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.
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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
🧬 Stop second-guessing your CMC strategy. Our fast-track CMC roadmap assessment identifies critical gaps that could derail your timelines and gives you the clarity to build a submission package that regulators approve. Secure your assessment at https://stan.store/SmartBiotech/p/get-cmc-clarity-in-1-week--investor-ready
Some years ago, I came across a study that stopped me in my tracks. Cyclists who ate pistachios before a 75-kilometer time trial performed worse than those who didn't. Not marginally—4.8% slower. Statistically significant. Why would a healthy nut sabotage athletic performance?
The culprit: raffinose, a trisaccharide in pistachios. During intense exercise, your gut becomes more permeable—and raffinose from the colon leaked into the bloodstream. Once there, it correlated with oxidative stress and leukotoxic effects. Your body was literally fighting the fuel you just gave it.
This concept is discussed in greater detail in the Smart Biotech Scientist Podcast, hosted by David Brühlmann, founder of Brühlmann Consulting.
I thought: if raffinose can slip through a stressed gut barrier and trigger metabolic effects in humans, what does it do to CHO cells under bioprocess stress? That question led to one of the most surprising findings of my PhD work.
Before we dive into that story, let me give you the roadmap for today. First, I'll walk you through why glycosylation matters for biosimilars and what happens when it goes wrong. Then, I'll show you the biology of how raffinose changes glycan processing. After that, I'll take you through the experimental journey—what we tried, what failed, and what finally worked. And I'll close with the real-world impact this had on actual development programs.
But let me tell you what this episode is really about. It's about media design that increased high mannose glycans 2.8-fold in CHO cell culture runs. It works in multiple cell lines. It doesn't require cell line reengineering or expensive glycosidase inhibitors. It's not a universal fix—I'll tell you exactly when it works and when it doesn't—but if you're 6 to 12 months from filing, this could save your timeline.
Before we go further, a quick primer for anyone who's new to glycosylation or needs a refresher.
Glycosylation is the process where sugar chains get attached to your protein as it's being made. Think of it like decorating a cake as it comes out of the oven—except the decorations determine whether your drug works or gets cleared from the bloodstream in 10 minutes.
This happens in two cellular compartments. First, the endoplasmic reticulum adds a core oligosaccharide structure—14 sugars arranged in a specific tree-like pattern. That's the starting template. Then, the protein moves to the Golgi apparatus, where the real action happens. Enzymes in the Golgi trim off some sugars and add others, sculpting that core structure into the final glycan pattern.
For monoclonal antibodies, glycosylation affects a lot. Half-life in circulation. Immune activation through Fc receptors. Protein stability. And most importantly for biosimilar developers: whether regulators will approve your product.
Get it wrong, and you're looking at 6 to 12 months of rework. Get it right, and you're on track for approval.
Now, back to raffinose and why it matters.
Let me paint you a scenario that's all too common in biosimilar development.
You've spent 18 months developing a biosimilar mAb. Your cell line is stable. Titer is great—3 grams per liter. You're hitting your productivity targets. The team is feeling good.
Then analytical drops the bomb. Your high mannose levels are 1.4 percent. The reference product? 6 percent. You're out of spec.
Now, you might be thinking: why does this matter? It's just a small difference in sugar composition.
Here's why it matters. High mannose glycans affect antibody-dependent cellular cytotoxicity—ADCC. They affect receptor binding kinetics. They affect serum clearance rates. And most critically, they affect regulatory comparability. If your glycan profile doesn't match the reference product within an acceptable range, regulators will ask for additional studies. Or they'll reject your filing outright.
So what are your options?
1️⃣ Option one is a temperature downshift to ~32–33 °C. It can push the glycan profile toward higher high mannose, but the productivity tradeoff is clone- and process-dependent.
2️⃣Option two: kifunensine. It's a glycosidase inhibitor that blocks mannosidase enzymes in the Golgi. It's effective—you get high mannose, no question. But it's expensive, hard to scale, and regulatory agencies scrutinize any process that uses enzyme inhibitors. You'll spend months justifying it in your CMC package.
3️⃣Option three: re-engineer your cell line. Go back to the drawing board. Select a new clone with better glycosylation characteristics. This works, but it adds 12 to 18 months to your timeline. If you're racing to market against competitor biosimilars, that delay could cost you hundreds of millions in lost revenue.
What you really need is a media-based solution. Something that scales. Something that doesn't torpedo your productivity. Something that regulators understand because it's just a media component adjustment.
That's where raffinose comes in.
Let me explain the biology of what's happening when you add raffinose to your cell culture.
Raffinose is a trisaccharide—three sugars linked together: galactose, glucose, and fructose. CHO cells can't metabolize it efficiently, so it accumulates in the culture medium and in the cell.
It turns out that raffinose competes with a specific enzyme in the Golgi: N-acetylglucosaminyltransferase, or GlcNAc transferase. This enzyme is responsible for adding the first branch to the core glycan structure. Think of it as the fork in the road where a simple glycan becomes a complex, multi-antennary structure.
When raffinose is present, it binds to the active site of GlcNAc transferase. It doesn't shut the enzyme down completely—it just slows it down. The result? Your antibody ends up with Man5—mannose-5—instead of the complex, fully elaborated glycans you'd normally see.
This is different from kifunensine. Kifunensine blocks mannosidase I and gives you Man8 and Man9—much earlier intermediates. Raffinose arrests glycan processing at a later step, giving you predominantly Man5.
We confirmed this mechanism with transcriptomics. When we looked at gene expression in cells treated with raffinose, we saw downregulation of galactosyltransferase—GalT—and other late-stage Golgi enzymes. The cells were adapting to the metabolic stress by dialing back their glycan elaboration machinery.
The key thing to understand is this: raffinose doesn't shut down glycosylation entirely. It arrests it at a specific step. That's why you get Man5 enrichment, not a complete block. And that's why it's tunable—you can dial the concentration up or down to hit your target glycan profile.
Now, knowing the mechanism is one thing. Making it work in practice? That's where things got interesting.
Let me take you through what we actually tried in the lab. Because the path to the breakthrough was not straightforward.
1️⃣ Attempt one: standard supplements.
We started with the usual suspects. Manganese chloride—known to affect glycosyltransferase activity. Galactose and GlcNAc at various concentrations—trying to push or pull the glycan pathway. We tested these in 96-well plates across a range of concentrations.
Result? Minor glycan shifts. Nothing reproducible. Batch-to-batch variability was all over the place. No clear dose-response relationship. We were tweaking the system, but we weren't controlling it.
2️⃣ Attempt two: osmolality manipulation.
We knew that osmotic stress affects Golgi function. So we tried increasing osmolality by adding raffinose at high concentrations—50, 75, 100 millimolar. The idea was to stress the Golgi and slow down glycan processing.
Result? Growth crashed. At 50 millimolar raffinose, cell viability dropped below 70 percent by day 5. Titer tanked. We thought we'd hit a dead end. Raffinose looked like a non-starter.
But then we had a realization. We were confounding two variables: raffinose concentration and osmotic stress. High raffinose meant high osmolality, and we couldn't tell which factor was causing the growth inhibition.
3️⃣ Attempt three: the constant osmolality pivot.
This was the key experiment. We adjusted sodium chloride in the medium to keep osmolality constant at 315 milliosmoles per kilogram—regardless of raffinose concentration. So when we added 30 millimolar raffinose, we subtracted enough NaCl to maintain constant osmolality.
Result? Growth recovered. Viability stayed above 90 percent through day 14. And now we could test raffinose concentrations up to 100 millimolar without killing the cells.
Once we isolated raffinose's effect from osmotic stress, the data became crystal clear. We had a dose-response curve. We had reproducibility. We had scale-up potential.
It turns out that the breakthrough wasn't in finding a magic ingredient. It was in controlling the variables so we could see what raffinose was actually doing.
Here's what worked.
50 millimolar raffinose increased high mannose 2.8-fold compared to the control. The effect was robust. It worked in two different cell lines. It worked in two different basal media. It worked at every scale we tested: 96-well plates, shake tubes, benchtop bioreactors.
The surprise? The glycan profile was predominantly Man5. Not Man8 or Man9 like you'd see with kifunensine. This told us we were hitting a different step in the Golgi processing pathway.
Now, I was fortunate to apply this approach in actual development projects. I can't share specifics—those programs are confidential—but I can tell you this: when you're staring at a glycan mismatch 8 months before your IND filing, having a validated media lever in your back pocket is the difference between making your timeline and explaining to leadership why you need another year.
One more thing about this approach that's often overlooked: it's regulatory-friendly. Raffinose is a simple trisaccharide. It's available as a GMP-grade material from multiple suppliers. It's not an enzyme inhibitor. It's not a genetic modification. It's a media optimization—something process development teams do all the time.
If you're pre-IND, this is straightforward. You're optimizing your process, and media composition is part of that. Document it. Include it in your development report. Done.
So we proved raffinose works. But here's the hard part: how do you actually implement this in your process without spending 6 months on design-of-experiments studies?
In Part 2, I'll give you the exact decision tree. When to use raffinose. When it won't work. And the three experiments you need to de-risk it before committing to your manufacturing campaign.
If you want to dig into the full methods and data, the paper was published in the Journal of Biotechnology, 2017, volume 252, pages 32 to 42. DOI: 10.1016/j.jbiotec.2017.04.026.
Thanks for joining me in exploring the biology behind raffinose and glycan control in CHO cell culture.
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.
See you in Part 2.
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
🧬 Stop second-guessing your CMC strategy. Our fast-track CMC roadmap assessment identifies critical gaps that could derail your timelines and gives you the clarity to build a submission package that regulators approve. Secure your assessment at https://stan.store/SmartBiotech/p/get-cmc-clarity-in-1-week--investor-ready
A new era is emerging in cancer diagnosis and therapy—one that extends beyond common indications like prostate cancer to address notoriously difficult-to-treat tumors that have long challenged oncologists. Radiopharmaceuticals, with their unique ability to both detect and destroy disease, are rapidly redefining what is possible in clinical research.
In this episode of the Smart Biotech Scientist Podcast, David Brühlmann speaks with Bryan Miller, Director of Scientific and Technical Operations at Crown Bioscience UK, about how advanced preclinical models are used to help clients advance innovative cancer therapies toward the clinic.
I think we are also seeing radiopharmaceuticals developed for a wider range of cancers. We do have therapies available for prostate cancer and neuroendocrine cancer, but we are seeing this expanding—and this will further expand over the coming years, which I think is a really exciting prospect. I think particularly the value that Crown Bioscience brings is what we touched on before in terms of the value of the models that Crown has in the radiopharmaceutical field. We can now offer our platforms of preclinical models to our clients to run radiopharmaceutical studies.
David Brühlmann [00:00:34]:
Welcome back. In Part One, Bryan Miller walked usthrough his journey from cancer research toradiopharmaceutical development and explained why this therapeutic class is capturing the attention of oncology researchers worldwide. Now we’re shifting gears to explore how Crown Bioscience is scaling its preclinical capabilities to meet exploding demand, the strategic partnerships shaping the field, and the technological innovations that could define radiopharmaceutical development over the next five years. Let’s dive back in.
Now I’d like to touch upon a different part of the technology. There’s a lot of innovation happening. Many are saying that we’re entering a new era for radiopharmaceutical innovation. What makes this moment or season different, and how are you as a company positioned to help shape that evolution as this field scales?
Bryan Miller [00:02:44]:
Yeah, I completely agree. I think we are entering a new era for it. It’s quite exciting what we’re starting to see, and one of the things that is most exciting to me—I touched on it a little before—is the development of diagnostics. We’re certainly seeing increasing interest and excitement about the potential of radiopharmaceuticals.
We’re also seeing increased interest in the theranostic use of radiopharmaceuticals—this idea that you can use the same material as both a diagnostic agent and a therapeutic agent. There’s huge potential here, particularly for patient stratification and rapid movement from diagnosis to treatment.
We’re also seeing radiopharmaceuticals developed for a wider range of cancers. We do have therapies available for prostate cancer and neuroendocrine cancer, but this is expanding and will continue to expand over the coming years, which is very exciting.
I think particularly the value that Crown Bioscience brings is in the preclinical models we offer for radiopharmaceutical studies. Our platforms give clients the opportunity to run studies using very advanced, well-characterized models, which opens up a lot of possibilities for research and development in this field.
David Brühlmann [00:04:14]:
What kind of limits do these models have, and where do you see the development going? What kinds of limitations are we likely to overcome very shortly?
Bryan Miller [00:04:26]:
I wouldn’t suggest there are strict limitations as such. As mentioned, we have a number of different platforms, so we can literally take a study from two-dimensional (2D) cell lines to three-dimensional (3D) organoids, then into CDX in vivo or PDX in vivo. That gives a very strong workflow in terms of maturing a study over time. By the time you’re running a study in a PDX model, you’re working with a model that recapitulates the heterogeneity of human tumors and provides highly clinically relevant information, which can generate very powerful data.
David Brühlmann [00:05:04]:
Perhaps give a definition once again of PDX and CDX. What does CDX mean?
Bryan Miller [00:05:11]:
CDX is a cell line-derived xenograft. These are xenograft models where a human cell line is implanted into a mouse to form a tumor, either subcutaneously or orthotopically.
For PDX, that’s a patient-derived xenograft. These models are set up from patient tumor samples—they’ve never been cultured in vitro. The tissue is grown in animals, then cryopreserved from the animal tumors. This approach retains many features of the original tumor, so it provides a more physiologically and clinically relevant output than a CDX, where the cells have undergone selection and adaptation to culture. PDX models retain much more of the tumor’s original characteristics.
David Brühlmann [00:05:51]:
The power of using models, as you highlighted before, is that you can move faster. But another purpose is to have more predictive models to get closer to your targets. What strategies do you use to achieve that?
Bryan Miller [00:06:09]:
I would say there are two main strategies, depending on whether you’re at the in vitro or in vivo stage.
Our organoid models are incredibly valuable. They form 3D structures that begin to recapitulate features you would see in an organ. That gives a much more clinically relevant output than a traditional 2D cell culture model.
With PDX models, the key is that they retain many features of the original tumor. Crucially, they maintain heterogeneity, unlike a tumor grown from a cell line. This intrinsic heterogeneity means that the model’s response to therapy better reflects what we would see in a clinical context.
David Brühlmann [00:07:04]:
I’m curious, Bryan. What I’m hearing is that there’s a lot happening in the space—innovation, changes, breakthroughs. What breakthroughs are you most excited about?
Bryan Miller [00:07:17]:
For me, it’s theranostics. That’s something I find very exciting, particularly their applicability to treating cancers. Take PDAC (pancreatic ductal adenocarcinoma)—very difficult to treat. Current treatment options are limited, and the five-year survival rate is still very low.
What excites me is the development of radiopharmaceuticals with theranostic applications for these hard-to-treat cancers. I see a lot of potential here, and we are already starting to see more development in this space. I expect it will continue to advance significantly in the coming years.
David Brühlmann [00:08:01]:
And how do you balance, in your role, moving fast and innovating quickly while also working in an industry where everything will eventually be regulated? Even at early stages, how do you factor in regulatory considerations and ensure that patients will ultimately receive what’s being developed? How do you manage these two worlds?
Bryan Miller [00:08:26]:
Well, what I would say is that robust quality standards are always absolutely crucial. At Crown Bioscience, we’re dedicated to delivering scientific studies of the highest possible quality. In setting up our strategic collaboration with Medicines Discovery Catapult (MDC), we partnered with an organization that shares our values and commitment to quality.
Having these robust quality systems in place allows our teams to perform work rapidly to meet essential timelines while maintaining extremely high standards. There’s no conflict between high-quality research and speed. If you conduct a low-quality study, that will set your timelines back—poor quality prevents you from getting the answers you need and delays the entire research program.
It’s crucial to work with partners who uphold very high standards of quality and QC, and this is ingrained in the culture at Crown. Our clients know the standards we work to, and we consistently deliver on timelines while maintaining the highest quality.
David Brühlmann [00:09:35]:
Let’s make this practical. What advice would you give a smart biotech scientist who’s curious about radiopharmaceutical development? Where should they start, and what can wait until later?
Bryan Miller [00:09:53]:
I would suggest working with the right partners. Developing a robust strategy for your study is critical—how you’re going to target the tumor, construct your therapeutic, and select preclinical models. Drawing on the knowledge of experienced people in the industry is crucial, and that’s a lot of the value a CRO can bring.
At Crown, we have extensive experience helping clients in the preclinical space. We can advise on study development, help design your study, guide model selection, and assist with isotope choice and labeling strategy for your test material. Partnering with the right experts early on ensures you have a strong strategy to deliver a successful study.
David Brühlmann [00:10:48]:
How do you make a partnership like this successful? Entering into a relationship with a CRO—or later, a CDMO—is sometimes like a marriage. It's very similar. You have to select very well your partner. What is your approach there? Because I'd say not every company fits every CRO. There are differences and it doesn't always make sense for a company to go to one because it's the best or whatever reason everybody recommends. Sometimes it makes sense to go to another. Right.
Bryan Miller [00:11:19]:
I think people choose CROs on different criteria. At Crown, we do work a very wide array of clients with different needs and different interests. And we can always tailor a study to the individual client. One of the things that we always try to do is we do like to see ourselves as an extension of that client's lab. We work in close collaboration in the design of the study. And there's very open communication during the progress of the study as well. I think the success is seen by the amount of repeat business that we get. And we do form these close collaborations with our clients because we are really dedicated to helping them achieve the objectives of their studies. And we've had the feedback time and again that they start to see us as part of their team. They almost see us as part of the research team and they collaborate very closely with us in terms of delivering the study successfully.
David Brühlmann [00:12:14]:
Now I would like to look forward into the future, since there’s a lot of innovation going on. What is your picture? Where do you see this field in five years?
Bryan Miller [00:12:26]:
It’s a dynamic field at the moment. There’s already a lot of exciting progress, and there’s more to come. We’re certainly seeing rapid growth in interest in radiopharmaceuticals, and I expect this to continue in the coming years.
What excites me is the prospect of increasing the number of cancer indications for which radiopharmaceuticals are a viable option—for diagnosis and therapy. This is particularly important for hard-to-treat cancers, where current treatment options are limited. I think radiopharmaceuticals offer the potential to really advance new treatments in these areas. So I anticipate we will see a significant impact of radiopharmaceuticals in treating these challenging cancers in the years ahead.
David Brühlmann [00:13:18]:
Bryan, what is the most important takeaway from our conversation?
Bryan Miller [00:13:24]:
I think the most important takeaway is the level of excitement and potential in radiopharmaceuticals, especially for oncology. Radiopharmaceuticals also have applications beyond oncology in other disease areas.
As I’ve mentioned, Crown Bioscience is a strong partner for these types of studies. We have a fantastic library of models, both in vitro and in vivo, available for radiopharmaceutical research. In partnership with Medicines Discovery Catapult (MDC), we have the expertise to help design and deliver highly successful radiopharmaceutical programs.
David Brühlmann [00:14:09]:
Where can people get a hold of you and further have a conversation about your work?
Bryan Miller [00:14:15]:
People can contact me through LinkedIn and I'd be delighted to give them any more information that they need on breeder pharmaceutical programs at Crown or indeed any other services that Crown offers.
David Brühlmann [00:14:25]:
Fantastic. I’ll leave the links in the show notes on Smart Biotech Scientist. Bryan, thank you so much for coming on the show today.
Bryan Miller [00:14:33]:
Thank you, David.
David Brühlmann [00:14:34]:
And that wraps up our conversation with Bryan Miller.
The radiopharmaceutical revolution is just getting started, and the preclinical strategies we discussed today could reshape how these therapies move from concept to clinic. If you are navigating the complexities of biotech development—whether in radiopharmaceuticals or beyond—I hope this conversation sparked some new ideas.
If it did, take 30 seconds to leave a review on Apple Podcasts or wherever you’re listening. Your feedback helps us bring more insights to the biotech community.
Thank you for tuning in. Until next time.
For additional bioprocessing tips, visit 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 Bryan Miller
Bryan Miller is Director of Scientific and Technical Operations at Crown Bioscience, with over a decade of experience in oncology research and preclinical drug development. He earned his PhD in Biochemistry from the University of Leicester and completed postdoctoral training at the University of Toronto and the Beatson Institute, specializing in in vivo and in vitro models of colorectal and pancreatic cancer.
After moving into the CRO field in 2015, Bryan has focused on translating strong science into effective preclinical strategies for oncology programs. Since joining Crown Bioscience UK in 2019, he has played a key role in guiding scientific operations and supporting clients across diverse cancer indications and therapeutic approaches.
Connect with Bryan Miller 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
Imagine treating cancer with something as precise as a guided missile—radioactive payloads delivered only where they're needed most. Radiopharmaceuticals are redefining both diagnostics and therapeutics, creating new momentum in oncology by pairing tumor-seeking molecules with potent radioisotopes. But what does it really take to develop these agents, and why are investors and scientists alike turning their attention to this field?
In this episode of Smart Biotech Scientist Podcast, David Brühlmann sits down with Bryan Miller, Director of Scientific and Technical Operations at Crown Bioscience UK, a Contract Research Organization (CRO) specializing in translational oncology and immuno-oncology drug discovery and development.
Radiopharmaceuticals are diagnostic and therapeutic agents. So they consist of a targeting agent—typically a small molecule or an antibody—and this will target the tumor. There’s also a radionuclide, and there’s a linker that connects the two. The radionuclide can act as either an imaging agent for diagnosis of cancer or as a therapeutic agent, where the radionuclide delivers a lethal dose of radiation to the tumor.
David Brühlmann [00:00:27]:
What if the next breakthrough in cancer treatment isn’t a pill or an infusion, but a precisely targeted radioactive payload that hunts down tumor cells like a guided missile? Today we’re diving into the radiopharmaceutical revolution with Bryan Miller, who is the Director of Scientific and Technical Operations at Crown Bioscience UK. From his early days studying colorectal cancer models to now leading preclinical development for one of oncology’s hottest therapeutic classes, Bryan is taking us inside science that’stransforming how we fight cancer.
Welcome, Bryan, to the Smart Biotech Scientist Podcast. It’s good to have you on today.
Bryan Miller [00:02:25]:
It’s great to be here, David.
David Brühlmann [00:02:27]:
Bryan, share something that you believe about early discovery research that most people disagree with.
Bryan Miller [00:02:34]:
So I’ve found that research progress is rarely linear or predictable. Over my career, I’ve seen the undruggable become druggable. I’ve seen false starts in some areas and surprisingly rapid advances in others. And I think that’s why I find it so dynamic and exciting—and why I love being a part of it.
David Brühlmann [00:02:51]:
Excellent. Draw us into your story. Tell us what sparked your interest in working on cancer models, and then what were some interesting pit stops along the way that got you to your current role?
Bryan Miller [00:03:03]:
Yeah, it’s quite interesting. So my PhD was actually in cardiac disease. So that’s where I started out. It was when I did postdoctoral fellowships—first at the University of Toronto and the second at the Beatson Institute (UK)—that really took me towards oncology models, both in vitro and in vivo. My postdocs were focused on models of colorectal and pancreatic cancer. And I think what motivated me at that point is exactly the same as what motivates me now. I was passionate about contributing to the development of new therapies for cancer and identifying new therapeutic targets. And really that’s what motivates me to go to work every day to this day. Being part of Crown Bioscience allows me to work across a large array of different cancer indications with a diverse range of clients. And it’s really motivating when you’re contributing to the development of new therapies. It’s fantastic when you’ve contributed to a research program that has led to a therapy going into the clinic.
David Brühlmann [00:03:58]:
Tell us a bit more about your current work. What does that look like when you’re developing these new therapies?
Bryan Miller [00:04:05]:
So we are a preclinical contract research organization, oncology focused, but we do work with a large array of different clients working on various therapeutic types. We have a range of advanced preclinical models available to our clients to run their research programs with. We work across most cancer indications and across most therapeutic types. So it’s a very diverse area of work that we conduct at Crown, which makes it really interesting. It’s very varied, it’s very interesting, and it’s absolutely fantastic to support this variety of client research projects.
David Brühlmann [00:04:42]:
And when you say it’s very diverse, that includes the various diseases you’re working on, and I imagine also very diverse companies with very diverse needs. Before we dive a bit further into radiopharmaceuticals, what is the main need companies have when they come to you? What is the main problem they want to solve?
Bryan Miller [00:05:04]:
They typically come to us with a target in mind and usually with a therapeutic that they wish to test. One of our main roles is to guide them towards the most appropriate preclinical model to address the question they’re asking. We have a range of different models available. We’ve got an excellent array of in vitro models, and we’re particularly well known for our organoid platform. So certainly at the in vitro phase, we have advanced models that can be very valuable. We’re also very well known for our PDX library. We have PDX models that cover most cancer indications, and we have genomic data associated with them. If you have targets in mind, we can guide you towards the most appropriate PDX model for your work. In addition, we have an array of TDX models, genetically engineered models, and humanized models. So really, while we’re cancer focused, for most questions around the development of cancer treatments—including neuro-oncology therapies—we will have appropriate models to support your research program and help you filter down to the most relevant ones.
David Brühlmann [00:06:08]:
When you say models, are these animal models, or are these 2D cultures, 3D cultures, or a combination of various models?
Bryan Miller [00:06:16]:
All of that, actually. So yes—2D cell culture, 3D cell lines, organoids, and various in vivo models. That includes syngeneic models, CDX models, PDX models, and humanized models. So it really is quite a wide array.
David Brühlmann [00:06:31]:
Excellent. So let’s dive into one specific area—radiopharmaceuticals. A lot of our listeners, I imagine, are not familiar with that. Can you start with the basics? What are radiopharmaceuticals, and how do they differ from traditional cancer therapies?
Bryan Miller [00:06:52]:Yeah, so radiopharmaceuticals are diagnostic and therapeutic agents. They consist of a targeting agent—typically a small molecule or an antibody—which targets the tumor. There’s also a radionuclide, and there’s a linker that connects the two. The radionuclide can act as either an imaging agent for the diagnosis of cancer or as a therapeutic agent, where the radionuclide delivers a lethal dose of radiation to the tumor.
David Brühlmann [00:07:18]:
Can you elaborate a bit more? Give us an example. How are they different from a traditional cancer therapy? Is it the morphology, the mechanism, or what exactly are the main differences?
Bryan Miller [00:07:32]:
I’d say there are probably two real advantages of radiopharmaceuticals. The first is safety, because you’re dealing with a therapeutic that can very specifically target a tumor. One of the early stages is to characterize the level of accumulation within the tumor and make sure you don’t have accumulation in other organs of the body. So they can have a greater safety profile than more traditional therapies. That’s one advantage.
The other thing that distinguishes them is the idea of theranostics. You can use an agent that combines diagnostics with therapy, using the same scaffold. You can use one radionuclide to diagnose a tumor and then switch to a second radionuclide to target and kill the tumor. Because you’re combining those within the same strategy, this allows for really good stratification of patients and very rapid progression from diagnosis through to therapy.
David Brühlmann [00:08:28]:
Okay, I see. So it’s faster, and there are a lot more things happening in parallel, giving you a much broader picture.
Bryan Miller [00:08:36]:
Yeah, and I think safer as well. A lot of them are much safer than traditional chemotherapies.
David Brühlmann [00:08:41]:
The radiopharmaceutical field has gone from a niche therapy to one of the hottest areas in oncology. What’s driving this rapid transformation, both on the scientific side and on the commercial and investor side?
Bryan Miller [00:09:04]:
That’s what we’re finding as well. There does seem to be growing interest in the area. It’s quite dynamic right now, and there are a lot of exciting things happening. I think it goes back to some of the advantages I just described, particularly the theranostic approach, which gives these therapies a considerable advantage.
I think the safety profile is another aspect that a lot of companies we work with find very valuable during development. And maybe the third aspect is the widening range of cancer indications that radiopharmaceuticals are being applied to. We’re starting to see more radiopharmaceuticals coming through that target a much more diverse range of cancers.
David Brühlmann [00:09:44]:
And the main purpose of all that is basically to make faster, more actionable, and more predictive decisions with all your models. I’m curious—on top of your models, do you add a layer of machine learning, AI, or advanced data-driven approaches? How does that work in your field.
Bryan Miller [00:10:05]:
In terms of model selection?
David Brühlmann [00:10:08]:
Yeah, and also in terms of interpretation of the data.
Bryan Miller [00:10:11]:
I would say that is certainly applicable to the selection of models. It’s absolutely crucial—you need to get your preclinical model right in order to have a successful research program. We have large datasets associated with all of our models. So certainly, if you’re looking for particular targets or particular gene profiles, we can help you with that. Our models are very well characterized, and that’s information we’re happy to share to guide you towards the most appropriate model.
We’re also very excited by the prospect of AI. I think that’s something that’s much more common now, and it’s something we’ll be incorporating into workflows as well.
David Brühlmann [00:10:51]:
Yeah, you’re making a very good point—you have to select the right preclinical model, because that’s the foundation, isn’t it?
Bryan Miller [00:10:59]:
Exactly. Yes. If you get the order wrong, you’re not going to have a successful program.
David Brühlmann [00:11:02]:
Yes. You’re going to answer the wrong question at the end of the day if you choose the wrong model. Besides that, what are other pitfalls when developing a new drug? What other choices or strategies really have to be right?
Bryan Miller [00:11:17]:
Yeah, particularly in the radiopharmaceutical field, it can be quite a challenging area. One early point is getting the targeting strategy for the tumor correct. That’s a very important aspect—you need high specificity in tumor targeting.
You also need to select an appropriate isotope and the correct labeling position and labeling strategy. Another important aspect is ensuring that you either have, or are working with someone who has, a reliable source of radionuclides. These materials have to be labeled fresh before dosing, so the supply needs to be reliable.
It’s also really crucial to have a robust labeling strategy and to ensure that proper and thorough quality control (QC) is performed on the labeled material. Nothing should be dosed unless it passes stringent QC tests. And, as we discussed, making sure that the model you select is the most appropriate one is critical to giving you the best chance of success.
These are all areas where we can assist—providing advice, guidance, and helping to design the most appropriate study design.
David Brühlmann [00:12:24]:
And compared to conventional drug development, what are the unique challenges associated with radiopharmaceutical development? You’ve mentioned quite a few things that need to be right. How different is radiopharmaceutical development compared to conventional drug development, and what are the unique challenges?
Bryan Miller [00:12:46]:I mean, obviously there are some commonalities. But I think the unique challenges are the need to prepare material fresh—you can’t make it in bulk and store it. You have to have fresh material that can be dosed almost immediately. There’s also the complexity of some of the strategies, particularly designing an appropriate radiolabeling strategy. Because this is quite a specialized area, you really need to work with people who have specialist expertise and can advise on the most appropriate way to label the molecule, the correct isotope to use, and, crucially, the linker. Those are all aspects of radiopharmaceutical design that are absolutely critical for success.
David Brühlmann [00:13:26]:
And how do you, as a company, manage all that and navigate these difficulties? What are some specific approaches you’ve developed?
Bryan Miller [00:13:35]:
One absolutely crucial element is the collaboration we’ve established with Medicines Discovery Catapult (MDC). We’ve recently launched this strategic collaboration, which brings together the range of preclinical models and preclinical expertise from Crown Bioscience with the radiolabeling expertise and radiopharmaceutical experience from MDC. It’s a really strong partnership, combining complementary strengths to deliver highly successful radiopharmaceutical studies for our clients.
David Brühlmann [00:14:10]:
And what kind of clients do you usually work with? Are they small companies, mid-sized companies, or large pharma organizations?
Bryan Miller [00:14:20]:
All three, actually. We work with a diverse range of clients—small, medium, and large. As we’ve alluded to earlier, there’s growing interest in the radiopharmaceutical field, so we’re seeing more and more organizations approaching us to discuss radiopharmaceutical development and appropriate strategies for further studies. That ranges from quite small companies through to larger pharma organizations.
David Brühlmann [00:14:39]:
I imagine their needs are quite diverse. Can you give us a sense of how those needs differ between small biotech and large pharma? What are the typical differences?
Bryan Miller [00:14:53]:
One of the main differences we see is the maturity of the program. Some clients approach us at a very early stage, without a defined radiolabeling strategy. In those cases, we may need to do significant optimization work—guiding isotope selection, developing the labeling strategy, and performing validation to ensure the material can be labeled effectively and is suitable for use.
Other clients come to us with more mature programs. They may already have an optimized labeling strategy and a defined protocol. In those cases, our role may be to perform the labeling through protocol transfer, rather than developing the process from scratch. So those represent two quite different types of client needs that we commonly see.
David Brühlmann [00:15:42]:
Since interest in radiopharmaceutical development is increasing on both the scientific and business sides, I imagine demand is growing rapidly. How do you ensure that you can scale your preclinical and translational capabilities to meet that demand?
Bryan Miller [00:16:04]:
We have a lot of experience in this area. Both Crown Bioscience and Medicines Discovery Catapult have worked in our respective fields for many years, and we’re accustomed to running a wide array of projects simultaneously. Scalability is something we’re very experienced with.
You’re absolutely right—we’re seeing increasing demand for these types of studies. But we approach scalability in the same way we do for other programs: scaling our in vivo and preclinical capabilities on the Crown side, and scaling the radiolabeling and radiochemistry capabilities on the MDC side. Both partners bring significant experience in delivering complex studies at scale.
David Brühlmann [00:16:43]:
That’s where we’ll pause for today. In Part Two, we’ll explore the specific platforms accelerating radiopharmaceutical translation and get Bryan’s predictions on where this field is heading. If you’re finding value in these conversations, please leave us a review on Apple Podcasts or your favorite platform.
It helps other biotech scientists like you discover the show. See you in Part Two.
Alright, 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 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.
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About Bryan Miller
Bryan Miller is Director of Scientific and Technical Operations at Crown Bioscience. He completed his PhD in Biochemistry at the University of Leicester, followed by two postdoctoral fellowships at the University of Toronto and the Beatson Institute. During this time, his research focused on the development and application of in vivo and in vitro models of colorectal and pancreatic cancer.
Since 2015, Bryan has worked in the contract research organization (CRO) sector, supporting oncology drug discovery and development programs across a range of therapeutic modalities. He joined Crown Bioscience UK in 2019, where he leads scientific and technical operations, leveraging advanced preclinical models to help clients progress innovative cancer therapies toward the clinic.
Connect with Bryan Miller 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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