Brito [00:09:30]:
I think we are on a path that may eventually lead to the replacement of animal models, although a lot of validation is still required. There is a strong effort to develop multi-organ systems in which pharmacodynamics and pharmacokinetics can be studied. So far, what regulators have mainly pushed for is the replacement of animal models in types of readouts where we already know that the systemic component is not required. But I would say we are clearly on a path that could take us there—we just need strong validation.
There are also efforts from the European Commission, including dedicated calls and roadmaps, to move us in this direction. With support from regulators and agencies, we may eventually get there.
David Brühlmann [00:10:17]:
This will definitely be a paradigm shift for the industry—a very different way of developing drugs.
And speaking of different approaches, let’s zoom in on 3D models and advanced models. Tell us more about that. What exactly is a 3D model, and why is it called “advanced”?
Catarina Brito [00:10:35]:
The notion of “advanced” goes beyond simply growing cells in three dimensions. These models need to recreate key aspects of the tissue they are meant to represent. The tissue microenvironment shapes how cells behave, so the goal is to have a bioactive model in which cells influence the system and are influenced by it as well—ideally in a way that achieves reproducibility and robustness.
There are several important aspects. First, architecture—the three-dimensional structure is essential for cell polarity, spatial organization, and physical confinement. Then there is the presence of diffusion gradients. These are not only gradients of oxygen and nutrients, but also of signaling molecules, which are distributed in tissues and strongly influence biology. They also affect drug penetration, drug binding, and clearance.
Another important component is mechanical cues and the extracellular matrix. Cells sense stiffness and tension, which are crucial for survival, migration, and—for example—immune evasion, which is particularly relevant in cancer therapeutics.
Then there is multicellular complexity. Tissues are composed of different cell types that interact with each other. If we think about tumors, they interact with stromal cells and immune cells. In neural tissue, neurons interact with glia and microglia. These interactions are essential.
Advanced models aim to recapitulate biology more closely to what happens in the body, allowing us not only to study disease mechanisms but also to better predict therapeutic responses.
David Brühlmann [00:12:26]:
In those 3D systems, do people need to visualize this in a particular way? For example, if we take a liver cell type—do we reconstruct a mini liver organ in a bioreactor? Or is it more like having a large number of individual cells, maybe floating?
Catarina Brito [00:12:47]:
We try to recapitulate tissues by capturing this multicellular complexity—having the different cell types that compose the tissue interacting with each other.
It also depends on the tissue. In the liver, for example, hepatocytes are tightly connected through junctions. You also have endothelial cells with fenestrations—essentially “windows” that allow molecular exchange. Then there are macrophages, immune cells that move within the tissue space.
So the goal is not necessarily to reproduce the exact anatomical architecture, but rather to recreate the relevant cell–cell interactions and how they contribute to tissue function.
David Brühlmann [00:13:34]:
And how long can you keep these cells in culture? If you want to start a new experiment, do you have to start from scratch every time? Can you keep something like a cell bank—maybe that’s not the right term—but how does that work?
Catarina Brito [00:13:49]:
It depends a lot on the cell of origin. With primary cells, they are typically used for a limited time. They may last several weeks in the model, but they cannot be propagated extensively.
If we’re talking about pluripotent stem cells, then we can differentiate progenitors and at least bank those progenitor populations. This allows us to work in a multi-step way that facilitates reproducibility.
The duration of the model itself also depends on the culture system. We use a lot of bioreactor technology under perfusion, precisely to prolong the lifespan of these models.
David Brühlmann [00:14:37]:
And how do you handle diversity? You’re working with liver cells, neural cells, stem cells—many different cell types with different requirements in a bioreactor setting. How do you manage that? Do you change conditions, media, bioreactor size or shape?
Catarina Brito [00:15:16]:
It really depends on the tissue. Each tissue has its own requirements.
For example, neural cells are extremely sensitive to oxygen tension and mechanical stress. We use bioreactors, but the design has to minimize shear stress, and oxygen levels must be kept low and very stable. Tight control of process parameters is critical.
In the liver, oxygen requirements are completely different. There, maintaining functionality is key, and perfusion flow is essential to support metabolic competence. It’s also crucial to optimize the ratios of different cell types—not only hepatocytes, but also the so-called non-parenchymal cells. These ratios must be tightly controlled.
In solid tumors, heterogeneity is central. We need systems that allow extracellular matrix remodeling, which is a key feature of tumors. This enables the formation of tumor niches, including hypoxic niches, which are highly relevant. On top of that, we aim to incorporate immune cells and study immune cell infiltration.
All of these requirements are driven by biology. For each model, we carefully choose design variables to meet those needs. Across all systems, robustness and scalability are essential. We aim to design models that are as modular and bioreactor-compatible as possible, ensuring reproducibility and throughput.
Finally, we place strong emphasis on validation, to confirm physiological relevance and ensure that it is not lost during scaling throughput.
David Brühlmann [00:17:22]:
I’m just curious, and I may have missed this earlier, but the purpose of all this, as you said, is better reproducibility and, ultimately, faster development. So how do these advanced systems compare, for instance, to animal models? Why can development be accelerated so significantly?
Catarina Brito [00:17:42]:
The throughput is completely different. When you develop models in scalable systems, you achieve much higher throughput, and these models can be applied much earlier in the drug development process.
They can be introduced earlier in pharmaceutical development than animal models, which are usually brought in quite late. This allows for much more selection and decision-making earlier on.
Additionally, these models are increasingly being adopted because much of the relevant biology—particularly human biology—is not captured in animal models. Even in cancer research, many of the newer therapeutic modalities, such as cell engagers and multispecific antibodies, rely on biological mechanisms that are not reproduced in animal systems.
So even when animal models are available, there is still a strong need to bring human-based models to the forefront.
David Brühlmann [00:18:28]:
We’ve just scratched the surface of how advanced 3D models are transforming drug development. In part two, Catarina will dive into the critical role of innate immunity in predicting therapeutic responses and share her vision for AI-powered personalized medicine platforms.
If you’re finding value in these conversations, please leave a review on Apple Podcasts or your favorite podcast platform—it helps other biotech scientists discover these insights. See you in part two.
All right, smart scientists, that’s all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your preferred podcast platform. By doing so, we can empower more scientists like you.
For additional bioprocessing insights, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech conversations in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Catarina Brito
About Catarina BritoCatarina Brito is a Principal Investigator at ITQB NOVA and Head of the Advanced Cell Models Laboratory at iBET and ITQB NOVA in Portugal. Her research focuses on the development of complex human cell models to investigate disease microenvironments and therapeutic responses, particularly in cancer immunology and neuroinflammation.
By integrating fundamental cell biology with translational research, her work aims to accelerate the development of advanced therapies while reducing reliance on animal models. She has coordinated more than 19 research projects, authored 90 peer-reviewed publications, and works closely with pharmaceutical partners and clinicians to advance innovation in preclinical research.Connect with Catarina Brito on [LinkedIn](https://www.linkedin.com/in/catarina-brito-ibet/).
Connect with Catarina Brito on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
For years, protein engineering has struggled with a fundamental bottleneck: machine learning models, powerful as they are, have been starved of meaningful data. Most rely on just 10,000 antibody-antigen structures from the Protein Data Bank—a drop in the ocean compared to the billions of parameters powering modern large-scale AI models. The result? Promising molecules often get stuck in preclinical limbo, not because the models aren’t smart, but because they keep hallucinating with incomplete data.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Troy Lionberger, Chief Business Officer at A-Alpha Bio, whose team has quietly shattered the data ceiling by measuring and curating more than 1.8 billion protein interactions..
Data is always going to be, I think, the constraint in industry—whether it's training your models or validating them. I think there's an increased appreciation that we need differentiated sources of data. A-Alpha Bio is hopefully going to play some part in that ecosystem, but we need more than just a handful of companies.
And I think the second thing I hope your audience walks away with is that, with the right data, AI can be an incredibly powerful tool in accelerating your work. That said, everyone would be forgiven for being confused by the claims of many, and so I would just encourage diligence as we watch this space unfold.
David Brühlmann [00:00:40]:
Welcome back to Part Two with Troy Lionberger from A-Alpha Bio. In the first half, we explored why protein–protein interactions are so fundamental yet historically difficult to characterize, the limitations of traditional antibody discovery methods, and where de novo design fits into the picture. Now we dive into the solution: how A-Alpha Bio’s platform generates massive datasets at unprecedented scale, what happens when you feed 1.7 billion measurements into machine learning models, and why this data revolution is transforming therapeutic development. Let’s jump back in.
Now I would like to move on to machine learning and data in general, because AI and machine learning are powerful, but they depend on good data. I always hear my former boss—he was quite skeptical about technologies—telling us, garbage in, garbage out. And that’s very true for modern technology. For AI, you need good data, and currently there’s a limitation because engineering models today are constrained by public-domain data. So can you paint us a picture of what that looks like and what the impacts are today on drug discovery?
Troy Lionberger [00:03:11]:
I think when we look at modern protein engineering models, I mean, many of them have been trained, as you said, on public data. In that case, it's typically the PDB, so the Protein Data Bank that is being referenced in many of these cases. And just to put a perspective on that, there are a little more than 10,000 antibody structures of antibodies bound to their target antigen in the PDB today. So when you compare 10,000—the number 10,000—to how much data has been generated to train ChatGPT, for example, or other large language models, I mean, there's a 10-order-of-magnitude difference between the amount of data that has gone into generalizable machine learning models in large language models compared to what data is available for brilliant protein engineering teams today to develop their next model.
The constraint is really felt when the biggest failure mode in many of these models is what they call hallucinating. So if you have one example of a solid structure that you're training your model on, it's actually quite difficult to then ask the model, if I make a mutation in one of these residues, will my molecule still bind? And if so, how tightly? Oftentimes these models are hallucinating because they've seen the structure before, there's a minor modification to it, so the tendency—the bias, if you will—is to revert to the solution that they've seen before.
So what's missing is an ability to generate data that can give these models many, many, many examples of both positive and negative data. So being able to definitively tell this model, no, that aspartic acid was changed to an alanine, I know from my experiments it did not bind anymore. Now correct yourself so that you stop hallucinating, right? I mean, that's basically what we're trying to do with AlphaSeq—is generate large amounts of data that introduce minor variations to these molecules and directly tells these models not only do they still bind, but what is the strength of that binding interaction.
And one thing for your audience to, I think, appreciate is when we talk about making generalizable models, I think the grand ambition in the industry is that we can one day teach a machine learning model physics. If you want to teach a machine learning model physics, the missing ingredient is not just the structures, but physical parameters like Gibbs free energy (ΔG). And it just so happens that binding affinity is directly related to ΔG. So what the AlphaSeq platform is giving us is not just structural information, but also energetic information that is really going to be, I think, transformative in how these models ultimately become predictive. And the predictive nature is going to increase the better they get at understanding the underlying physics.
David Brühlmann [00:06:07]:
Wow, that's powerful. That's powerful. Even physics, yeah. What can you do today with the technology, and what will be possible as you build your models? If I'm not mistaken, you have measured 1.7 billion protein interactions, or have—I’m not sure if this is by your company or in general. Where is this going to lead?
Troy Lionberger [00:06:30]:
I think there are multiple layers to that right now. I think the latest count is 1.8 billion, but what's 100 million between friends, I guess. So that data has been used—and we generated that data—in large part to train our internal models and also to do partnerships with therapeutic developers as well.
To answer the first part of your question, what are we doing today? Today we're offering access to our platform under fee-for-service arrangements. Someone comes to us with an antibody that they want to diversify or increase the affinity of or make more developable. We have a very standard process now that three months later these customers are getting what they want, and it's very easy for us to do this work, so we're happy to do it.
Where I think things get more challenging are applications where you require multiple iterations of the AlphaSeq platform. So what I just described as a kind of fee-for-service—someone comes with a molecule, we’ll improve it for them—that requires one iteration, if you will, of our experimental platform.
For more challenging examples, I'll give you two. One would be work we did with Gilead, where we were able to essentially optimize biologics specific for HIV. So to make a neutralizing biologic for HIV, the challenge that was put in front of us is: can you make a molecule that can hit not just one or two versions of HIV variants that are present in the population, but over 800 variants? If you had asked me before I joined this company three months ago if it would have ever been possible for someone to optimize a molecule against more than 800 different variants of a target, I would tell you there's just no way. You might get one or two, but not 800. And the fact that they were able to simultaneously improve the efficacy of this molecule for virtually every variant of HIV available is really exciting.
And I think that, for clarity, took three turns of the crank. So that's how powerful I think things get when you iterate a few times on this.
Another example that I'll give you that I didn't think possible before three months ago is reliably and reproducibly making biparatopic molecules. So what I mean by that is in most cases, the parts of antibodies called the variable domains that ultimately interact with your target antigens typically will bind to a single antigen—so one variable domain, one target antigen.
What are called dual-specific antibodies are those that actually have the ability to have that same domain bind to two different antigens. I've seen a few examples of this in the past where it was serendipitously discovered. And I think a lot of therapeutics historically has been kind of serendipity—it's bespoke and artisanal. We're getting to the point where now these things are tractable and systematic.
And this is one example where we can now almost arbitrarily introduce two different paratopes into this antibody and have them interact with two different antigens. And this opens up all kinds of unique modes of action and safety profiles for therapeutics that really haven't been possible before. But this is where this ability of AlphaSeq and AlphaBind to reproducibly engineer these molecules is, I think, quite exciting.
David Brühlmann [00:09:54]:
Yeah, definitely—that's quite exciting. And it's powerful to have all these tools at our disposal. And now this leads me to the next question. Because if you're developing your company, especially if it's a smaller company or a mid-sized company, you always face the question: Well, we do have very knowledgeable and successful CROs. We have service providers. We have all kinds of technologies. So what do we do in-house? What is the capability we're building in-house? What is the knowledge or the understanding we want to generate in-house versus sourcing out? So as you are working with different companies, what is your observation or your recommendation?
Troy Lionberger [00:10:33]:
David, I think you're right that many companies want to keep their core intellectual property and contributions to these molecules in-house. I absolutely understand the tendency to want to do that.
I think my response is: there's a reason that everyone decided to move to Microsoft Word instead of typewriters. I mean, at some point we realized that there are more efficient ways to move forward. And I think it just comes down to dollars and cents.
The reality is that these therapeutic developers, they don't want to be spending their time engineering molecules—if we're honest with ourselves. I mean, what they want to be spending their time doing is de-risking molecules, not making them. And so the quicker that they can get to de-risked molecules, the better for everybody.
And that is really where I would encourage those in the field to ask the question: Could you accomplish more, faster, and cheaper than service providers? If the answer is no, then everyone should use a service provider. That is really how we're operating now in many of our applications.
We're simply trying to accelerate these teams by making their work easier, faster, and cheaper—and more importantly, de-risking that entire process, because we're quite confident that we will be able to ultimately deliver a molecule. There isn't as much of a question anymore in terms of risk. I mean, we haven't yet had a project that we haven't been successful with. Another aspect of this is just mitigating the overall risk.
David Brühlmann [00:12:01]:
Troy, before we wrap up, what burning question haven't I asked that you are eager to share with our biotech community?
Troy Lionberger [00:12:09]:
If I'm honest, there’s something that we’re really excited about—it’s coming, and we’ve been talking about it publicly, so I’m happy to share it here.
But back to this question about being ultimately limited to the entirety of the PDB and only having about 10,000 structures to train your models on—what can we do about that? Crystallography is not going to solve that problem. We have around 10,000 structures, and only a few hundred are added each year. That’s not going to give us the scalability that we ideally need for these models.
And if I were to predict where the field is going—at least on the machine learning and protein engineering side—I think there’s going to be an appreciation across the industry that synthetic epitopes will become an indispensable tool for training these models.
This took me a bit to get my head around, so I’ll try to distill it. The basic idea is that if you have one antibody, it will bind to one target—but that’s not necessarily always true by physics. You could design other antigens—synthetic antigens—specifically to bind that same antibody.
So we’ve been working on something called the Synthetic Epitope Atlas. This is a program that generates predictions about synthetic partners for antibodies, for the purpose of validating that we can actually see a structure and measure the ΔG for that interaction. And even though these data are purely synthetic—these binders do not exist in nature—we’re showing that you can train a model almost just as well as with the PDB, but using things that don’t exist in nature.
You’re teaching these models structure, epitopic diversity, and feeding them ΔG and affinity measurements. That, for me, is really exciting to be a part of at these early stages. And increasingly, we’re starting to hear across the industry that more and more groups are pursuing synthetic epitope development specifically for machine learning and model training.
Just to put this into perspective: I mentioned that there are about 10,000 antibody structures available today to train models on. In just one of our measurements, we’ve shown that you can generate about 1,000 viable structures that are all validated, along with 29,000 confirmed non-binders—molecules with simple mutations that break affinity. That’s roughly 30,000 distinct measurements from a single experiment that can be used to train models. That’s pretty exciting when you think about how an alternative to the Protein Data Bank could ultimately grow.
David Brühlmann [00:14:51]:
Yeah, that’s exciting. With everything we’ve covered today, what is the most important takeaway?
Troy Lionberger [00:14:57]:
The most important takeaway I hope your audience walks away with is, first of all, that data is always going to be the constraint in this industry—whether it’s training models or validating them. There’s an increased appreciation that we need differentiated sources of data.
A-Alpha Bio is hopefully going to play some part in that ecosystem, but we need more than just a handful of companies. And the second thing I hope your audience takes away is that, with the right data, AI can be an incredibly powerful tool for accelerating your work.
That said, everyone would be forgiven for being confused by the claims being made today, and so I would just encourage diligence as we watch this space unfold.
David Brühlmann [00:15:41]:
Troy, this was fantastic. Thank you so much for sharing your insights and your perspective on where drug discovery is heading. Definitely exciting times ahead. Where can people get in touch with you and learn more about your technology?
Troy Lionberger [00:15:56]:
Yeah, our website is www.aalphabio.com—there’s a lot of information there. You’re also welcome to connect with me on LinkedIn. I’d be happy to connect with your audience.
David Brühlmann [00:16:10]:
Excellent. I’ll put the links in the show notes. Smart biotech scientists, please reach out to Troy. And Troy, once again, thank you so much for being on the show today.
Troy Lionberger [00:16:20]:
Thank you, David. It was great speaking with you.
David Brühlmann [00:16:22]:
What a conversation. From measuring billions of protein interactions to predicting the next generation of therapeutics, Troy Lionberger has given us a glimpse into the future of drug discovery. The integration of high-throughput biology and machine learning isn’t just incremental progress—it’s transformational.
If this episode sparked new ideas for your own work, share it with a colleague and leave a review on Apple Podcasts or wherever you listen. Until next time, keep innovating. Thank you so much for tuning in. Until next time, keep innovating.
For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Troy Lionberger
Troy oversees Business Development and Alliance Management at A-Alpha Bio. He started his career in the lab after earning a PhD from the University of Michigan and completing postdoctoral training at UC Berkeley. During nearly a decade at Berkeley Lights, Troy held senior leadership roles in R&D, product strategy, and business development, driving commercial initiatives that secured over $200M in deals across biopharma, agriculture, and industrial biotech.
Before joining A-Alpha Bio, he was Chief Business Officer at Abbratech, guiding the company from stealth-mode antibody discovery to a partner-focused biotech. Troy combines deep technical expertise with a track record of scaling platform companies through strategic, transformative partnerships.
Connect with Troy Lionberger on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
Antibody therapeutics have transformed modern medicine, but for many scientists, developing new candidates still feels like searching for a needle in a haystack—a slow, expensive, and unpredictable process. Structural biology and high-throughput data generation are now collapsing that haystack, offering unprecedented visibility into the molecular handshake that drives life: protein-protein interactions.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Troy Lionberger, Chief Business Officer at A-Alpha Bio, a biotechnology company harnessing synthetic biology and machine learning to measure, predict, and engineer protein-protein interactions.
As a biologist, I would tell you that ultimately life doesn't exist if proteins aren't on some level interacting with other proteins. So whether it's catalyzing force in your muscles or replicating DNA, proteins have to interact with other proteins to carry out all of the cell functions that are necessary for life. If there's ever cell dysfunction, it's oftentimes in some way, shape, or form tied back to some sort of protein–protein interaction that's both the origin of many disease states, but also the opportunity for therapeutic intervention.
David Brühlmann [00:00:35]:
Protein–protein interactions govern almost every biological process and hold the key to treating cancer, infectious diseases, and neurological disorders. Yet, with only 10,000 antibody–antigen structures in public databases, we're building tomorrow’s medicines on yesterday's data. Today, Troy Lionberger, Chief Business Officer at A-Alpha Bio, reveals how measuring millions of interactions simultaneously changes everything. By generating unprecedented quantities of high-quality data, they are accelerating the discovery of rare antibodies, engineering better protein therapeutics, and training AI models that predict what works before you ever touch the lab bench.
Let's explore how. Welcome, Troy, to the Smart Biotech Scientist. It’s good to have you on today.
Troy Lionberger [00:02:42]:
Thanks, David. It's a pleasure to be here.
David Brühlmann [00:02:43]:
Troy, share something that you believe about biotherapeutic development that most people disagree with.
Troy Lionberger [00:02:51]:
It's an interesting question. I think the most controversial view I harbor right now is—given my background—there is an overwhelming historical acceptance that antibody therapeutic development is artisanal and bespoke, that you're really hunting for needles in a haystack, if you will, as is often the common analogy.
I think the controversial statement I would make is that it’s far more systematic today than I ever imagined. For example, most people are surprised when I tell them that there are tractable and reproducible ways to make antibodies that have the same affinity for their human therapeutic target as animal targets. I mean, not just cross-reactive, but the same quantitative affinity, which could help streamline preclinical development, for example, of therapeutic antibodies. Most people I’ve spoken with seem to think that is a flight of fancy. Fundamentally, there are processes that make this happen today. That is the most surprising thing I share with people on a day-to-day basis.
David Brühlmann [00:03:52]:
And this will open avenues to novel therapeutics and also more efficacious drugs.
Troy Lionberger [00:03:59]:
That's right. I mean, preclinical development of antibodies is fundamentally constrained by the challenges in developing these therapeutic molecules. In large part, getting them to work with the animal models required in your studies is problematic. Oftentimes, the affinities of your molecules for those animal targets are far worse than for your human targets. So while you may have a drug that works quite well in humans, you can’t get it to the clinic because the animal might have toxicity issues, simply because you had to administer so much drug into it.
David Brühlmann [00:04:33]:
Before we dive deeper into today's topic, take us back to the beginning. What first sparked your interest in biotech, and how did that journey lead you to A-Alpha Bio?
Troy Lionberger [00:04:43]:
The origin for me was really in college, when a faculty member teaching structural biology started describing proteins as nanomachines. That visual has always stayed with me. It got me interested in science and wanting to understand how these fascinating machines, which operate with very different materials and properties than anything we’ve created as human beings, function and work.
That naturally led to understanding that these proteins, which we barely understand, are ultimately at the root of all human disease—leading to cell dysfunction, which we describe as disease states. It’s ultimately the understanding of these basic building blocks of life that drives biotechnology: figuring out how these proteins can be manipulated and controlled to elicit therapeutic effects.
To answer your question about how I ended up at A-Alpha Bio, my career in biotechnology started at a life science tools company called Berkeley Lights, where I helped invent an exciting technology to discover therapeutic antibodies. That experience naturally led to working with many teams in the industry to support their discovery efforts, and I became increasingly aware of the next major constraint: the preclinical development of those drugs. That is, in large part, the problem we are trying to solve at A-Alpha Bio right now.
David Brühlmann [00:06:05]:
When I looked at your website, what struck me is that your company, A-Alpha Bio, describes itself as a protein–protein interaction company. Why are these interactions so fundamental to drug discovery, and why are they also so difficult to characterize at scale?
Troy Lionberger [00:06:22]:
It's a great question. My background is in biology. As a biologist, I would tell you that ultimately life doesn't exist if proteins aren't on some level interacting with other proteins. So whether it's catalyzing force in your muscles or replicating DNA, proteins have to interact with other proteins to carry out all of the cell functions necessary for life. If there's ever cell dysfunction, it's oftentimes in some way, shape, or form tied back to some sort of protein–protein interaction.
That's both the origin of many disease states, but also the opportunity for therapeutic development.
Being able to characterize these protein–protein interactions—there are many technologies that have come forth to help do this. Surface plasmon resonance (SPR) is an industry-standard way to study protein–protein interactions, and we call this affinity. Understanding the strength of those interactions, or the affinity of those interactions, is ultimately how biophysical characterization describes these protein–protein interactions.
The problem historically is that, despite how advanced these technologies are, they are also quite costly and difficult to use. And when I say difficult, I don’t mean it’s impossible—people do this every day in labs all around the world. It’s just that if your goal is to make millions and millions of those measurements, it’s not a scalable technology.
A great example: to generate the amount of affinity measurements that come off just one of our experiments using SPR, it would take a few weeks. At A-Alpha Bio, the equivalent amount of affinity data would cost you between $1 and $500 million if done at a CRO. We do a million measurements at a time. That math illustrates the fundamental constraint in the industry. Despite increasing awareness that this volume of data is transformative for understanding biology, no one is going to pay $100 million for a weeks-long experiment.
So the constraint we’re trying to solve is making these data—otherwise far too expensive and too hard to generate—easier, more affordable, and more economical.
David Brühlmann [00:08:40]:
That's exciting, and that's definitely the way to go—to be able to screen a lot more and then find, quote-unquote, “needles in the haystack,” but for a much smaller, modest budget. If we just look at the general picture—because drug discovery has been done for decades—how do companies do this traditionally? What are the traditional workflows and methods? Let’s start with the basics.
Troy Lionberger [00:09:06]:
The branch of therapeutic discovery that I come from is called in vivo discovery. In in vivo discovery, you are typically relying on an animal model whose competent immune system is ultimately responding to the presence of an antigen that’s presented, raising an immune response against that antigen. On the discovery side, scientists will access those antibody-producing cells, identify the ones producing an antibody very specific to your disease target, and then sequence those antibodies to move forward in developing them into a drug.
There’s a complementary approach called in vitro discovery, where you use what are literally called panning methods. You can imagine gold miners panning for gold, which gives you an appreciation for the basic philosophy behind current discovery: needles in a haystack, mining for gold. In phage panning, you use bacteriophage to express a version of your therapeutic and access very large diversities—many different combinations of molecules. You expose these to your therapeutic targets, find those that bind, sequence them, and move them forward in the development process.
David Brühlmann [00:10:20]:
I imagine there are a lot of advantages to this traditional approach. Can you highlight what those advantages are, and also what the limitations are?
Troy Lionberger [00:10:27]:
The advantages of the in vivo approaches using animals are that you're taking advantage of really one of the world's most sophisticated ways of generating diverse sequences of antibodies, which is a competent immune system. To date, while there is promise in AI, AI has not been able to generate the diversity of functional antibodies that a competent immune system can. That is the promise for the future of in silico methods. But to date, hands down, one of the finest ways of generating a diverse antibody response is using an immune system.
The advantages are the diversity. The disadvantages are that, in many cases, you're not able to get human antibodies because it would be unethical to immunize human beings for the purpose of generating antibodies for therapeutics. So we’re limited to animal models that produce antibodies that then have to be further engineered to be compatible with human biology. We have developed humanized animal models to solve that problem, but these are expensive and not commonplace. That is the challenge there.
On the in vitro side, using phage panning, it's much faster. The downside is there are often biophysical characterization issues with those molecules. For example, we’re phage panning at room temperature, but antibodies have to survive body temperature. If they melt or denature at body temperature, that's a problem. So there are other liabilities with the in vitro technologies.
David Brühlmann [00:11:58]:
With the new technologies advancing very rapidly, what is the picture you're seeing? Are we going to have a side-by-side approach, or eventually will AI, machine learning, and so on take over?
Troy Lionberger [00:12:11]:
It's definitely top of mind for me personally, and I should be upfront and say this is the first time I've worked on the machine learning and AI side of the industry. I'm definitely new to the game. So with that caveat, I’ll just say I’ve mentioned in vivo, in vitro, and now, as you mentioned, in silico approaches, which are now complementing the first two antibody discovery approaches.
De novo antibody design is the name of the field that is essentially trying to predict sequences of antibodies that will bind efficaciously to a therapeutic target. Right now, I see all of these as complementing one another. As I said, there are advantages to in vivo and in vitro technologies. In silico approaches often take the output of those approaches as inputs to their models. They’re absolutely interlinked today.
I think the promise of in silico methods is to eventually amass enough data that you can generalize these models so that you don’t actually need a wet bench. But I would argue the constraint is always—even if you didn’t need data to train your models, you will need data to validate your outputs. There’s no escaping the data cycle in this space.
There’s a lot of talk in AI about AI models taking the lead. I think there are advantages to de novo design in terms of epitopic accessibility and creating next-generation molecules. But as it stands today, I would describe them as complementary.
David Brühlmann [00:13:43]:
And I imagine that with AI and machine learning, you’ll be able to accelerate the workflows. It's not one or the other, as I’m hearing, but it’s definitely making certain steps of the process faster and more efficient.
Troy Lionberger [00:13:58]:
That's absolutely right. And I’ll give you a great example—an area that A-Alpha Bio is heavily involved in right now: optimizing antibodies to be lead candidate molecules for preclinical teams. Typically, after the discovery of an antibody, you want to optimize that candidate. That could involve making it more human so that it doesn’t interact negatively with the human immune system when injected into patients. It could involve improving developability, like reducing the propensity for aggregation or increasing how much you can produce from cells at scale. But you’re also optimizing affinities.
Historically, this has been a very complicated process. You’d focus on driving affinities to where you want them, then worry about secondary characteristics of molecules that may affect manufacturability downstream. After making those changes, you’d have to go back to ensure your affinity hasn’t veered off course. It’s a slow, recursive, iterative process that’s expensive and time-consuming at each phase, often ping-ponging back and forth through various parts of the value chain. This could take over a year of hard work and significant investment.
What we’re doing now, leveraging disruptive data generation to inform machine learning models in a bespoke way, is transformative. We can generate datasets that train models to predict higher-affinity antibodies while simultaneously optimizing other characteristics. For example, we can insert up to 21 mutations into an antibody and still achieve greater than 50% accuracy in maintaining higher affinity than the parental antibody. That shows the flexibility now possible—where previously you could only add two or three mutations to achieve desired characteristics.
We can now predict sequences that are more human, more developable, higher expressing, higher affinity, and more stable—all at the same time—in a three-month process. This condenses what used to take potentially years of effort into just a few months.
David Brühlmann [00:16:25]:
So traditionally, drug discovery would take years—like two years, three years? What is the benchmark?
Troy Lionberger [00:16:33]:
It really depends. Having been in this space, it depends on the approaches taken and the complexity of the target. But in terms of lead optimization, the part of the process I’m referring to, that could traditionally take one to three years, which we are now compressing into a three-month process.
David Brühlmann [00:16:54]:
Wow, that’s massive. Yeah, that’s a big change.
Troy Lionberger [00:16:56]:
Yeah. And after that three-month process, we’ve also done things that historically couldn’t be done. For example, we can drive the affinities of preclinical animal model antibodies to match human target affinities. That’s where it gets really exciting—asking how this changes the dynamics of the overall ecosystem if everyone’s projects could be accelerated through preclinical development. These molecules are essentially tailor-made for the experiments planned in preclinical studies.
David Brühlmann [00:17:30]:
At the end of the day, we need to find effective antibodies—the purpose of the whole drug discovery process is to find the most efficacious antibody. Now what I’m hearing is that we have technologies to accelerate the workflow. My question is: do these technologies also enable finding that very effective antibody, or do we need additional technologies on top of that?
Troy Lionberger [00:17:59]:
No, I would argue that the problem I just described is not just about making a faster, more efficient process. We are also hitting the target product profiles required for these therapeutics. There’s no sacrificing quality by accelerating the timeline, which is, I think, a rare example. In this three-month process, you’re actually starting to guarantee results—something I never thought would be possible with a service provider.
David Brühlmann [00:18:28]:
Can you lead us into how you’re achieving that? What is the technology used, and what are the major steps?
Troy Lionberger [00:18:34]:
At a high level, here’s how it works: we generate tens of thousands of mutations of a parental antibody. We randomly insert arbitrary point mutations, making mutants with one, two, or three mutations at a time. We then measure the affinity of those tens of thousands of antibodies against a panel of different targets—not just the human target, but maybe mouse and cynomolgus targets, as well as point mutations of targets or comparable family members. This helps avoid early readouts of nonspecific binding.
We gather data not only on binding affinity and cross-reactivity, but also specificity from each of those tens of thousands of molecules. We then use all that data to fine-tune AlphaBind, our computational platform. The AlphaBind model has been trained on close to a billion different affinity measurements generated by the company. Fine-tuning the model with data from a specific parental antibody trains it to predict mutations that can be introduced into the original molecule.
The machine learning picks up on synergistic and compensatory mutations that might not be obvious to the human eye but are clear in the data. As a result, we can be greater than 90% confident in generating antibodies with higher affinity, even with up to 15 point mutations.
In parallel, we use off-the-shelf developability models to downselect molecules. While optimizing affinity, we simultaneously evaluate expression, solubility, and melting temperature to ensure the molecules are manufacturable.
David Brühlmann [00:20:33]:
So you generate millions of affinity measurements in a week. Are you using yeast display and genomics to do that, or what’s the trick behind it?
Troy Lionberger [00:20:43]:
Yeah, let me go a bit under the hood. Our wet-lab technology is called AlphaSeq. It’s a yeast display system that takes advantage of yeast mating pathways in nature. In yeast genetics, the A and α (alpha) strains have mating receptors that engage to form a diploid cell containing the genetics of both parent cells.
What our co-founder and CEO, David Younger, proved while in David Baker’s lab is that there’s nothing extremely specific about these mating receptors. You can attach molecules of interest to the outside of these cells, and if those molecules have measurable affinity, they will increase the rate of diploid formation—or yeast mating.
Here’s how it works in practice: you have a culture with a library of A strains expressing an antibody library and α strains expressing targets of interest. Under optimized culture conditions, mating occurs. After some time, you sequence everything. The number of times you see a specific gene pair correlates with binding affinity.
The diploid cells contain the genetics of both “mom” and “dad,” so each readout provides a genomic barcode corresponding to a specific antibody–antigen pair. You can quantitatively relate that to affinity in molarity, which correlates well (about 0.85) with standard measurements like SPR (surface plasmon resonance) or BLI (biolayer interferometry).
The trick here is extracting a biophysical measurement from a genomic readout. Next-generation sequencing provides the scalable data we need, and we can reliably relate it to binding affinities for millions of molecules simultaneously.
David Brühlmann [00:23:18]:
You have a good correlation. I’m curious—what is the affinity range you’re able to measure?
Troy Lionberger [00:23:25]:
Great question. The affinity range we usually see—though we can tune our culture conditions to adjust sensitivity—is typically from hundreds of picomolar up to tens of micromolar. So it’s a very large dynamic range that covers the therapeutic range most people are interested in.
David Brühlmann [00:23:53]:
That wraps up part one of our conversation with Troy Lionberger on the data revolution in antibody discovery. We’ve explored the limitations of traditional methods and how A-Alpha Bio’s AlphaSeq platform is changing the game.
In part two, we’ll discover how machine learning transforms this massive dataset into predictive power. If you’re finding value in this episode, please leave us a review on Apple Podcasts or your favorite platform—it helps other scientists like you discover the show.
All right, smart scientists, that’s all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your preferred platform. By doing so, we can empower more scientists like you.
For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Troy Lionberger
Troy Lionberger serves as Chief Business Officer at A-Alpha Bio. He started his career as a research scientist after earning his PhD from the University of Michigan and completing postdoctoral training at UC Berkeley. During nearly a decade at Berkeley Lights, Troy held senior leadership positions spanning R&D, product strategy, and business development.
Before joining A-Alpha Bio, he was Chief Business Officer at Abbratech, where he guided the company from stealth into a partner-focused antibody discovery biotech. At A-Alpha Bio, Troy applies his strong technical background and experience scaling platform companies through strategic partnerships to help position the company as a key enabler in the industry.
Connect with Troy Lionberger on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
For generations, silkworm pupae were simply a byproduct of silk spinning. Today, the biotech spotlight is shifting to their dormant power: transforming these “waste” organisms into natural protein factories. Turns out, when silkworm pupae are harnessed as living bioreactors, they can produce complex recombinant proteins and vaccine antigens at a scale—and cost—that makes mammalian cell culture systems look cumbersome.
In this episode of the Smart Biotech Scientist Podcast, host David Brühlmann speaks with Masafumi Osawa, a global strategy leader at KAICO with an unconventional path into biotechnology. Originally trained in cultural anthropology, Masafumi’s career was shaped by hands-on experience in pharmaceutical business development across diverse markets.
Silkworms were once used purely for producing silk, and their pupae were often considered waste. Today, those same silkworm pupae have the potential to address major global health challenges and offer new modalities for vaccines and therapeutic proteins. If any researchers listening today are struggling with difficult protein expression—whether it's a VLP, membrane protein, or complex antigen—I would be very happy to explore how we can support your R&D.
David Brühlmann [00:00:36]:
Welcome back to Part Two with Masafumi Osawa from KAICO. In Part One, we explored how silkworm pupae function as natural bioreactors expressing complex proteins using a baculovirus expression system. Now we’re moving from platform science to product reality. KAICO isn’t just offering contract services—they’re developing injectable and oral vaccines for both human and animal health. We’ll examine their development pipeline, discuss the unique regulatory considerations when your bioreactor is alive, and explore where silkworm-based manufacturing fits into the future of biologics production. Let’s continue our conversation.
Let’s shift gear, Masa. Let’s focus on your product pipeline. What are the kind of molecules you're developing? How well advanced are you in these various molecules? I'm curious to see what you're working on right now.
Masafumi Osawa [00:02:50]:
Our pipeline is designed around a stepwise regulatory strategy, starting with segments that allow for rapid entry and progressing toward human applications after livestock validation. Our most advanced program is the PCV2 oral immunization product for pigs, registered in Vietnam as a functional feed additive. In real farm environments, it has shown performance comparable to injectable vaccines while substantially reducing labor and stress costs. This provides strong validation of oral antigen delivery using silkworm-derived proteins in livestock.
We are also developing oral vaccines for cats, including FPV (feline panleukopenia virus), FCV (feline calicivirus), and FHV-1 (feline herpesvirus type 1), and producing purified CPV (canine parvovirus) antigens for dogs. Our aim is to reduce vaccination stress in animals and offer alternatives to in-clinic injections, with implications for human health as well.
Our recombinant injectable human norovirus vaccine is preparing for Phase I clinical trials next summer under Japan’s AMED SCARDA program. This marks a major step toward establishing insect-based platforms in human pharmaceuticals. Beyond internal programs, we collaborate with partners to express complex proteins and antibodies, leveraging the unique capabilities of the silkworm system. Overall, our pipeline reflects a long-term progression from livestock to companion animals to human injectables and eventually oral medical vaccines for humans.
David Brühlmann [00:04:39]:
Oral vaccines are an exciting delivery approach because they reduce distress during administration. Are there specific antigen characteristics that work better—or worse—for oral vaccine applications, and where are the limits?
Masafumi Osawa [00:04:57]:
Silkworm-derived antigens offer significant advantages for both injectable and oral vaccines, although the reasons differ by modality. For injectable vaccines, the key strength lies in the ability to express antigens that are difficult or sometimes impossible to produce in other systems. This includes complex structural proteins and virus-like particles (VLPs), which often do not require mammalian-type N-linked glycosylation and therefore assemble particularly well in insect-based platforms.
The silkworm pupal environment provides a dense, multicellular physiological setting that naturally supports proper folding, multimerization, and high-yield expression. This is why we can obtain enough purified antigen from a single pupa to immunize several hundred pigs. When manufacturing injectable swine vaccines, this level of efficiency is extremely difficult to match with conventional cell culture systems.
For oral vaccines, the advantages are even more distinct. Previous plant-based oral vaccine approaches—such as rice or other edible crops—have struggled due to low expression levels or sharply increasing production costs at industrial scale. As a result, despite scientific interest, few plant-derived oral vaccines have reached commercial feasibility.
Silkworm pupae, however, function as naturally concentrated bioreactors, delivering expression levels far higher than those typically achieved in plants. At the same time, silkworms can be mass-produced at low cost, making them well suited for oral vaccine applications where dosage volumes are much larger than injectables. In our PCV2 oral program, for example, we formulate approximately 1.5 g per pig, accounting for variation in feed intake and ensuring sufficient mucosal exposure. By contrast, injectable formulations—with higher purity and potency—allow a single pupa to cover hundreds of animals.
Additionally, silkworm-based production avoids large bioreactors, extensive culture media, sterile water systems, and intensive cleaning operations. These factors significantly reduce manufacturing costs and environmental burden, giving our platform a strong economic advantage—particularly in vaccine applications where global accessibility and scalability are critical.
David Brühlmann [00:07:49]:
During the COVID pandemic, we heard over and over how important it is to be able to develop vaccines very quickly. I’m curious—using the silkworm platform, how fast can you develop a new vaccine? Is this comparable to, for instance, mRNA, or how does it differ?
Masafumi Osawa [00:08:08]:
As long as the DNA sequence is available, we can produce any kind of recombinant protein. That’s the first point. One example is that during COVID we also produced SARS-CoV-2 recombinant spike protein. It took just three months after the outbreak of COVID-19. I think that is one remarkable aspect of our platform.
David Brühlmann [00:08:32]:
That’s remarkable—very fast. And the advantage I see with your platform is that because you’re scaling out, it’s very easy to expand production rapidly and produce massive amounts of vaccines in a short time. On our podcast we usually focus on human medicine, so I’d like to take a quick deep dive into the animal side of things because that’s also very important. We often forget that there’s a huge market for animal health. What are the main regulatory differences between the two? Can you give us the two-minute version? Obviously, I'm sure there's a lot of more details, but what is the two minute version of the differences between human and animal health?
Masafumi Osawa [00:09:14]:
Regulatory pathways for silkworm-derived products differ significantly between human and animal health. Human vaccines follow globally harmonized standards, but because silkworm-derived antigens are unprecedented, we must work closely with Japan’s PMDA (Pharmaceuticals and Medical Devices Agency) to define raw material controls, GMP, CMC expectations, and quality control frameworks.
In the animal health sector, pathways differ by country. In Vietnam, our PCV2 product was registered as a functional feed additive rather than as a pharmaceutical, enabling rapid market entry. Companion animal vaccines follow pharmaceutical regulatory frameworks, but typically with shorter timelines than human vaccines. These differences allow us to pursue a staged development strategy—starting with faster, more accessible applications, generating real-world validation, and gradually advancing toward more tightly regulated markets.
David Brühlmann [00:10:18]:
Okay, so it’s country-dependent. And as I recall, there are also major differences between human health and animal health regulations?
Masafumi Osawa [00:10:27]:
Yes. However, for companion animals, regulatory standards are often harmonized through VICH (International Cooperation on Harmonisation of Technical Requirements for Registration of Veterinary Medicinal Products). For products like our PCV2 immune-enhancing feed additive, since there is no comparable product globally, the regulatory classification depends on how local authorities decide to position it.
David Brühlmann [00:10:55]:
Let’s look ahead. You’re still a relatively young company. What does the future hold? If we look at therapeutic areas, product types, or services, what are your next steps?
Masafumi Osawa [00:11:10]:
While vaccines represent our core focus, the silkworm system has broader therapeutic potential. Many antibodies and complex recombinant proteins are difficult to express in standard systems due to folding challenges or instability. Silkworm pupae, with their diverse cell types and chaperone-rich environment, offer an alternative solution for these difficult targets. Sustainability is another strong feature of silkworm biomanufacturing. Because the system requires minimal water, no bioreactors, and low energy inputs, the overall environmental footprint is significantly lower than conventional platforms. This opens possibilities for distributed manufacturing models where production can occur closer to the end user, including in emerging regions with limited infrastructure. In the long term, the flexibility of small-batch production means that silkworms may contribute to personalized biologics or rare disease therapeutics.
David Brühlmann [00:12:16]:
When do you think we’ll see the first biologic approved that was produced in silkworms? Do you have a sense of timing—five years, ten years?
Masafumi Osawa [00:12:27]:
That’s very difficult to answer because we are about to enter a Phase I clinical study next year—next summer, to be precise. I don’t know how many more years it will take, but it’s becoming very real. In the past, no one believed that a live silkworm body could be a source of APIs or vaccine antigens, but now it’s becoming a reality. Entering a Phase I clinical study means that products derived directly from silkworms are about to be administered to humans. So I cannot give a timeline, but this is a major step.
David Brühlmann [00:13:10]:
That’s a major milestone and shows that you’ve done the homework and achieved initial regulatory acceptance. Obviously, there’s still a lot ahead, but entering Phase I trials shows that regulatory bodies see the potential and trust the technology.
Masafumi Osawa [00:13:32]:
Yes. Regulatory authorities now accept our quality control strategy and how we manage consistency and safety, which opens the door to further pharmaceutical development opportunities.
David Brühlmann [00:13:45]:
Absolutely. You’ve demonstrated proof of concept, and once that foundation is laid, you can build on it. That’s wonderful. Before we wrap up, Masa, is there any burning question I haven’t asked that you’d like to share with our biotech community?
Masafumi Osawa [00:14:04]:
One thing I may not have mentioned is the production volume of a single silkworm pupa. Productivity is another strong advantage. One silkworm pupa, about 2 to 3 centimeters in size, can express 10 to 20 milligrams of norovirus virus-like particles (VLPs). After purification, this typically yields 1 to 2 milligrams per pupa, which is still a substantial amount. This is why scaling out is such a strong advantage of our platform. If we need 100 milligrams of product, we simply require 100 silkworm pupae. The total space needed is about the size of a laptop. Compared to large-scale manufacturing equipment, this is extremely compact, making it a key benefit of our platform.
David Brühlmann [00:15:09]:
As we wrap up, Masa, what is the most important takeaway from our conversation?
Masafumi Osawa [00:15:15]:
Silkworms were once used purely for silk production, and their pupae were often considered waste. Today, those same pupae have the potential to address major global health challenges and offer new modalities for vaccines and therapeutic proteins. If any researchers listening are struggling with difficult protein expression—whether VLPs, membrane proteins, or complex antigens—I would be very happy to explore how we can support your R&D.
David Brühlmann [00:15:49]:
This has been great, Masa. Thank you for sharing your work and for helping democratize life-saving therapies by pushing boundaries beyond what many people think is possible. Where can people connect with you?
Masafumi Osawa [00:16:08]:
Please feel free to connect with me on LinkedIn: Masafumi Osawa.
David Brühlmann [00:16:17]:
There you have it, Smart Biotech Scientists. Please reach out to Masa and learn more about the technology. Once again, thank you very much for being on the show today.
Masafumi Osawa [00:16:28]:
Thank you very much for having me, David. It was my pleasure.
David Brühlmann [00:16:33]:
Thank you for joining us for this deep dive into silkworm-based biomanufacturing with Masafumi Osawa. From ancient silk production to modern vaccine development, KAICO is proving that nature still has lessons to teach us about efficient bioprocessing. If this episode expanded your thinking about alternative expression platforms, please leave a review on Apple Podcasts or your favorite platform. Your feedback helps us bring you more cutting-edge biotech insights. Thank you so much for tuning in today and I'll see you next time.
All right, smart scientists, that's all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, Visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Masafumi Osawa
Masafumi Osawa brings more than a decade of experience in the pharmaceutical sector, with a strong focus on driving innovation to address global health needs. He is the Business Development Lead at KAICO Ltd., a Japan-based biotechnology start-up specializing in recombinant protein production using silkworms as biological reactors.
At KAICO, he leads strategic partnership development, represents the company at industry events and technical forums, and applies his strengths in market analysis, CRM, and communications to clearly articulate KAICO’s vision and promote its distinctive technologies, including oral vaccine platforms for both human and veterinary use.
Connect with Masafumi Osawa on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
For centuries, silkworms have spun threads that bound empires and launched markets. Today, the quiet revolution brewing at KAICO is transforming these creatures from textile icons into potent bioreactors, with the potential to rewrite the rules of recombinant protein production.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Masafumi Osawa. Trained in cultural anthropology—and shaped by frontline experiences in pharmaceutical business development—Masafumi now leads global strategy at KAICO.
His journey, from observing healthcare access disparities in Indonesia to championing silkworm-based biomanufacturing, brings a fresh perspective that’s bridging science, business, and public health in unexpected ways.
Right now we are working on developing a human norovirus vaccine which is about to enter into a Phase I clinical study next year. So our local authority also asked us how does KAICO manage the quality of the silkworm? So one answer is that we use SPF-grade parent brood (PB) from specialized cell culture facilities, strictly control diet quality, rearing conditions, environmental monitoring, and breeder documentation.
So we collaborate closely with the PMDA, Japan’s Pharmaceuticals and Medical Devices Agency, to establish the acceptance criteria and ensure alignment with pharmaceutical expectations in terms of the differences of each silkworm PB.
David Brühlmann [00:00:50]:
For over 4,000 years, silkworms have spun silk that would eventually connect civilizations and through ancient trade routes. But what if these creatures could do more than weave fabric? What if they could manufacture life-saving biologics? Today’s guest, Masafumi Osawa from KAICO, is pioneering exactly that transformation. His team has developed the silkworm–baculovirus protein expression platform that turns these living organisms into natural bioreactors. Join us as we explore how this technology is evolving from university research to GMP manufacturing reality and what it means for the future of protein production.
Welcome Masafumi, to the Smart Biotech Scientist. It's good to have you on today.
Masafumi Osawa [00:02:51]:
Thank you very much for having me today, David. I'm truly excited to be here.
David Brühlmann [00:02:56]:
Masafumi, share something that you believe about bioprocess development that most people disagree with.
Masafumi Osawa [00:03:04]:
Many people assume a living organism can't be a reliable or consistent biomanufacturing unit, especially when it comes to pharmaceutical-grade materials. But based on our work, I believe the opposite is true. A silkworm can be a highly powerful and surprisingly consistent bioreactor, capable of producing a wide range of recombinant proteins, including vaccine antigens. There has never been a human medicine produced directly from a live silkworm. That's precisely why people question the quality and safety. Yet our experience is showing that silkworm-derived proteins can meet modern pharmaceutical expectations. And with the right controls, this platform can create new possibilities for biomanufacturing.
David Brühlmann [00:03:53]:
I'm looking forward to our conversation, Masafumi, to unpack this and to understand how you produce pharmaceuticals in silkworms. Before we do that, let's talk about yourself. Draw us into your story—what sparked your interest in biotech, and what were some pivotal moments that led you to your current role?
Masafumi Osawa [00:04:15]:
Unlike most of the listeners of Smart Biotech Scientist, my academic background is actually in cultural anthropology, not molecular biology or biochemistry. So during my university studies, I conducted fieldwork on Indonesian society as part of my research. Indonesia is a country rich in cultural diversity, but through my study I also witnessed large disparities in access to healthcare, limited access to clean water, financial barriers to basic medicines, and gaps in essential health services. These experiences made me want to contribute to global health in a more structured way, and this led me to join Towa Pharmaceutical, a Japanese company specializing in orally disintegrating tablets. I began my career as a medical representative and later moved into international business development. I conducted market research in Taiwan and Mongolia, identified product–market fit, and supported regulatory strategies for each region. It was fulfilling to watch new products reach patients and see their real-world impact.
But during the COVID-19 pandemic, coincidentally around the time my child was born, I began reevaluating my career path. I appreciate the importance of generics, but I also realized that generics only exist because someone first innovates and pushes the boundaries of drug development. I felt drawn toward innovation, toward work that might genuinely shift the trajectory of public health. Around that time, JEPRO was strengthening its focus on domestic vaccine development. That was when I discovered KAICO and the silkworm–baculovirus protein expression platform. While my first reaction was a mixture of shock, fascination, and respect, the idea that a silkworm—a small and fragile creature domesticated for thousands of years purely for silk—could produce complex recombinant proteins normally requiring expensive bioreactors was astonishing. I remember thinking, if this organism can produce such complex molecules, it could change the way we address disease.
However, when I entered KAICO, I faced an immediate challenge. I had only worked with small-molecule drugs and needed to learn the fundamentals of proteins, expression systems, and the differences between manufacturing platforms. Thankfully, with 90% of KAICO's employees coming from technical backgrounds, I was surrounded by researchers who generously supported my learning. This environment helped me rapidly bridge the gap, and interestingly, my anthropology background became a strength rather than a mismatch. Understanding how different societies collaborate, how decisions are made in different cultural contexts, and how technologies are adapted across regions became extremely valuable.
KAICO now works actively with international partners in Vietnam, Thailand, and Europe, and my ability to navigate cross-cultural communication has become central to my role. And today, as Business Development Lead, I introduce KAICO's platform globally, support partners working with complex protein targets, promote our first immune-enhancing feed additive product for pigs, and build co-development alliances not only for vaccines, but also for broader protein-based R&D programs. Looking back, joining KAICO was a natural extension of my original interest—connecting people, bridging cultures, and contributing to public health, this time through biotechnology.
David Brühlmann [00:08:11]:
I love listening and discovering your story, and I love seeing that it's not linear. You started at one end and now you ended up in biotech. How fascinating is that? And also what resonated with me is when you were saying you had a non-biotech background, but actually this very experience from your studies is a huge advantage. I love that. So tell us a bit more about how KAICO started as a university spin-off and what the vision is behind your silkworm platform.
Masafumi Osawa [00:08:45]:
Okay, so KAICO was founded at Kyushu University, one of Japan's leading institutions for entomology, with over 100 years of history and more than 450 unique silkworm strains. Our CEO, Mr. Yamato, encountered the silkworm–baculovirus expression system while studying in an MBA program. He was searching for dormant academic technologies with commercial potential, and when he discovered this platform, he recognized its significance immediately. Together with Professor Kusakabe, the principal scientist behind the system, they founded KAICO in 2018.
The company initially focused on two goals: producing recombinant research reagents derived from silkworms and collaborating with pharmaceutical companies to develop recombinant vaccine antigens and APIs. But beyond those objectives was a broader vision. If silkworm pupae could reliably express complex proteins at high yield, they could transform the landscape of biologics manufacturing. Making that vision a reality became KAICO's mission—changing the world with silkworms.
David Brühlmann [00:10:09]:
So let's look at the silkworm more specifically. How do you inject a recombinant baculovirus into a silkworm, and how do you quote-unquote culture your silkworms? Do you just let them grow and eat mulberry leaves, or are they swimming in a bioreactor, or how does that work?
Masafumi Osawa [00:10:28]:
Let me walk you through the basics. So the silkworm system works through a straightforward but powerful mechanism. First, we design the DNA sequence for the protein of interest. This sequence is inserted into a baculovirus vector that infects only silkworms. Once the recombinant virus is ready, we inject a small amount into the silkworm pupa. Over the next four to five days, the virus spreads throughout the pupa, infecting its cells. Each infected cell begins producing the target protein based on the inserted gene.
During the metamorphosis stage, the pupa becomes a highly active biological environment with diverse cell types, abundant molecular chaperones, and physiological conditions that support the correct folding and assembly of complex proteins. So practically speaking, the entire pupa functions as a compact, pupal cell–contained bioreactor with extremely high cellular density.
David Brühlmann [00:11:36]:
And these worms, where do you keep them? Are they in a container or where do they live?
Masafumi Osawa [00:11:44]:
So we purchase all the silkworm cocoons from local farmers or certified manufacturers. Inside each cocoon there is a pupa. We first cut open the cocoon, remove the pupae, and place them in containers, which are then stored in a refrigerator. In the refrigerator, they go into hibernation, and we can keep them for up to one month, or sometimes up to two months, before injecting the baculovirus.
David Brühlmann [00:12:14]:
And how long does the quote-unquote production process last? Is this a few days or weeks until you harvest?
Masafumi Osawa [00:12:22]:
So after inoculating the baculovirus, it takes just four to five days until the target protein is fully expressed inside the body of the pupa. After that, we purify the protein to obtain the reagent. So overall, it can take one to two months if we already have the right construct for the target protein.
David Brühlmann [00:12:41]:
And what I heard is that since the virus infects the entire worm, all different cells express your protein of interest. Is that correct?
Masafumi Osawa [00:12:51]:
Yes, you're correct.
David Brühlmann [00:12:52]:
Okay, let's compare this now to more traditional platforms such as E. coli or mammalian cells, for instance, or conventional insect cell cultures. What are, I'd say, the key advantages of your system, or perhaps also some drawbacks versus the other systems?
Masafumi Osawa [00:13:10]:
So when comparing silkworms to other expression systems, several differences stand out. Compared to E. coli and yeast, silkworms offer more natural folding and more mammalian-like post-translational modifications. These characteristics are especially important for structural proteins and multi-subunit complexes.
Compared to insect cell lines like Sf9 or Hi5, silkworms often provide higher yields, better folding integrity, and dramatically simpler scale-out production. Insect cell lines require large bioreactors, expensive media, and extensive facility infrastructure. Silkworms require none of those. And compared to CHO cells, the gold standard for therapeutic production, silkworms avoid the need for costly media, large-scale tanks, and significant water consumption.
So silkworm-based production follows a fundamentally different philosophy. Instead of scaling up by building larger tanks, we scale out simply by increasing the number of pupae.
David Brühlmann [00:14:19]:
And I guess because you're scaling out and not up, you can much more quickly adapt to different demands, right? Because you're much more flexible. Now, something that comes to my mind—and also that resonates with your first statement about what you think is different from perhaps other people in our field—there are a lot of advantages to using a living organism, as you said. Definitely the cost is much lower. That's one of them. And the drawback, or potential drawback, that comes to my mind is how do you manage the variability? Because if you, for instance, have a CHO cell line, that's a clonal cell line, so it's always the same clone. But I guess in your system you have some genetic variability between one worm and another. How do you manage this?
Masafumi Osawa [00:15:08]:
Thank you very much. That's a very important question. Actually, right now we are working on developing a human norovirus vaccine, which is about to enter Phase I clinical study next year. So our local authority also asked us how does KAICO manage the quality of the silkworms. One answer is that we use SPF-grade parent brood (PB) from specialized sericulture facilities, strictly control diet quality, rearing conditions, environmental monitoring, and breeder documentation. We also collaborate closely with the PMDA, the Japanese Pharmaceuticals and Medical Devices Agency, to establish acceptance criteria and ensure alignment with pharmaceutical expectations in terms of variability among individual silkworm PB.
David Brühlmann [00:16:00]:
And by doing this you can manage the variability. So you can make sure that from one batch to another you get the same product at the end of the day. Because in biologics we say the process is the product. So I imagine that in your system this is true as well.
Masafumi Osawa [00:16:17]:
Yes, you're right.
David Brühlmann [00:16:18]:
Now, I've read on your website that you describe your silkworm pupae as equivalent to about 100 to about 1,000 milliliters of insect cell culture. Can you tell us a bit more about how you came up with these numbers, and what that means for the process economics? Does that mean that you can produce a lot more volume or more product on a smaller footprint?
Masafumi Osawa [00:16:42]:
So this number is not just an estimate; it comes from a published comparative study titled Comparison of recombinant protein expression in a baculovirus system in insect cells and silkworms. In that study, 45 different recombinant proteins were expressed in Sf9 cells, silkworm larvae, and silkworm pupae. When expression levels were normalized, the researchers found that a single pupa yields, on average, the equivalent recombinant protein amount produced by approximately 120 mL of Sf9 culture, with some proteins reaching much higher equivalencies. This is where the 100–1,000 mL per pupa framework originates.
From a production economics perspective, this has important implications. As you know, conventional recombinant protein production requires large bioreactors, sterilized media, and massive amounts of water, followed by extensive cleaning steps. These processes contribute significantly to environmental footprint and operating cost. In contrast, silkworm pupae function as self-contained biological culture vessels. They require no bioreactors, no large volumes of water, and no cleaning validation. The physiological environment is preassembled by nature, eliminating significant upstream costs.
For developers like us, this also means that scaling is far easier. Instead of scaling up by building larger tanks, which adds engineering risk, you simply scale out by increasing the number of pupae, just as I mentioned earlier. This reduces infrastructure burden and supports long-term cost efficiency. This advantage will make it easier to offer stable pricing and a consistent global supply.
David Brühlmann [00:18:48]:
Now I'm curious, Masa, how do you do the downstream processes? Because once you finish your production run, you have these worms that have expressed a certain amount of protein, and you mentioned that then you do the harvesting. So how does that work? And then how does the purification work? Is purification very close to a traditional purification process we see with E. coli, for instance, or with yeast? Or are there some major differences?
Masafumi Osawa [00:19:16]:
The downstream purification process is similar to conventional protein expression systems.
David Brühlmann [00:19:22]:
And how do you get the protein out of your worms? Is that similar to what you would do with E. coli, for instance? How do you quote-unquote harvest your worms?
Masafumi Osawa [00:19:31]:
So after the target protein is fully expressed inside the body of the pupa, we homogenize the whole pupa with buffer, then ultracentrifuge the extract and apply standard chromatography steps to purify the protein. So basically, the system is partly similar to the traditional approach.
David Brühlmann [00:19:54]:
Yeah, I see. And that's also where I imagine you start applying cleanroom and closed-process conditions to make sure that at the end of the day your product is sterile and safe to use. Correct?
Masafumi Osawa [00:20:08]:
Yes, you're correct.
David Brühlmann [00:20:09]:
I'd like to touch upon the quality side of things, because you mentioned glycosylation, which is an important part, especially for more complex molecules. Where do you see the limits of your platform versus CHO? Are there certain molecules that are too complex to produce in worms, or do you think you can produce pretty much any kind of molecule?
Masafumi Osawa [00:20:32]:
We can produce pretty much any kind of molecule. So far, one of our strongest technical advantages is our consistent expression success. Across more than 130 protein expression projects—many of them challenging targets—we have observed successful expression in every case. This includes membrane proteins, intrinsically disordered proteins, allergens, multi-subunit proteins, large virus-like particles, and even certain GPCRs. Many partners approach us after unsuccessful attempts in E. coli or mammalian systems. The silkworm pupal physiological environment provides favorable conditions that artificial bioreactors struggle to replicate.
David Brühlmann [00:21:23]:
As you're interacting now with health authorities, I imagine you have some very interesting conversations with them, as this is a novel host and a novel way to produce pharmaceuticals. What are the unique GMP and regulatory challenges you have encountered so far with your living organism?
Masafumi Osawa [00:21:43]:
So because silkworms are living organisms, GMP considerations focus heavily on raw material controls. This overlaps with what I mentioned earlier. We use SPF-grade parent brood, and there are dedicated facilities that supply only SPF-grade, pharmaceutical-grade silkworms. These facilities strictly control diet, rearing conditions, environmental monitoring, and breeder documentation. So how to monitor safety and quality at this level is something that makes our discussions with local authorities quite unique.
David Brühlmann [00:22:27]:
That's it for Part One. We have explored how KAICO emerged from academic research and how silkworm pupae function as remarkably efficient bioreactors. Next time, we'll dive into production economics, post-translational modifications, and KAICO's vaccine pipeline. If you are finding value in these conversations, please leave a review on Apple Podcasts or your preferred platform. It helps other biotech scientists like you discover these practical insights.
All right, smart scientists—that's all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Masafumi Osawa
Masafumi Osawa has over 10 years of experience in the pharmaceutical industry and is dedicated to advancing innovative solutions for global health challenges. He serves as Business Development Lead at KAICO Ltd., a Japanese biotechnology start-up that develops and produces recombinant proteins using silkworms as bioreactors.
In this role, he identifies and establishes strategic partnerships, represents the company at trade events and workshops, and leverages his expertise in market research, CRM, and public relations to effectively communicate KAICO’s vision and showcase its unique technologies, including oral vaccines for humans and animals.
Connect with Masafumi Osawa on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
Ever watched judges' faces light up during your pitch ? Neither had I – until that competition day when everything changed. The stakes were high.
Ten teams, seven minutes each, and most presenters drowning their innovations in data tsunamis while executives checked emails. And we all wonted the 5-figure prize money.
You're nodding because you've been there. That knot in your stomach before presenting? The fear that your brilliant science will get lost in translation? The voice whispering, "Just show the data and get off stage"? I've felt that too. It's like being fluent in a language nobody else in the room speaks.
When our turn came, we didn't start with methods or specifications. Instead, we told a story about frustrated scientists, failed batches, and patients waiting. The atmosphere shifted instantly. Phones went down. Questions became strategic. We won first prize not because our science was superior, but because our story made our impact unforgettable.
In the next ten minutes, I'll reveal exactly how we did it – and how you can do the same. Let's begin.
This concept is discussed in greater detail in the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.
Every compelling story follows a three-act structure that's been powerful since ancient times. This isn't just artistic tradition – it's how our brains naturally process information.
Let me show you this structure in action with perhaps the greatest product launch of all time: Steve Jobs unveiling the iPhone in 2007.
Jobs began with Act 1 – setting the stage: "This is the day I've been looking forward to for two and a half years." He established anticipation and context by reminding us of Apple's history of revolutionary products – the Macintosh that changed the computer industry and the iPod that transformed music.
Then came Act 2 – the problem. Jobs implied the problem existed in the fragmented, confusing world of separate devices that consumers struggled with. This created tension the audience wanted resolved.
For Act 3 – the resolution – Jobs delivered that unforgettable moment: "We are introducing three revolutionary products... a widescreen iPod with touch controls, a revolutionary mobile phone, and a breakthrough Internet communications device." He paused, repeated the list, then delivered the punchline: "Are you getting it? These are not three separate devices. This is one device. And we are calling it iPhone."
Notice what Jobs didn't do. He didn't start with technical specifications. He didn't begin with the development process. He created a narrative that built tension and then resolved it brilliantly.
For scientific presentations, this translates beautifully. Instead of diving into methods and technical details first, start with the human impact. Who benefits from your work and how? Then clearly define the problem or unmet need – the villain of your story. Finally, present your solution as the transformative hero, supported by focused, relevant data.
This structure works because it follows our brain's natural information-processing patterns. It creates tension that seeks resolution. It builds from context to solution, not solution to context. And it positions your data as support for a compelling narrative, not as the narrative itself.
Think about it – what's the last presentation that truly captivated you? I'd bet it followed this structure, perhaps unconsciously. The presenter likely started with why the work matters before explaining how it works.
Building on this three-act foundation, Donald Miller's StoryBrand framework provides a powerful seven-element structure that's particularly effective for scientific communication. I've been using this framework successfully for many years now in keynotes, pitches, technical presentations, and more – it consistently helps transform complex scientific concepts into compelling narratives that resonate with diverse audiences.
First, we have a Character – the patient, healthcare system, or company facing limitation. They encounter a Problem – the current technical, medical, or business limitation that's causing pain. Then they meet a Guide – your innovation or approach (not you personally). This Guide gives them a Plan – how your solution works (the science, simplified). The Guide then Calls them to Action – the decision or support you need. This helps them Avoid Failure – consequences of maintaining the status quo. And finally, it Ends in Success – a data-supported vision of improved outcomes.
Notice how this differs from conventional scientific presentations. Traditional presentations often position the researcher as the hero overcoming obstacles. But in effective storytelling, your audience is the hero. Your innovation is merely the guide helping them succeed. This subtle shift makes your presentation instantly more engaging because it centers their needs, not your accomplishments.
This isn't about manipulating your audience. It's about respecting how human minds process information. Even the most analytical brain responds better to structured narrative than to random data points.
Now, let's get practical. What about those high-stakes situations when you have just 3–5 minutes with decision-makers? This is where the Minimal Viable Pitch becomes essential.
This streamlined approach uses the same StoryBrand elements we just discussed, but boils everything down to the strict minimum. The goal is to be simple but not simplistic – it's a fine line.
You could go as extreme as just putting one sentence for each element:
Character: "Commercial manufacturing engineers struggle with batch failures costing $2M monthly."
Problem: "Current monitoring systems can't detect critical quality shifts until it's too late."
Guide: "Our real-time PAT platform uses novel spectroscopy to detect changes 4 hours earlier."
Plan: "Integration takes just four weeks with our plug-and-play system."
Call to Action: "Approve the $100K pilot in Plant 3 next quarter."
Failure Avoidance: "Without this, we'll continue losing 30% of batches to quality deviations."
Success: "With implementation, batch failures drop by 70%, saving $1.4M monthly."
The key principles here are starting with the end in mind – what decision do you need? One slide should equal one key message. Your data should support your narrative, not be your narrative. And technical details belong in appendix slides or follow-up materials.
This isn't about oversimplifying complex science. It's about prioritizing what matters most to your specific audience in your limited time slot.
I know what you're thinking. "But my topic is too complex for storytelling." Actually, more complex topics need stronger narratives, not weaker ones. Richard Feynman, Nobel laureate physicist, explained quantum mechanics through stories about spinning tops and everyday objects. He didn't simplify the science; he made it accessible.
Or perhaps you're thinking, "My boss expects technical presentations." That's a common challenge. The solution? Layer technical details within a narrative framework. Use appendix slides for deep dives after establishing relevance. Often, leadership appreciates this approach because it makes their decision-making process clearer.
Short on time? Start with just the opening two minutes – hook them first. Try this template: "Currently, [stakeholders] are struggling with [problem], costing [consequence]. Our [solution] addresses this by [approach], resulting in [benefit]." That opener alone can transform how your audience receives everything that follows.
How do you know if your scientific storytelling is working? Look for engaged body language during presentations. Notice if questions focus on implications and next steps rather than basic clarifications. Pay attention to whether people accurately relay your key points to others. And track if you're invited to present to broader audiences.
But the real measure is simple: did you move your audience to action?
Remember, brilliant science that no one understands remains just unrealized potential. Your ideas deserve better. By structuring them as compelling stories, you're not compromising scientific integrity – you're ensuring your science has the impact it deserves.
You're staring at your laptop screen, aren't you? Rehearsing that upcoming presentation in your mind, wondering if your slides have too much data or too little context. Maybe you're thinking, "I just need to show my results – that's what matters."
I understand completely. When you're racing against deadlines, mastering storytelling feels like one more impossible task on your already overflowing plate.
But remember what science teaches us: when we present information as a story, our audience's brains release dopamine that improves focus and memory. They produce oxytocin that builds trust and connection. They experience endorphins that create positive associations with your ideas.
This isn't just presentation theory — it's neuroscience. Your brain is literally wired to respond to stories. And so are the brains of every decision-maker, investor, and colleague you present to. You've spent years becoming an expert in your field. You've designed elegant experiments and solved complex problems. Telling your story effectively isn't betraying your science – it's ensuring it reaches the people who need it most.
Your research deserves more than a footnote in a journal. It deserves to change lives. And now you know exactly how to make that happen.
Need help with an upcoming presentation? Book a free 20-minute consultation. We'll help you get started crafting a compelling scientific story that resonates with your audience - whether it's for your next team update, executive briefing, or investor pitch. No obligation, just practical guidance to make your next presentation unforgettable.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
🧬 Stop second-guessing your CMC strategy. Our fast-track CMC roadmap assessment identifies critical gaps that could derail your timelines and gives you the clarity to build a submission package that regulators approve. Secure your assessment at https://stan.store/SmartBiotech/p/get-cmc-clarity-in-1-week--investor-ready
Ever stood in front of a room full of executives, your heart pounding, wondering why your brilliant science isn't connecting? You've spent months perfecting your data, yet their eyes are glazing over faster than cells in a bad freeze-thaw cycle.
I get it. You're thinking, "I'm a scientist, not a salesperson. My data should speak for itself." The frustration is real—you've dedicated your career to rigorous methods, not crafting stories. It feels almost wrong to "package" your science, like you're somehow betraying your training.
But here's the truth: the most brilliant bioprocess technology in the world changes nothing if nobody understands why it matters. In the next ten minutes, I'll show you exactly how I transformed a technical presentation into a compelling story that won first prize—without sacrificing scientific integrity. Your ideas deserve to be understood, not just documented. Let's begin.
This concept is discussed in greater detail in the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.
Picture this: A Zoom call with ten small boxes showing judges' faces, most with cameras off or looking down at other screens. We were one of the many teams to present that day, armed with a few slides filled with our most compelling data - carefully curated process optimization results, key analytical findings, and critical technical specifications that we knew would demonstrate the value of our innovation in our limited 7-minute window.
We had spent weeks preparing. Our science was solid. Our innovation had potential to monitor critical quality attributes in real time. But as we shared our screen, I could feel our opportunity slipping away. In the tiny thumbnails I could see, one judge was clearly typing emails. Another had that glazed-over expression that screams "I'm mentally somewhere else." Without body language cues or eye contact, we were losing them before we'd even begun.
That's when it hit me. These judges – a mix of executives and technical leaders – had to evaluate 10 complex projects in a few hours, all through the exhausting filter of video calls. They weren't specialists in our particular technology. How could we expect them to grasp our innovation's significance when we were just another set of slides on their screen?
Unlike most scientific presentations, we had purposefully chosen a different approach from the beginning. We started with WHY – sharing my experience from just a few weeks earlier when I was part of a troubleshooting team where our innovation would have completely changed the game. I painted the picture of frustrated scientists, failed batches, and a therapy that couldn't reach patients reliably – then showed how our technology bridged that gap.
The atmosphere in the room shifted instantly. Judges put down their phones. They leaned forward. Questions became strategic rather than merely clarifying. While other teams had equally strong technical solutions, we won because our story made our impact memorable and clear.
One judge later simply told us, "Your pitch nailed it." We hadn't changed our technology – we had changed how we communicated it.
This experience transformed how I approach scientific communication forever and it revealed a paradox many of us face as scientists.
As scientists, we spend years mastering technical knowledge – cell culture optimization, analytical methods, protein characterization. We become experts at designing experiments and interpreting data. But here's the painful truth: most of us receive almost no training in how to communicate that knowledge effectively.
Think about the last scientific presentation you sat through. Chances are it started with methods and technical details. The slides were probably packed with small font and excessive data. The presenter likely followed the same chronological structure as a scientific paper. And emotion? Storytelling? These elements were probably nowhere to be found.
This approach works fine in the lab. But step outside that environment, and it fails spectacularly. Why? Because decision-makers – whether they're executives, founders, or investors – have limited time and varied technical backgrounds. Their brains, like all human brains, are wired for stories, not data dumps.
Even at scientific conferences, have you noticed how you remember the presentations with clear narratives while forgetting those that were technically sound but lacked a compelling story? That's not coincidence – it's neuroscience.
Like many scientists do you recoil at the idea of "selling." It feels inauthentic, perhaps even contrary to scientific principles. But here's the uncomfortable reality: you're already selling, whether you acknowledge it or not.
You sell when you pitch your project to leadership for approval. You sell when you ask peers to collaborate. You sell when you present to investors for funding. You sell to regulatory bodies for approvals. And ultimately, you sell to patients and healthcare providers who need to adopt your innovation.
Simon Sinek captured this perfectly: "People don't buy WHAT you do; they buy WHY you do it."
There's a persistent myth in scientific circles that "good science speaks for itself." If that were true, the most funded science would always be the most technically sound. But is that what we observe? Often, the most funded science is well-communicated science – work whose importance is made crystal clear through effective storytelling.
Think of it this way: your brilliant bioprocess optimization means nothing if you can't secure the resources to develop it further. Your groundbreaking assay is worthless if regulators don't understand its value. Your life-saving therapy won't help patients if physicians can't grasp why they should prescribe it.
Let me share a fascinating experiment conducted by Rob Walker and Joshua Glenn. They purchased ordinary objects from thrift stores for around $1.25 each. Then, they added fictional stories to each object and sold them online. The result? These $1.25 items sold for an average of 6259% markup. Objects worth about $129 in total sold for nearly $8,000 – simply because they had stories attached to them.
Why does storytelling have such power? Neuroscience offers compelling answers.
When we experience suspense or cliff-hangers in a story, our brains release dopamine – a neurotransmitter that improves focus, motivation, and memory. When stories evoke empathy, our brains produce oxytocin – the "trust hormone" that builds human connection. And when stories include humor, our brains release endorphins – reducing stress and creating positive associations.
This isn't just psychological theory – it's biological reality. Our brains are literally hardwired to respond to stories.
The simple Pixar Story Spine format demonstrates how accessible storytelling can be:
"Once upon a time there was [blank]. Every day, [blank]. One day [blank]. Because of that, [blank]. Until finally [blank]."
Apply this to science, and suddenly complex information becomes digestible. Abstract concepts gain emotional resonance. Key points become memorable long after your presentation ends.
Consider a technical presentation about cell culture media optimization. You could start with methodologies and statistical analyses. Or you could begin with:
"Once upon a time, there was a promising therapy that couldn't be manufactured at commercial scale. Every day, batch failures threatened patient access. One day, we discovered a critical nutrient limitation. Because of that, we developed a new feed strategy. Until finally, we achieved consistent 95% batch success rates – meaning thousands more patients could receive treatment."
Same data. Completely different impact.
If you're feeling resistant to these ideas, you're not alone. Let's address the most common objections I hear from fellow scientists.
"Storytelling means sacrificing accuracy and detail." This assumes stories and data are mutually exclusive. They're not. Stories provide the framework into which technical details fit. Think of it this way: the story is the map, while data are the landmarks. Without the map, landmarks exist in isolation with no clear path between them.
"Emotion has no place in scientific communication." Research contradicts this directly. All decisions – even technical ones – have emotional components. We justify decisions rationally after making them emotionally. Even the most analytical mind responds to emotional engagement, often unconsciously.
"I'll lose credibility with my peers." This fear is particularly strong among scientists. But examine the most cited papers in your field. Chances are they tell compelling stories about why the research matters. Clear communication doesn't diminish credibility – it enhances it.
Before preparing your next presentation, ask yourself three questions:
Remember, you're not "dumbing down" science when you tell stories – you're making ideas accessible. You're not "selling out" – you're ensuring your science has impact.
Try this simple exercise: practice explaining your current project to a smart 12-year-old. If they understand why it matters, you've found your story. If they're confused, keep refining.
The most brilliant science never changes the world if it stays trapped in the lab. Your ideas deserve to be understood – and storytelling is how you make that happen.
And speaking of making your ideas understood – in our next episode, I'll share a practical framework you can apply immediately. We'll explore the three-act structure for scientific presentations and I'll give you a step-by-step template for what I call the Minimal Viable Pitch – perfect for those crucial 3-5 minute opportunities with decision-makers. You'll learn exactly how to transform your next presentation from data-heavy to decision-ready.
I know what you're thinking right now. "This all sounds great, but I've got assays running, deadlines looming, and a team meeting in thirty minutes. When am I supposed to learn storytelling on top of everything else?"
I hear you. The weight of scientific excellence already feels crushing some days.
But here's the truth: storytelling isn't an extra burden—it's a lifeline. It's the difference between your brilliant work gathering dust and changing lives. Between getting funded or forgotten. Between influencing decisions or being ignored.
You've already mastered complex cell cultures and protein characterization. You've decoded genomic and metabolic mysteries and optimized bioprocesses. Compared to that, storytelling is the easy part.
The world desperately needs your innovations. And you may need additional funding to keep your startup going. Patients are waiting. Don't let your breakthroughs stay trapped in technical jargon and dense slides. Your science deserves to be understood. Your ideas deserve to spread.
And you, brilliant scientist, already have everything you need to make that happen
Need help with an upcoming presentation? Book a free 20-minute consultation. We'll help you get started crafting a compelling scientific story that resonates with your audience.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
🧬 Stop second-guessing your CMC strategy. Our fast-track CMC roadmap assessment identifies critical gaps that could derail your timelines and gives you the clarity to build a submission package that regulators approve. Secure your assessment at https://stan.store/SmartBiotech/p/get-cmc-clarity-in-1-week--investor-ready
