Why do so many promising biotech ideas stall—long before they reach the clinic or marketplace? For many, the answer lies hidden in the earliest phase of bioprocess development: upstream processing. It’s where strain selection, media optimization, and culture conditions set the stage for everything that follows. Yet, the smallest missteps here can snowball into expensive roadblocks downstream.
Joining Smart Biotech Scientist Podcast host David Brühlmann is Sebastian Blum, a microbiologist with more than two decades of experience in the life sciences and currently Market Development Manager at Beckman Coulter Life Sciences.
There are a lot of bottlenecks in the early bioprocess development in the upstream processing. There is where you do all your strain development, trying to find the best media, setting up those initial culture conditions to get the best growth and productivity, and very often see things get stuck in these early upstream phases. And if you're not doing enough comprehensive screening and optimization right after the beginning, like with your strain selection or media development, you're often setting yourself up for suboptimal titers for product quality and those issues become way harder and more expensive to fix later on when you are further downstream or trying to scale up for example.
David Brühlmann [00:00:40]:
Welcome to The Smart Biotech Scientist. Are you struggling to choose the right screening approach for your early-stage bioprocess development? Wondering if you're creating bottlenecks that will haunt you downstream? Today, we're tackling the critical decisions that separate efficient process development from costly delays. I'm joined by Sebastian Blum, who is a microbiologist with over two decades in life sciences and Market Development Manager at Beckman Coulter Life Sciences. We're diving into high-throughput screening strategies that actually work. And thank you to Beckman Coulter Life Sciences for sponsoring today's episode. Welcome Sebastian. It's good to have you on today.
Sebastian Blum [00:02:36]:
Hey David. Fine now. Happy to hear. Thanks.
David Brühlmann [00:02:39]:
Sebastian, share something that you believe about bioprocess development that most people disagree with.
Sebastian Blum [00:02:46]:
Well, that's an interesting one. So I think maybe the James Bond question, or at least I call it the James Bond question, which often arises in the community. Is it stirred or is it shaken? And in my opinion, in the early screening phase in R&D of microbial cultivations, it's not about how the oxygen is introduced, but how much is introduced. So what is the kLa and what is the oxygen transfer rate? So in my view for this question, at that stage it's irrelevant whether it's stirred or shaken.
David Brühlmann [00:03:12]:
You have quite a long career already in bioprocess development. Take us into your story in the very early days. What drew you into this field and what were some interesting steps along your career path?
Sebastian Blum [00:03:27]:
Actually, I've been in the life science industry for over 25 years now. My background is in biology with a focus on microbiology. Back in, I think it was 2010, there was a small company in my network which really caught my eye and they had invented a micro-fermentation system and they were really focused on using it for bioprocess development across different markets. I was super hooked. So I started talking to potential customers, just asking what they thought about having a tool like that in their lab. And their feedback was quite positive. And that totally convinced me at that time and led me to join the m2p-labs team in 2011, which was then acquired by Beckman Coulter Life Sciences roughly 10 years later. At that point it wasn't an easy decision for me because I just had my first child and honestly I was looking for some stability at that time. But in the 14 years since I have been there, I've never regretted the choice a single day.
David Brühlmann [00:04:16]:
I'm curious Sebastian, because you work with a lot of different companies. Please paint us a picture of how certain biotech companies approach process development differently.
Sebastian Blum [00:04:27]:
Okay, that's maybe really interesting because when I look at biotech companies, they actually tackle process development quite differently and often it depends on who they are. What do I mean by this? For instance, if you've got smaller startups or academic spin-offs, they're usually all about speed and keeping costs down. So they might start with simpler solutions or lower-throughput methods, things like shake flasks, for example, just to quickly prove their concept. And it's all about getting that initial data very fast.
And then on the other end of the spectrum you have the big established pharmaceutical companies and they tend to invest heavily in really robust data-rich early-phase development. For example, they are using advanced automation high-throughput systems like the BioLector XT Microbioreactor right from the start. The main goal is normally to de-risk the process as much as possible, ensure it's scalable afterwards and build this super comprehensive understanding of all the critical process parameters.
Why are they doing this? Because they have got to meet stringent regulatory requirements down the line. And then of course you have the CDMOs, those contract development and manufacturing organizations. They really have to be super flexible, offering a whole spectrum of approaches, simply because they are serving so many different and diverse clients, each with their own unique needs and at different development stages. One common thread that I've seen among all these successful companies, regardless of their size, it's the commitment to generate good data and doing it early and often. Hope that answers your question.
David Brühlmann [00:05:55]:
Just following up on your answer because this is very insightful. What is the main difference between the small and the large companies? Is that mainly cost or is it also expertise or the ultimate strategy? What is your observation?
Sebastian Blum [00:06:10]:
I would say also costs very often, but sometimes you're underestimating the startups where strong investors are, for example, included and they want to push the startups as quickly to the market as possible, and then all of a sudden they also have quite a high budget. But yes, of course startups normally have less budget and fewer resources compared to big pharmaceutical companies, for example.
David Brühlmann [00:06:33]:
Among the projects you are involved in, where are the most common bottlenecks or challenges?
Sebastian Blum [00:06:41]:
I see that there are a lot of bottlenecks in the early bioprocess development. In the upstream processing there is where you do all your strain development, trying to find the best media, setting up those initial culture conditions to get the best growth and productivity, and very often see things get stuck in these early upstream phases. And if you're not doing enough comprehensive screening and optimization right after the beginning, like with your strain selection or media development, you're often setting yourself up for suboptimal titers for product quality and those issues become way harder and more expensive to fix later on when you are further downstream or trying to scale up, for example.
David Brühlmann [00:07:19]:
This is an excellent point. Smart biotech scientists, please listen to this again. I'm going to repeat it. You need to focus on it early on because if you make mistakes early on, these can become very expensive. So thank you very much for saying this, Sebastian, because this is also a message I try to repeat as much as I can. And very important advice. If you look now at early-stage bioprocess development, what are the most common misconceptions you come across working with a diverse group of people?
Sebastian Blum [00:07:49]:
I think most scientists are aware of the particular importance of the early screening phase, so I wouldn't call it a misconception. However, I more often see scientists screening in, for example, batch mode, even though it's already foreseeable that the final process will run in a fed-batch mode, so including pH control. In my experience this can lead to an incorrect ranking, which then only becomes apparent during the scale-up process. This result can lead to a significant loss of time, as you essentially have to start all over again afterwards. I mean, this can be accounted for in project planning, but why not directly screen in the mode in which the final process will ultimately run afterwards?
David Brühlmann [00:08:27]:
Yes, this is important to know what will be the system your process will be running in. Or in other words, start with the end in mind. This is important. And then work everything backwards, right?
Sebastian Blum [00:08:38]:
Yeah, exactly.
David Brühlmann [00:08:39]:
Now, early-stage development can be quite overwhelming if you have never done it before, especially if you're launching your own startup. You have a lot of things to look after. And now, since there are so many different bioreactor systems, how should a founder go about—or even say a more advanced biotech company? Because you have very different systems. So perhaps let's start like this. Sebastian, can you paint us a picture of the various small-scale systems that are available and perhaps just give us the two-minute version of when you should work with one or the other system?
Sebastian Blum [00:09:17]:
I would say when scientists are looking at bioreactor systems today, it's like they are choosing from a pretty clear spectrum. And I would say each option has its own trade-offs. You can start, for example, with the shake flasks. They are super cheap, easy to set up, and good for just rough initial screening and checking viability, for example. But that's the con. The process control is terrible. pH, DO, temperature, they are all over the place. You get limited real-time data. Reproducibility isn't great, not to talk about scalability. Plus, if you have a lot of samples, it's really labor-intensive, not to mention the cleaning and potential cross-contamination. So there are, of course, fields where you can use them, but you need to really check carefully when to use them.
Then, for example, if you look at benchtop bioreactors, 1-liter to 10-liter stirred-tank bioreactors, for example, the advantage is you get excellent process control. They are great for actual scalability studies and they are much more representative of large-scale production compared to, for example, the shake flask. And you can get really rich kinetic data from them. But downsides also exist: they are expensive per experiment and your throughput is normally low. You can only run a few in parallel, which takes a lot of lab space as well. And there is still a lot of manual work involved and they are just slower for extensive optimization. So these are, in my experience, ideal for process characterization and initial scale-up once you have already identified your key parameters.
Then finally, the high-throughput platforms. The advantages—the pros—are this is where you get massive parallelism. We're talking about dozens of experiments all in parallel. And you get advanced process control even at the microscale, with real-time non-invasive measurement for things like optical density, pH, dissolved oxygen, different fluorescent proteins. And they are reproducible and generate tons of rich data for DOE studies, for example, really speeding up early-stage optimization. But also there we have disadvantages. The initial capital investment is higher normally, and you really need good experimental design and data analysis skills to get the most out of them. So these are best for, in my opinion, rapid strain screening, media optimization, and doing really comprehensive process development in those early and mid-development phases.
The big takeaway is I think you've got to pick the system that truly aligns with your development stage, how much data resolution you need, your throughput requirements, and of course your budget. And often companies will use a combination of these systems in a phased approach, for example.
David Brühlmann [00:11:39]:
Basically we have three different main types of bioreactors. Correct? We have the shake flasks or the spin tubes, which are very simple in handling, but you don't have a lot of control over them. We have the bioreactors where obviously the throughput is not very high, but you have a lot of advantages because you can control these bioreactors. And then in the middle we have what we call these high-throughput systems where you can do a lot of screening and you can do, to a certain extent, also some process control definitely.
Sebastian Blum [00:12:13]:
So the BioLector XT Microbioreactor really stands out quite a bit, both for how easy it is to use and the quality of data it gives you, especially compared to traditional methods and even some other automated systems out there. So usability-wise, it uses a microtiter plate format, which is well known and easy for high-throughput work, and setting up an experiment in the BioLector XT Microbioreactor software is intuitive and allows the user to monitor in real time and visualize all your data. Plus, it has this non-invasive optical measurement technology, meaning you don't need to have to put probes into individual wells. That just makes handling so much simpler, reduces contamination risks, and also lets you run it continuously unattended. It's a big advantage. It cuts down on manual labor massively compared to shake flasks or even a lot of benchtop fermenter systems.
Data quality-wise, it gives you real-time online measurement for critical parameters like biomass, which is measured via scattered light, pH, dissolved oxygen, and fluorescence sensors, for instance for GFP expression. And it does this with a time difference of just a few seconds. So unlike systems that just take occasional offline samples, the BioLector XT Microbioreactor captures all those dynamic changes, giving a much richer kinetic profile of how your microbes are behaving under different conditions.
David Brühlmann [00:13:24]:
How does the BioLector XT Microbioreactor differ from, let's say, an ambr® 15 or ambr® 250 plate? I imagine a lot of you listening have worked with either one of the systems. Can you just tell us what are the different volumes, how many conditions you could typically test, and what are some main differences or also some commonalities between the three systems?
Sebastian Blum [00:13:45]:
The BioLector XT Microbioreactor system is using working volumes from 800 microliters up to 2.4 mL in a 48-well format. The 48 wells are batch fermentation. If you want to go for fed-batch fermentation with feeding, it's 32 experiments on one single plate, because we need 16 reservoirs in this plate for carbon source and for adjusting the pH. So compared to ambr® systems, 250 milliliters, there's of course a difference.
We are measuring the pH with so-called optodes, optical sensors in each of the wells individually. By this, we also don't need to take any samples, for example, for biomass measurement, which we also try to avoid, of course, because the volume is not that high and that can also be a disadvantage. If, for example, a higher volume is needed, then I would say the ambr® system with 250 mL offers much more possibilities than an 800- or 1,500-microliter reaction volume.
I would say some of our customers are using, for example, the BioLector XT Microbioreactor also to reduce or de-risk their ambr® 250 runs. That means they are using both systems in combination just to get a better-selected choice into the maybe more expensive ambr® runs and to de-risk their experiments.
David Brühlmann [00:14:56]:
Can you run perfusion at all in the BioLector XT Microbioreactor?
Sebastian Blum [00:15:02]:
No, that is not possible. We cannot run a perfusion in the BioLector XT Microbioreactor.
David Brühlmann [00:15:06]:
Okay, understood. And what are the cell types that are best suited for the BioLector XT Microbioreactor?
Sebastian Blum [00:15:11]:
BioLector XT Microbioreactor is developed for microbial applications, so everything that needs high oxygen transfer or changes the pH quickly is made for BioLector XT Microbioreactor. Definitely do not use mammalian cells in the BioLector XT Microbioreactor. We tried this in the past because we were curious, but the results were quite poor. So we said, okay, that's not our target market here.
But the possible microorganisms that can be used range from strictly anaerobic applications to phototrophic organisms, fungi or filamentous fungi, even bacteria, yeast, E. coli, Pichia, Saccharomyces, Bacillus, you name it. So it's very flexible in terms of microorganism cultivation.
David Brühlmann [00:15:49]:
Can you give us some specific projects or cell types you have worked on recently?
Sebastian Blum [00:15:55]:
We are working with Pichia pastoris and methanol induction, which is very common in the industry as well and gives a big advantage. But also Lactobacillus, for example, in the food industry is often used with BioLector XT Microbioreactor and low-pH sensors that we are offering because of the low pH at which Lactobacillus is cultivated. So I would say these are some strong organisms that we are working with together with customers at the moment.
David Brühlmann [00:16:18]:
I'm just curious, because what I am hearing, Sebastian, is that the BioLector XT Microbioreactor excels at screening experiments because you have 48 wells you can use for media screening, for different culture conditions, and now giving different approaches, especially modeling approaches and so on. I mean, there are smarter ways to define your experiments and there are not-so-smart ways to define your experiments, let's say it like that in a very simple way. So I'm just curious, how do you guide companies also to make the best use of the BioLector XT Microbioreactor? Can you give us some examples what companies typically need, or are they very experienced and they know exactly what they need to do?
Sebastian Blum [00:17:00]:
Typically when they get in touch with BioLector XT Microbioreactor, we normally also do demonstrations of the system, and at that point in time we understand what customers need and the customer understands what the system can offer to them. So that helps a lot in the process of seeing whether the system gives an advantage to the customer at all.
But of course, there is intensive training because not everyone is very familiar with high-throughput screening systems or with the BioLector XT Microbioreactor specifically. And also the software is, of course, new. So there is normally a training of two to three days with application specialists involved, where we go through what customers want to see and also what kind of complexity there is.
Our system allows scripting, which means you are able to do more complex protocols. These complex protocols are done in LUA. There you need to have somebody on board who is able to script in LUA. Of course, that is not always the case, and we have also developed a user interface that helps you do that.
But very often we face, especially in the industry—less in academic areas—customers who are not able to do LUA scripting on their side. And therefore m2p-labs / Beckman Coulter Life Sciences is also offering this to be done by us, so we can get in touch with customers and talk about what they need to be able to program this LUA script especially for them. I would say from the biological part they are very experienced normally, but of course there needs to be training on how they can use their knowledge on the BioLector XT Microbioreactor most effectively.
David Brühlmann [00:18:24]:
That's it for part one. We've covered critical ground on process development strategies and early-stage decision-making. In part two, we'll continue exploring screening technologies and practical advice for maximizing your data quality. If you're finding value in these insights, please leave us a review on Apple Podcasts or your favorite platform. It helps other scientists discover practical advice like this. Thank you so much for tuning in, and I'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 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 Sebastian Blum
Sebastian Blum is Market Development Manager at Beckman Coulter Life Sciences, specializing in high-throughput bioprocess development. He has more than 25 years of experience in the life sciences industry and supports international R&D teams in addressing microbial process challenges efficiently.
Sebastian holds a degree in Biology with a focus on Microbiology from Heinrich Heine University Düsseldorf. Before joining Beckman Coulter Life Sciences, he gained hands-on experience in laboratory automation and bioprocess technologies at Hamilton Robotics GmbH and m2p-labs GmbH.
His work bridges scientific depth with practical, scalable bioprocess solutions.
Connect with Sebastian Blum 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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Bioprocess development has always been slowed down by legacy assumptions: big stainless-steel bioreactors, endless timelines, and the belief that complex therapeutics can only be produced in closed systems. But what if you could grow life-saving antibodies in a greenhouse instead?
In this episode of Smart Biotech Scientist Podcast, host David Brühlmann sits down with Professor Waranyoo Phoolcharoen, co-founder of Baiya Phytopharm, to explore how she’s transforming cutting-edge academic research into life-saving biopharmaceuticals made from plants.
During the COVID-19 pandemic, we conducted Phase 1 and Phase 2 clinical trials for COVID-19 vaccines produced using our technology. We received many questions from regulators. With scientific questions, we used scientific knowledge to explain and answer them. That is why we were approved for both Phase 1 and Phase 2 clinical trials.
David Brühlmann [00:00:26]:
Welcome back to our conversation about molecular farming. In Part 1, Waranyoo Phoolcharoen explained how plants become biofactories and why Southeast Asia’s biotech landscape shaped Baiya Phytopharm’s strategy.
Now we tackle deployment. How does their platform address everything from oncology to infectious diseases? What enabled Asia’s first plant-derived COVID-19 vaccine to reach clinical trials and achieve a production capacity of 5 million doses per month in Bangkok? And where is this technology heading next? Let’s find out.
Let’s look at the technology itself. Your platform is very interesting—a unique and different way to produce life-saving drugs and medicines in a more affordable way.
However, scientifically speaking, you are competing with legacy systems—microbial systems, yeast expression systems, and mammalian cell systems like CHO cells.
How does your platform compare to these, and what makes it a strong choice for certain molecules?
Waranyoo Phoolcharoen [00:02:44]:
In the pharmaceutical industry today, we all know that CHO cells (Chinese Hamster Ovary cells) are the standard platform. CHO cells and microbial systems are incredibly powerful and have enabled modern biologics manufacturing. However, they were built for environments with significant capital investment and stable infrastructure.
That is not always the reality in low- and middle-income countries.
The biggest limitations are cost and complexity. CHO cell manufacturing requires expensive facilities, complex equipment, and tightly controlled conditions. This makes it difficult to build and operate in settings where capital, supply chains, and utilities may be limited.
Another issue is scalability. CHO cell systems scale by building larger bioreactors, which increases cost very quickly. That model works well for high-margin products, but it is harder to apply for affordable vaccines or monoclonal antibodies.
If you consider microbial systems, they are fast and relatively inexpensive. However, they cannot produce complex biologics like monoclonal antibodies due to the lack of proper post-translational modifications.
Molecular farming takes a different approach. Plants are naturally scalable—you grow more biomass instead of building larger reactors. The infrastructure is simpler, the system is more robust, and the cost structure is better aligned with the needs of low- and middle-income countries.
Molecular farming is not replacing CHO or microbial systems. But for products where speed, cost, and access truly matter, it is often a better structural fit.
David Brühlmann [00:04:41]:
One additional advantage I see is that you have much more flexibility with your system in terms of demand. When scaling up a biologics process, you typically need to build a larger facility or install larger bioreactors. But if I understand correctly, Waranyoo, in your case—if demand changes quickly—you could simply plant more plants in a short time. Is that correct?
Waranyoo Phoolcharoen [00:05:06]:
Yes. Because we use plants, we can grow them continuously. The plants we grow do not need to be committed to a specific product immediately—we can decide later which product to produce.
To scale up, we simply grow more plants. This is very different from scaling up CHO cell manufacturing, where obtaining additional bioreactor capacity is a major investment. For us, increasing production for each product is relatively straightforward—grow more plants. That makes scaling easier.
At the beginning of our conversation, you mentioned the importance of accelerating process development. How does your platform speed up process development?
Waranyoo Phoolcharoen [00:05:45]:
When we talk about molecular farming—using plants to produce proteins—at our company, we do not create transgenic plants. Instead, we use what is called transient expression.
This means we do not need to generate stable cell lines. Once we have the gene sequence, we introduce it into the plant and begin producing the protein almost immediately. The plant acts as a living bioreactor, translating the genetic information very quickly.
Within three to four days, we can express the target protein. From gene sequence to harvest typically takes about one week—not months.
This is why our platform enables rapid product changes and accelerated development. The key advantages are speed and flexibility.
David Brühlmann [00:06:41]:
What is the regulatory perspective on this technology? Since it is quite a different production approach, have you received buy-in from regulators?
Waranyoo Phoolcharoen [00:06:52]:
The technology itself is not new. Most regulators, including the U.S. Food and Drug Administration (FDA), have been familiar with plant-based expression systems for more than 20 years. There may be additional questions because there are still relatively few products on the market using this platform. However, regulators understand the scientific principles.
Regardless of whether a product is produced in CHO cells, bacterial systems, or plant cells, regulators ultimately evaluate the product itself. You must demonstrate purity, safety, and efficacy. The standards are similar, even if the production platform differs.
We have evidence of regulatory acceptance through our COVID-19 vaccine program. During the COVID-19 pandemic, we conducted Phase 1 and Phase 2 clinical trials for vaccines produced using our technology.
We received many questions from regulators. But scientific questions can be addressed with scientific data. By providing strong evidence and explanations, we were approved to proceed with both Phase 1 and Phase 2 clinical trials.
David Brühlmann [00:07:59]:
Beyond the COVID-19 vaccine and other vaccines, what additional molecules are in your pipeline, and how advanced are these programs?
Waranyoo Phoolcharoen [00:08:08]:
Right now, we have several products in our R&D pipeline. We are focusing more on monoclonal antibodies. We use our platform to engineer molecules and try to develop “biobetters.”
For example, we are working on cancer antibodies. We have animal data showing that our plant-produced monoclonal antibodies can be used for cancer immunotherapy.
We are also engineering different glycan structures to study whether glycosylation patterns affect antibody half-life. That is one of our key research areas.
Beyond oncology, we are also working on infectious diseases. We are developing antibodies against RSV (Respiratory Syncytial Virus), using RSV antibodies as a model to improve half-life extension strategies.
Another important focus is rabies monoclonal antibodies. Rabies remains a significant problem in Thailand and Southeast Asia. In our region, equine-derived polyclonal antibodies are still commonly used.
What we are trying to demonstrate is that plant-produced monoclonal antibodies can perform effectively as an alternative. That program is currently in progress.
David Brühlmann [00:09:31]:
As you work on these programs—especially monoclonal antibodies and your oncology pipeline—what manufacturing, development, or regulatory challenges do you anticipate before reaching market approval?
Waranyoo Phoolcharoen [00:09:51]:
After we stopped the clinical trials for our COVID-19 vaccine, we restructured the company. We have subsidiary company. We still want to focus on developing drug and new product. But I would say we need to think about revenue. We need to think about how to sell the product. Then we structured the company that I would say now maybe our goal is not develop one drug from the beginning and we aim for marketing that product. Then with Baiya Phytopharm, we still focus on developing new drugs, but we focus on what we are good at. Let's say we're thinking about developing things like if we can have molecules, antibody that have longer half-life, then we might spin off this and try to do Phase 1 clinical trial if we can. And license that product to our partner or someone else. But we have two more subsidiary companies.
One subsidiary focuses on molecular farming services. Because we have a GMP facility, we realized that many groups are interested in producing proteins using plant-based systems but lack the infrastructure or technology. By offering our platform as a service, we can support external partners, test our system across different proteins, and generate revenue while strengthening our expertise.
Another subsidiary is BGF Pantry. BGF Pantry focuses on growth factors and other proteins that can be sold as raw materials for cosmetic applications. These products face lower regulatory barriers and can reach the market more quickly. They use the same plant-based platform but provide faster commercialization pathways.
This part of the business generates cash flow and helps maintain our team and manufacturing capabilities while our therapeutic programs continue to advance.
The key point is that we must develop multiple parts of the company simultaneously to push the platform forward. We still aim to develop high-value therapeutic antibodies, but the journey to market approval is long. That is why we need multiple business engines to move the company forward. Balancing these efforts allows us to continue advancing plant-based medicines.
David Brühlmann [00:12:30]:
I’d like to go back to the GMP facility. You built it with the goal of producing a COVID-19 vaccine there—if I’m correct, with a capacity of up to 5 million doses per month. Can you tell us what the biggest technological and regulatory challenges were, especially as you were preparing during a pandemic?
Waranyoo Phoolcharoen [00:12:50]:
When I think back to that time, during the COVID-19 vaccine development, many people asked how difficult the regulatory process was. Surprisingly, it was not as difficult as many expected.
We worked closely with the Thai Food and Drug Administration. They were knowledgeable and maintained strict quality standards and protocols. Most of their questions were scientific, and scientific questions can be answered with scientific data.
Looking back, I did not see major bottlenecks from a laboratory or regulatory perspective.
The more serious challenge was actually the supply chain. Many critical pieces of equipment, reagents, and consumables required for biomanufacturing—such as filters, chromatography resins, and single-use components—were not produced locally in Thailand. We had to import everything.
During the pandemic, when every country was trying to produce vaccines at the same time, these supply chains became a major constraint. Lead times increased significantly, shipments were delayed, and that became a major bottleneck for GMP production.
That experience taught us an important lesson: pandemic preparedness is not just about having the technology or a GMP facility. It requires building the entire ecosystem.
David Brühlmann [00:14:26]:
Yes, it’s often much more global and holistic than we think, right?
Waranyoo Phoolcharoen [00:14:31]:
Exactly. In normal situations, you never think about those aspects.
David Brühlmann [00:14:35]:
As we wrap up, what are some interesting technological innovations in molecular farming that you see emerging in the future?
Waranyoo Phoolcharoen [00:14:47]:
At this point, when people ask me about our next plan, I’m not always sure which specific product we will sell. In biotech, product direction can change quickly.
But for me, the most important goal is to prove that plants can be recognized as a mainstream production platform. I don’t want plant-based systems to be viewed merely as an alternative option.
When we think about producing a protein, we typically consider mammalian cells, insect cells, E. coli, and other microbial systems. I want plants to be part of that standard list of platform choices.
Our goal is to demonstrate that plants are a reliable and scalable platform for producing medicines.
David Brühlmann [00:15:35]:
This has been great, Waranyoo. With everything we covered today, what is the one key takeaway you want listeners to remember?
Waranyoo Phoolcharoen [00:15:44]:
My key takeaway is that manufacturing platform choice truly matters.
If you are in biotech and struggling with long development timelines or scale-up challenges, it may not always be the molecule—it may be the system you are using.
Molecular farming offers a different set of trade-offs: faster development and flexible scaling. It will not replace traditional microbial or mammalian systems, but it provides scientists with another practical option.
So don’t default to only one platform. Step back and consider alternatives. And if you would like to explore this approach, feel free to reach out to me.
David Brühlmann [00:16:31]:
That’s a very good point.
Waranyoo Phoolcharoen [00:16:32]:
We are very happy to collaborate and test your molecules on our platform.
David Brühlmann [00:16:39]:
Where can people reach you?
Waranyoo Phoolcharoen [00:16:42]:
You can visit our website at www.baiyaphytopharm.com. We are also active on LinkedIn.
David Brühlmann [00:16:51]:
There you have it, smart biotech scientists. I will put the link in the show notes. Please reach out to Waranyoo.
Waranyoo Phoolcharoen [00:16:56]:
Yes, we are very open to new collaborations and excited to explore new molecules using our platform.
David Brühlmann [00:17:06]:
Well, there's your offer. Please take advantage of it and reach out to Waranyoo and her team. And Waranyoo, thank you so much for sharing your new technology, what you're doing in Thailand. It has been a wonderful conversation. Thank you so much.
Waranyoo Phoolcharoen [00:17:21]:
Thank you.
David Brühlmann [00:17:23]:
Waranyoo’s journey—from molecular farming researcher to building pandemic-ready biomanufacturing capacity in Bangkok—reveals a powerful truth: plants can help democratize medicine production.
Weeks instead of months. Regional resilience instead of dependency. Accessible biologics instead of supply chain bottlenecks.
As her oncology pipeline advances, the next chapter of molecular farming is unfolding.
If this conversation changed how you think about biomanufacturing’s future, please leave us a review on Apple Podcasts or your preferred platform.
Thank you 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 joining us on your journey to bioprocess mastery. 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 Waranyoo Phoolcharoen
Waranyoo Phoolcharoen is a pioneer in molecular pharming and co-founder of Baiya Phytopharm, a biotechnology company developing plant-based platforms for rapid and scalable biopharmaceutical production.
As Chief Technology Officer and a professor at Chulalongkorn University’s Faculty of Pharmaceutical Sciences, she bridges academic research and real-world impact—using plant biotechnology to develop medicines for infectious diseases. Her work focuses on making biologics faster, more accessible, and more affordable while fostering a collaborative scientific culture and strengthening Thailand’s biotechnology ecosystem.
Connect with Waranyoo Phoolcharoen on LinkedIn.
You may also enjoy exploring these additional conversations from the podcast, featuring ideas, insights, and perspectives across biotechnology and innovation.
Episodes 141 - 142: How Microalgae Cuts Antibody Costs by 70% and Redefines Biomanufacturing with Muriel Bardor
Episodes 163 - 164: How Moss Enables Production of Unproducible Protein Therapeutics with Andreas Schaaf
Episodes 229 - 230: Cyanobacteria Biomanufacturing: Achieving Carbon-Neutral Production at Lower Cost Than Fermentation with Tim Corcoran
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
Bioprocess development has always been slowed down by legacy assumptions: big stainless-steel bioreactors, endless timelines, and the belief that complex therapeutics can only be produced in closed systems.
But what if you could grow life-saving antibodies in a greenhouse instead?
Today's guest on Smart Biotech Scientist Podcast with David Brühlmann is Waranyoo Phoolcharoen, co-founder of Baiya Phytopharm in Thailand, a pioneer in molecular farming—turning plants into agile biofactories for recombinant proteins, vaccines, and antibody therapeutics.
I came across a scientific paper on molecular farming, which is the idea of using plant to produce pharmaceutical proteins. Then I think at that time, I just realized from the paper that, oh, plants is not just sources of food or raw material, but we can use it as efficient biological system to produce so many things. They grow using sunlight, water, and time. They naturally produce complex molecules. So I began to question why are we relying on expensive control industrial system, big fermenter to make medicine if this plant can produce those kind of things as well?
David Brühlmann [00:00:35]:
What if you could grow life-saving antibodies in a greenhouse instead of stainless steel bioreactors? Professor Waranyoo Phoolcharoen spent years pioneering molecular farming, which means turning plants into biofactories for rabies treatment and COVID vaccines, before co-founding Baiya Phytopharm in Thailand. Today she reveals how her platform produces biopharmaceuticals in weeks instead of months and why Southeast Asia's biotech landscape offers unique advantages, and what pivotal moment pushed her from publishing research to building a clinical-stage company that's redefining pandemic preparedness. Welcome, Waranyoo Phoolcharoen, to the podcast. It's great to have you on today.
Waranyoo Phoolcharoen [00:02:36]:
Hello. Thank you very much.
David Brühlmann [00:02:38]:
Waranyoo, share something that you believe about bioprocess development that most people disagree with?
Waranyoo Phoolcharoen [00:02:46]:
If we think about bioprocess development, one thing that people disagree on is that bioprocess development does not have to take a long time. I think timing is quite important, which is something people often consider, especially for pharmaceutical bioprocess development. This idea likely stems from traditional technology. From my experience with molecular farming and bioprocess development, if you start thinking about the process early—considering manufacturer scalability and downstream constraints from day one—then that should shorten the timeline. I believe the real challenge is not that bioprocess development is inherently slow, but rather that we often treat it as though it must be slow.
David Brühlmann [00:03:37]:
I'm looking forward to this discussion today because you're making a very good point, and we need to find ways to accelerate process development. So take us back to the beginning; what sparked your passion for plant biotechnology, and what were some pivotal moments along your journey?
Waranyoo Phoolcharoen [00:03:57]:
That's a very interesting question. I did not plan to work in plant biotechnology at all. I completed my undergraduate and master's degrees in Thailand. I was interested in science in general; like other students, I just enjoyed learning, but I did not have a clear passion or long-term vision for my career. The real turning point came later during my PhD when I received a scholarship from the Thai government to pursue my PhD in plant biotechnology at Arizona State University. It was not necessarily because this was my dream; rather, it was a requirement of the scholarship.
At the same time, I knew that upon returning to Thailand, I would have to teach at the Faculty of Pharmaceutical Sciences due to this scholarship. I began asking myself the ethical question: what should I focus on to connect plant biotechnology with pharmaceutical science and medicine? With this question in mind, I began to read and search for more research publications. And I came across a scientific paper on molecular farming, which is the idea of using plant to produce pharmaceutical proteins. Then I think at that time, I just realized from the paper that, oh, plants is not just sources of food or raw material, but we can use it as efficient biological system to produce so many things. They grow using sunlight, water, and time. They naturally produce complex molecules.
So I began to question why are we relying on expensive control industrial system, big fermenter to make medicine if this plant can produce those kind of things as well? And at first it's just the field that I had to study, but over time I became more interested in when I saw the result. I saw that we can really make medicine more affordable and easy to bring it to people who don't have access. I mean, this part did not start with big dream, but during my PhD, then I used plant to produce antibody and vaccines for Ebola. And I saw that it really worked in animal model. And later I can see that people use this work to test in human. And I think that experience changed what I believe. It changed how I think because I feel like this is not just publication. It's not just in theory, but we can use it, the plant, to produce something to save people's lives. And I think that is the moment that I saw that this technology have potential in the real world.
David Brühlmann [00:06:31]:
Very exciting. And what I love about your story is that it’s driven by both curiosity and a sense of purpose—making therapies more accessible to more people. I think that's a very important topic, especially in your region. I'm eager to dive into that a bit further later in our conversation. At this point, I'm curious about your journey as you finished your PhD and postdoc. At one point, you had the idea or opportunity to co-found a company. Can you share what sparked this idea and what ultimately made you decide to take that step? Founding a company is quite a leap.
Waranyoo Phoolcharoen [00:07:12]:
Actually, I observed how biotech companies operate while I was in Arizona. I had the chance to work at a biotech company in San Diego, and it was my first exposure to that environment since we don’t have such companies in Thailand. However, I knew I had a scholarship that required me to return to be a professor at a university. After coming back to work at the university in Thailand, I followed what I believed was the right academic path. I worked as a professor, taught students, conducted research, and published papers. On paper, everything looked successful. I secured research funding, and many students contributed to scientific knowledge.
However, over time, I began to feel uncomfortable with this situation. It seemed like all my research ended the same way—with publications. We utilized a lot of research funding, which is public money, and given that Thailand is a low and middle-income country, we don't have abundant resources. This led me to question what tangible benefits our country derives from just publications. I couldn't find a satisfactory answer. Yes, I trained students, and I take pride in that—they went on to secure good jobs, but most of them, let's say none of them, continued in research. It was a simple reality: researchers in our country do not get paid well, and many students end up selling products online, earning more than they would in a laboratory. Accepting this was a hard reality.
At some point, I realized that I needed to do something differently. Publishing papers alone wasn’t sufficient. If this technology truly works, I thought, then I need to take action. Starting the company was an idea we had—it stemmed from wanting to do something different, not out of sheer confidence. When we co-founded Baiya Phytopharm in 2018, our initial idea was simply to commercialize something from our research. We didn't have a clear business plan or roadmap; we were looking to try something new. That decision led us to where we are today.
David Brühlmann [00:09:40]:
When I looked at your website, what stood out was the phrase, "We grow the plant of life." What does this actually mean? Also, can you share with our listeners what kind of plants you're using and how you utilize them to produce medicine?
Waranyoo Phoolcharoen [00:09:56]:
Because the plant of life is not just a metaphor for us, but it's the real way to describe what we do. At Baiya Phytopharm, we use plants as a living system to make medicines. Instead of producing drugs in stainless steel tanks, we can grow them, produce the medicine in the plant cells. And you can think of a plant as a natural factory. Plants already know how to grow quickly. They know how to make complex molecules. And what we do is just give the plant clear instructions, telling the plant how to make specific medical proteins, such as vaccines, monoclonal antibodies. And then for a short period of time, the plant can follow this instruction and produce medicine inside the leaf. And the plant can stay healthy and continue to grow. But during that time, it becomes a biofactory.
And the real advantages of this approach are speed and flexibility. We can go from a gene sequence to producing useful protein in a week, not years. And we don't have to build new factories or redesign the whole system for each new product. The same platform can quickly adapt to medicines for new diseases or new variants. Then we can design the whole process as one system, from how plants grow to how we harvest them, how we purify these medicines. This allows us to meet pharmaceutical quality standards while keeping the benefits of plants, like lower cost and easy scalability.
So when we say that we grow the plant of life, we mean exactly that. We are growing plants to make real medicines—medicines that can be produced faster, more affordably, and closer to the people who need them. The plant is not just a tool, but the foundation of a new way of making medicines.
David Brühlmann [00:11:58]:
When you say plants, this means that you are literally growing the whole plant, not as many people do plant cell cultures, just individual cells in a bioreactor?
Waranyoo Phoolcharoen [00:12:08]:
No, we grow the whole plant.
David Brühlmann [00:12:11]:
And what kind of plants do you usually use?
Waranyoo Phoolcharoen [00:12:14]:
The plant we use is one type of tobacco plant. The scientific name is Nicotiana benthamiana, and it is a species of tobacco that contains a lower amount of nicotine. It is a plant that most plant biologists use in the lab. They use it to study phenotypes and genotypes because it is quite susceptible to Agrobacterium transformation.
David Brühlmann [00:12:38]:
Now, before we dive a bit more into the technology and the science itself, as you were transitioning from a more academic role into an entrepreneurial role, what were some key mindset shifts you had to take in order to succeed in that role?
Waranyoo Phoolcharoen [00:12:55]:
I did not plan to change from scientist to entrepreneur at the beginning. I think I changed over time. And I think the biggest mindset shift is that we moved from just asking simple research questions—we didn’t want our work to end with publication. As professors, we know the pathway: getting grants, doing research, publishing, training students, and so on.
But as an entrepreneur, we have to ask different questions. It’s not just, “Oh, this is interesting and we want to do it.” The mindset comes from asking different questions: Who is going to pay for this? Does it make commercial sense? These are questions I never thought about when I was only a scientist.
I think the biggest shift comes from the questions we ask, because when you have the right question, it leads to the right answer.
David Brühlmann [00:13:56]:
Thinking about whether it makes commercial sense, the question I want to ask is: as you were thinking about this, what were the products that came into your mind?
Waranyoo Phoolcharoen [00:14:09]:
When we started the company, yes, we wanted to make medicines—we wanted to make drugs and vaccines. That’s the goal, because we believe it’s very important. And as a professor in the Faculty of Pharmaceutical Sciences, that’s what we want to do.
But when we started the company, we began with products that could reach the market quickly with lower regulatory barriers. For example, we started producing different types of growth factors that could be sold as raw materials for cosmetic products. The regulation is different compared to vaccines or drugs.
Actually, I learned this from our CEO, who has more experience from a business perspective than I do. What we do together is that I support the technology—what we can do scientifically—while she works on the market size, which products we can realistically bring to market and generate revenue from. The technology and the business side have to work together, and from that, we determine which products we are going to make.
David Brühlmann [00:15:19]:
Translating an innovation or technology can be quite a struggle. I’ve talked to a lot of scientists, and I’ve also been part of technology innovation initiatives. I’d say it’s not only in academia—even in larger corporations, scientists sometimes struggle with this. I would love to hear your perspective.
Based on your journey, what advice would you give to researchers who want to create impact-driven biotech companies, especially in a context like yours, where resources may be limited?
Waranyoo Phoolcharoen [00:15:50]:
I think the first thing I would say is that there are many ways to create impact from research. Building your own company is just one of them. Not every scientist needs to become an entrepreneur.
There are many pathways. You can file patents and license them to other companies, or you can transfer or sell your intellectual property. There are different ways to create impact.
But if you want to build your own company, I think the most important step is to be very honest with yourself. You need to understand what role you can perform well.
In my case, when I started the company, I didn’t have many choices because there was no one else who could take this research to commercialization. That’s why I felt I needed to do it myself. But I also needed to understand what I could do and what I could not do.
For example, I knew I could not be both CTO and CEO. Some scientists can manage both roles, but for me, management, finance, and marketing involve many skills that would take time to learn. That’s why I found a partner—someone who is strong in the areas where I am not. I think that is very important.
Another important lesson is timing. If you wait until you feel completely ready, you will probably never start. You have to begin when you feel unready. In a resource-constrained environment, perfect conditions never exist.
You will make mistakes. You will fail. But speed matters. Move fast, fail fast, and fail forward. Each failure gives you information and lessons learned. You cannot get that from planning alone.
Before I started the company, someone gave me very simple advice. He said, “Be brave.” At that time, it sounded simple, and I didn’t fully understand it. But along the journey, I realized how true it is. You will face many things you are afraid of—regulatory uncertainty, funding gaps, technical challenges, and many other issues.
Courage doesn’t mean you are not afraid. It means you move forward anyway. For me, one of the most important things was simply to start and learn from doing. Learning itself is already a form of impact.
Different countries and regions are different. You need to start, act, and learn from your own environment.
David Brühlmann [00:18:39]:
Yes, I love that. What I’m hearing are three things:
I would love to hear your perspective and also for you to tell our listeners more specifically about the landscape where you are building your company—in Thailand. You studied in the United States, and many of our listeners are from Europe and the U.S. They may not be very familiar with the scientific and technological landscape in Southeast Asia.
Could you paint a picture for us of what research looks like in Thailand, and how the pharmaceutical industry operates there and more broadly across Southeast Asia?
Waranyoo Phoolcharoen [00:19:27]:
I think Southeast Asia has an interesting and often misunderstood biotech landscape. It is not yet a global biotech hub like Boston or California, but it is certainly not an empty space.
What we see today is a region with strong scientific talent and growing infrastructure, but still relatively immature in terms of the translation ecosystem. In countries like Thailand, Singapore, Malaysia, and Indonesia, there are many excellent academic researchers, strong clinical talent, and increasing government investment in the life sciences.
However, the systems that connect discovery to commercialization—such as venture capital, experienced biotech operators, contract development and manufacturing organizations (CDMOs) at scale, and advanced laboratory know-how—are still developing. This gap creates both challenges and opportunities.
From the opportunity perspective, Southeast Asia has real unmet medical needs, especially for infectious diseases and biologics that are either too expensive or not prioritized by Western markets. This creates a strong case for locally developed, cost-effective biologics. We are also close to diverse patient populations and regional clinical sites, which is valuable for translational and clinical research.
Another advantage is the cost structure. Biotech is never cheap, but building R&D and manufacturing capacity here in Southeast Asia can be significantly more cost-efficient than in the U.S. or Europe. If you design the process carefully, this is especially important for technologies like molecular farming, where scalability and cost of goods sold matter from the beginning.
At the same time, the challenges are real. Capital is more limited here. Investors tend to be more risk-averse when it comes to deep-tech biotech. We also have fewer people with experience in taking biologics from the lab through clinical development and into regulatory approval.
As a founder, you are not just building a company—you are also helping to build the ecosystem around it. You need to develop talent, partnerships, regulatory pathways, and even public trust. Education is part of the work as well.
These are both challenges and opportunities.
David Brühlmann [00:22:20]:
Yes. And if I may add to that—something you said, Waranyoo, about cost-effectiveness being very interesting in Southeast Asia—I can confirm that. Recently, I’ve done a lot of research in the region regarding CDMO capabilities, and there are increasingly more excellent CDMOs in Singapore, Thailand, Malaysia, and even Vietnam.
There is a lot happening in Southeast Asia. I agree with what you’re saying—it is often misunderstood. It’s important to raise awareness that the region has a strong competitive advantage and is evolving very quickly.
Waranyoo Phoolcharoen [00:23:02]:
Yes, and I think now more people are starting to realize that. I see more companies from other regions looking toward Southeast Asia.
David Brühlmann [00:23:14]:
This wraps up Part 1 of our conversation about molecular farming. We’ve explored how Waranyoo transformed academic breakthroughs into Baiya Phytopharm’s commercial endeavor, how molecular farming addresses structural limitations of closed production systems, and what building biotech in Thailand reveals about emerging markets.
In Part 2, we’ll examine the platform’s plug-and-play capabilities, how they achieved Asia’s first plant-derived COVID-19 vaccine to enter clinical trials, and the development of a GMP manufacturing facility for pandemic response.
If this resonated with you, please leave a review on Apple Podcasts or your favorite platform and share it with a colleague. 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 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 tips, visit 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 Waranyoo Phoolcharoen
Waranyoo Phoolcharoen is a plant biotechnology scientist, entrepreneur, and professor at Chulalongkorn University. She co-founded Baiya Phytopharm in 2018 to translate molecular pharming research into scalable biopharmaceutical solutions, using plants as biofactories to produce medicines for infectious diseases such as rabies and COVID-19.
Dr. Phoolcharoen holds a B.Sc. in Biochemistry from Chulalongkorn University, an M.Sc. in Molecular Genetics and Genetic Engineering from Mahidol University, and a Ph.D. in Plant Biology from Arizona State University. Alongside her academic work, she leads innovation at Baiya, advancing plant-based pharmaceutical manufacturing while mentoring the next generation of scientists in Thailand.
Connect with Waranyoo Phoolcharoen on LinkedIn.
You may also enjoy exploring these additional conversations from the podcast, featuring ideas, insights, and perspectives across biotechnology and innovation.
Episodes 141 - 142: How Microalgae Cuts Antibody Costs by 70% and Redefines Biomanufacturing with Muriel Bardor
Episodes 163 - 164: How Moss Enables Production of Unproducible Protein Therapeutics with Andreas Schaaf
Episodes 229 - 230: Cyanobacteria Biomanufacturing: Achieving Carbon-Neutral Production at Lower Cost Than Fermentation with Tim Corcoran
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
Complex automation often arrives wrapped in hype, but the reality is more nuanced. Biotech teams wrestle with CMC development, data validation, and the balancing act between risk and innovation. Nobody wants to drown in complexity or bankroll the latest tech trend that solves nothing. So: what matters, what’s just noise, and how do you build systems that actually elevate the process?
This episode features Anthony Catacchio, CEO of Product Insight and veteran in new product development for medical devices, warehouse logistics, and bioprocess automation. Anthony Catacchio brings a practical, systems-minded lens—grounded not in technology for its own sake, but in designing solutions that fit real-world lab and manufacturing workflows.
You really need to find your problems when you're still at a whiteboard. Once you've developed all the software and done all this work, if your iterations are too slow, you just don’t learn these lessons until it’s too late. And the later it is in the process, the longer it takes to fix and the more financially painful it becomes.
By front-loading as much validation as possible and really pushing to create data — wherever that data comes from — it doesn’t really matter. We’ll always design the most appropriate experiment for the project. But you have to have that data. You have to be willing to try and fail.
David Brühlmann [00:00:38]:
Welcome back to our conversation about robotics and automation. In Part 1, Anthony Catacchio from Product Insight explained his phased approach to hardware development and why a clear problem definition prevents over-engineering solutions.
Now we confront the hard questions. Where does AI genuinely transform bioprocess automation beyond buzzwords? How do you validate functionality through minimal testable products without premature scaling? What does the discovery phase actually uncover about variable bioprocess conditions? And critically, when should early-stage biotech companies automate versus staying manual?
Let’s separate automation wisdom from expensive mistakes. Let’s assume you now have a problem that’s worth solving — one that could cost several hundred thousand dollars or even millions. What is your strategy to develop that solution? Do you follow a minimal viable product approach? Do you focus on prototyping? Do you leverage existing technologies? How do you approach it?
Anthony Catacchio [00:02:59]:
It depends massively. If we're looking at doing custom robotics, you have a really high-value problem and there's just nothing on the market that fundamentally works. And we need to make custom mechanical assemblies or custom software or whatever it is, generally speaking, and we have a fair amount of experience doing kind of those first-party tools. I have 500 locations that do this operation all day, every day. And so it makes sense for me to invest the money to build the right thing and to build it myself.
There's a fine line in terms of how big your organization has to be and how much work you need to be doing to do that kind of project. But if you're doing that and you're doing kind of a larger scale automation initiative across multiple sites. Generally speaking, the way we work and the way we try to run those projects and develop those technologies is to, again, do the upfront work with the system concept development and then do some kind of requirements validation. And this varies depending on what the product is. But like I talked about the system concept development, in a lot of ways, that's really just about trying to make sure we really understand the requirements, that we have them all. And we show those concepts to a bunch of different users, a bunch of different stakeholders, because again, their feedback feeds those requirements.
And when you show somebody a solution, you get a lot better information than asking them just a generic question. And so we go through that, then we'll often build what we call a tech demo and a usability demo is kind of how we think about them. And the goal there is to take how we plan to solve the hardest parts of the technology problem and essentially just make sure they're feasible. Again, it varies massively depending on what the program is because it's one of the ways we leverage our expertise and experience is to say which parts of this problem are actually hard. What technology or what mechanism or what operation, if it doesn't work the way we think it's going to work, means none of this works. So go find those 3 or 4 things that, yeah, this is a little weird.
We're pretty sure it's going to work, but if we're wrong, we can't get back from that. Those 3 or 4 things that kind of underpin your system concept and underpin your architecture, go and build and test those for real. But only that little piece. We don't need to build a sheet metal box. We know we can make a sheet metal box, but do we know this mechanism will work right with this? We're trying to detach and attach hoses that were actually designed for humans to do. Can we actually do that? And does the way that we're intending to do it actually work repeatedly? And we might need to prove that up front.
And then on the user side, usually we'll build a completely functional system from the user's perspective. That's a complete lie inside. There might even be a person standing behind a wall that's pretending to do the things that the automation will do. It's sort of like the caricature of the tech demo that a lot of startups do to raise money. Generally, we're doing it very transparently. We're telling the client, look, we have developed any of this stuff yet. We just want to make sure that when we do, it's going to work for users. It's going to accomplish the process. And you can do that in a couple of different ways. If you're doing things for a bioprocess thing and you're saying, well, we're going to put a robot here and it's going to move like this and it's going to be this kind of robot.
I can make a person behave like a robot, right? I can give a person the limitations that that robot will have and I can test the system with a bunch of humans because I don't have to develop anything. I can just tell them what their capabilities are and maybe I'll 3D print some stuff to make their hands less useful. Yeah, you gotta pick it up with something that looks like an end effector. We'll find a way to fake it so that we can actually validate that our solution will do what we think it's gonna do and that we've found all the requirements. And then from there, really our next goal is to move to what we call minimum testable product.
Minimum viable product works if you're developing software. If you're developing hardware and robotics, your product isn't gonna be viable. You shouldn't go all the way to viable before you get to testable. And what we mean by that, particularly if you're doing something that's going to go to higher volume production, hundreds or thousands of something, you want to build a version that very much isn't an engineered product, but works and will work in the real environment. Again, you're not going to test every requirement that way, but you want to develop something that can be tested, can be deployed in the same way that, and we use a lot of parallels to agile software development in our process where you want like continuous deployment and continuous improvement.
With hardware, you really can't just like launch a product and then revise it 7 times. That just doesn't work because you can't over-the-air update hardware. So you really don't want to rush to the end. You don't want to rush to minimum viable. You want to get to minimum testable and then do controlled pilot deployments and learn and iterate. And then from there, usually your next deployment is minimum viable, but again, still controlled in pilot settings.
So we do a lot, a lot of work in more controlled pilots because you really want the information you want the learning, you want the iteration, you want your development team to get that feedback, but you can't just launch a product in these spaces. It just doesn't work. Labs don't want to be your beta tester. So you better have real data before you deploy into production. And the only way to generate that and to really validate that your product works is to build that kind of handheld deployments and real testing into your development pipeline. You can't do it on the backend. You can't beta test with sellable product in this space and software loves that approach.
David Brühlmann [00:08:21]:
That’s a big challenge in bioprocessing. You need to enter the lab with a well-established product.
Anthony Catacchio [00:08:29]:
Right. And that's hard. And part of how we do that right is just by either simulating the environment and building our robot and running our robot through simulated environments and then kind of doing the inverse like I described with people where you're simulating the robot, you're simulating the product in the real environment.
You really need to do both. It's very easy to get trapped in, well, it works in the lab kind of mindset of always have a real product and simulate the environment is a real trap in product development that we see all the time where you get all the way to the end, but you never actually went out to where these things actually get used and tested it, either tested your robotics for real or brought the capabilities that your robotics bring through people or through whatever other means. You didn't test your process. You didn't test your system concepts in the real world. You didn't test your requirements in the real world. And so then when you go to launch or scale or whatever it is you're doing, often you've missed these core requirements that make these things actually work in the real world because there's just nothing like being in the real environment. You're always going to find things the first time you put a product into an environment. You just are. And you have to try to do that early and you kind of have to eat it on the development side. It's not something you can push onto your customers. It's, it's just not a risk anyone wants to take.
David Brühlmann [00:09:47]:
How do you identify these quote-unquote hidden, perhaps, requirements or underlying mechanisms? Because developing the equipment is one thing, that's already quite a challenge. But now in bioprocessing, especially in the upstream process, we work with living cells. So we have a lot of variability, we have a lot of things going on that are independent of the technology, but as we're combining the two, it can get very messy and like very quickly.
Anthony Catacchio [00:10:16]:
Yeah, I mean, a lot of ways that just comes down to your test planning. You have to do a lot of testing. And again, this comes back to our rush to not a minimum viable product, but a minimum testable product. How can we start producing data about what this looks like? Because particularly when you have a lot of variability like that, the only real way out is statistics. I need to know what your yield or your success rate or your failure rate, however you want to look at it.
I need to know what it is today without this product or without these process changes or this automation system applied. And then I need to build a way to simulate this automation system in a world that has all that variability. And so that's where you would potentially take people and put those people in a lab and have them work the way that the automated system will eventually work, because that way you will start to tease out, we can create some data here and we can see that when we do this the way we're talking about doing it in our automated workflow, oh, this kills yields like this. We're missing something here, but in a way that you can iterate very quickly and you're not overinvested before you get there. If you go all the way to everything works and everything's perfect and I've got a shippable product, it's really hard to make those big changes once you find those problems.
And a lot of time what ends up happening is you get kind of like go fever. They talk about in aerospace where you're so far down the line that you find a problem towards the end and no one really wants to fix it because the investors don't want to hear that part of our strategy is political too, to say you've got to find these problems up front or they're just going to get buried because no one wants to say you've got an architectural issue or a process issue 2 months before you launch after you've done all the hard engineering. You've got to find that stuff up front because otherwise you just don't get a chance to fix it. You're going to be wrong. You're always going to be wrong in this world.
Once you put a process into a real environment and you stop simulating the environment, you're going to find stuff that you didn't understand fully, or there's variability that just no one actually understands. That's always the fun one. The people, the operators on the line just compensate for that variability, but no one ever documents it, right? No one ever sees it. You've got people in the loop who just kind of make it work. Often when you put automation in, those people go away and you're like, hey, wait a minute, you were doing stuff that no one knew you were doing. So you really have to take out whatever expertise you think you're going to take out of the system and make sure it still works before you go all the way down the path of fully developing and deploying a product.
David Brühlmann [00:12:40]:
If we zoom out, the ultimate goal is to accelerate bioprocess development and make manufacturing more robust. From your perspective, how does this data-driven development approach accelerate development?
Anthony Catacchio [00:12:58]:
Yeah, it's a lot about what I was just talking about and the idea that you can make changes so quickly if you test early. You really need to find your problems when you're still at a whiteboard. Once you've developed all the software and you've done all this stuff, if your iterations are just too slow, you just don't learn this until it's too late. And the later it is in the process, the longer it takes to fix it, the more painful it is financially. And so by front-loading all of that validation as much as you can and really pushing to create data — and whatever that data, wherever it comes from, it doesn't really matter — we're always going to design whatever experiment is most appropriate for the project, but you have to have that data. You have to try to fail.
The “fail fast” term has gotten very polluted and broken because there's so much in the engineering culture that's sort of grown out of software development, and organizations just sort of expect everything to work the way that software works. And it just doesn't. I mean, the way you fail fast is by putting concepts in front of real users, by running trials where you have people instead of robots. Those are the places where you fail. You want to find those big glaring requirements that you missed. And the earlier you find them, the faster you can fix them.
You really want to — and that's again why we lean so hard on trying to validate our requirements early — because in a lot of ways that's the hard part and it's the only part that matters. If you have the wrong requirements up front, you can engineer the world's most beautiful solution, but it does the wrong thing. It doesn't solve the problem, so no one cares.
And so that's really our focus: making sure we're building the right thing and that we understand the broader sensitivity analysis. You want to make the right thing. You also don't want to try too hard. Those are the two things. Why do you want to know the requirements? You want to know which parts are really important, but you also want to know which parts really don't matter because you need to focus on the right aspects of a technology, on the right aspects of a process.
If you don't need a ton of precision somewhere, then don't build that precision. Don't go to the end of the earth refining exactly the placement of something or temperature control of something if it doesn't actually matter. And so that's really it. That's the key in hardware development as we see it: validating that you really understand the problem and that you understand the requirements of what an optimal solution looks like before you engineer that whole thing.
Sort of just assume that you're wrong upfront and continuously work to prove yourself right with real, statistically driven data. It's kind of a “go slow to go fast” approach. And people bristle at it sometimes — like, what do you mean you're going to spend two or three months just drawing pictures and having people pretend to be robots? It's like, yeah, those two or three months are incredibly valuable. Don't skip those. Don't pretend like you know the answers and just skip right to engineering. You will always regret it. And so that's really how we go fast.
David Brühlmann [00:15:47]:
Let's make this very practical. Is there perhaps one or two questions a biotech scientist could ask to quickly determine whether it's worthwhile doing a more in-depth study about a certain problem — whether to automate or not?
Anthony Catacchio [00:16:02]:
I mean, the biggest question is just how consistent of a process is it and how much of the work — you're a scientist, right? You're highly educated. You know what you're doing. How much of what you're doing on a day-to-day basis is you understanding and solving problems versus doing rote, repetitive tasks?
I am not an AI booster as a general statement. I think there are a lot of technologies that are really interesting going on right now with machine learning and deep learning, and a lot of those things have really great applications. If you know what you're doing and you feel like you're using your skills and your brain to do a task correctly — and that it doesn't work unless you understand what you're doing — chances are that's not a good candidate for automation.
If it's something that — yeah, I do this and when I do it, I'm thinking about what I'm going to make for dinner and what my plans for the weekend are going to be because my brain is off and I'm just moving my body in a way that gets the job done — those things are much, much better for automation.
So in a lot of ways, that's a good way to think about it: how hard do you have to try to do this? How much are you thinking while you're doing this? If you're thinking a lot, chances are automating it is not going to be great because it's a highly variable process and you probably can't ever figure out all the requirements of it.
If it's something that's just rote and repetitive and, man, if I didn't have to do this every day, I'd have another hour to work on problems that actually require my expertise — that's where we want to be from an automation perspective. We want to get that stuff off the plate of these highly skilled researchers because the goal is to get to some sort of treatment that works as fast as possible. That's essentially the goal of biologics development. You want to do as many experiments as you can, as fast as you can. And the more that we can enable researchers to do that, that's really where our value is.
David Brühlmann [00:17:51]:
Before we wrap up, Anthony, what burning question haven't I asked that you're eager to share with our biotech community?
Anthony Catacchio [00:17:59]:
I think one of the biggest things that is sort of on everybody's mind — and I alluded to it a little bit — is this idea of AI in research and how that will develop and where that fits.
I think it's really interesting. Again, there are a lot of really cool applications in robotics, and most of them, like I said, are really around machine vision more than anything else. From a pure research perspective, again, if you have to turn your brain on a lot and problem-solve, AI won't ever really do that — not in the form we have today.
But there are a lot of opportunities for things like data analysis, for things like predicting the outcome of a process, or those sorts of things. One of the things I think a lot of people don't understand about leveraging AI — particularly building first-party models — is the amount of data that you need to produce in order to be able to make those things meaningful.
And I think this will be one of the things we see in capital equipment development and in lab equipment development: building in methods of collecting much, much more data about the processes than we do today.
So I think that's going to be one of the real opportunities. It's not necessarily, “Oh, how do I automate this process?” It's, “How could I change this process so it produces more and better data?” I think that's going to be one of the big questions that biotech labs ask themselves more and more moving forward as everybody leans harder and harder in that direction.
It's going to be about producing data. And again, I am not an AI doomer. I don't think large language models are coming for scientists or for highly skilled labor in that way. But I do think that in order to get value out of those types of services and technologies, capital equipment in particular is going to need to change in a lot of ways to collect far more data than we do today in order to drive that value.
David Brühlmann [00:19:41]:
Excellent. This has been fantastic, Anthony. What is the most important takeaway from our conversation?
Anthony Catacchio [00:19:48]:
In my mind, the most important thing we think about in this world is that if you want to automate something, you need to look at the whole picture. That’s the biggest thing — any kind of automation is really about understanding and codifying a process. So you need to deeply understand the process and the environment to do an effective job. It’s not actually a technology problem. It’s a systems development and requirements development problem.
David Brühlmann [00:20:16]:
Excellent. Thank you so much, Anthony, for coming on the podcast. Where can people connect with you?
Anthony Catacchio [00:20:23]:
LinkedIn or through our website. They can always submit an inquiry there through www.productinsight.com.
David Brühlmann [00:20:29]:
There you have it, Smart Biotech Scientists. You’ll find the links in the show notes. Please reach out to Anthony. And Anthony, once again, thank you so much for being on the show.
Anthony Catacchio [00:20:38]:
Thanks, David. It was great.
David Brühlmann [00:20:39]:
Anthony’s framework reveals a fundamental truth about bioprocess automation. Success isn’t about deploying the most advanced technology. It’s about disciplined discovery, phased validation, and knowing when innovation beats invention.
Teams that skip these principles waste resources on systems that cannot handle real manufacturing complexity. Get the approach right and automation accelerates your program. Get it wrong and you may build expensive failures.
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 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 Anthony Catacchio
As Owner & CEO of Product Insight, Anthony Catacchio helps companies translate complex automation challenges into scalable, real-world hardware solutions. With a background spanning engineering leadership and product development, he focuses on structured, phased execution that validates core assumptions before full-scale buildout.
By combining robotics, AI, and disciplined systems engineering, he enables organizations to build and commercialize hardware products efficiently—while minimizing early-stage complexity, cost, and risk.
Connect with Anthony Catacchio 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.
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Automation is not a silver bullet for CMC or bioprocess challenges—misapplied, it just adds costly complexity. The real edge? Thoughtful system design anchored in process understanding, not chasing the latest robotics hype.
Anthony Catacchio, CEO of Product Insight and a specialist in translating industrial robotics for high-value bioprocess and CMC operations, joined David Brühlmann on the Smart Biotech Scientist Podcast to unpack why most automation projects fail—and how to ensure yours doesn’t.
Fundamentally the purpose of a product is to embody a process. And so I've always sort of kept that same thread of thinking again, whether it's surgical equipment or it's industrial robotics, it doesn't really matter as long as you take the time to really understand the process that you're trying to affect and that you're trying to improve and you build real system requirements and you really explore the solution space and understand what you're looking at. In a lot of ways, everything translates really well as long as you take the time to understand the process. All the technology translates incredibly well.
David Brühlmann [00:00:33]:
Bioprocess hardware development sounds straightforward until you try to build it. Anthony Catacchio, CEO of Product Insight, has spent years helping biotech companies navigate the challenges. Because most automation projects fail because teams never clearly define the problem they're solving and overengineer solutions with unnecessary complexity. Today, he reveals his phased approach to developing robotics and AI-driven systems that actually work. Reducing risks, accelerating timelines, and producing validation-grade data without burdening internal teams with complexities they are not equipped to handle. Let's dive in.
Welcome, Anthony. It's great to have you on today.
Anthony Catacchio [00:02:32]:
Yeah, it's great to be here, David. Really excited. I can talk about robots forever, so I'm always excited when I get a chance to.
David Brühlmann [00:02:38]:
That's awesome. Anthony, share something that you believe about bioprocess development that most people disagree with.
Anthony Catacchio [00:02:45]:
I guess I don't know what everybody agrees or disagrees with me, but one of the things I think that's going to be really interesting to see over the next several years is as we see more and more generalized robotics and sort of industrial robotics technologies all kind of creeping further and further into lab processes of all kinds. I mean, absolutely in the CMC and bioprocess world on the manufacturing side, but also on the research side as accessibility goes up and up, then deployment should go up and up with it. And so that's what we see a lot of, whether it's cobots or AGVs — wheeled robots that move stuff around in labs — we expect more and more of that kind of stuff.
I think there's this idea that biotech and labs are wholly different from industrial robotics. And realistically, I think that's going to be the biggest thing that people see over the next several years. It's not so much that you're going to see tons and tons of lab-specific or bioprocess-specific robotics and AI. You're going to see a lot of the stuff that's been cutting edge or has been really well proven out in a lot of these industrial spaces start to make that move from the manufacturing line or assembly — whatever it is — into some of these higher-value spaces. Obviously these high-value spaces are very risk-averse, but the robotics industry has a pretty long history of bringing automation to complex processes. And I think we're going to see more and more of that kind of migrate across from industrial logistics and those sorts of spaces where a lot of this stuff's been developed and refined into these somewhat lower-volume, higher-value use cases. I think that's maybe the biggest thing. A lot of people sort of view their niche as — I don't want to say special — but special. That we, oh, you can't just use that robot that you use on a factory floor or in a warehouse.
You can't use that here. And that might be true in some ways, but the fundamental principles are there. And it's really just about someone making the investment and taking the time to do it right and develop the right systems for whether it's bioprocess or other lab spaces or medtech spaces where the technology's already there. And the question is just: is someone going to take the time to design the system correctly and create the right quality levels and reliability levels?
David Brühlmann [00:04:57]:
Anthony, draw us into your story. What got you started in robotics automation and what were some interesting pit stops along the way?
Anthony Catacchio [00:05:05]:
I didn't really set out to be a roboticist. I'm a mechanical engineer by training. My whole career has really been kind of in the new product development space, starting in medtech, really. My first job out of college was all about surgical positioning equipment. It was a small company. The engineers did a lot of things there, and that included research and process mapping and really understanding how to bring mechanical systems that, again, aren't necessarily anything novel or specific to the medical world — or the technology isn't — but understanding how to design those systems in a way that worked well in a surgical setting.
So I did a lot of work in arthroscopy and in trauma care and that sort of thing, building medical devices. But really, even as far back as then, it didn't really matter. Are you making a medical device? Are you making something for a lab? Are you making something for the automotive industry? The principles are all the same. There's a different level of system design — that's fundamentally what it comes down to.
In labs or in CMC, your system and process are just so important because you have so much value flowing through those materials. And so it's not a technology problem. It's a system design problem.
As my career progressed, I moved from that company to a consulting group. When you go into consulting, you see a lot of different types of problems. I got a chance to work on some surgical robots. I worked on a bunch of other medical devices — whether it's a cart-based system, an implantable device, or whatever it is. You get to see all those same basic ideas of engineering and robotics. It’s like, now you're just applying them. You're building a different type of system around them.
It was in medical and surgical robotics where I got a lot of experience, as well as starting to move into the industrial robotics space. In the consulting world, as you well know, it's very referral-heavy. Sometimes you do a medical device project, and then someone leaves that medtech company and goes to work at a robotics company. They still call you — even though you're the med device guy — because they need ideas about how to do robotics and how to design systems so they can achieve their goals.
So I got a chance to start getting into the industrial robotics world. Over the last seven or eight years, I've leaned pretty heavily into that on the logistics side — warehouse robotics in particular.
That's really how I got here. My background is in product, but product has always meant system in my mind. I think of product development as the physical embodiment of a process. Products exist to enable processes — whether those processes physically aren't possible without that product, or you're building the product to make them higher throughput, more reliable, lower labor, or whatever it is you're trying to achieve.
Fundamentally, the purpose of a product is to embody a process. I've always kept that same thread of thinking — whether it's surgical equipment or industrial robotics. It doesn't really matter as long as you take the time to understand the process you're trying to affect and improve, build real system requirements, and explore the solution space to understand what you're looking at.
In many ways, everything translates really well if you take the time to understand the process. The technology translates incredibly well.
I've had the opportunity throughout my career to move across different industries and realize: this doesn't need to be different and special. We just need to use it the right way.
Maybe you can use the same robot that you use in a warehouse — you probably can. You might need to put an enclosure on it. You might need to make sure it stays out of the way or can operate in a sterilizable environment. You might need a different end effector with different requirements. But fundamentally, the core technology is the same.
That’s what I see in the future of biologics processing and labs in general — bringing the wall down a little bit and showing how these technologies can translate with good system design.
David Brühlmann [00:09:04]:
What kind of problems are you solving today in the bioprocess world so that people can see exactly what you're working on today?
Anthony Catacchio [00:09:13]:
Yeah, I mean, unfortunately, the consulting world — the big downside, fundamentally — is I can never really show anyone the stuff we're working on, right? Because we're always inside someone else’s R&D environment.
But a lot of what we're looking at today, and the kinds of things we're working on, involve understanding how to move materials around efficiently. That’s a lot of the work we see. And again, it sounds simplistic, but there’s this crossover where you need people who understand lab operations and lab automation. It’s very hard to remove all the people from the process.
There are many workflows where you still need expertise. You still need high-touch interaction. You can’t necessarily aim for fully lights-out automation all the time — and you shouldn’t.
So a lot of what we spend our time thinking about is how to design systems that accommodate today’s workflows — especially the parts that aren’t friendly to automation — while removing physically demanding work, repetitive work, or slow, high-risk-to-the-process tasks.
We spend a lot of time looking at how to structure the entire process flow to enable high-throughput automation in that space. You have to be very careful about how you delineate and protect the expert’s role in those environments. You can’t just throw in a bunch of large industrial robots, put everything in cages, and turn it into an inaccessible manufacturing line. It’s not realistic — and it’s not the goal.
The goal isn’t to eliminate human labor. The goal is to reduce errors, increase throughput, and improve reliability. It takes thoughtful system design to introduce automation in a way that doesn’t exclude human expertise but amplifies it. That’s really what we focus on. In robotics, a lot of the time, the core job is simple: pick something up and put it down somewhere else — without contaminating it, damaging it, or altering it along the way.
When people talk about AI in robotics, many of the real advancements in recent years have actually been in vision systems. The ability to identify objects, the scaling of edge computing to process visual data locally, and the improvement of machine learning models — that’s what has changed things.
What that enables is a broadening of acceptable inputs and outputs. Twenty-five years ago, you had to know exactly where the object was. You had a pre-programmed robot path that moved to a fixed coordinate and picked up the item.
Today, with vision-guided robotics, you can operate much more flexibly. You might know that the object is somewhere on a bench, and the system can locate it dynamically — something that simply wasn’t possible before modern computer vision capabilities.
So it’s really the merging of AGVs (autonomous guided vehicles), mobile robots, wheeled quadrupeds — which are an incredible emerging technology — and vision-guided robotic manipulation.
David Brühlmann [00:12:05]:
We have so many technologies at our disposal now, and you could do a lot of things. But the question I often ask myself is: what is really making the difference, and where is it just hype? Where is it just the tech person with a great idea? How do you advise people? What are the best areas to implement these automation or robotic systems?
Anthony Catacchio [00:12:28]:
It’s very much a case-by-case basis. The way our process generally works is that we always start with a really rigorous problem definition. “Implement automation” is not a problem statement. We need to know before we start: What is the goal? What are your challenges? Is the problem throughput? Is it that you can’t hire enough labor? Is it error rates? Compliance? What are we optimizing for? What are we trying to achieve?
Once we understand that, the next step is what we call system concept development or system architecture development. We take the time to look at: Now that we understand the problem, what are all the possible ways to solve it?
There’s a real challenge in this space. Many of the people who understand robotics capabilities and know what technologies should be used often don’t have aligned incentives. If you ask someone who sells a particular robot whether it will work for your application, they’ll try very hard to say yes.
So one of the first things we do is lay out all the different ways to solve the problem. At the end of the day, it’s about return on investment. We measure the potential impact and outcomes of different system concepts and layouts.
You might solve it with a single 6-degree-of-freedom robotic arm. That might require developing a custom end-of-arm tool. Maybe you mount it on a mobile robot. Or maybe you redesign the layout so the robot doesn’t need to move at all — place the robot in the center and arrange the work cells around it.
We typically walk through dozens of whiteboard-level concepts. How could this system be arranged? What are the true requirements? What’s the optimal way to meet these constraints and achieve the client’s objectives?
A large part of this phase is communication. Sometimes I’ll show a client five concepts knowing that none of them are correct. I’m not trying to present a final design in the first review. I’m trying to understand their problem more deeply.
People struggle to fully enumerate their requirements upfront. But when you show them a potential solution, they’ll say, “That would be great, except for this, this, this, and this.” And that’s valuable. They couldn’t articulate those requirements until they saw a concrete concept. That’s a huge part of our process — exploring the entire solution landscape before committing.
From there, we might simulate concepts, or what we call “human-bot testing,” where instead of deploying a robot, we assign people to behave like robots and test the workflow physically before automation is introduced.
David Brühlmann [00:15:21]:
So it starts with defining the problem extremely well, then exploring potential solutions. How do you guide your clients in balancing this? You could build an amazing system that costs millions. You could build something very simple. Or you might conclude that a robot isn’t necessary because people can do the job effectively. There’s always a balance between over-engineering, innovating, and inventing new things. What’s your take on that?
Anthony Catacchio [00:15:54]:
That’s exactly what we try to address in the concept phase. The last thing we want is to spend a client’s money and not deliver real value. That doesn’t lead to repeat business. It doesn’t lead to good morale.
There’s nothing worse than engineers working on something and thinking, “Why are we doing this? This doesn’t make sense.”
That’s why the early system concept phase is so important. A couple of years ago, a company approached us with a request. They had parts that needed to be wiped down with isopropyl alcohol (IPA) and boxed in a cleanroom environment. They sent us a 15-page RFQ with detailed specifications for a robotic solution.
Before even bidding, we reviewed it and asked ourselves: Why not just use a glove box and a person? Put the dirty parts in one side, wipe them down with IPA, place them in a box, and pass them out the other side. No need for a full cleanroom. No need for a robot.
We could absolutely build the robot. It would cost around $1 million. But we asked them: Are you sure you need this? You don’t have the throughput to justify it. You won’t eliminate all personnel anyway. This just isn’t complex enough to warrant automation.
So we gave them two answers:
Here’s the quote for the robot you requested.
And here’s a much simpler way to solve your problem.
Interestingly, I sometimes think the peak of our value proposition is talking people out of projects. It sounds counterintuitive — projects generate revenue. But many times, people jump straight to a solution and assume robotics is the answer.
Yes, you could solve the problem with a robot. But you shouldn’t.
Our goal in system concept development is to validate whether this is even the right project. If there’s a dramatically simpler and cheaper way to solve the client’s real problem, we’ll recommend that.
Because if we save someone from spending $1 million and solve it for $10,000 instead, the next time they face what looks like a million-dollar problem, they’ll call us.
We focus on validating problem-solution fit and ensuring we’re not off by orders of magnitude in cost. And the only way to do that is to invest time upfront exploring all possible approaches
If you skip that step and jump straight to buying a robot because someone said, “Go solve this with automation,” you often create unnecessary complexity.
Sometimes the better solution is simply to let a person do it. There’s something to be said about not trying too hard to replace the incredible machine that is a human being.
David Brühlmann [00:19:00]:
You’re making such a good point. What I’m hearing is that common sense matters. You can solve many problems with robotics, but some problems are better solved simply — cheaper and faster — without automation.
And I agree: telling a client, “Yes, we could build this, but you don’t need it,” builds long-term trust. That’s how you win in the long run, not just the short term.
Anthony Catacchio [00:19:24]:
Yeah — in the long term. It has its challenges in the short term, but it’s the right way to do it. Because eventually someone is going to notice that you could have solved this for $1,000 instead of $1 million. You generally want to be the person who notices that first. You don’t want to wait for someone else to point it out.
David Brühlmann [00:19:43]:
This wraps up Part 1 of our exploration of Product Insight’s hardware development approach and why data quality matters for GMP validation. We’ve also explored why a clear problem definition prevents over-engineering.
In Part 2, we’ll continue this conversation about building automation systems that actually solve bioprocessing challenges without unnecessary complexity. If these insights on hardware development resonated with you, please leave us a review on Apple Podcasts or your favorite platform to help other scientists discover this episode. Thank you 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 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 Anthony Catacchio
Anthony Catacchio is Owner & CEO of Product Insight, where he leads the development of robotics and AI-driven systems that automate complex physical business processes. After joining the company in 2021 as Director of Engineering, he expanded the technical team and refined a specialized, phased hardware development process designed to significantly reduce risk and compress timelines.
His approach emphasizes early validation, robust data generation, and clear system architecture—ensuring clients can make confident manufacturing and scale-up decisions without overburdening internal teams.
Connect with Anthony Catacchio 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
The promises of biotech often hinge not on bold science alone, but on rigorous CMC decisions. Skipping steps or cutting corners early can sabotage even the most innovative programs down the line, derailing therapies before they reach patients.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann welcomes Henri Kornmann, Head of Biologics Innovation Centre at Ferring Pharmaceuticals, whose experience stretches across multiple commercial launches and clinical lifecycles. Henri’s “house building” approach demystifies CMC’s complexity, showing why early diligence—paired with regulatory fluency and scientific insight—pays dividends for years.
You need to understand the regulation. As a CMC biotech scientist, you serve two customers. You serve the patients that are in the clinic. You want them to receive a safe and efficacious product, but you serve also health authorities. This is your client. You need a clear understanding of what they need. And it's there, it's accessible, you have guidelines, but you need to access them, and you need to understand them, and you need to apply them.
David Brühlmann [00:00:28]:
Welcome back. In part one, Henri Kornmann explained how early CMC decisions, including your cell line, your analytical panel, your specifications, create the foundation that determines success or failure of your CMC program. But a solid foundation isn't enough. Now we tackle the structures you build on top. How do you scale from lab to commercial manufacturing without cracks appearing? How do you control raw materials, manage impurities, and handle stability through lifecycle changes? And critically, how do you maintain process consistency post-approval while still improving? Let's find out.
What are now the next logical steps? Because you mentioned process validation, you mentioned we are in Phase II, eventually we go into Phase III and we have higher volumes, more clinical demand. So what are the next things, let's say, a startup founder should think about, or even somebody who is a CMC leader in a larger company?
Henri Kornmann [00:02:45]:
So I will go back to my house analogy. Phase I is the foundation message again, don't mess up with the foundation, there are things if they are not well done, you will really pay later during the program. Phase II, my approach is we don't change a lot. So you are still on that foundation. From a CMC point of view, or at least that's how I see things, I don't see big difference between supply for Phase I and supply for Phase II, except that suddenly you produce more batches and you create clinical exposure to your CQA.
Then you arrive to supply clinical Phase III. And here the recommendation is to supply clinical Phase III with your final process, with the process that will be used for commercial. So most of the time you need to scale up. So going back to the house analogy, when you think about scale-up is I start to build the structure of my house. Can this structure stand and be extended? Can I put a second floor and a third floor and maybe a garage next to it, making sure that the house stands? So this is the scale-up for commercial, and you need to deliver a commercial process which is robust, which has a good capability, which has limited environmental footprint, where you have secured the supply of your raw materials, where you have maybe for your critical raw materials, dual sourcing.
I have seen some companies, for instance, that have two sources of Protein A resin. They have the main source, but they have also qualified an alternative source from another supplier. So this is what you need to develop. It's a heavy CMC package, scale-up for commercial, but it's fundamental that at the end of this work package you have something that is robust, that you can operate long term because your process will stay for 10 to 15 years. This is usually the lifecycle of a drug. It can stay long term. Going back to the analogy, this is really building the structure, the height of the house.
David Brühlmann [00:04:59]:
And at that stage, typically process validation and process characterization is done. For certain people, it's sometimes difficult to understand what the difference is. Can you just give us a 2-minute version of what do you do and what is the purpose of those activities?
Henri Kornmann [00:05:15]:
I will go back to my house analogy. So the foundation, then I have the house, and then you move— so you have developed a process for commercial, the house is almost ready, and then you enter the house and you check, can I live in that house? Are lights working? Can I heat my house? This type of thing. And this is what we call process validation. This is another big work package that is happening after scale-up and development for commercial. Process validation according to FDA, and here I'm using the FDA terminology, they divide it into 3 stages: process validation Stage 1, Stage 2, and Stage 3.
Process validation Stage 1, this is all the studies that are needed just to design and justify your process control strategy. Just to give an example, I'm operating my bioreactor between 36 degrees and 37.5 degrees. That's my range. This is what I will put in my dossier. I need to justify those two numbers. So I justify it either because I control it very tightly or I need to test it. So this is process validation Stage 1. At the end of it, you have what we call the process control strategy, which is how do I control my process? How many samples do I take? How many cycles can I do on my chromatographic resin? How long is my hold time in my process? If I have a hold time, can it be overnight? Is it 3 days? Is it 1 weekend? So this is all process validation Stage 1.
So maybe one message, because sometimes this process validation Stage 1 is difficult to sell to management because it's requiring a lot of resources. So when you look at benchmarks, process validation Stage 1 is typically 12 to 18 months for a team of 10 to 15 FTEs. So it's massive. But again, it's a requirement because you need to justify all your limits and how you control your process.
Then you move into process validation Stage 2. And here, this is what we call PPQ batches. So you run your process full scale.
David Brühlmann [00:07:40]:
What does PPQ mean? Can you explain that for us, please?
Henri Kornmann [00:07:43]:
PPQ means Process Performance Qualification. And this is your process at final scale, essentially commercial scale, with the process control strategy in place, that you operate a certain number of times. And this is actually to prove that you can run this process and that it's delivering a drug substance and a drug product that are within the specifications that you have set.
There are two tricky elements to consider, at least to me. One is the number of PPQ batches that you will produce. Twenty years ago, this number was 3 and everybody was happy about it— if you have 3 consecutive successful PPQ runs, that's okay, your process is good and validated. Nowadays, you need to justify the number of PPQ batches. So you have a lot of literature and publications and discussions about this number of PPQ batches. But this number is one tricky element of process validation Stage 2.
The other tricky element is the timing of it. Because obviously there is a lot of value in this material. I mean, this is material you can sell and distribute. They are GMP batches. So you want to run those PPQ batches not too early because then you will have shelf life issues. If you run them too early, then you need to go into BLA, the dossier is reviewed until you get your approval. And not too late because they have to be part of your dossier. So the timing has to be thought through. So this is process validation Stage 2.
And then building on the house analogy, you move into process validation Stage 3, which is continued process verification. And this is usually happening after BLA approval. And here in the house analogy, this is, can I maintain my house? Can I improve my house? And this is typically process validation Stage 3.
And this is also a very important CMC package because I said before that your process will be there for 10 to 15 years, maybe 20 years, and it will deviate. People say that in the lifecycle of a process, the process will deviate by one sigma, by one standard deviation. Of course, some processes never deviate, other processes deviate much more, but your process will deviate because some raw materials will change, because one vendor will stop supplying that filter and you will have to use another filter, because you will be interested to optimize your process and maybe increase the productivity, so you will fine-tune your process parameters and so on and so forth. So this is why process validation is important, to make sure that once the process has been registered, because it will by definition deviate, it is still in a state of control.
David Brühlmann [00:10:47]:
This is a very important point because the house building is extremely important, but then you want to live in the house for a long time. So you have to also make sure that you can tweak certain things. And I think that's also where good control strategy comes into play and that you still can post-approval change a bit or also react to certain raw material changes between lots and so on, that you have some levers.
Henri Kornmann [00:11:13]:
Exactly. And I think the main message I wanted to convey to the CMC colleagues and the scientists that are listening to this podcast is CMC is obviously important. There are mistakes that you could do that can never be fixed down the road, hence having a huge impact on the value of your program. And I think this message is supported by those two FDA analyses about how much CMC impacts the complete response letters from FDA and refusals to file an IND.
David Brühlmann [00:11:49]:
I would also like to touch upon the human aspects because you worked in different companies, you have seen a lot of different scenarios, different ways of working. I'd just be curious—because you have a lot of things to orchestrate, you have people from engineering, process development, manufacturing, and oftentimes even external stakeholders, especially when you're in a smaller company working with a CDMO or a CRO—what are some pieces of advice you can give to the people listening to manage the leadership part or the management part of this whole CMC program?
Henri Kornmann [00:12:27]:
You just said: a CMC program is multiple elements connected together. We discussed about the cell bank that needs to feed process development to to different supply, drug substance, drug product, then distribution to the clinical site. So to me, the efficacy of CMC program strongly relate with good project management skill. I think one piece of advice is make sure you have a very solid project management in place that will be able to build the plan, connect all the work package together, and most importantly, adapt the plan on the go because by definition your plan is not correct and you need to redesign constantly that plan and realign all those work packages. This is one element.
And the other element of solid CMC package is obviously solid scientific background. Of course it makes a lot of difference when you have access to expertise of people that can advise because it's a multiple choice things every day, every week. When you are part of those those CMC drug product development, you take decision and direction, and you need guidance. Sometimes you need guidance. So I would really also making sure that in one way or another, I could access this expertise.
And finally, and that will be my third point, is you need to understand the regulation. As a CMC biotech scientist, you serve two customers. You serve the patients that are in the clinic. You want them to receive a safe and efficacious product, but serve also health authorities. This is your clients. You need a clear understanding of what they need. And it's there, it's accessible. You have guidelines, but you need to access and you need to understand them and you need to apply them.
David Brühlmann [00:14:23]:
Yes, most of these guidelines are public now, so you should be able to access them. But as you said, you also need the scientific and technical background to make meaningful conclusions from them.
Henri Kornmann [00:14:37]:
You are right. The guidelines are guidelines, so by definition, they are generic. And this is how do you translate those guidelines to your program that is key. But yes, they are by definition, they are accessible and public. They are issued by public institutions. So they are accessible.
So that's the three elements
There is another analogy I like to use when I mentor CMC scientists. I say to them, you know, you are half of a scientist and half of a lawyer. I mean, you need to understand the regulation because you need to navigate that regulation. If you are only a good scientist, it's not helping. Of course, if you have a perfect top-notch on regulation, it's not helping too. You need to master the two aspects.
David Brühlmann [00:15:25]:
Before we wrap up, Henri, what burning question haven’t I asked that you are eager to share with our biotech community?
Henri Kornmann [00:15:33]:
I think you did a pretty good job asking the right questions. Again, the main message I would like to convey is: a CMC program is like building a house. If you approach it that way, you will be successful.
David Brühlmann [00:15:49]:
Great. That’s awesome. So what is the most important takeaway from our conversation?
Henri Kornmann [00:15:54]:
Never underestimate CMC. If you do, you will pay for it later.
David Brühlmann [00:16:06]:
Never underestimate CMC. That’s a great way to conclude our conversation, Henri. It has been fantastic. Thank you for the memorable analogy and for helping us understand the critical importance of CMC—and how to approach it practically. Where can people connect with you?
Henri Kornmann [00:16:28]:
The easiest way is probably on LinkedIn.
David Brühlmann [00:16:33]:
Fantastic. I’ll leave the link in the show notes. Henri, thank you again for being on the show today.
Henri Kornmann [00:16:40]:
Thanks for the invitation, David. It has been a pleasure. Bye everyone.
David Brühlmann [00:16:45]:
Henri’s house-building analogy reveals a profound truth: biologics development isn’t about heroic late-stage rescues. It’s about disciplined foundational work at the beginning of CMC development. Solid ground, Quality by Design, risk-based structure, strong project management systems will get you there.
Get these right early—and your therapy reaches patients.
Get them wrong—and no amount of late effort can fully save you.
If this conversation changed how you think about CMC development, leave us a review on Apple Podcasts or your favorite platform. Thank you for tuning in, and I’ll see you next time.
Alright, Smart Scientists—that’s it for today on the Smart Biotech Scientist Podcast. Thanks for joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review and help us empower more scientists like you. For additional bioprocessing tips, visit smartbiotechscientist.com. Stay tuned for more 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 Henri Kornmann
Henri Kornmann, PhD, brings over 20 years of leadership experience in CMC development and GMP manufacturing within the biotechnology and medical device sectors. At Ferring Pharmaceuticals, he led the development of the biologics pipeline and was instrumental in advancing Adstiladrin to approval, marking the company’s first gene therapy and expanding treatment options for patients with bladder cancer. His work reflects a strong focus on translating scientific innovation into compliant, scalable manufacturing solutions.
Throughout his career, including senior roles at Biogen, Merck, and Medtronic, Henri has guided complex programs from early development through regulatory approval and lifecycle management. He is widely regarded for his strategic approach to CMC, regulatory readiness, and building high-performing technical teams.
Connect with Henri Kornmann 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
In the competitive world of biotherapeutics, making the right choices in early CMC development can mean the difference between regulatory approval and costly setbacks. The industry has seen increasing regulatory expectations, and what worked a decade ago just won’t cut it today.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann and Henri Kornmann, Head of Biologics Innovation Centre at Ferring Pharmaceuticals, explore the critical foundations of CMC development— likening it to building a house where mistakes made early on can have lasting, irreparable consequences.
The first step when you build the house is having very solid foundation. And to me, the foundation of the house is the supply of your clinical Phase I. You have to see that as the foundation of the house. Here you cannot make mistakes. And there are mistakes I have seen during my career. When they are done at that stage, they cannot be repaired. A typical example of this type of mistake could be the clonality of your cell banks. Developing cell banks has been a topic for years, but also the technology and the expectations from regulators have also evolved. So you cannot develop a cell bank today as you were doing it 10 years ago.
David Brühlmann [00:00:51]:
Many biologics that fail in late-stage development share one fatal flaw: a weak CMC foundation laid years earlier. Henri Kornmann has spent two decades developing biologics the right way. From Merck to Biogen to leading Ferring Pharmaceuticals's first gene therapy approval for bladder cancer. Today he reveals why your clonal cell line selection, analytical methods, and early characterization decisions determine whether your therapy reaches patients or collapses under regulatory scrutiny. If you're developing biotherapeutics, the choices you make today will haunt or save you at commercial scale. Let's dive in!
Welcome, Henri. It's good to have you on today.
Henri Kornmann [00:02:51]:
Hello, David. It's good to be with you and with the listeners to your podcast today.
David Brühlmann [00:02:56]:
Henri, share something that you believe about bioprocess development that most people disagree with.
Henri Kornmann [00:03:03]:
That's a difficult question. I think there is something very important for people to understand in drug development in general, but this is even more valid for all the CMC aspect of the drug is that a CMC program is like building a house. You need to start at the beginning of your program at preclinical stage and at, let's say, clinical Phase I, you need to start with very, very solid foundation. And from those foundation, as you progress with the clinical Phase II and Phase III and then request for approval, this is like adding floors and rooms in the house. And I have been in this industry for 20 years, mainly in big and mid-pharma, never working for startup, but I have performed a lot of due diligence. And when I have to due diligence a program, I often realize that colleagues in startups have difficult to understand that. And of course, they have limited financing and they need to go fast, but in that environment, they often forget about some of the fundamentals of CMC that are critical.
David Brühlmann [00:04:21]:
Yeah, I share your observation, Henri. As a consultant, I speak to a lot of startup founders, and that's exactly what I noticed, that a lot of startup founders have next to nothing, or in some cases, little knowledge about what CMC entails. And also, I like your way of looking at it, building a house and you need to lay a solid foundation. And that's also the reason I'm very excited about today's podcast, because we're gonna look at how are we gonna build that house that is gonna endure and not gonna fall eventually. But before we do that, Henri, take us back to the beginning and tell us what sparked your passion for biotech and what were some interesting pit stops along the way?
Henri Kornmann [00:05:08]:
My career is rather simple. If you draw a line between CMC development and GMP manufacturing, during my career I constantly jump from one side to the other. For instance, I started my career at Merck as junior CMC development scientist being specialized in downstream processing. But then I jump and I became Lean Six Sigma Black Belt, improving GMP manufacturing process. And then ultimately progressing into being responsible for the GMP manufacturing in a multi-product facility based in Switzerland. And then I moved back to CMC development, developing biosimilars. Then I made a 3-year excursion out of biotech working for medical devices, working for an American company called Medtronic. But of course I miss biotech. So after 3 years, I jumped back into TechOps working for Biogen, being part of this fantastic facility that they have in the middle of Switzerland, close to Solothurn. And then ultimately, and this is where I am today, I was called to join Ferring Pharmaceuticals. Ferring is mid-pharma company and I joined Ferring to develop the CMC development side for biologics.
David Brühlmann [00:06:34]:
Tell us about this last part because you were involved in the approval of Ferring's first gene therapy. Tell us more about that and then also tell us how that experience reinforced the importance of getting your CMC foundation right.
Henri Kornmann [00:06:51]:
This program is a gene therapy helping patient fighting with bladder cancer. This therapy has been now approved by FDA. Ferring gets involved in that product at the very end of clinical Phase III, and then the mandate was to take it from there and to bring it to BLA. And it's a very good program. It has been developed by one of our sister companies based in Finland called FinVector. This gene therapy program is called Adstiladrin®. It's gene therapy helping patients fighting for bladder cancer. And of course, one of the first thing you do is review the program and you assess the various CMC work package to see if they are complete or if there are some gaps, because of course when you go into registration, you submit your dossier and you get questions. I think every development program has CMC challenge and that one was not different than others. So we started to close some of those gaps, but it was interesting because there were some gaps that were difficult to close because we were late in the program. And actually, I was saying before that all programs have challenges.
There has been this review from FDA mid of last year, I think in July, where they published the list of the complete response letter starting from 2020. So all the complete response letters are starting— most of the complete CRL, the complete response letters are starting from 2020. And people started to analyze the cause and why the program were not approved first time. And actually 70% of the CRL were having a CMC challenge. So I think this indicates to colleagues working in CMC that this is extremely important because you don't want to arrive there and having your program reject out of CMC topic. There has been another FDA analysis. I think it's an older one from 2020 that was not on the CRL but more on the IND. And that study showed also that a significant amount of those IND, around 10%, they were not allowed to proceed to clinical Phase I, also because of CMC issues. So at the end of the development and at the beginning of the development, CMC can cause delay, increase cost and delay in program if it's not properly done.
David Brühlmann [00:09:29]:
You're making an excellent point. And I just want to reiterate that because this is such an important statement. And also I want to raise awareness because as I'm having conversations with people who want to go into an IND filing, it's so key to understand that you need to get these things right early on. And as you said, Henri, if you make wrong choices or you have some gaps, these can become very costly, especially at the later stage of your program as you're getting closer to your BLA. And that's the very topic of today, to help biotech scientists understand this is important but also have a strategy and have tactics to make better choices. So let's go into your analogy of building this house. Tell us, where do you start? What is the foundation? What do we have to look at? And then we'll go step by step.
Henri Kornmann [00:10:22]:
I like this analogy very much. I'm using all the time when I mentor the next generation of CMC scientists and the next generation of CMC leaders, because I think it's quite self-explaining. So maybe the first step when you build the house is having very solid foundation. And to me, the foundation of the house is the supply of your clinical Phase I. You have to see that as the foundation of the house. Here you cannot make mistakes. And there are mistakes I have seen during my career. When they are done at that stage, they cannot be repaired.
A typical example of this type of mistake could be the clonality of your cell banks. Developing cell banks has been a topic for years, but also the technology and the expectations from regulators have also evolved. So you cannot develop a cell bank today as you were doing it 10 years ago. I have faced, for instance, during due diligence programs where you could not demonstrate the clonality of the cell bank. So what do you do? What do you recommend to your business development department when you realize that it has not been properly done? And that's the analogy with the foundation.
Another example of one of those basic things linked to the cell bank, but also linked to the material you will produce for Phase I is how do you store it? I mean, it has to be stored according to the Good Distribution Practice (GDP). And I have seen examples where people have developed properly a cell bank and then they place it in a cryoconservator that is not according to the GDP rules. And so what do you do again when you observe that and you have to make a recommendation? Do we buy or not this program when you're part of a due diligence? What do you do? It's a challenge, right?
David Brühlmann [00:12:23]:
What about raw materials when you're creating your cell bank? I think that's another critical aspect, right?
Henri Kornmann [00:12:29]:
You are totally right. This is a key aspect where you need to make sure you have collected all the proper documents about your raw material. Something as basic as a TSE/BSE statement for one of the materials you used to create your cell bank can become important. Ten years down the road, when you are at filing, you may receive questions about it, and it can become a major challenge — even though you may not know it at the time, it can still be questioned. So people have to remember that.
The message is that, in a CMC program, there are many things that can be fixed later, but there are a few critical elements — the foundation — that you cannot get wrong. I gave some examples. The idea is not to create an exhaustive list, but to emphasize that foundations are very important.
You also mentioned that you speak with CMC colleagues in startups. They need to be aware of this because if they miss some of these critical elements that cannot be corrected later, it can destroy part of the value of their program.
David Brühlmann [00:13:34]:
Besides the cell bank, is there any other aspect that is fundamental in this foundational part?
Henri Kornmann [00:13:41]:
I'm sure that there are more. One I think about is how deep you characterize your substance. My approach or my recommendation is even if you are short in money and you have to make some decision, do not save money on the characterization at the beginning as part of the foundation because you don't want to discover something funky in your molecule at later stage. So full state-of-the-art characterization of your drug substance prior to Phase I will be a recommendation. There has been programs where you realize that you have very high level of misincorporation in your molecule. This you want to discover rapidly.
David Brühlmann [00:14:31]:
Absolutely. So we have now laid a solid foundation. What is the next step, Henri?
Henri Kornmann [00:14:37]:
The next step is— so you have your foundation, you have supply for your clinical Phase I. You move into supplying clinical Phase II. My approach and my recommendation is not to change things between supply of Phase I and Phase II. Some people might want to scale up because they believe it will require less batches and maybe saving money on the supply of clinical Phase II. My approach, it is usually simpler. You stay with your Phase I process. Maybe you produce a little bit more batches to supply your Phase II. This is actually not stupid move because with more batch, you start to build your knowledge about your CQA and you have clinical exposure of your potential CQA.
David Brühlmann [00:15:34]:
Can I ask you something about this? Because you mentioned CQAs. So just explain what that is. And it leads me to a further question. When do you start thinking about the quality by design approach. Just explain also what are these different things we should think about?
Henri Kornmann [00:15:52]:
So the Quality by Design (QbD) approach has to be embedded in your program starting clinical Phase I. You need to think about what could be your potential CQAs - your critical quality attributes -, which means the quality attributes of your molecule that may have an impact on the efficiency and the safety of your product. And you need to think about your potential critical process parameters (CPPs), which are the process parameters that are impacting the potential CQA. So this has to start Phase I and it has to be part of your IND. Of course, at Phase I, you know very little about your process and you know very little about your product, but there is a lot of things that are published. If you work with platform molecules such as antibody, there is a lot of things that are produced, so you can also use that, but it has to be embedded in your IND for Phase I.
David Brühlmann [00:16:53]:
What is a typical mistake or pitfall you see with respect to CQAs or CPPs?
Henri Kornmann [00:17:01]:
I think one of the challenge, QbD, is identifying CQA. There are ones that are very obvious, but there are ones you don't know. And of course in the clinical trial you have are not designed to test your CQA. They are designed to test the safety and the efficacy and the dose of your product. So this is a challenge, and here you need history and literature. And the other challenge is the link between process parameter and CQA, which is usually covered much later in the program during your process validation stage 1. But even there, it's also difficult to cover everything. And this is the reason actually why you have a process validation stage 3, what the regulator called the continued verification. It's because we know this understanding of the process during clinical development is limited. And we will continue to learn down the road when the product is on the market.
David Brühlmann [00:18:09]:
We have explored what makes solid ground for biologics development: a robust clonal cell line, comprehensive analytical panels, justified specifications, and why it's essential to define your critical quality attributes early. In part two, Henri walks us through scale-up challenges, controlling raw materials and impurities, lifecycle management, and building post-approval control strategies that actually work. If these CMC fundamentals resonated, leave a review on Apple Podcasts or your favorite platform to help other scientists like you find this conversation. Thank you so much for tuning in today, and I'll see you next time.
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 Henri Kornmann
Henri Kornmann, PhD, is an executive leader with more than two decades of experience in pharmaceutical CMC development and GMP manufacturing across biotech and medical devices. At Ferring Pharmaceuticals, he joined in 2019 to build and lead the Biologics Innovation Centre at Biopôle in Lausanne, assembling a multidisciplinary team dedicated to advancing innovative biotechnology-derived medicines from early development through commercialization. He played a key role in strengthening the company’s biologics capabilities and development strategy.
Prior to Ferring, Henri held senior leadership roles at Biogen, Merck, Medtronic, and Nestlé Health Science, consistently bridging breakthrough science with robust, scalable GMP manufacturing and global supply. He earned his PhD in Bioprocess Science from the Swiss Federal Institute of Technology Lausanne and is recognized for building strong CMC foundations that enable long-term program success.
Connect with Henri Kornmann 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
If biotech had a “cheat code” for sustainable manufacturing, what would it look like? Imagine harnessing sunlight and seawater to create valuable molecules—no fermentation tanks, minimal waste, virtually carbon neutral.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann speaks with Tim Corcoran, CEO and Co-Founder of Deep Blue Biotech. Tim is on a mission to transform cyanobacteria from scientific curiosity into a foundation for commercially viable, carbon-neutral specialty chemicals.
One of the very first pieces of research we did was to look at what our cyanobacteria were predisposed to make. Because if all you're doing is giving it a little nudge and encouraging it and joining up a pathway here and there, it's akin to pushing a car down a hill as opposed to pushing a car up a hill. Again, I'm always wary of making it sound easy because it's definitely not, but it's a much more efficient R&D process when you do it that way. And so again, it comes back to looking at what are we able to make, but also how does that suit the market? And when you join all of those things up together, hopefully you get a much more viable company.
David Brühlmann [00:00:34]:
Welcome back! In part 1, Tim Corcoran explained how Deep Blue Biotech’s ocean-derived cyanobacteria produce molecules through photosynthesis while secreting them directly into the media — a game-changing advantage. Now, we tackle the hard questions: How do you choose your first product when hyaluronic acid sells for $2,000 per kilogram but your ultimate target biofuel sells for $2 per liter? How do you scale photobioreactors when the infrastructure barely exists? And what separates technologies that commercialize from those that die in the valley of death? Let's find out.
We're going to get to the scale-up and the huge scale in a minute. I just want to focus a bit more on your deliberate choice to go into the consumer care market. This is quite a competitive market. You have all these big players out there. How do you compete against these players, especially with a limited budget?
Tim Corcoran [00:02:46]:
I guess one of the key factors is what we're doing is B2B rather than B2C. So that means things like marketing costs are less of a worry. If you try and produce a consumer product, the cost — I think Amyris is a good example. They're a synthetic biology company who got themselves into a bit of trouble. I think they've bounced back now, but they tried to launch consumer products and they spent an enormous amount of money on marketing. So being a B2B model helps. It means, for example, if you target the 10 biggest personal care manufacturers in the world, that's relatively achievable through a few conversations, a few emails, a few meetings. Then you've got distributors who are seeking an advantage over their competitors. You know, they want to be able to sell their hyaluronic acid at the expense of their competitors.
So being able to offer a distributor a unique product with significant advantages that we've already discussed over existing products, potentially that gives you the opportunity to tap into quite a large distribution network.Now the other factor is the actual amount that you can supply. If you speak to one of the large personal care companies, potentially they want tonnes of the product, and it will take a little while to scale up to that. So initially you need to engage with some of the smaller, more agile personal care companies as well, who might only want a few kilograms of the product so that they can do a product launch and get it out there. That serves a number of advantages. It means you've got products in other products that are on the market now. It means you're generating revenue quickly, and you're able to do that before you've scaled up to significant industrial scale. And it gives you some really useful case studies. People can see how these products perform.
David Brühlmann [00:04:17]:
Moving on to the scale-up aspects, what I see, Tim, is you have a promising technology — it's novel, you have a tremendous market, you have a great business model, and potentially you will have a huge demand and you will need to scale up not just to 20,000 liters, probably to 100,000 liters or beyond. What is your strategy there? Because the reason I'm asking this question is you have a novel technology and not many CDMOs have photobioreactors for photosynthetic organisms. What are you going to do, or what is the best strategy from your point of view?
Tim Corcoran [00:04:50]:
It is the flip side of a novel technology, as you say, that the infrastructure isn't abundant, shall we say. Now, fortunately, there are companies like A4F – Algae for Future in Portugal, for example, who can act as both a CRO and a CMO. They have very large-scale photobioreactors, so we're working with them at the moment. Plans to initially scale up one of our strains to about a 1,000-liter photobioreactor scale. But they don't have the downstream processing capabilities that you need. So you then need to take what comes out of that and find a suitable downstream processing CMO. So in the very first instance, our first commercial sales will be working with probably two different CMOs, one to grow it, one to process it. And that gets us that commercial revenue, which is an important stepping stone in the development of a company.
Now, our goal then is to build our own pilot facility so that we can refine the process. And the pilot facility, because of the sort of highly efficient process and the profitability profile that we have, that pilot will be profitable. It will make the company self-sustaining from a research point of view. And it will prove the technology at a reasonable scale, and it will provide you with enough samples and small volumes to generate regular commercial quantities, and for the big companies to be able to do all the testing they need to do in their formulations.At that point, potentially you can secure pre-sale agreements, which then you take to the bank against building an industrial facility. Now, whenever you speak to investors in particular, the idea of building a large industrial facility is a concern. But if you can show that you've got pre-sales from these big companies, then it becomes much more viable.
Now, our plan is to build one industrial facility. We don't want to build loads and loads of them. If we can build one, show that it's profitable, show that it's working, show that it's doing all the things that it should be doing, at that point, the intention is to move to a sort of technology licensing model because you can scale and reach the market much faster that way. So working with other chemical companies, licensing the technology to them, supporting them to scale it up. A lot of them will have much of the downstream processing side of things in-house already. So the bit that they will need to invest in will be the photobioreactor side.
And the interesting thing is one of the fringe benefits of this is as they do that, the photobioreactor technology and the industry as a whole will grow and it will develop and more efficiencies will be built in. But yeah, once you start licensing, obviously you're able to scale. You do leave a bit of profit on the table. I think it's not as profitable long-term as building multiple facilities, but I think it is a faster way to reach the entire market.
And because of the CO₂ aspect, because of the cost of production aspect, I think our method for hyaluronic acid will become the default mechanism for making hyaluronic acid. Much in the same way as precision fermentation took over from extraction from rooster combs about 25 years ago. This will be a substantial change in the industry. And just the cold logic of, well, it's carbon-neutral and it's cheaper, why wouldn't I do it this way, will lead it to become the dominant method.
David Brühlmann [00:07:42]:
Since you're going to build a facility, do you also have in mind becoming a CDMO at one stage or renting part of your capacity to other developers?
Tim Corcoran [00:07:51]:
We have a strong interest in helping the industry develop, but we are not particularly thinking at the moment of becoming a CMO or CRO, partly because we have so much additional research we want to do for ourselves. When you look at, as I say, the number of other chemicals we can make is in the hundreds. And so there's a lot of additional research. Now, potentially what we can do is take that pilot facility and take that industrial facility down the line and repurpose them to focus on these second-, third-, fourth-generation chemicals, proving it each time and then licensing them out and then moving on to the next one. The goal ultimately is that we have a full spectrum of chemical solutions — carbon-neutral, cost-efficient — you start to, when I'm sort of daydreaming, you start to think about the potential scale of the company as a whole and you think it could rival some of the really, really big chemical manufacturers.
David Brühlmann [00:08:38]:
Yeah, definitely. What comes into my mind, Tim, is your technology needs a lot of light. So where is the best location to build your facility? Because not only do you need a lot of light, you need skilled labor, which is probably quite difficult to find right now. So where are you going to build your facility?
Tim Corcoran [00:08:57]:
We're based in Sheffield, and from a skilled labor point of view, we've been very fortunate. The University of Sheffield is brilliant at cyanobacteria. They have two separate labs working on it. So we've been able to tap into, from an R&D perspective, we've been able to tap into that. Now, as you scale, that engineering side of things becomes more and more important. Again, actually the University of Sheffield is very, very strong at that. So that gives us a good starting point.But when you think about where you're going to get your light from, there are two main sources. One is natural light and the other is LEDs. Now if you want natural light, obviously moving somewhere where that is abundant helps. My feeling is probably it'll be natural light supported by LEDs, but time will tell.
So Portugal has a thriving and growing ecosystem around photosynthetic production, around photobioreactors and cyanobacteria and microalgae. So there's expertise and there's a degree of infrastructure and there's natural light there. They have relatively cheap electricity. Portugal is really attractive. Plus I'm a big fan of Lisbon. I think it's a lovely city.But the other end of the spectrum, interestingly, is Iceland. Iceland is developing a photobioreactor industry built around cheap, extremely clean geothermal electricity. So the cost of electricity and the CO₂ footprint of the electricity there are very favorable. So at that point you're not going to be relying on natural light — obviously Iceland being where it is — it's going to be LEDs. But if the LEDs are powered by geothermal electricity, it's cheap and it's carbon-neutral.
Now, obviously Iceland is a little bit further away, it's a bit colder, but there are companies operating there now who are working with microalgae in particular. And when you speak to them, they say, no, we have absolutely no problems attracting people to come and work here. People who want to work on this will travel. So that's another interesting location for us.
David Brühlmann [00:10:38]:
And how does the business case change as you're factoring in the additional electricity costs for LED lighting?
Tim Corcoran [00:10:45]:
It does make a difference. I think if you go somewhere like Iceland where the cost of electricity is, I think it's less than a quarter of what it is in the UK, then it becomes much less of a factor. But certainly I think if you're using artificial light, then your electricity profile is going to change quite substantially. So you do have to think about that. One of the advantages of Portugal is if you can get sensors which detect the intensity of the light, you can potentially say, okay, it's a cloudy day, we'll dial up the LEDs, or it's a particularly bright day, we can turn the LEDs off. And then you're using LEDs sparingly, you're using them as and when. The other factor is to look at which wavelength and intensity of light the cyanobacteria most respond to because you can make it a much more efficient process if you understand the mix of wavelengths and the intensity of light that benefits them, and you can calibrate it really quite precisely.
David Brühlmann [00:11:32]:
I'd like to focus on the lab-to-market journey. You have seen a lot of companies succeed, a lot of companies fail. From your perspective, what makes a company succeed in that? Because finally, we have great technology in the lab, but if we fail to transfer it into a commercial setting, it will be of no use to society.
Tim Corcoran [00:11:53]:
I agree. I think one of the big factors — one side in particular — as I alluded to earlier, around 2023, money tightened up substantially. A lot of synthetic biology companies had grown up and developed based upon access to easy, cheap money. And when that stopped, a lot of them suddenly struggled, and they'd got these long timeframes for their research and development and all of a sudden they couldn't afford that.
At the same time, because they thought they had access to all this money and all this time, they were targeting commodity products — for good reasons: big markets, potentially the biggest environmental impact. But it meant that to get to a point where they were economically and financially viable, where they could compete with the products they were replacing, required an enormous amount of research. And again, that made it very hard for them. And a lot of them ran out of money and went bust. And some of them have since bounced back, I'm pleased to say.
I think the synthetic biology industry has grown a lot leaner and a lot cleverer about how it works. So it has improved. It's learned from that. That pressure has forced the evolution of the industry as a whole, and it's in much better shape now than it was.I mentioned, I think it was Amyris who launched consumer brands. That is a real challenge. As I say, things like marketing costs can drain your resources very quickly. My preference — I come from a B2B background — would always be to operate in a B2B fashion because generally it's easier from a commercial point of view. The sales and marketing process is simpler.
And then the other one that I think ties in — and it probably particularly relates to the earlier stages of synthetic biology — was people would choose their favourite microbe and they would try and make their favourite product — oversimplification. But what it meant was there was a lot of genetic engineering to make whatever it might be — Escherichia coli, yeast, whatever — produce this product.
Now, as I mentioned earlier, one of the very first pieces of research we did was to look at what our cyanobacteria were predisposed to make. Because if all you're doing is giving them a little nudge and encouraging them and joining up a pathway here and there, it's akin to pushing a car down a hill as opposed to pushing a car up a hill. Again, I'm always wary of making it sound easy because it's definitely not, but it's a much more efficient R&D process when you do it that way.
And so again, it comes back to looking at what we're able to make, but also how does that suit the market? And when you join all of those things up together, hopefully you get a much more viable company.
David Brühlmann [00:14:02]:
What piece of advice would you give to a brilliant scientist sitting on the fence about starting their own company?
Tim Corcoran [00:14:08]:
I would 100% encourage them to do it. There are so many good ideas that don't get exploited, and it bothers me that there are all these brilliant things that may never see the light of day. Now, the scientists can do the technical bit. The other side of it, the commercial, the corporate bit, that's where they're going to need help. And there are, I would say, depending upon their context, there are three ways they can go about it.
One, if they're at a university, go and speak to your commercialization department or technology transfer office. They will have people who have expertise and knowledge about how to do this. Often you can get funding from the university to help you achieve that.
The second, people like me, like I used to be — business development consultants who will work with early-stage companies. They might work with you as a consultant, they might join you as a business partner. They can take care of that side of things, and that works.
And then the last one, I would always recommend it to anyone that wants to try it, is Carbon13 Venture Builder. Their concept is all about bringing technically minded people together with commercially minded people, putting the two in a room and seeing what comes out. So as I said, that's how I met my co-founder. It was a brilliant process. We weren't the only ones. A lot of good companies have come out of that. I'm sure there are other venture builder programmes. That's just the one that we worked on. But I thought Carbon13 did a brilliant job of creating the opportunity for these ideas to be realised.
David Brühlmann [00:15:19]:
Before we wrap up, Tim, what burning question haven't I asked that you're eager to share with our biotech community?
Tim Corcoran [00:15:27]:
Oh goodness. If I was thinking about it, the thing that I always ponder when I'm sort of looking at it is where can synthetic biology go? What is its ceiling? Because it's a relatively young industry. It's learning from its mistakes and it's improving.
Generally speaking, you see synthetic biology focusing on fuels and plastics and materials and that sort of thing. But what else could it do? Certainly I think there is potential in biodegrading products and dealing with issues like microplastics potentially. There is a lot of scope for it.
There's a cyanobacteria company I came across a little while ago who were developing a cyanobacteria-based paint which would be photosynthetic. So you'd paint it on a building and it would capture CO₂ and fix it. There's all sorts of areas it can potentially go. And frankly, I'd like imagination to give the answer to it. But I think the potential is enormous and it's worth anyone with either a commercial or a technical perspective thinking, I wonder if it could do this. Hopefully over time it becomes a significant part of the answer to the challenges we face with climate change.
David Brühlmann [00:16:28]:
This has been great, Tim. What is the most important takeaway from our conversation?
Tim Corcoran [00:16:34]:
A: I love cyanobacteria. And B: science has enormous potential, but it needs to be aligned with commercial expertise. If you take the two and they work together, I think you can achieve great things.
David Brühlmann [00:16:51]:
And that's the way forward, I think. Thank you so much, Tim, for sharing your passion, letting us into the world of cyanobacteria. Where can people get a hold of you?
Tim Corcoran [00:17:01]:
You can reach us — I'm on LinkedIn. I discovered actually there is more than one Tim Corcoran on LinkedIn. Or you can email me. My email address is tim@deepbluebiotech.com. Or you can visit our website, deepbluebiotech.com. I'm always happy to talk. It's one of my philosophies. I'll try and talk to anyone with an interesting idea or question, because sometimes you get some really interesting opportunities as a consequence.
David Brühlmann [00:17:21]:
Smart biotech scientists, use this opportunity. You'll find the links in the show notes. And thank you once again, Tim, for being on the show today.
Tim Corcoran [00:17:30]:
Thank you very much for having me, David.
David Brühlmann [00:17:31]:
It's been a pleasure. Tim's journey from three decades in commercial and leadership roles to founding Deep Blue Biotech reveals a critical truth: breakthrough science needs disciplined commercialization strategy. Start with high-value products, prove the case, move down the value chain, build one factory, then license broadly. And balance organism health with yield optimization. These principles separate innovations that reach market from those that don't.
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 Tim Corcoran
Tim Corcoran, Co-Founder and CEO of Deep Blue Biotech, brings more than 25 years of commercial and leadership experience across multiple sectors. He has a proven track record in designing growth strategies, advising start-ups and scale-ups from early stages to successful exits, and building robust networks of investors, partners, and clients.
Tim’s experience spans both established international corporations and entrepreneurial ventures, giving him a unique perspective on driving innovation and creating long-term business value.
Connect with Tim Corcoran 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
What if the ocean’s tiniest inhabitants held the secret to decarbonizing the entire chemicals industry? With mounting pressures for sustainability, biotechnology is urgently seeking efficient, eco-friendly alternatives to traditional manufacturing—and marine microbes just might be the missing link.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann speaks with Tim Corcoran, CEO and Co-Founder of Deep Blue Biotech, whose unconventional path led him from a commercial background to pioneering synthetic biology with ocean-derived cyanobacteria.
Generally, when people think about cyanobacteria, it's often in the news for negative reasons. It's clogging up a lake somewhere. There's a loch in Northern Ireland where it seems to do that quite a lot. But actually, the things people don't realize about cyanobacteria are that they are among the most efficient photosynthetic organisms on the planet. They are the reason our planet developed a breathable atmosphere in the first place. So that was our starting point. We thought, well, that's got great potential.
And then you look at the similarities with other microbes — Escherichia coli, Streptococcus, yeast, and so on — that have been engineered to make useful products. And we think, well, can cyanobacteria do that? And the more we looked at it, the more we realized cyanobacteria could do that for a whole swathe of chemicals.
David Brühlmann [00:00:43]:
What if the ocean held the key to decarbonizing the entire chemicals industry? Today's guest spent three decades in commercial and leadership roles before discovering a remarkable microbe floating off Singapore's coast — cyanobacteria that could revolutionize how we manufacture everything from cosmetics to fuels. Tim Corcoran, CEO of Deep Blue Biotech, joins us to reveal how this marine organism secretes high-value molecules directly into seawater, why photosynthesis changes manufacturing economics, and what finally makes cyanobacteria commercially viable after years of failed attempts.
Welcome, Tim. It's good to have you on today.
Tim Corcoran [00:02:42]:
Thank you for having me.
David Brühlmann [00:02:44]:
It's a pleasure, Tim. Share something that you believe about bioprocess development that most people disagree with.
Tim Corcoran [00:02:53]:
Oh goodness. I think a lot of people view scaling bioprocesses as inherently difficult and quite off-putting. I don't think it necessarily needs to be the case. I think the technology is improving. People are learning at a tremendous pace. We personally have designed our technology specifically with scale-up in mind to make it as easy and as straightforward as possible. But certainly, when you speak to people, that is often one of the primary concerns on their minds.
David Brühlmann [00:03:22]:
Take us back into your story before we talk about the exciting science and technology you're developing. Tell us what sparked your interest in commercial and leadership. You're coming from a different background. And tell us also what led you to science and finally to the role you're currently in.
Tim Corcoran [00:03:41]:
I started off, I think when I was about 16, with my first sales job, just a summer job selling double glazing, which does not have a great reputation here in the UK, and it may well be the case elsewhere. But I realised that I quite enjoyed it and that I was competent. So when I left university, I didn't know what to do with my economic history degree. So I thought, I've been doing this through university to pay some bills, I'll carry on doing it. And then over the years that developed from sales into broader commercial and operations roles. I enjoyed looking at how businesses could commercialise, how they could grow revenues, how they could improve profits.
For a good number of years, I worked for a market research company that was dealing with innovation, specifically within the FMCG market. They were looking at what successful innovations did as case studies and why some innovations failed. And I always found that very thought-provoking. There were certain traits you saw time and time again in successful innovations. And so that informed a lot of my thinking about product development and about how you could create new things that people actually wanted and that would succeed.
I then became a business development consultant, and I was working with a lot of early-stage companies, helping them take what was often just an idea on a piece of paper and turn it into reality — helping them with investment, planning, strategy, and commercialisation. In particular, I was running a company called Master Investor at the time, working with a chap called Jim Mellon, who’s a fairly visionary investor. He's very keen on science in particular — how it can fix a lot of our problems and how you can use that to make money at the same time.
In particular, he was very interested in alternative proteins. He has a company called Agronomics — and still does — which has invested heavily in the alternative proteins market. Through him, I was exposed to a lot of really interesting companies. And it was that gradual process of thinking about innovation, thinking about how you commercialise things, talking to people who were frankly brilliant at what they did, and learning from them about how they could transform that into a living, breathing, hopefully profitable company.
David Brühlmann [00:05:42]:
What was that pivotal moment then that made you take the leap and found Deep Blue Biotech? And what was the vision behind it?
Tim Corcoran [00:05:49]:
It had been percolating away at the back of my mind for a while. I'd been working with these companies and I knew I'd been able to help them. And I thought, well, why don't I try and do it for myself?
At the same time, I have a longstanding and deep concern about climate change. I'd wanted to do something about it. I wasn't sure exactly how I could contribute, but I wanted to do something meaningful. I thought, well, if I help to commercialise climate-friendly technologies, then potentially that means I can sleep at night — I feel as though I've made a positive difference.
So I joined an accelerator — or more precisely, a venture builder programme — run by an organisation called Carbon13. They are focused on creating climate-focused companies, and they bring together technical people — scientists and engineers — and commercial people like me, essentially putting them in a room together for a three-month period to see what emerges.
That’s where I met my co-founder. He's a chemical engineer by training. He had been working as VP of Sustainable Innovation at Unilever, and he'd grown increasingly frustrated because all the sustainable ingredients and chemicals brought to him were either too expensive or not as performant as the ingredients they were already using. And Unilever weren’t going to accept that — and consumers weren’t going to accept it either. They didn’t want to pay more, and they certainly didn’t want something less effective.
So he had left, looking for a way to create a technology that could overcome that trade-off. Over the course of those three months at Carbon13, we looked at different technological options. We explored several approaches and eventually settled on cyanobacteria. And the more we looked at them, the more we thought this has huge potential.
David Brühlmann [00:07:15]:
That sounds exciting. And tell us more about these cyanobacteria and why this is an interesting host organism to work with.
Tim Corcoran [00:07:22]:
I have a tendency to get overexcited at this point, so I'll try to keep myself calm. Generally, when people think about cyanobacteria, it's often in the news for negative reasons. It’s clogging up a lake somewhere. There’s a loch in Northern Ireland where it seems to do that quite a lot.
But actually, what people don’t realise about cyanobacteria is that they are among the most efficient photosynthetic organisms on the planet. They are the reason our planet developed a breathable atmosphere in the first place. So that was our starting point. We thought, well, that’s got great potential.
And then you look at the similarities with other microbes — Escherichia coli, Streptococcus, yeast, and so on — that have been engineered to make useful products. And we think, well, can cyanobacteria do that? And the more we looked at it, the more we realised cyanobacteria could do that for a whole swathe of chemicals.
Now, traditionally, people have been held back with cyanobacteria because they grow more slowly than some of these other microbes, and the yields were often quite disappointing. So it was hard to commercialise. There was also a relative lack of scientific knowledge about cyanobacteria — the tools for genetically modifying them and the understanding of how to cultivate them efficiently were less developed.
That has changed significantly over the last few years. The strain that we're working with was discovered about five years ago, so it's a relatively recently discovered and not widely characterised strain. That does create challenges when you're trying to engineer it, because you're learning things for the first time that no one else has encountered. But equally, it has huge potential because it grows much faster than many other cyanobacterial strains. It achieves relatively high biomass productivity and has been shown to yield commercially relevant amounts of chemicals and ingredients.
So when you take that as your base chassis organism and then think, okay, how can I improve it? It holds enormous potential to finally realise what cyanobacteria can truly do.
David Brühlmann [00:09:06]:
How is that vision linked to the discovery of this cyanobacterial strain? Tell us more about that.
Tim Corcoran [00:09:11]:
Our strain of cyanobacteria is an ocean-based strain. Now, that's important because it means it tends to be more robust. It’s used to dealing with a range of different light intensities, temperatures, CO₂ concentrations, and varying nutrient levels in the ocean. Because it’s robust, it can tolerate environmental fluctuations much better. And potentially — and we're working on this at the moment — you can fine-tune its cultivation conditions to reach a point where growth and product formation are optimised.
The fact that it's an ocean-based strain also means the chemicals it produces can legitimately be described as ocean-derived. In certain industries that may not matter as much, but in personal care — which is the industry we're focused on at the moment — that matters a great deal. Consumers respond positively to ocean-derived ingredients. They may pay more for them and choose them over other alternatives. There is a clear brand and marketing advantage.
Now, our first product is hyaluronic acid. Our hyaluronic acid would be the only ocean-derived hyaluronic acid on the market. And when you speak to personal care companies about that — and we've spoken to many — that’s the point where they start to see strong commercial potential. They can envision unique products with distinctive marketing claims that justify premium pricing, grow sales, and improve profits.
At the same time, we can say: the primary carbon feedstock here is CO₂. So the process is carbon-neutral and it is potentially carbon negative if we choose our electricity sources quite carefully. And because it's such a simple mechanism, it's a very clean, efficient process. We can make these ingredients for less than you are currently paying. So that green premium I mentioned earlier, that's no longer a factor. Worries about whether or not it's effective or not are no longer a factor because it's a drop-in solution. And you've got these unique marketing claims around the ocean-derived side, and it creates quite a compelling proposition.
David Brühlmann [00:10:54]:
You definitely have a lot of unique selling points — net zero, ocean-derived — it’s compelling. Help people better understand the differences between cyanobacteria and some more established production hosts. It can get confusing quickly. We have Escherichia coli, we have moss, we have microalgae. What are the main differences?
Tim Corcoran [00:11:15]:
I guess the starting point is prokaryotes, which include Escherichia coli and cyanobacteria — organisms without a membrane-bound nucleus — versus eukaryotes, which include microalgae, moss, yeast, and essentially most multicellular organisms. Eukaryotes are fundamentally more complex. Prokaryotes are simpler, and from a genetic engineering perspective, that simplicity can be advantageous. It can also make them metabolically efficient.
So from a cyanobacteria point of view versus microalgae, for example, which are essentially very small single-cellular plants. Cyanobacteria are simpler, and that means the photosynthetic process tends to be more efficient. Because the photosynthetic process is more efficient, that conversion of CO₂ into chemicals is more efficient. So that's sort of where it lies.
Other prokaryotes like E. coli and things like that, they're not photosynthetic. Cyanobacteria are somewhat unique in being essentially photosynthetic bacteria. So they sit almost in between the two, and arguably you could create a whole separate sort of classification for them.
David Brühlmann [00:12:09]:
Besides photosynthesis and the CO₂ aspect, are there other advantages to working with cyanobacteria?
Tim Corcoran [00:12:15]:
Because the inputs are so limited, you're not feeding sugar. Take Escherichia coli as an example — it generally requires sugar-based feedstocks. Cyanobacteria use CO₂ as their carbon source and light as their energy source, which means you can potentially leverage natural sunlight. That reduces both your carbon footprint and your input costs.
Another key aspect is the cultivation medium and the resulting broth composition. Compared with organisms like Streptococcus species or E. coli, the medium is much simpler. For marine cyanobacteria, it’s essentially water with defined mineral salts. That simplicity can make downstream processing more straightforward.
It’s worth noting that Gram-negative bacteria, including cyanobacteria and E. coli, do contain lipopolysaccharides (endotoxins). However, depending on the product and application — particularly for non-parenteral uses like cosmetics — the regulatory and purification requirements are different.
Because the cultivation inputs are defined and relatively simple, and because we design the system for secretion of the target molecule into the medium, downstream processing can be highly efficient. That efficiency is a key driver in reducing our overall cost of production and improving competitiveness versus incumbent manufacturing methods.
David Brühlmann [00:13:02]:
So this leads me to this question then. What I'm hearing, Tim, is that the current strain has many advantages and significant potential. But why haven’t more people worked with cyanobacteria historically? And why does it seem that now several companies are starting to see the opportunity? Why now?
Tim Corcoran [00:13:24]:
People have been trying to make this into an industrially viable organism — or platform technology, depending on how you want to describe it — for well over a decade. Most cyanobacterial strains grow several-fold more slowly than conventional production hosts. That’s a challenge from the outset.
Then there’s titre. Even after genetic engineering, product titres were often only a small fraction of what you might achieve with Escherichia coli, Streptococcus, or yeast. That combination — slow growth and low titres — is what historically put people off.
As I mentioned, the discovery of this relatively recent strain was one of the triggers for renewed interest. It’s still not as fast as those heterotrophic microbes, but the gap is significantly reduced. That enables greater volumetric productivity. At the same time, the molecular biology toolkit for cyanobacteria has improved considerably. Genome annotation, transformation methods, promoter systems, CRISPR-based editing — these tools have matured.
We’re working with researchers such as Alastair McCormick at the University of Edinburgh, who has developed tools for more efficient genetic modification of cyanobacteria, including our strain. That makes the R&D process far more tractable. I wouldn’t say it’s easy — it isn’t — but it makes development feasible within reasonable budgets and timelines.
David Brühlmann [00:14:38]:
One advantage I see is that your product is directly secreted. When you work with E. coli, for example, you often have to lyse the cells first. That could offset slower growth rates or even lower titres.
Tim Corcoran [00:14:53]:
Definitely. Secretion into the culture medium simplifies downstream processing substantially. We estimate that it reduces cost of goods by roughly 25–35%, which is significant. It reduces the number of unit operations and simplifies purification.
It also lowers energy demand because you’re avoiding mechanical cell disruption and some of the associated clarification steps. Fewer and simpler downstream steps mean lower overall energy consumption and a reduced carbon footprint.
David Brühlmann [00:15:17]:
And how about scale-up? You need light, CO₂, temperature control. You mentioned that scale-up was built into your thinking from the beginning.
Tim Corcoran [00:15:27]:
One of the things that we liked about cyanobacteria is that they typically grow in photobioreactors, which are essentially a series of glass tubes. They're modular by nature, and that means you don't see a significant difference in performance between, say, 100 or 1,000 litres or 10,000 litres because you're just adding more glass tubes. It's more a case of scaling out than it is scaling up. Now, I don't want to minimise the challenges involved. There will always be some challenges. So as you scale up, you have to think about access to light. You've got to make sure all of the microbes are getting access to the light coming in. You've got to make sure that the temperature remains roughly consistent because again, the more light generally, the more heat you get with it. So you've got to try and keep that controlled.
And you've also got to think about the CO₂ mixing, making sure all of the cyanobacteria are getting equal access. But actually there is enormous headroom on photobioreactor development. There's some really interesting companies coming up with some unique models to tackle these things and make it more repeatable and scalable. Companies like Algenie, for example, in Australia, who are developing a helical photobioreactor for continuous production. It makes the entire process substantially more efficient. Now, we've actually not factored in these yet. We're hoping to do some testing with these new photobioreactors in the future where potentially our cost of production comes down even further because of them. In contrast to bioreactors, I think people have been investing in and developing bioreactor technology and infrastructure for a long time now, and that reached a fairly sophisticated level. I think photobioreactors will take a similar path, but they're probably 10, 20 years behind. So the headroom for improvement is really quite exciting.
David Brühlmann [00:17:02]:
Now let's talk about the business side of things. And since you have a commercial background, I'm very curious about your thinking behind what kinds of products you have chosen. And tell us, what were the reasons behind the choices and also the business choices you made?
Tim Corcoran [00:17:17]:
It's a good question. So at the time that we formed in 2023, it was around the time that money was getting quite expensive. Interest rates were going up. People weren't investing in lending money quite like they had done for the previous 10 or 20 years. So we consciously thought about how can we be profitable quickly so that we don't have to keep going back to the well? I think the days when synthetic biology companies take 10 years to develop a product that was commercially viable are gone. You won't get the time for that. The very first piece of research we did was what is our cyanobacteria predisposed to make? What chemical precursors does it contain? And there's a really long list. So that was a good starting point. It did then make a lot of work for us though, because what we had to do is take that list and combine it with market sizes and market prices, because we wanted something with a large market and ideally a high price. If it's got a high price, then we could be competitive quickly. So when you take that Venn diagram and you sort of transpose all of those different factors, there were a number of candidates in there, but the single best candidate to start with was hyaluronic acid because it's expensive. You're generally looking $2,000 per kilogram, often sometimes quite a bit more than that. The market is big and it's growing quickly.
But also the other factor was hyaluronic acid is very popular in the personal care sector where storytelling matters. Now you can use it in pharmaceuticals, for example, and sort of therapeutics, but the storytelling matters a lot less there. If you're in that sector, people just want it to work. Whereas in personal care, when you talk about the origins of the ingredient, you talk about ocean-derived, you talk about carbon neutral, that matters and that adds value to the product. People will pay to a greater or lesser extent for that.
The other factor that we like about personal care sector is from a regulatory point of view, it's much more accessible. If you want to go into pharma or food, the regulatory barriers are somewhat intimidating. They are time-consuming and they're expensive to deal with. Personal care, obviously there are regulatory barriers, particularly safety and efficacy testing and things like that. But relatively speaking, it's a shorter, less expensive process, and there is a well-trodden path for biotech-type solutions in personal care products. So all of that together, it meant that we ended up with a nigh on, as far as we were concerned, a more or less perfect business case for hyaluronic acid. We do have a list of second, third, and fourth generation products that we're going to tap into once we've got the hyaluronic acid up and running and profitable. But also as the technology improves and becomes more efficient, you can move down the value chain and tackle slightly less expensive products until hopefully you get towards the sort of the commodity end of the market. Because from an environmental perspective, that's where you'll have the biggest impact.
David Brühlmann [00:19:51]:
Your approach sounds a lot like the Tesla model where you start with the premium products to earn some money quickly and then you walk down, should I say, the value chain, or at least the cost. This is an important message. I just want to stress that again because a lot of people listening are scientists and we think about the science. Tim, you have a commercial background, so tell the scientists listening why your approach is such a game changer. What changes when you start with the premium product first?
Tim Corcoran [00:20:20]:
When you start with a premium product, it means the technology doesn't have to be perfect to go to market. You've got a far better chance of taking something which is good enough and turning it into a profitable business. Now, in the years I worked as a business development consultant, I worked with a lot of brilliant people who were fantastic at what they did, but they didn't have that commercial expertise. They didn't know how to translate that. There are people all over the place, business development consultants like me. If you're at a university, they will have entire departments dedicated to taking technical ideas and translating those. And what I would say I'd say to any scientist with an idea out there is go and speak to these people. Ask them, do you think this could work? How could it work? Because they can help you with the planning. They can help you with identifying the customers.
One of the things when I was working in innovation market research a few years ago, one of the key characteristics of successful innovation, successful new products, was understanding the market, understanding what people want. Now, that's not necessarily a job for scientists. They'll need people to help them with that. But speak to your potential customers. Understand what it is they want. I think if you go back 20 years, maybe more, companies used to come up with a new product and then try and find a way of chucking it at the market and hoping it stuck. I think in the last 10 years in particular, companies have become a lot more efficient, a lot more intelligent about it. They look at the market, they look at what the market wants, where the gaps are, and then they start on the innovation process. And they end up with products which hopefully are perfectly suited to an unmet need in the market. So that market research, customer discovery is a vital part of the process. Before you start sinking significant amounts of money into sort of the development and commercialisation, you need to understand that because that help guide your subsequent research.
David Brühlmann [00:21:55]:
And what are some commodity products you think could still be interesting, but where you have a huge ecological benefit? What are you thinking about?
Tim Corcoran [00:22:04]:
From our point of view, biofuels is an area of strong interest. Now, cyanobacteria are able to make things like butanol, for example. Now, butanol currently isn't used as a biofuel because there isn't really a clean, efficient way of making it. But actually, as a biofuel, it holds great potential because it has a far closer profile to petrol and diesel than ethanol does. So if you can make butanol in a carbon-neutral fashion, it has great potential as a biofuel. Now, the challenge is butanol is currently, it's about $2 a litre, so it's cheap. And generally speaking, people don't want to pay a huge amount more for their fuel. So the goal is to be able to make butanol efficiently. Now, the thing that the triggers for us to be able to do that, and it's somewhere in the future, but we are going to be working on it further, is we need to get the yields from the cyanobacteria up. Now, the good news is for our cyanobacteria străin, the yields that we've already got are actually looking far more promising than we ever expected. So we actually think there are multiples of what we thought were achievable are now achievable. The cost of productions are much lower than we initially expected, and the technology continues to improve.
Scaling up to deal with a biofuel to make a significant impact on the petrol market or the diesel market or something like that, that's a somewhat intimidating prospect because you think about the scale of the petrochemical industry. But if you want to replace them, you do need to be able to scale it up. So if you want to have a photobioreactor producing butanol that isn't the size of a small country, the critical points will be that yield, getting a few grams per liter, perhaps 5 to 10 grams per liter into that photobioreactor, at which point it starts to look potentially like quite an efficient way of doing it. The environmental impact is sort of absolutely mind-blowing.
David Brühlmann [00:23:46]:
We have explored why cyanobacteria's unique biology, photosynthesis, CO₂ utilization and direct secretion finally makes commercial sense. In part 2, we'll dive into the strategic decisions that separate successful synthetic biology from brilliant failures. We'll talk about choosing hyaluronic acid over commodity fuels, navigating photobioreactor scale-up, and building toward a licensing model instead of capital-intensive facilities.
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 Tim Corcoran
Tim Corcoran is the Co-Founder and CEO of Deep Blue Biotech and an experienced business development consultant with over 25 years of experience guiding start-ups and scale-ups.
He specializes in growth strategies, investor relations, and building strong partnerships that create long-term business value.
Connect with Tim Corcoran 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
Last time, we covered the biology of how raffinose works and the experimental journey that led to a 2.8-fold increase in high mannose glycans. Today, we're getting practical. I'm going to walk you through when raffinose works, when it doesn't, and the exact three-experiment protocol you can run in 8 weeks to validate it for your process.
Let's dive in.
This concept is discussed in greater detail in the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.
First, let's talk about scope. Because raffinose is not a universal solution, and I don't want you spending time and resources on something that won't work for your program.
✅ Use raffinose when you need to increase high mannose for biosimilar matching. Specifically, when your cell line's baseline high mannose is 1 to 3 percent and you need to get to 5 to 8 percent. That's the window where raffinose shines. You have room to move, and the effect size is large enough to hit your target.
✅ Use raffinose when you have analytical bandwidth to track Man5, Man6, Man7, and Man8 individually. If you're only measuring total high mannose, you're flying blind. You need to see the distribution because raffinose shifts the profile toward Man5. If your reference product is heavy in Man8 or Man9, raffinose won't get you there.
✅ Use raffinose when you're in process development—before you've locked your process for regulatory filing. Media optimization is expected at this stage. Regulators understand it. It's low risk.
Now, when should you not use raffinose?
❌ Don't use it if you need to decrease high mannose. If your baseline is already 10 or 12 percent and you need to bring it down, raffinose will make it worse. In that case, look at feed strategies or temperature shifts to drive glycan elaboration.
❌ Don't use it if your baseline high mannose is already above 10 percent. At that point, you have a cell line issue, not a media issue. Media tweaks won't fix a cell line that's fundamentally not processing glycans correctly. You need to go back and select a better clone.
❌ Don't use it if you need Man8 or Man9 specifically. Raffinose gives you predominantly Man5. If your reference product has a different high mannose distribution, you need a different tool. Kifunensine might be your answer, despite the cost and complexity.
❌ And don't use it if your titer is already marginal—below a few grams per liter. In that case, prioritize productivity first. Get your titer up, then worry about glycan matching. You can't afford to take a 20 percent titer hit when you're barely viable.
🔑 The key thing to understand is this: raffinose is tunable. The sweet spot for most processes is 15 to 50 millimolar. At concentrations above 65 millimolar—even with constant osmolality—you start seeing growth inhibition and titer hits. So you have a working range, and you need to find your optimal point within that range.
That's what the three-experiment protocol is designed to do.
Here's the roadmap. Three experiments. Eight weeks total. Clear go/no-go decision points at each stage.
1️⃣ Experiment 1: Dose-response screen in 96-well plates.
Test four concentrations: 0, 10, 30, and 50 millimolar raffinose. Do this in your current basal medium. Maintain constant osmolality by adjusting sodium chloride. This is critical—if you don't control osmolality, you're back to confounding variables.
Track three things: viable cell density, titer, and glycan profile at harvest. You need all three data points to make an informed decision.
Go/no-go decision: If you see at least a 2-fold increase in high mannose at 30 millimolar with less than 20 percent titer loss, proceed to Experiment 2. If you don't hit that threshold, stop here. Raffinose won't solve your problem. You'll need to revisit your cell line or explore other glycan control strategies like temperature shifts.
2️⃣ Experiment 2: Spin tube confirmation.
Take your top two concentrations from Experiment 1 and run them in spin tubes. Spin tubes give you better metabolic profiling than 96-well plates. You can sample every two days and track glycan evolution over the entire culture duration.
This is where you see if the high mannose increase is transient or stable. Some media additives give you a Day 5 effect that disappears by Day 10. You need to know if raffinose holds through to harvest.
Optional but insightful: measure intracellular UDP-galactose and UDP-GlcNAc if you have the analytical capability. This tells you whether raffinose is affecting nucleotide sugar pools, which would explain part of the mechanism. But if you don't have this capability, don't let it block you. It's not required for the go/no-go decision.
Go/no-go decision: If the high mannose increase is consistent across the time course and titer recovers by day 10 to 12, proceed to Experiment 3. If you see a glycan reversion after day 7 or if titer stays suppressed, you have a problem. Either adjust your concentration downward or reconsider the approach.
3️⃣ Experiment 3: Scale-up in bench-top bioreactors.
This is where you validate robustness. Take your lead concentration and run it in controlled pH and dissolved oxygen conditions—the environment your manufacturing process will actually see.
And here's a tip: challenge your process with stressed conditions. Run one batch at pH 6.9 instead of 7.0. Run another at 35 percent dissolved oxygen instead of 40 percent. Spike glucose on day 12 to see if metabolic stress affects the glycan profile. You want to know your boundaries before you commit to manufacturing.
Go/no-go decision: If all three batches hit your high mannose target and you don't see unexpected issues—aggregation, charge variant shifts, titer collapse—you have a robust process. Document it. Lock it in. Move to your next process development milestone.
Let me share three mistakes we made during this work—and how you can avoid them.
❌ Mistake 1: Waited too long to involve analytical.
We optimized media formulations in 96-well plates for weeks before getting our first glycan data back. We were measuring titer and viability, but we were blind to the quality attribute we were trying to control.
The fix? Get analytical buy-in on Day 1. You need rapid turnaround—ideally 48 hours or less—from sample harvest to glycan data. If your analytical team can't support that, this project will drag on for months. Build that partnership early. Make it a priority.
❌ Mistake 2: Didn't map the design space early.
Remember earlier when I said we tested raffinose at fixed pH? We never explored pH-by-raffinose interactions. We never tested temperature-by-raffinose interactions. We simply didn't check whether the raffinose effect would hold across different pH or temperature conditions.
The fix? Once you have a lead concentration from Experiment 1, do a mini design-of-experiments: raffinose by pH by temperature. Understa nd your boundaries. Know where the effect is strong and where it's weak. That knowledge will save you when you hit an unexpected process deviation at scale.
❌ Mistake 3: Didn't check feed interference.
We optimized raffinose in basal medium and assumed the effect would carry over when we added our standard bolus feed on day 7. We didn't test whether feed components might interfere with the raffinose mechanism.
Given what we learned about osmolality—that it can completely mask or confound the raffinose effect—feed interference could be equally substantial. Feed compositions vary widely and often contain components like manganese, galactose, or other supplements that could promote or inhibit glycan processing.
The fix? Test raffinose in your actual feed schedule from the start, and test higher and lower feed additions. Feed composition matters. Don't optimize basal in isolation and assume it will carry over.
These mistakes cost time. They cost materials. They cost credibility with your manufacturing partners. You can avoid them by planning more carefully upfront.
Here's what this research taught me, and it goes beyond raffinose.
Glycosylation isn't downstream of the process. It's not something you fix at the end after you've optimized titer and viability. Glycosylation is designed into the media from Day 1.
Most scientists optimize for titer first. They pick a cell line. They tune the feeds. They hit 3 or 4 grams per liter. Then analytical comes back with glycan data, and it's out of spec. Now they're scrambling. Temperature shifts, feed adjustments, maybe a late-stage media tweak. It's reactive.
The teams that win? They co-optimize titer and glycosylation from the first design-of-experiments study. They set up their 96-deepwell screens with glycan profiling built in. They track high mannose, galactosylation, sialylation, and fucosylation alongside titer and viability. They see the trade-offs in real time. And they make informed decisions about where to land on the productivity-quality curve.
Raffinose is one lever. There are others—we'll explore them in future episodes. Manganese, galactose, feed timing, temperature profiles. But the principle holds: your media is your glycoengineering platform.
In short, media optimization can be a powerful way—faster, cheaper, and less risky than cell line reengineering—to optimize the quality attributes of your recombinant protein.
If you lock that mindset in early, you'll avoid the late-stage scrambles. You'll hit your regulatory milestones on time. And you'll save your team months of rework.
If you want more details, you can access the full peer-reviewed paper in the Journal of Biotechnology, 2017, volume 252, pages 32 to 42. DOI: 10.1016/j.jbiotec.2017.04.026.
If you found this episode valuable, I'd love your feedback. The best way to share it is by leaving a review. It helps other scientists discover these insights and lets me know what's resonating with you.
Thank you for taking this journey with me into media-based glycosylation control for biologics manufacturing.
Until then—smarten up your biotech.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
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
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