Carbon neutrality pledges echo across the biopharma industry, but the question lingers: how do you actually measure and shrink your true environmental impact, when most data is missing and every facility operates on a different baseline?
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann welcomes Niklas Jungnelius, a veteran in process modeling and sustainability at Cytiva, who’s spent years uncovering what really drives emissions—and how small process changes can have outsized effects.
Typically, when you do a life cycle assessment (LCA), you look at different damage categories. There are many aspects of environmental sustainability—perhaps the most important one at this point in time being carbon emissions.
But if you’re producing your product in an area where there’s a water shortage, that may be your focus area, because our industry is highly water-intensive. Whereas if you have plenty of water available, you may focus less on that.
Then there are other aspects, such as resource depletion. How many resources are you consuming in your process? It’s quite obvious, with all the single-use plastics we generate, that consumables are a major focus area.
David Brühlmann [00:00:45]:
Welcome back. In part one, Niklas Jungnelius opened our eyes to the hidden economics of bioprocessing. Now, in part two, we’re shifting gears to tackle the elephant in the room—sustainability.
How do you actually quantify environmental impact? What’s stopping biotech from hitting those ambitious carbon-neutral targets? And with AI and continuous manufacturing changing the game, how do you model technologies that barely have historical data? Niklas brings his process modeling expertise to answer these burning questions. Ready to future-proof your bioprocessing strategy? Let’s go.
I’d also like to focus on another part. Obviously, process economics is important. The other key area is the environment. Many leading biopharma companies have set ambitious sustainability goals—being carbon-neutral by 2030 or 2040, depending on the context. What are the biggest technical and economic hurdles preventing biotech companies from reaching these goals?
Niklas Jungnelius [00:03:06]:
That’s an interesting question. I think if we focus on the relative targets that many companies have set—such as an 80% to 90% reduction in CO₂ emissions compared to a given baseline year—it really depends on what your baseline is.
And that’s not as easy to determine as it sounds, because there’s still a lot of missing data. When trying to assess your true carbon emissions, we often have to make assumptions, and over time we’re getting more and more accurate data. So things may shift slightly—it’s somewhat of a moving target.
But the baseline largely determines what your viable reduction options are. For example, if your baseline situation involved heavy use of fossil energy in your operations, that will have a very high impact on your carbon footprint. In that case, the most obvious way to reduce emissions would be to switch to renewable energy sources, which can make a huge difference in your CO₂ footprint and substantially cut emissions.
On the other hand, if your manufacturing facility was already using renewable energy in the baseline year—say, in Switzerland, for example, where I know, David, that a large portion of the electricity grid is renewable—you already have a cleaner baseline profile. But that also means it’s much harder to achieve those 80–90% reductions.
So you’ll need to focus more on other factors—typically consumable-related emissions. Those are your Scope 3 emissions—the emissions you’re not directly responsible for, but that come from your supply chain and vendors.
As a supplier to the biopharma industry, Cytiva has to work extensively not only to introduce new, more sustainable products with lower carbon footprints, but also to reduce the emissions of existing products. Our customers can’t simply replace every product in their process to make it more environmentally friendly.
So if we, on our end, can reduce emissions from existing products—by, for example, switching to renewable energy, working closely with our suppliers, or identifying alternative raw materials—we can significantly help our customers reach their sustainability targets.
David Brühlmann [00:05:33]:
If we look at the consumables or raw materials we’re using, what comes to mind is obviously single-use plastics—that’s where a lot of the CO₂ footprint lies. But biopharma also uses a lot of water. Are these really the main drivers, or not? I’m curious—what are the real drivers?
Niklas Jungnelius [00:05:54]:
I think it depends on which damage category you’re looking at, because when you perform a life cycle assessment (LCA), you evaluate several different impact categories.
There are many aspects of environmental sustainability — perhaps the most important one at this point in time being carbon emissions. But if you’re producing your product in a region facing water scarcity, then water conservation becomes a key focus area, since our industry is highly water-intensive.
Whereas if you have plenty of water available, you might focus less on that. Then you also have factors like resource depletion — how many resources are you consuming in your process?
If we stay with carbon footprint for a moment, I think that’s a great point — it’s top of mind for most people. It’s very obvious, with all the single-use plastics generated in our processes, that consumables are a major focus area.
However, I would argue that based on the data we have today, our main focus shouldn’t necessarily be on recycling. Even though it’s tempting — and very visual — to focus on those piles of plastics, the relative benefit from recycling, compared to emissions generated during production, is actually quite small.
Also, because biopharma is a relatively small global industry, we face logistical challenges — such as transport emissions — in the recycling chain for these materials.
In contrast, during the production phase, we have large waste streams, and manufacturing in cleanrooms is highly energy-intensive. I think that’s where we’ll find our true hotspots.
And one thing I should add about environmental sustainability is that it often surprises people when they conduct an LCA and discover what the real impact drivers are. Sometimes it turns out to be a single, obscure chemical you’d never expect — yet it has a disproportionate environmental impact. If we can eliminate or replace that chemical, it can make a huge difference to the overall sustainability of a product.
David Brühlmann [00:08:00]:
Let’s get tactical here — some of our listeners working in process development or manufacturing might be thinking, “That’s great, I want to monitor this, but where do I start?” Can you give some advice on how to begin and how to measure these key parameters?
Niklas Jungnelius [00:08:18]:
I think there’s a lot of potential to work more with process modeling. We’re seeing many large biopharma companies now integrating process modeling early in development — to design smarter, more efficient processes.
One key principle is to think manufacturing from the start. In process development, the time horizon is often limited — especially if you’re a startup without the resources or expertise to fully map your future manufacturing process. But starting with the end in mind — including commercial-scale production — leads to better decisions along the way.
You also need to define the scope and ambition level of your modeling. What benefits do you want to achieve? How much effort is it worth investing at different stages?
Should you model individual unit operations or entire processes? That may depend on whether you already have a platform process.
The level of detail in your models determines how you structure your organization. If you want broad adoption — for example, having people in every PD or MSAT lab trained on process modeling — then the models need to be relatively simple.
But if you want high accuracy and detailed parameter tracking, you’ll likely need dedicated experts who work with process modeling daily. It’s difficult for someone to stay up-to-date with all inputs and assumptions if it’s just an add-on to another role. So for higher precision and more robust results, it’s worth having a few specialists focus on process modeling full time.
David Brühlmann [00:10:05]:
Niklas, what burning question haven’t I asked that you’re eager to share with our biotech community?
Niklas Jungnelius [00:10:13]:
I’d say that there’s no single manufacturing technology that’s superior in every situation. It really depends on your inputs and assumptions.
For example, if you compare fed-batch with continuous manufacturing, the outcome changes drastically depending on the starting titer. If you double the titer, that could make or break the business case entirely.
So asking, “What’s the best manufacturing technology?” is a bit like asking, “What’s the best way to get to work?” The answer is always — it depends. It depends on your traffic, how far you live, what transportation options are available, and how much you’re willing to spend on commuting.
Similarly, in manufacturing, understanding the implications of each option helps you make the best decision for your specific needs. Process economic modeling can guide you toward the manufacturing strategy that best aligns with your individual objectives.
David Brühlmann [00:11:12]:
And if we look ahead, Niklas, is there any emerging trend in bioprocessing that could completely change our models or assumptions?
Niklas Jungnelius [00:11:22]:
I think what’s shifting now is how we work with technologies like intensified fed-batch, where productivity in batch bioreactors increases by front-loading the cell expansion phase.
As I mentioned, titer is a key parameter here — and these improvements can actually shift the balance back toward batch manufacturing in certain cases. We’re also seeing hybrid processes — for example, perfusion bioreactors combined with batch downstream processing.
Looking further ahead, we might see new manufacturing technologies such as cell-free expression systems. If we’re talking about transformational change, that’s where it could happen — but those technologies are still quite uncertain and varied, each requiring significant optimization for specific manufacturing contexts.
David Brühlmann [00:12:13]:
We’ve covered a lot of ground today. From everything we’ve discussed, what’s the most important takeaway you’d like listeners to remember?
Niklas Jungnelius [00:12:22]:
From my perspective, it’s about understanding that depending on your focus areas, prerequisites, and ambitions, different solutions will suit your manufacturing process differently. And perhaps, David, I’ll turn the question back to you — have you learned anything today that you think could be useful in your work as a strategic advisor to the biopharma industry?
David Brühlmann [00:12:48]:
Absolutely — that’s a great question, and I like it. One thing I’ve learned is that there’s really no one-size-fits-all solution. It depends on your scale and on the phase you’re in — whether you’re still in the clinical stage or moving closer to commercial manufacturing. The choices you make will differ greatly depending on that context — for instance, whether you opt for single-use systems versus stainless steel, or for smaller versus larger-scale operations.
Another important factor is your modality — ultimately, the volume required and the process productivity determine what’s most suitable. As you said, Niklas, it’s critical to consider these parameters right from the start. Ideally, you aim for as high productivity as possible in your process, because that’s where you can gain a lot — in both time and cost savings.
Niklas Jungnelius [00:13:40]:
Thanks a lot, David. That’s a very good answer — I’m very happy with that.
David Brühlmann [00:13:46]:
That’s great — I passed the test!
Niklas Jungnelius [00:13:48]:
Yes, you did!
David Brühlmann [00:13:51]:
This has been fantastic, Niklas. Thank you for your insights — and for that thought-provoking question at the end. I loved it. Where can people reach you if they’d like to connect or learn more?
Niklas Jungnelius [00:14:03]:
The easiest way is to connect with me on LinkedIn. Perhaps you can include the link in the podcast notes.
David Brühlmann [00:14:09]:
Sure — we’ll do that. Just check out the Smart Biotech Scientist show notes for this episode. You’ll find Niklas’s contact information there. And please, take the opportunity to reach out to Niklas with your process modeling or bioprocess optimization questions. And once again Niklas, thank you so much for being on the show today.
Niklas Jungnelius [00:14:26]:
Thank you so much, David. It's been a pleasure and happy to connect with anyone who wants to discuss more about this very interesting topic.
David Brühlmann [00:14:34]:
This wraps up our conversation with Niklas Jungnelius from Cytiva. From quantifying environmental footprints to navigating emerging technologies, Niklas has given us a true masterclass in thinking beyond just cost of goods. Remember — every process decision you make today shapes both your economics and your environmental impact tomorrow.
If this episode sparked new ideas, do us a favor and leave a review on Apple Podcasts or your favorite podcast platform. Your feedback helps us serve you better — and thank you for tuning in! Until next time, keep doing biotech the smart way. All right, smart scientists — that’s all for today on the Smart Biotech Scientist Podcast.
Thank you for joining us on your journey toward bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your preferred podcast platform. By doing so, you help us empower more scientists like you. For additional bioprocessing tips and insights, visit us at www.bruehlmann-consulting.com
Stay tuned for more inspiring biotech conversations in our next episode. Until then — let’s continue to smarten up biotech!
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Niklas Jungnelius
Niklas Jungnelius serves as Process Modeling Leader at Cytiva, guiding biopharmaceutical manufacturers, industry groups, and internal stakeholders in evaluating the impact of various process technology options. His work supports organizations in making strategic choices that enhance process efficiency, productivity, and environmental performance.
Niklas earned his master’s degree in Chemical Engineering from Chalmers University of Technology and has more than 25 years of experience in the life sciences sector, including over a decade in strategic roles at Cytiva and GE Healthcare.
Connect with Niklas Jungnelius 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
Is your manufacturing strategy bleeding money in ways you can’t see? The production floor is full of hidden cost traps—capital investments, labor, resin lifetime, and facility flexibility—that often dictate the business fate of biologics and biosimilars.
This episode of the Smart Biotech Scientist Podcast turns the spotlight on process economic modeling: the tool that’s reshaping how manufacturers understand and control cost drivers behind monoclonal antibody and biologics production.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Niklas Jungnelius, Process Modeling Leader at Cytiva, a global biotechnology leader dedicated to helping customers discover and commercialize the next generation of therapeutics.
The key cost drivers — two of them would be the scale of manufacturing and process productivity. The production volume is critical, as we see a very strong benefit from economies of scale when looking at the manufacturing cost per gram of product or per dose produced.
With a larger facility and larger bioreactors, you can reduce factors such as labor cost and capital cost investment per gram of product produced.
Similarly, if you have higher productivity in bioreactors — meaning higher titers — that’s also very helpful in reducing overall cost.
David Brühlmann [00:00:41]:
Ever wonder if that expensive new technology will actually save you money in the long run — or which process parameters are secretly eating your budget alive? Today, we're diving into the world of process modeling with Niklas Jungnelius, who is a Process Modeling Leader at Cytiva.
With over 25 years in the life sciences industry, Niklas helps biotech companies make smarter decisions about manufacturing economics. Whether you're comparing fed-batch vs. perfusion or trying to justify a capital investment, this episode will change how you think about process costs. Let's jump in. Niklas, welcome — it’s great to have you on today.
Niklas Jungnelius [00:02:37]:
Thank you so much, David. Glad to be here.
David Brühlmann [00:02:40]:
Niklas, share something you believe about bioprocess development that most people might disagree with.
Niklas Jungnelius [00:02:47]:
Sure, David. I would say that I personally do not believe that fully continuous bioprocessing will become the dominant manufacturing mode for new mammalian cell culture processes within the next 15 years.
That may go against what many people in the industry expect, but the main reasons I think this way are as follows:
For these reasons, I believe that intensified fed-batch processes — offering many of the performance advantages of continuous without the same level of complexity — will remain a very attractive and practical option for most companies moving forward.
David Brühlmann [00:04:02]:
I’m curious, Niklas — what originally drew you into life sciences, and ultimately to process modeling? What were some interesting steps along your career path?
Niklas Jungnelius [00:04:15]:
Yeah, I think it really started back in high school. During my final year, we had a new biology and biochemistry teacher — a man named Anders — who had previously worked at a university doing both research and teaching. He told us all these fascinating things about new methodologies and tools emerging within biopharma and life sciences, and that really caught my attention.
Originally, I had planned to study computer science, but I changed my mind and instead applied to university for chemical engineering and biochemistry. After completing my degree, I explored a few different areas — I worked in marketing, customer service and support, and commercial operations. But about 14 years ago, I applied for a position within what was then GE Healthcare’s Strategic Marketing organization. I was hired and immediately felt at home, initially working with portfolio strategies for chromatography products.
Over time, I expanded my scope to include all types of downstream operations, and as part of that, I became involved in process modeling. I found it fascinating to understand how different technological and operational choices affect performance and economics, and to assess the value and impact of those choices. That analytical aspect really intrigued me, and I wanted to work more closely with it — and also more closely with our end users.
When the company transitioned to Cytiva, I was asked to lead our work in process economic modeling. That sounded very appealing, and in that role, I now collaborate with both internal stakeholders — helping assess the impact and benefits of new technologies, supporting product portfolio roadmapping and technology evaluations — and with external customers, helping them make the best process technology choices for their specific needs. That’s what I do today.
David Brühlmann [00:06:23]:
For those listening who might be new to process modeling, can you explain what it actually involves? What does it do, what’s the purpose, and what are you essentially building when you create these models?
Niklas Jungnelius [00:06:40]:
Yes, absolutely. First of all, I like to clarify that what I do is process economic modeling, not mechanistic modeling. Mechanistic modeling focuses on simulating physicochemical properties — for example, how a chromatography separation behaves under different conditions. In contrast, process economic modeling looks at the manufacturing process from an economic and capacity perspective — things like cost structure, equipment utilization, and increasingly, environmental sustainability.
In these models, we account for all key variables that influence process efficiency and output. The questions can be quite diverse. Sometimes we focus on individual unit operations, which we might model in Excel — that gives us full control over the parameters, calculations, and outputs.
Other times, we model entire manufacturing processes end-to-end. For that, we typically use third-party software, most often BioSolve (by Biopharm Services). BioSolve includes a lot of predefined parameters — which can be customized — and allows us to evaluate both the performance of individual steps and the downstream impact when one operation changes.
In all cases, these models rely on mass balance calculations — we track how much product flows through each step, the duration of each step, and from that, determine equipment sizing, consumable requirements, labor needs, and so on.
We also include assumptions about labor costs, capital costs, and facility investments, which together allow us to estimate the total cost of goods (CoG) with reasonable precision. When modeling environmental sustainability, we extend this by analyzing consumable usage, material composition, and facility requirements — such as cleanroom areas and air handling needs. For instance, larger cleanrooms require more air changes per hour, which in turn drives energy consumption. So all these factors come together to give a holistic view of both economic and environmental performance.
David Brühlmann [00:09:21]:
For example, the cost of manufacturing — or more precisely, the cost of goods (CoG) — has gained a lot of attention in recent years, especially as cost pressures on biologics have increased.
Before diving into the details, can you give us a high-level picture of the main cost drivers and how they influence biologic production — or even the price patients ultimately pay for life-saving therapies?
Niklas Jungnelius [00:09:55]:
Yeah, that’s a good question. To answer it properly, I need to take a step back and not just look at modeling the manufacturing cost, because as you know, there are many other costs involved in biopharmaceutical development.
For innovator molecules, the price per dose is not primarily determined by the manufacturing cost — the margins are typically high. That’s because companies need to recover the significant investments made in R&D, clinical trials, regulatory approvals, and commercialization. So traditionally, in the case of innovative biologics, the main cost pain point for biopharma companies has not been at the manufacturing stage.
However, we are now transitioning into a market with an increasing number of biosimilars being introduced. And perhaps I’m a bit mAb-focused here — since monoclonal antibodies are where we do most of our work — but in the biosimilar segment, the landscape is much more competitive. Margins are lower, and the cost to bring these products to market is significantly less than for new, innovative therapies.
As a result, manufacturing cost becomes a much more critical factor for biosimilar producers. Being able to reduce production costs directly improves competitiveness against other biosimilar manufacturers.
That said, whether we’re talking about innovator biologics or biosimilars, the top priority remains having a robust, high-quality process with sufficient production capacity to ensure uninterrupted product supply. Any supply interruption or manufacturing downtime can be extremely costly and damaging to both the business and patients.
David Brühlmann [00:11:33]:
From your modeling work, Niklas — especially as you mentioned for mAbs (monoclonal antibodies) — what parameters have the biggest impact on mAb production costs? And have you found any surprising insights there? Let’s start with fed-batch processes, and then we can move on to other process formats.
Niklas Jungnelius [00:11:51]:
I’d say the key cost drivers — two of the most important — are manufacturing scale and bioreactor productivity. The production volume has a huge impact because we see very strong economies of scale when we look at the manufacturing cost per gram of product or per dose. With a larger facility and larger bioreactors, you can reduce costs like labor and capital investment per gram of product produced.
Similarly, if you achieve higher productivity in your bioreactors — meaning higher titers — that’s extremely helpful in lowering overall costs. This is true for fed-batch manufacturing, but it’s even more critical for perfusion processes, where media consumption represents a much larger portion of the total cost. If you can make that operation more efficient — for example, producing more product per gram of media consumed — it significantly improves the process economics.
We should also mention the difference between stainless steel and single-use facilities. In stainless steel setups, you have substantial capital investment costs, as well as higher labor costs due to all the cleaning and line clearance activities required.
In contrast, single-use processes require significant spending on plastic consumables — such as bags, tubing, and connectors — but they bring major advantages. They reduce labor needs, shorten turnaround times between batches, and increase facility throughput, which helps improve overall productivity and agility.
David Brühlmann [00:13:33]:
Right, and I imagine that while cost is one driver, single-use systems also give you a lot of flexibility. So it’s always a trade-off between running and maintenance costs versus the flexibility you gain — and also the scale at which you operate.
Niklas Jungnelius [00:13:52]:
Yes, definitely. Over the past couple of decades, we’ve seen a major shift toward single-use technologies precisely because of that increased flexibility.
As you said, you have lower facility construction costs, shorter lead times to get capacity online, and generally more agile operations. At the same time, I’d like to point out that we still see many large stainless steel facilities being built — especially in regions like Korea, the U.S., and Europe — where companies are investing in 10,000-liter or larger stainless steel bioreactors. That’s because the economy of scale at that size is so favorable; for very high-volume products, large stainless steel production can be extremely competitive.
David Brühlmann [00:14:46]:
What about the intermediate scale — say, up to 2,000 liters — where you could use either stainless steel or single-use systems? Are there situations where one is clearly preferable, or is it more of a case-by-case decision?
Niklas Jungnelius [00:15:07]:
It’s very much case by case, but generally speaking, the smaller the scale, the more advantageous single-use becomes.
That’s because stainless steel facilities involve very large fixed investments, almost regardless of scale. And just to clarify, I’m mainly referring here to mammalian cell culture capacity — the picture might look quite different for microbial fermentation.
The flexibility of single-use systems has tremendous value, even if it’s not always easy to quantify. Having the freedom to make late-stage decisions or bring new capacity online faster can reduce the safety margins you need in your supply chain. That agility can make a big economic difference over time.
David Brühlmann [00:15:48]:
Now let’s talk about continuous manufacturing. Even though you’ve expressed some reservations — that not everyone will adopt it because of the added complexity — it does offer economic advantages, especially when moving toward intensified or even end-to-end continuous processes.
From a purely economic standpoint, what are the main differences between these process types? What are the advantages and drawbacks?
Niklas Jungnelius [00:16:20]:
If we start with fed-batch, in the classical setup, your bioreactor productivity isn’t that high because much of the run time is spent on cell growth — expanding the cells to a suitable density before significant product formation begins.
Once you reach downstream processing, you typically have large batch volumes that you can process efficiently — so your downstream utilization is high. However, if you only have a single bioreactor feeding downstream, you’ll still have idle time between runs, which lowers your overall facility utilization and increases your capital cost per gram.
In a perfusion or continuous process, your bioreactor operates at high productivity, delivering product continuously to downstream. But here, the downstream line becomes the limiting factor — it can only operate at the same throughput as upstream. You don’t gain much benefit from trying to speed up individual steps, because everything is connected in real time.
For example, in a continuous mAb process, you might have an upstream productivity of 1 g/L/day, feeding into the capture step. After Protein A capture, you concentrate the product — say, from 1 g/L to 10–20 g/L — which reduces the downstream process volume dramatically. So, if your bioreactor produces 1,000 liters per day, that might translate to only about 2 liters per hour through the later purification steps. It’s a very slow, “dripping” flow, which isn’t the most efficient way to run those unit operations.
You could add multiple perfusion bioreactors to the same downstream line to improve utilization, but that brings its own technical and operational challenges. So, in summary, the key question is: Where do you want your productivity gains? In fed-batch, you can keep your downstream line fully utilized by staggering multiple bioreactors. In continuous, you gain upstream efficiency but lose some flexibility in downstream.
One additional point — in clinical or low-frequency manufacturing, resin cost becomes a major factor. Chromatography resins are a sort of hybrid cost item — part consumable, part capital investment — with long lifetimes, often up to 100–200 cycles.
If you only run a few batches and can’t fully use that lifetime, the effective resin cost per batch becomes very high. In continuous or perfusion processes, you can use smaller columns and cycle them repeatedly over longer runs, which reduces the total resin requirement and spreads out the cost more efficiently. So resin cost tends to be a major driver in fed-batch processes, but less so in continuous ones.
David Brühlmann [00:20:04]:
That’s a really good point about resin lifetime, especially for clinical-scale manufacturing. Beyond that high resin cost, are there other hidden costs that companies might overlook before doing proper process economic modeling?
Niklas Jungnelius [00:20:20]:
Yes, absolutely — there are several. Some are hard to capture directly in a model, like risk of process failure or the value of operational flexibility. We can include proxy parameters for those, but there’s always a subjective element.
Another common oversight is the cost of underutilized capacity. When launching a new product, companies often build facilities designed to meet future demand projections — say, five years down the line. But if actual sales don’t reach those forecasts, you end up with idle capacity, which is expensive unless you can repurpose it for other products.
This is where flexible, single-use facilities really shine — with shorter build times and modular capacity, you can align production more closely to real demand, avoiding excessive safety margins on capacity.
Also, from a modeling perspective, it’s easy to over-optimize. As a process modeler, I often work in an idealized world — assuming everything performs exactly as expected. But in reality, you need to build in safety margins to account for process variability and unforeseen issues.
So my advice is: make realistic assumptions, don’t over-optimize, and always include reasonable safety factors in your calculations. That gives you a more credible and practical cost estimate.
David Brühlmann [00:22:32]:
Thank you for tuning in. Today we covered the fundamentals of process modeling and the critical cost drivers that can make or break your manufacturing strategy. In Part Two, we’ll dive into sustainability modeling and emerging technologies that are reshaping bioprocess economics.
If you found value in this conversation, please leave us a review on Apple Podcasts or your preferred platform — it helps other biotech professionals discover these insights.
Alright, smart scientists — that’s all for today on the Smart Biotech Scientist Podcast. Thanks for joining us on your journey toward bioprocess mastery. If you enjoyed this episode, please leave a review and visit smartbiotechscientist.com for additional resources and tips. Stay tuned for more inspiring biotech insights in our next episode — and 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 Niklas Jungnelius
Niklas Jungnelius is the Process Modeling Leader at Cytiva, where he advises drug manufacturers, industry associations, and internal teams on the implications of different process technology choices. He helps organizations make informed decisions to achieve goals in process economy, productivity, and environmental sustainability.
Niklas holds a master’s degree in Chemical Engineering from Chalmers University of Technology and brings over 25 years of experience in the life sciences industry—half of which has been spent in strategic roles at Cytiva and GE Healthcare.
Connect with Niklas Jungnelius 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
Cell and gene therapies have redefined what’s possible in modern medicine, but their road to market is anything but straightforward. Instead of a slow ramp-up, these therapies often leap from small early-stage trials to global commercialization at record speed—putting immense pressure on analytical workflows, supply chains, and every partnership in between. Staying ahead means making smart, sometimes tough choices about what to build internally and what to hand off to expert partners.
This is the second part of the conversation with Daniel Galbraith, Chief Scientific Officer at Solvias, a Swiss-based global CRO providing analytical services for small molecules, biologics, and cell and gene therapies. The discussion is part of the Smart Biotech Scientists podcast, hosted by David Brühlmann.
The development of these products, the cell and gene therapies, is so much faster that when you're working together, understanding where the endpoint is—where are we trying to get to?—and for everybody to be on that same race in parallel, working in step with each other, so that as each success of a clinical study goes, and success with the regulators, what does that mean? How are we going to staff that up? How are we going to enable that whole supply chain that you have to work on a global basis? And that's really where the partnership between a CRO and a drug developer in that CGT space becomes just a fundamentally different way of working together. It's a really true partnership.
David Brühlmann [00:00:45]:
Welcome back to part two of Daniel Galbraith from Solvias. In the first half we explored his biotech journey and the seismic shifts in analytical development. Now we are getting tactical. Daniel will share the top analytical challenges that consistently derail bioprocessing teams and how to implement these phase appropriate strategies that actually speed up development and the critical factors for selecting the right CRO partner. Plus his predictions on which therapeutic modalities will dominate the next five years. Let's jump in.
Given the multiple technical and scientific challenges, how do you at Solvias or other CROs, how do you enable a successful commercialization of novel therapies? What is that thing that makes a project successful?
Daniel Galbraith [00:02:51]:
I think for us in the cell and gene therapy space, the thing that’s made it successful is the commitment and the partnership that you have to understand right from the start is needed to actually make these products successful. So if you think about cell and gene therapy, normally you start off with a very small number of patients or batches to treat — phase one clinical studies, all of that. So you’re investing a lot of time and effort to actually test and release quite a small number of batches of product for that early-stage clinical trial.
But with cell and gene therapy, especially in some of these conditions where there’s an abbreviated approval process — and you can read up about how the FDA does that — there are a number of ways that we can take these treatments that target rare conditions, where you can go from very early on in a phase one study to phase three and now approval, and that can be done in a relatively short period of time.
What you can find is that you’re going from actually testing a small number of products to testing quite a large number of products. You’re going through an approval process, validation of the systems, and all of the analytics around that. And then you’re in a commercial situation where you’re trying to release maybe hundreds or perhaps thousands of these products each year.
That scale-up is a challenge for everybody because you’re going from testing, I don’t know, one batch a month to maybe 100 batches a month. And from a CRO position, you need equipment, lab space, people, training, SOPs. Sometimes you want your testing to be done on another continent — so maybe you’re testing in Europe, and you need to do releasing in the U.S. So have you got a facility in the U.S.? Have you got them set up to be able to do that testing as well? The converse is true — from the U.S. to Europe, or then people want to set up in Japan, Korea, wherever.
That scaling — going from a small number to a global enterprise, as it were — to actually make these drugs available to a global population is much faster than any monoclonal or ADC or anything like that would be, where you would maybe have five or ten years to go from a phase one to a commercial sort of lab release of these products.
The cell and gene therapy development is so much faster that when you’re working together, understanding where the endpoint is — where are we trying to get to — and for everybody to be on that same race in parallel, working in step with each other, so that as each success of a clinical study goes and success with the regulators, what does that mean? How are we going to staff that up? How are we going to enable that whole supply chain that you have to work on a global basis?
And that’s really where the partnership between a CRO and a drug developer in that CGT space becomes just a fundamentally different way of working together. It’s a really true partnership. And I know people use that term partnership quite frequently, but I think when you really understand what you’re trying to do with cell and gene therapy products, that partnership just becomes more and more important.
David Brühlmann [00:05:57]:
Absolutely. I think it should be a partnership because it's. As a drug developer, I might have just one shot at my goal and it has to work out. So the partnership has to be perfect. It's not just a transaction. I think it's much more. And this leads me to another point, because since a lot can go wrong, what can we do to avoid pitfalls? And what are pitfalls you're seeing when working with a CRO?
Daniel Galbraith [00:06:26]:
In some ways, there is a choice that companies have. They can have the choice to sort of bring in the testing and actually do it themselves. And some companies do decide that they want to actually control all of these things, and they want to establish their own labs and do their own testing. That’s a perfectly valid way to approach the analytics. It’s an expensive way to do it, and it’s a time-consuming way to do it, but it’s a way to do it.
The pitfalls of working with a CRO are that sometimes you’re relying on them for scheduling the work and making sure that the work is done in a timely way so that you get the results when you need them. That can be quite a challenge if you’ve not made the CRO very aware of your timelines, and if your timelines are changing. It becomes particularly acute when there’s an issue.
Sometimes there’s a problem with the manufacturing — we’re getting unexpected results. Can the CRO provide resources to actually do that investigation as fast as you would like it done? That’s where having that good relationship, making sure they’re aware of what’s going on and what the implications of these results are, becomes really important. You need that really good working relationship.
But it’s a separate company — they have multiple customers, and they need to balance that. So a CRO can’t always be as reactive as maybe you would like them to be because they have other commitments they’ve made. So I think understanding what capacity the CRO is working at is important. You obviously want them to be successful — you want the CRO to be making money and all of that good stuff — but you also want to know they have a little bit of flexibility in their capacity.
If something does go wrong, can we jump in and get those resources? Because time costs money in the drug manufacturing world. And if you’re delaying things — delaying clinical trials and all of that — that’s a challenge financially and, obviously, for the patients. So you want to make sure your CRO is working, as I say, lock-and-step with you when these issues come up. I think most people would find that one of the main pitfalls when working with a CRO is that scheduling and resource issue.
David Brühlmann [00:08:32]:
So what I'm hearing, Daniel, it's number one, it's the communication is important, that you let your CRO know that this is highly critical. And then it's also the timely manner that you need to be able to have these results when you need them, especially to troubleshoot, right?
Daniel Galbraith [00:08:51]:
Yeah. I mean, you want to be sure that they can troubleshoot, so you challenge them. Do they understand the product? Do they understand the process? Do they understand the technical capabilities of the assays that you’re doing? You want to make sure they’ve got the competence — essentially, absolutely.
But I see that relationships become challenging when pressure is applied. And pressure is usually applied when there’s a crunch time — when it comes up to submissions and acquiring that data at the right time. That’s where these relationships become challenging, and it’s where the good CROs can pull through.
David Brühlmann [00:09:25]:
A big challenge in developing a drug is to decide what expertise or know how you're building inside of your company and what is the part you're outsourcing to your CRO to be faster. So there is a trade off, obviously. You outsource to your CRO, usually it's faster, but you will not have perhaps the knowledge inside your company. So what is a good way to find the sweet spot there?
Daniel Galbraith [00:09:51]:
There is a dynamic. You always have the drug developer, and then normally, well, if you look at cell and gene therapy, you would have a CDMO — someone that does the manufacturing. Some of the manufacturing will be done, and some of the testing and release of the product will actually be done by the CDMO. They will have a level of expertise — they’re right there, they have the samples available, they can do it very quickly.
And then there’s some part of that subset of the characterization that the CDMO usually doesn’t have the expertise to handle, so they would then outsource that to the CRO. And then there’s that dynamic between the drug developer, the CDMO, and the CRO — now managing that relationship. So, these three parties are in play.
From a drug developer point of view, you really need to — or I would advise you to — invest in that project management capability within your company, because there’s going to be a lot of things happening, a lot of decisions that need to be made, and they need to be made very quickly. You need to be able to focus on these and actually control them and manage them to your expectations.
So, the CDMO will do the manufacturing, they’ll then ship samples to the CRO, they’ll then get results, and then someone has to collate all of these results — usually your project management team and the people that are reviewing them: your quality team, regulatory, all of those people bringing them together.
There are so many moving parts in that relationship now. Everybody is experienced, they’ve done this before, they know how it’s done. It’s this manufacturing process, this product, this clinical trial. What you’re trying to do — you’re in control, you should be making the calls, making the decisions: where are things being done, what are the results, how are we analyzing the results, and all of that.
And that’s where investing in that, I think, would be the number one suggestion I would make — in that logistics and project management of the system. Let the CDMO and the CRO do what they know best — they know how to do a lot of these things. Don’t replicate what they do, because I think that’s a folly there. But definitely manage. And that management, that solid process, I think is something to invest in.
If there was something else that you wanted to invest in more, I think some of the characterization of your drug is probably something that should be outsourced, because that’s a once-and-done thing. So investing in that internally — I don’t see a return on investment from that.
I think if you have a drug that has a particular and very difficult way of measuring its activity, and maybe you can’t find a CRO that can actually help with that, that might be one other thing that you may want to spend a bit of time investing in. Either you work very closely with the CRO to actually put that in place, or you may want to do it yourself.
The issue tends to be that bringing things internally for just one or two analytical techniques tends to be quite an expensive thing to do, as you mentioned. So you would have to do a kind of return-on-investment assessment — is it worth investing in a CRO, getting them up to speed, and actually letting them do it, or is it something I really need to control myself?
Most people usually go down the CRO route, because it’s an added task that sometimes stretches the company a bit too far. So the CRO route is probably preferable. But sometimes we do see companies decide that, for very complicated drugs, bringing some testing internally makes sense. For most people, for most modalities, though, the CRO route is probably the best way to go.
David Brühlmann [00:13:25]:
Before we wrap up, Daniel, what burning question haven't I asked that you're eager to share with our biotech community?
Daniel Galbraith [00:13:33]:
I think the most common question I get is to try and predict the future — what’s going to be the next big thing, and all of those questions — and I think that sort of thing is quite entertaining. I don’t have a crystal ball; I can’t tell the future.
I think the only comment I would probably make in that respect is that, from what I’ve seen, not one single modality is going to win out. We’ve got so many different mechanisms of actually treating disease nowadays, and I think the most interesting thing is how these things can be used together to treat conditions.
Where I think the challenge from an analytical point of view lies is in how we actually look at how these drugs work together to treat a condition. Is there a way that we can actually look at an antibody-drug conjugate and a piece of mRNA together, in an analytical situation, to see how these things can be used in combination? That’s going to be the future.
I’m not too sure, as an industry, that the CROs are ready for that — to look at these combinations of therapies working in concert and how they can improve patient outcomes. But it’s definitely the way the industry is going: drugs are not going to be used in isolation; they’re going to be used much more often in combination.
And I’m not too sure we’ve really understood, from an analytical point of view, how these combinations work. So that would be the challenge for the future, I think. But it’s actually not that far away — people are already starting to ask these questions.
David Brühlmann [00:15:10]:
Yeah, let's see what the future holds. It's definitely going to be exciting. With all that we've covered today, Daniel, what is the most important takeaway from our conversation?
Daniel Galbraith [00:15:21]:
I think, from an analytical point of view, I would say that understanding how to really work with a CRO is probably what I’d like to get across. We — most of the CRO industry — definitely want to be a partner. We want to share in your success. The more open and transparent everybody is about how to work together, how to learn to work together, and having an optimistic mindset when establishing those relationships, I think, is probably the best takeaway I would suggest. With CROs, we want to share in your success.
David Brühlmann [00:15:57]:
Fantastic, Daniel. Thank you so much for sharing all the insights. I have greatly enjoyed our conversation. Where can people connect with you?
Daniel Galbraith [00:16:07]:
I'll share my email address: daniel.galbraith@solvias.com but we attend many conferences throughout the year. I'd be happy to discuss anybody's analytical questions, concerns, interests, anything excellent smart biotech scientists.
David Brühlmann [00:16:23]:
You will find the links down in the description. And Daniel, thank you once again for being on the show today and sharing all these amazing insights. Thank you so much.
Daniel Galbraith [00:16:34]:
Thank you. It's been a pleasure. Thanks again, David.
David Brühlmann [00:16:36]:
What an insightful conversation with Daniel Galbraith. From avoiding over engineering early methods to selecting the right CRO partner, his three decades of experience shine through every recommendation. These are battle tested strategies that can save your program months and millions. And don't forget to grab the notion CMC Dashboard from the show notes. It also includes the proven map roadmap that consistently hits IND timelines and you will see you can use that to visualize your timelines, your task, your risks in a very convenient way. You will get the exact step by step process that eliminates months of trial and error saving your time and costly mistakes. And if Daniel's insights resonated with you, please leave us a review on Apple Podcast or on your favorite podcast platform. And I thank you so much for tuning in today and I will see you next time. Keep making biotech smart.
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 Daniel Galbraith
Daniel Galbraith, Ph.D. serves as the Chief Technology Officer for Large Molecules and Advanced Therapies at Solvias, where he leverages more than 25 years of experience across the life science and biopharmaceutical sectors. Throughout his career, Daniel has held key leadership positions at Merck Life Science, BioReliance, and Sartorius Stedim BioOutsource, driving progress in analytical science, product characterization, and advanced therapeutic development. His earlier roles at Covance Laboratories, MedImmune Vaccines, and Q-One Biotech reflect a strong foundation in virology and biosafety.
Daniel earned his Ph.D. in Immunology from the University of Abertay, along with an M.Sc. in Forensic Science from the University of Strathclyde and a B.Sc. (Hons) in Microbiology from the University of Glasgow. Renowned for his scientific insight and collaborative approach, he is deeply committed to advancing analytical excellence and innovation in next-generation biotherapies.
Connect with Daniel Galbraith 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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Many biotech teams are sprinting to create breakthrough therapies, but often encounter the intricate and numerous challenges of CMC development.
As the industry advances the frontiers of cell and gene therapies, distinct analytical and manufacturing obstacles are emerging—far more complex than those faced with traditional biologics. To succeed, companies must embrace new approaches that balance scientific precision with commercial practicality.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Daniel Galbraith, Chief Scientific Officer with Solvias, a Swiss-based global CRO providing analytical services for small molecules, biologics, and cell and gene therapies.
As a CRO, you want to be as excited, you want to be part of that journey with them. And I think that's something that I would say, you want to get a sense that these people have a commitment. They want to spend time talking about your product, listening about your product. If they're not willing to do that at the start, they definitely aren't going to be willing to do that later on. So get people that are excited and want to hear about your product and willing to listen.
Now, in our practical side of things, there's a couple of things. Everybody thinks the product is unique, but there are some common features of products. So you want to know that you'll spend time and effort looking at the unique part of your product, but there's also an ability to actually a lot of the routine stuff can be done very quickly and easily. We're not reinventing the wheel for every single aspect of the characterization you need to do for your products.
David Brühlmann [00:00:53]:
Are you struggling to find the right analytical strategy and partner for your CMC development? You are not alone. Many biotech teams waste months over engineering methods or partnering with the wrong CRO.
Today I'm joined by Daniel Galbraith, who is the Chief Scientific Officer at Solvias, who has guided analytical development for nearly three decades. In part one, we'll uncover his journey, the biggest industry shifts and critical techniques for safer cell and gene therapy development. I'm David Brühlmann. Welcome to the Smart Biotech Scientists Podcast. Let's dive in.
Welcome, Daniel, to the Smart Biotech Scientist. It's good to have you on today.
Daniel Galbraith [00:02:53]:
Thanks very much. Lovely to be here. Thanks.
David Brühlmann [00:02:55]:
Daniel, share something that you believe about bioprocess development that most people disagree with.
Daniel Galbraith [00:03:04]:
So you're asking me to be controversial. I like it just as a kickoff. One thing that I think in bioprocessing, if I look at specifically cell and gene therapy at the moment, everybody seems to think that it will follow the same path that monoclonal antibodies took 30 years ago. Monoclonals are very specialized, difficult to manufacture. And over a period of time, we gradually got better and better. And now we have huge scale and it's relatively cheap to produce. And we have that all over the world, really. And everybody seems to think that cell and gene therapy is going to follow that same path, but I'm not sure that's true. I think we've still got a long way to go before we've got these products that are going to be as scalable and manufacturable, if that's a word, as monoclonals. I'm a little bit nervous that people are very optimistic about it following the same pathway as mAbs. I think we've got a number of innovative processes to develop before we get anywhere near that. So I'm a little less optimistic than a lot of people on that one.
David Brühlmann [00:04:06]:
Well, this is a valid point, Daniel, because advanced therapies are much more complex than traditional biologics. So I think there's things that will translate over. But there are unique challenges that we have not seen in the biologic space. So also curious to see how this will play out.
Daniel Galbraith [00:04:26]:
There's a lot of pressure. You can see that in the market, the conferences. Everybody talks about scalability and making this cheaper, cost of goods, all of that. So there is a lot of pressure. Just don't know if we'll got the tools quite yet to be able to get there.
David Brühlmann [00:04:40]:
Before we dive deeper into the science, let's talk about yourself. Daniel, draw us into your fascinating journey from starting your career to now becoming the chief scientific officer at Solvias. What initially drew you into biotech and what were some interesting pit stops along the way?
Daniel Galbraith [00:05:00]:
Yes, interesting. Yes, long career. It's gone so fast in many ways that you think when people ask you that question, you've been in a long time, you think, I've not really. But then I started actually in 1996, actually in biotech, which is almost 30 years. Next year will be 30 years. What drew me into it was that mid-1990s period you started to get the approvals of the first real biologic monoclonal drugs, they were starting to kind of come.
The regulators were getting interested in putting regulations in place. So it really was that there was an inflection point definitely there where there was these new drugs were coming, different challenges. As I said earlier, they were difficult to produce in those days. They were expensive to produce, it was small batches, all of that thing. But they presented a lot of challenges in how to characterize them, how to make them safely. What were the risks? What were the first time we're making products really in cells? What were the virology risks? What were the bacterial contamination risks? How do we characterize them?
So that's what kind of started me off, starting to look at these drugs. It was novelty, it was new. And people that are attracted to the new. Along the way, there have been other things that have happened. We started off with them being every product was unique. And then all of a sudden, in the 2000, mid-2000s, 2005, 2008, we started to look at biosimilars. You know, all of a sudden we could make a copy of these drugs. And then the analytical challenge is with this unique product process, how did we make a copy of that with another product process? So then the analytics started to get more complicated again. This time we were using different technologies to look at these drugs. So again, that was really interesting, looking at that different aspect of it.
And then more recently, we've started seeing cell and gene therapy. And you know, just around pre COVID, the COVID time, two things happened. We saw what we could do as an industry around COVID. And I thought that's fascinating. We really put our mind to it as an industry. Sometimes I think we've forgotten very quickly what we managed to do during that COVID period. So that was. It keeps you very interested. When it is an industry, we can rise to that challenge. Along with that, we've seen the advent and the approval of so many cell and gene therapy, mostly autologous drugs, but a lot of drugs that can cure diseases that when I started, we thought we would never have a chance of curing, like sickle cell disease, all of these kind of conditions.
Now that we've got approved drugs for, that keeps you interested, that you can't fail to be interested when you're meeting a medical challenge like that. I'm not on the clinical side of things, obviously I'm more on the manufacturing side. But you're part of that journey. You're an important part of that journey. And to still be able to at least contribute even to a small extent, I think in that journey is it keeps you interested and it's also what's next? From when I started out in ‘96 and 2005, you've seen such huge changes now and you think, Well, another five years, where are you going to be now? So you're always looking to the future. I feel keeps you young.
David Brühlmann [00:08:06]:
I hope it's evident that you are curious and you've been very excited about all these amazing changes over these last 30 years. What was that spark that drew you eventually into a leadership position and now the CSO of quite a big company?
Daniel Galbraith [00:08:21]:
I think you have the scientist role, which is that technical role and understanding what’s going on. But I think you need a little bit of that entrepreneurial spirit — that interest in kind of going, “What’s next? What can we improve? What can we make better?”
And I think that’s where the CSO role comes in. I don’t think of it solely as a purely technical role. Obviously, you have to have that technical background, because that’s really where the vast majority of what you do is. But you also need to think: how does it work as a business function, as well as a technical function?
I think your job is to inspire. That’s really what made me want to move into a more leadership role — that ability to get people excited about the science, to get people excited about what we can do. If you can do that, either internally with your own company or externally to match the excitement that these innovator companies have with new drugs, I think that’s what makes you ready to be in that leadership role. Inspiring the science is really what I think of with that. I’m not saying I’m great at it, but I feel like I can help with that.
David Brühlmann [00:09:28]:
And with respect to the cell and gene therapies, as you said, we are now able to treat sickle cell disease and a lot of other diseases we previously thought it would never be possible. What are the specific analytical challenges we are now seeing with these new modalities?
Daniel Galbraith [00:09:46]:
That keeps you young as well. We’re trying to tackle some of these challenges. So when we think about these products, we’ve got cells, essentially, and cells are a population — they’re not all the same. Essentially, what we’re trying to do with the analytics is actually come up with some sort of measure on a population. And that’s, for number one, an analytical challenge: how do you characterize a population?
It’s different when you’re characterizing a batch of proteins or a molecule, a drug like aspirin or something, where everything is expected to be the same. With these products, we expect everything to be different — all of the cells will be slightly different in some way. So we need to sort of make an average or make a summation of what’s going on there.
At a fundamental level, we have to just think of these products differently. We can’t expect them to behave or to be measured in the same way that we would measure a monoclonal antibody. Even so, we have to bear that in mind.
I think also, cells don’t just have one activity — they do lots of different things, especially when we’ve manipulated them. Normally, we’ve inserted a gene or modified a gene or something like that — something has happened. We’ve upset them, we’ve upset their DNA in some way, usually to make them do something different. And cells will respond in different ways to that.
In some cases, in some of the CAR-T therapies, we can see adverse effects once we’ve manipulated them in certain ways. So we need to be able to measure that, to understand what the negative things could have been. Sometimes the cells will die; sometimes they’ll grow in a particular way.
The summation of all of these changing things means that, analytically, we have to apply techniques that can make an estimation of what’s going on. We need to understand that they’ll change over time — because if you take a sample on day one and then look again on day two, day three, or day ten, you expect change. The cells will grow, divide, or do something.
What are we doing, when are we doing it, and what parameters are we putting on there? What specifications do we want to put on there? Understanding what makes a good batch and what makes a not-so-good batch is a big challenge, because normally we’re dealing with really small patient batches. When I say “patient batches,” I mean the number of patients we treat — you might only be making ten batches for ten patients. So we don’t have a big population of batches to be able to make a specification. We need to understand that as well. The challenges are multiple, on different levels: what we’re measuring, how we’re measuring it, when we’re measuring it — and then, once we have measured it, how we’re determining what’s good and what’s bad.
David Brühlmann [00:12:26]:
And one major difference I see also between traditional biologics and cell and gene therapy is in certain scenarios, especially in the cells therapy side, you don't have the classical clinical study progression because you have a patient population of one, obviously. So how does this play out now when it comes to the requirements with respect to analytical methods? Because usually when we talk about biologics, we have a phase appropriate approach. Do we still see something similar on the cell and gene therapy side or how is this adapted?
Daniel Galbraith [00:13:02]:
To some extent, we do. I mean, the difference between what I would call autologous therapies compared to other therapies is really quite important, because you’re absolutely right — if we’re making one product for a particular patient for treatment, we have to consider that differently from what we would do for a product that we can give to multiple people, all of those sorts of things.
But just to take the autologous therapies — because those are the most common ones that we have at the moment that are out there and approved — we have to understand what the mode of action of the drug is intended to do.
So, you can just use the CAR-T therapies to start with. Essentially, they will express something on the cell surface — that expressed molecule on the surface will go in, make some interactions, and then, obviously, lead to a treatment outcome that results in an improvement in the patient’s condition.
Each batch, as you say, is patient-specific. So how do we do that? Essentially, we then look at an empirical level: do the cells express enough of this protein that we think will be clinically able to achieve a change in the patient that will lead to an improvement?
And I think that’s where you have to look at historical data — things that are published in the literature — and actually see if, in the past, when we’ve seen this level of expression of this type of protein, we’ve also seen an improvement.
For early-stage clinical trials, we’re really looking at historical data, and that’s all we can go on. Usually, the regulators would expect you to be able to reference other clinical trials that have shown that a certain level of expression of a particular molecule can achieve the treatment effect we’re looking for.
For these autologous therapies, we’re really just measuring the amount that’s there and then looking back and asking, is this good enough? As things move forward and they treat more and more patients, they gather enough data to say, “When we saw this amount of expression, we saw an improvement in the patients.” Gradually, they build up more and more information that way.
It’s really a step-by-step process, and they can slowly introduce more specification within it. It’s not — I wouldn’t say — in the same way that we would see for traditional biologics, where you have a phase one kind of validation and characterization of the assays, then phase two, phase three, and finally commercial release. You don’t have that. It’s more of an iterative process with autologous products, where they gradually see what works and what doesn’t, and they introduce changes that way. That seems to be the way they move with those ones.
David Brühlmann [00:15:36]:
We know that in the cell and gene therapy we have one big challenge. It's the cost, and we have amazing therapies, but only few people can access them today. So my question now, Daniel, is what technologies do you see that us make better therapeutics faster, cheaper, and perhaps even if we talk about allergenic, perhaps even scale to bigger scales, what do you see coming there?
Daniel Galbraith [00:16:06]:
One of the things I mentioned at the start was that, I mean, I think we still need some innovation in this space. I think we definitely do, because from what I’ve seen, it’s a tough nut to crack — making things to the same quality, to that high specification that we have for patient safety.
We have seen some products coming through. When we look at gene therapies and cell therapies that are using viruses to manipulate the cells and actually create the product, those are quite challenging to scale on an individual basis — really, just because of the vectors that we’re using.
So I think, if we’re looking much further into the future, we need to find methods that can manipulate the cells using techniques that don’t rely on some of these very expensive vectors that are currently part of the manufacturing process. I think we need to solidly look at that.
Now, there are some people using CRISPR technologies to modify the cells that way, but the efficiency of those — and the side effects and issues around using CRISPR — are still challenges. So I think we probably need some innovation in that space, to find a way to manipulate the cells in a more efficient way before we can properly scale these.
There are a number of techniques we can use to scale the amount of cells we produce — and that’s been around for quite some time. But to me, it’s more in the manipulation of the cells that actually create the gene therapy where we need to focus.
There are some innovative technologies looking at different ways of manipulating the cells to make them more receptive to the DNA being inserted or modified, or whatever the change may be. These are very early-stage techniques — not really taken into the clinic yet — but I think in the next few years, if we can get around that issue, the scaling of the cells themselves shouldn’t really be a problem for us.
We’ve got so much expertise within many companies looking at cells and how to grow them. The media we use to grow the cells, how we harvest them, and how we characterize the cells — all of that is already there.
But to me, the first step we need to get better at…
David Brühlmann [00:18:27]:
And which therapeutic modalities are the most popular right now? Which of those you think will gain momentum and perhaps other ones are maybe overhyped?
Daniel Galbraith [00:18:37]:
If you look across all modalities, everyone’s getting very excited about the peptides and what peptides can do. There’s obviously a big push in that direction. Looking at the biologics space, the antibody–drug conjugates (ADCs) over the past couple of years seem to have really come to the fore. The opportunities these are presenting — the payloads, the linker technology — have improved so much. There’s now a lot more choice, and many more manufacturing sites are able to handle these toxic compounds.
A lot of antibodies are also being repurposed — used as these antibody–drug conjugates rather than just as antibodies themselves. Another interesting development we’re seeing is that ADCs are being used in combination with other therapies as well — traditional chemotherapies, radiotherapies, and biologics. It’s really quite exciting to see these combinations of therapies coming together from that point of view.
Now, not to say they’re overhyped — I wouldn’t say that — but I do think ADCs still have a long way to go. Financially, a lot of companies see them as commercially viable, with a strong return on investment. So, in the biologics space, I’d say that’s probably the most exciting area right now.
On the other hand, one thing that seems to have gone a bit off the boil is some of the mRNA technologies. They were very exciting during COVID — seeing the opportunities that these mRNA vaccines presented. Before COVID, only a couple of companies were really interested in this technology. Then suddenly, everybody — all of the large pharma companies — had RNA as part of their portfolio.
That does seem to have cooled off a bit. Maybe we just need to see a bit more investment, a few more successes, and it might come back. But it’s been a little disappointing not to see a couple of blockbuster examples really showing what mRNA technologies can do.
Everyone still seems to be working on it, but progress has probably been a lot slower than expected, especially after what we saw with COVID and the opportunities that came out of the vaccine programs.
I guess now, because we’re moving away from the pure vaccine use of these technologies into other treatments, it’s taken an unexpectedly long time. It was probably a little bit overhyped — as you suggested — how quickly some of these could come through.
But overall, I’d say the ADCs definitely look like a great opportunity area.
David Brühlmann [00:21:09]:
On the podcast, we have covered and talked about choosing a CDMO several times already, but we have never had the opportunity to talk about an analytical partner, a CRO, which also can be or in oftentimes is a strategic partnership. So I would like to know a bit more about that. How to make the best decisions, how to choose the right partner.
Let's assume I am the CEO of a biotech startup and I need analytical support because we're lacking certain analytical methods. So when selecting an analytical partner, what are the critical questions I should ask to ensure that the collaboration will be successful?
Daniel Galbraith [00:21:55]:
You make a very good point. This product that you’re making as a manufacturer is the most precious thing you have — and you’re handing it over to someone in a CRO to analyze it, characterize it, and then produce reports that you’ll take to the regulators to get approval to move on to your next stage of clinical studies, or to get approval to make it a commercial product.
So this is really important — it’s your baby, really. You’re absolutely right: choosing the right partner from that respect is crucial. Now, there are a few aspects that, as a CEO, you’ll be interested in. One is: can they meet the timelines that we have for getting into the clinic and doing all the things we need to do? Can that partner meet those timelines? Does the company have the right expertise? Have they worked with these types of products before? Sometimes you do get very unique products where, with all due respect, nobody’s really got that specific expertise. But do they have the mindset — the inquiring mindset — to be able to work on your product and take an interest in it?
And I think that’s maybe one of the softer things to ask, and it’s more of a feeling: do they get excited about your product in the same way that you do when you describe it? I’ve been in many meetings — and I love being in these meetings — where the drug company is telling you about the patients they want to treat, about what this new drug will do and how much it will improve these patients’ lives. It’s lovely to hear that. And as a CRO, you want to be just as excited — you want to be part of that journey with them.
That’s something I’d emphasize: you want to get a sense that these people have commitment, that they want to spend time talking about your product and listening to you. If they’re not willing to do that at the start, they definitely aren’t going to be willing to do that later on. So, get people who are excited, who want to hear about your product, and who are willing to listen.
Now, on the practical side of things, there are a couple of points. Everybody thinks their product is unique — but there are some common features across products. You’ll spend time and effort looking at the unique part of your product, but there should also be an ability to handle a lot of the routine work quickly and easily.
We’re not reinventing the wheel for every single aspect of the characterization you need to do for your product. There are some off-the-shelf solutions that can give you quick and easy answers to certain questions. Ask those questions: What can you do for me quickly? What’s going to take time? Get an understanding of that — because not everything is difficult, but not everything is easy either.
So, where can I save time? Where can I save money by using existing expertise, so we don’t have to re-develop every analytical technique? There should be a lot of standard things the CRO can offer. Then, once you understand that aspect, you can look at what resources they need to put toward your product — to characterize it, to make it unique — and understand what they can do there.
Those are probably the key things you want to think about. There’s also some basic stuff — the GMP quality, the quality systems, the SOPs, all of the procedures. You want to make sure all of those are in place. But those should really be table stakes. You shouldn’t have to worry too much about that — if you’re going to a CRO and you intend to go into an IND, all of those things should already be in place. Still, you want to make sure they are. Just check that out.
David Brühlmann [00:25:16]:
That wraps up part one with Daniel Galbraith. We've covered his incredible journey and evolving landscape of analytical development. In part two, we'll tackle the practical challenges, from common pitfalls that trip up teams to phase appropriate strategies that actually accelerate your CMC timeline.
Speaking of streamlining your CMC journey, check out my new Notion CMC Dashboard that will help you transform chaos into confidence. This dashboard also includes a proven roadmap that will give you the exact step by step process that consistently hits IND timelines without the guesswork. And hey, if this episode added value, please leave us a review on Apple Podcasts or whatever platform you found us on. Thank you so much for tuning in 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 on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, visit us at www.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
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About Daniel Galbraith
Daniel Galbraith, Ph.D. is the Chief Technology Officer for Large Molecules and Advanced Therapies at Solvias, bringing over 25 years of experience in the life science and biopharmaceutical industry. He has held senior leadership roles at Merck Life Science, BioReliance, and Sartorius Stedim BioOutsource, where he drove innovation in analytical development, product characterization, and advanced therapeutic technologies. Daniel began his career in virology and biosafety, holding positions at Covance Laboratories, MedImmune Vaccines, and Q-One Biotech.
He holds a Ph.D. in Immunology from the University of Abertay, an M.Sc. in Forensic Science from the University of Strathclyde, and a B.Sc. (Hons) in Microbiology from the University of Glasgow. Known for his strategic vision and scientific leadership, Daniel advocates for innovation, analytical excellence, and collaborative partnerships in advancing next-generation biotherapies.
Connect with Daniel Galbraith 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 answer to battling antibiotic-resistant infections isn’t a new antibiotic, but harnessing viruses that have been quietly dominating bacterial populations?
Bacteriophages, viruses that target and kill bacteria, have been saving lives for a century, but their true potential is only now being unlocked by modern machine learning. The race isn’t just about discovering effective phages; it’s about deploying the right therapy, personalized to the patient, before time runs out.
This is part two of the conversation with José Luis Bila, CEO of Precise Health, a personalized phage platform aiming to improve and treat bacterial infections employing a combination of bacteriophage technology with data science and machine learning.
Can you isolate all the bacteriophages in the world? No. Even if you do isolate them, the bacteria are evolving and the bacteriophages are also evolving. How do you catch up to those evolutions altogether? It's insane, right? We start with the matching and maybe putting the bacteriophages into practice, etc. But in cases where you do not find any bacteriophage or there is no antibiotic that can be used, then what if we could engineer new bacteriophages in silico that could then be taken into production?
David Brühlmann [00:00:33]:
Welcome back to the Smart Biotech Scientist. I'm David Brühlmann, your host, and this is part two with José Luis Bila, who is the CEO of Precise Health, where we're exploring the business and regulatory sides of AI-powered phage therapy. In part one, we heard his compelling personal story and learned how artificial intelligence can revolutionize treatment selection against bacterial infections.
Now we're diving into the nitty-gritty: production scalability, regulatory hurdles, and the economic realities of bringing personalized phage treatments to market. Discover how AI is making life-saving therapies both accessible and affordable for patients worldwide.
How do you produce these bacteriophages, and also, how long would it take, for instance, in a case where we have 80 or 90% coverage, but unfortunately, a patient comes in with that 10% we have not covered? How fast could you start the bioreactor, and how long will it take? Also, can you tell us a bit about what kind of bioreactors we're using?
José Luis Bila [00:02:54]:
Producing bacteriophages is actually quite complex by itself, but to be honest, it's not the most complex thing—it’s been done for ages. I'm not a bioprocessing person, but I would reckon that it's a little less complex than synthesizing a chemical molecule. So, I’d say it’s quite manageable.
The type of bioreactors we use are standard bioreactors—the same ones used for bacterial growth are the same ones used for bacteriophage growth and production. In terms of timing, it depends on who is doing the production. Some hospitals already have their own in-house capabilities, which is a major advantage. Most hospitals, however, don’t have that, so you need to go to a commercial CDMO. For commercial CDMOs, the minimum timeline is typically two to four weeks, assuming they have an empty bioreactor and personnel ready to start production.
Then there’s another issue: stability and quality control. You need to go over all of these steps carefully. Everything needs to be considered—essentially, it’s the classic GMP process. All the quality management systems need to be in place to ensure production is efficient and correct.
David Brühlmann [00:04:22]:
I guess a dual system could work very well. You could use a CDMO to produce the 80–90% of bacteriophages you need regularly, and then have smaller bioreactors in a few hospitals to quickly produce whatever is lacking. That could be a solution.
José Luis Bila [00:04:41]:
Yes, that’s correct. The reality today is that only one hospital in Switzerland that I know of has GMP production capabilities. Most hospitals in the world do not have GMP capabilities. However, when they need phages for patients, they have processes in place that are not fully GMP but could be considered GMP-like, with proper sterilization methods, etc., to deliver bacteriophages to patients. Some hospitals have already invested in this capability.
But it’s a big assumption that most hospitals worldwide will have this. In Europe, many large, university hospitals may have this capability, but regional hospitals? That’s much more difficult. In US, again, in big hospitals, bigger cities and well-funded hospitals might manage, but rural areas? Not so easily.
We also have to consider that most antimicrobial resistance cases are coming from LMICs—low- and middle-income countries. Whatever strategy we develop today, we must account for that, because that’s where the most impact can be made.
David Brühlmann [00:06:01]:
Could we envision some mobile facilities somewhere in a remote area of Africa or Southeast Asia? Because as you mentioned, that's where the most cases will be. I think in the Western world we could cover it with a few centers and then quickly ship it if needed. But where it will take time is especially in those remote areas and probably that's also where the expertise is still lacking today. How could we go about producing that in a very economic way, in a cheap way, maybe with single use technology or some mobile reactors? We probably need some creativity there.
José Luis Bila [00:06:40]:
Yeah, exactly. I think if we manage to build a single use bioreactor for each patient that is cost effective some way, that will be a billion dollar idea for sure. Because this is something that I think all personalized approaches would need somehow. And the issue I feel is not really on the bioreactor itself, it's really on the downstream processing of things. Because you want to be able to build a bioreactor but you need to combine it with the downstream processing in a way that you get something that is pure. But how do you miniaturize it in a way that is going to be cost effective? If anyone has some ideas, we are happy to collaborate on those things because yeah, this is a billion
dollar idea for sure.
But for the LMICs, the way I see it is that in Switzerland, we are fortunate enough to have these big hospitals that make us think outside the box, initially without the thinking of the costs or something that we can already kind of start implementing, thinking of ideas of how to make it work. And then in a second phase we will have to be able to decrease the cost so that we can feed everybody else who does not have the same economic power.
Now for LMICs, we will have to implement it here first, make sure that it works and then how do we go down the line. That's going to take a bit of time. But I am from LMIC myself and like I said my parents died from the antimicrobial resistance. And in that case, even if we had bacteriophages today that could have saved them, I wouldn't have benefited them by just us focusing on financially powerful markets. So I need also to think of that and this is something that attracts me to my team every day. Whatever you do think long term, three to five years from now, if you were in Uganda, for example, how would this be used and how would it be implemented in a hospital where does not have the same capability as one big hospital in Switzerland?
David Brühlmann [00:08:47]:
Yes, that's a very good point, Jose. Start with the end in mind because finally it's about the end user and how are we going to bring it to them. Even if we have the best technology, but we cannot deliver it or it's too expensive, it will be of no use. Now just I want to double down a bit on the process again. So since you mentioned the downstream, can you tell us how different the downstream is from a standard, let's say MAP process or like a viral vector process? Is that very similar or is it very different?
José Luis Bila [00:09:15]:
From what I understand, it’s quite similar. I’m not doing this day-to-day, and I’m not very familiar with viral vector bioprocessing, but phages are viruses themselves, so I wouldn’t expect anything drastically different.
One thing I want to highlight, which I mentioned earlier, is stability—this is extremely important. Let me step back a bit: some doctors are concerned with logistics, like how to get the right phage at the right time. That’s one issue. The second issue is the lack of systematic clinical data. We have enough treated cases to show promise, but they haven’t followed classic clinical trial protocols with standardized data points that can be consistently implemented.
This is one issue. On top of that, there have been some clinical trials done back in 2017, if I’m not mistaken, on phage therapy. In one trial, they used a cocktail of phages at 10^6 PFUs for wound infections. Over time, they noticed that stability was not great. Instead of administering 10^6 PFUs to a patient, they ended up giving only 10^2 PFUs per milliliter. This is clearly insufficient—so instability was a major problem.
Another issue in that specific trial was that each patient’s bacterial isolate needed to be tested against the phage cocktail before administration. You couldn’t simply assume that the cocktail would work for every patient, unlike antibiotics, which are more broadly effective.
Returning to the stability issue: if we want to implement our strategy—taking the bacterial fingerprint of a specific region and stocking phages—we need to keep them stable for a long time. That’s crucial for the economics. If a batch only lasts three months, how likely is it that a patient will need that batch within that timeframe? If we can increase shelf life to two or three years, the economics make much more sense. We can produce batches, store them, and eventually someone will use them.
This is also where miniaturizing the bioreactor—your idea of a single-batch system delivered directly to the patient—comes in. Until we achieve that, we need proper phage stability. One way to achieve this is through lyophilization, which various groups are actively exploring to increase stability.
David Brühlmann [00:12:17]:
That's a very important point, José. I just want to also briefly touch upon the regulatory aspect because finally it's a drug you going to be used to treat patients. How do you navigate this with the health authorities? Especially as you're using machine learning and AI approaches to identify the fate. Is that relevant or not? Or how do you go about that?
José Luis Bila [00:12:39]:
There are two regulatory fronts, and this is always a big question, no matter which investor we talk to: What are the regulations around this?
The first front is phage therapy itself. If I’m a doctor and want to administer phage therapy, which set of regulations do I follow? The second front is the process to identify the right phages and produce them before they can be used under the first regulatory framework.
For the first front, many countries—and many stakeholders in the phage world—are exploring ways to implement this in hospitals. For example, in Belgium, they use magistral preparations for each patient, so GMP is waived. Hospitals prepare phages in-house and administer them directly. They maintain a library of bacteriophages, and doctors follow a manual process to identify and produce the required phage.
Last year, around November 2024, Portugal announced that it would follow Belgium’s model: bacteriophages can be used as long as there is a clear patient need, no alternative exists, and ethical approval is granted by the hospital committee. Some other European countries and even stakeholders outside Europe are following similar approaches. So there is some regulatory relaxation. However, the European Union still requires more clinical trials to establish certainty.
The second front is the use of machine learning for phage identification. This is still not fully mature for direct regulatory acceptance, but there are precedents. Consider antibiograms in hospitals: the process is the same, except instead of testing antibiotics, we test phages. If digital antibiograms exist, why not a digital phagogram?
We are not building a platform to be used recklessly. We want intelligent guidance for doctors. We are also pursuing CE marking and all necessary regulatory approvals. This builds confidence for medical doctors to use the platform and ensures we follow all regulatory pathways for clinical applications.
David Brühlmann [00:15:47]:
What is your vision for the future of this machine learning powered phage therapy?
José Luis Bila [00:15:52]:
It’s a deep question. There are many fronts to consider. Today, the most natural entry point for clinics in Europe—or anywhere that isn’t yet using phages—is natural phages. You extract them, isolate them from multiple sources, and then identify the right ones. Managing this identification and reducing logistical challenges is where machine learning can help first.
Looking ahead three to five, maybe even ten years, we believe this is where we can have the most impact. Consider this: estimates suggest there are 10 to 30 million bacteriophages on Earth—a huge number. Can you isolate them all? No. Even if you could, bacteria and phages are constantly evolving. How do you keep up with these changes?
That’s why we start with matching phages from existing libraries. But in cases where no suitable phage exists or antibiotics fail, what if we could engineer new phages in silico and take them to production? This is the vision of Precise Health: a system where you first match from existing libraries, and if nothing fits, you can engineer new phages with a single click, produce them, and potentially use them for the patient.
Of course, this is far more complex than it sounds and will take time. Implementing engineered phages is harder and slower, which is why we are starting with the matching approach first.
David Brühlmann [00:17:54]:
Wow. The future definitely looks bright or interesting at the least.
José Luis Bila [00:17:59]:
Yeah, true.
David Brühlmann [00:18:00]:
This has been great. José, before we wrap up, what burning question haven't I asked that you're eager to share with our biotech community?
José Luis Bila [00:18:08]:
Burning question? It’s not really a question—it’s more of a vision for me. Bacteriophages have existed for 100 years, and we know they work. They’ve been used multiple times successfully.
The real challenge is building the infrastructure and foundation to implement phages cost-effectively and accessibly, so they can have a global impact—not just in Europe, but worldwide. We believe machine learning is key to achieving this, and this is what we’re trying to demonstrate to the world.
So, maybe not a burning question, but definitely a burning comment.
David Brühlmann [00:18:59]:
Fantastic. With all that, what we've covered today, what is the number one takeaway you want our listeners to walk away with?
José Luis Bila [00:19:11]:
We don’t need to wait to find solutions for people who are dying. This could affect anyone—it could be us, or your loved ones. Antimicrobial resistance is one of those urgent issues, and current systems aren’t working.
If you have an idea to help—even if it’s outside your usual field—go out and try it. Eventually, someone will find the right solution. I think that’s the key takeaway.
To all the listeners out there, thank you so much for taking the time to listen.
David Brühlmann [00:19:54]:
Thank you so much for moving the needle, José . And thank you so much for investing your time and passion to solve a real problem of humanity. Also thank you very much for coming on the show. It has been a huge preasure to talk with you. Before we go, where can people connect with you?
José Luis Bila [00:20:11]:
We can be found at www.precisehealth.io, that's our website. But for those who want to reach me directly, it's jose.bila@precisehealth.io . I'm happy to connect with anyone who might have questions or I can help with anything.
David Brühlmann [00:20:30]:
Fantastic. I will leave the links in the show note so reach out to José . And José , once again thank you very much much for coming on the show. It has been a huge pleasure.
José Luis Bila [00:20:40]:
Thank you so much. It was a pleasure to me. My first podcast. I hope it's impactful to anyone who is
listening. Thank you so much.
David Brühlmann [00:20:47]:
José's vision for AI powered phage therapy offers real hope in our fight against antibiotic resistance. His journey from personal loss to breakthrough innovation proved that biotech can truly save lives. Do you have questions about process development or manufacturing challenges? Well, book a free consultation with me at www.bruehlmann-consulting.com/call and I'm happy to help you get started. And please rate us on Apple Podcasts or your favorite platform because every review helps us reach more biotech scientists ready to make their mark. Thank you so much for tuning in today and I'll see you next time.
All right, smart scientists, that's all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up Biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About José Luis Bila
Dr. José Luis Bila is the Co-founder and CEO of Precise Health, a company dedicated to making phage therapy faster, smarter, and more accessible through AI-driven innovation. He earned his PhD in Chemistry from EPFL and began his career in life sciences consulting, advising global biotech and pharmaceutical firms on strategy and innovation. He later joined a MedTech startup developing rapid STI diagnostics.
Blending scientific rigor with entrepreneurial vision, José leads Precise Health’s strategy, product development, and partnerships. His personal experience—losing both parents to antibiotic-resistant infections—fuels his mission to bring effective, precision therapies to patients where traditional antibiotics no longer work.
Connect with José Luis Bila 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
Antibiotic resistance isn’t just a looming problem. It’s a global crisis. Every year, more than one million people die directly from resistant infections, and another 5 million die indirectly. Routine infections are becoming life-threatening, and healthcare systems worldwide are under pressure.
Despite decades of warnings, pharmaceutical solutions are falling behind, while “superbugs” continue to outpace new drug development. If trends continue, by 2050, antibiotic resistance could claim 10 million lives annually and cost the world $1 trillion.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets José Luis Bila, CEO of Precise Health, a company dedicated to making phage therapy faster, smarter, and more accessible through AI-driven innovation.
Throughout my studies, of course, I've been passionate about life sciences in general. But when I was doing my bachelor’s, a very unfortunate event happened. Two events happened where I lost both my parents to antibiotic resistance due to co-infections and because they were immunocompromised at the time. In one case, it was that the doctors did not have time to react with the right antibiotic, but in another case, which was more extended, more chronic, they just couldn't find anything else off the shelf that they could try.
Unfortunately, I lost both my parents in one year after the other, 2010 and 2011. Since then, I've been really quite motivated to use my knowledge to go into personalized medicine because I believe that in their cases, it's just that they couldn't find something specific to them, most importantly.
David Brühlmann [00:00:48]:
Antibiotic resistance kills 1.3 million people annually and could cost $1 trillion by 2050. Traditional antibiotics are failing against superbugs, leaving patients with few options. What if artificial intelligence could revolutionize how we fight these deadly infections? Today's guest, José Bila, lost both parents to antibiotic-resistant infections, a tragedy that sparked his mission to develop AI-powered phage therapy solutions. His personal journey from chemistry PhD to biotech entrepreneur and now CEO of Precise Health reveals how cutting-edge science meets deeply personal purpose in the fight against antibiotic resistance. Welcome to the Smart Biotech Scientist. I'm David Brühlmann, your host. Join me today as José shares his journey from personal tragedy to pioneering AI
solutions that could save millions from deadly bacterial infections.
Welcome, José, to the Smart Biotech Scientist. It's a pleasure to have you on today.
José Luis Bila [00:03:09]:
Thank you so much. It's a pleasure to be here.
David Brühlmann [00:03:11]:
José, share something that you believe about bioprocess development that most people disagree with.
José Luis Bila [00:03:18]:
I wouldn't say it's directly the bioprocess itself. It's more on the economics of the bioprocess. Especially when you start thinking of more kind of personalized medicine, usually people think it's a problem with scale-up more than anything. But in reality, when it comes to the field in which we are specifically, it's more about the economics regarding batch utilization. This is something that is a little bit controversial, depending on who you speak to, of course. For instance, in the case of personalized medicine, you can have a thousand liters of the product, but if you're only using it for one patient, it doesn't change anything. You need to be able to have a high-scale batch utilization. These are things that can also be solved with different approaches like machine learning and so on. So it's not really a bioprocess per se. Like I say, it's more of a general thing regarding the economics of bioprocessing.
David Brühlmann [00:04:17]:
José, draw us into your story. Can you share what sparked your initial interest in biotechnology and how your personal experience shaped your very scientific focus and led you to fight antibiotic resistance?
José Luis Bila [00:04:32]:
So I'm a chemist. I studied chemistry my entire life. I did my bachelor's, master's, and PhD in chemistry. And throughout my studies, of course I've been passionate about life sciences in general. But when I was doing my bachelor’s, a very unfortunate event happened. Two events happened where I lost both my parents to antibiotic resistance due to co-infections and because they were immunocompromised at the time. In one case, it was that the doctors did not have time to react with the right antibiotic. But in another case, which was more extended, more chronic, they just couldn't find anything else off the shelf that they could try. Unfortunately, I lost both my parents in one year after the other, 2010 and 2011.
Since then, I've been really quite motivated to use my knowledge to go into personalized medicine because I believe that in their cases, it's just that they couldn't find something specific to them, most importantly. From there, after my master's, for instance, I worked as a chemist. Of course, I was designing molecules and synthesizing molecules for photodynamic therapy, so it’s another strategy that is used for cancer, for instance, and that was very deep personalized medicine. After my PhD, I went into management consulting, specifically in life sciences. There I learned a lot about gene therapies, personalized medicine, and so on, and all this knowledge and my personal story culminated in Precision Medicine, a startup that we have built today.
David Brühlmann [00:05:56]:
It's a very tragic story indeed. And at the same time, I love your purpose behind that — a greater purpose and not just science. Well, it comes out of a personal experience, a tragic one, and now you're using that for the greater good, which is very powerful. So I am wondering, José, to what extent is this tragic event an isolated case? Can you tell us a bit? What is the current state of antibiotic resistance? How many deaths do we have per year, and what is the cost to our healthcare system?
José Luis Bila [00:06:29]:
So estimations from the World Health Organization and all the different surveillance programs we have estimate that there are 1.2 to 1.3 million deaths globally per year caused directly by antibiotic resistance. But there are actually around 5 million which are indirectly caused by antibiotic resistance. Of course, we usually look at the direct ones, but the indirect ones also — these co-infections — lead to something else, which is reported not as a death by antibiotic resistance but by organ failure or something else that we never learn about. So the number is huge. Estimations again by 2050 indicate that there will be about 10 million deaths globally per year. This is huge if you look at it, and it's quite comparable to cancer today. The number is increasing day by day, and some newer estimates indicate even more than 10 million deaths today. This is a big problem. It's not only a question of the deaths themselves; there is also a cost burden associated with this. Because when a patient has an antibiotic-resistant infection, the tendency is that they will stay longer at the hospital, and this patient is not going to work.
You have more doctors having to focus on one specific patient, and so on. So the economics globally actually show about 1 trillion dollars in additional costs by 2050 globally for healthcare alone. This is huge, associated with the 10 million deaths that are expected. I think the bottom line is that we need to find a solution that works. The current antibiotics are failing, and this is not working as we have today. As you might know, and also your listeners might already know, a lot of the pharma companies are actually exiting the entire antibiotic space. The economics of it are not very attractive compared to cancer and many other indications. Since 1950, I think it’s been more than 50 years, and some reports show that there hasn't been a new antibiotic that has been very effective compared to what we already have today against gram-negative bacteria.
Gram-negative bacteria are causing a lot more damage than maybe gram-positive, relatively speaking. Finding new modalities is important. Finding new approaches is very important. This is why I think a lot of the innovation has to come from smaller companies because the big ones are exiting. We should have a collaborative approach from different institutions, academics, SMEs, private companies, and so on. So it's a big problem because out of these 10 million people that will be dying, it could be my parents, but it could be somebody else's parents, or it could be us — exactly. That could be the next person dying. So this is a big problem for sure.
David Brühlmann [00:09:24]:
It definitely is a big problem. And the numbers are crazy. Antibiotic resistance has been a hot topic for several decades already. So what is the current state now in 2025? Is it still the main drivers that we've talked about for the last decades, or is it something else that's causing even greater numbers of people to probably die in the future?
José Luis Bila [00:09:45]:
One of the drivers, of course, is what caused antibiotic resistance in the first place. You can think of antibiotics as these static molecules: once you synthesize a chemical molecule, it's static, it will not change. I mean, I'm a chemist, you're also a chemist, so you know that even changing one substitution on one of the positions of the molecule requires going through clinical trials again, which can take 10 to 15 years until you can actually commercialize it. So this static molecule is fighting against something that is changing; it’s adapting itself. If you are in a box fight and you keep throwing punches the same way in the same direction, if I am adaptable, of course I will know after a few punches that I need to run away and I know how to defend myself against that. That’s what bacteria is actually doing. We need an approach that is adaptable as bacteria is also adapting. So having static approaches is one of the things increasing the issue with antibiotics.
The other thing is that, over time, depending on which culture or country you come from, people, whenever they have a small symptom, their intuition is to go and get, for example, a medicine like Aristamol. But in some cases, most people just say, “Oh, just take an antibiotic.” So the misuse and overuse of antibiotics has been driving the entire issue with antimicrobial resistance (AMR).
On the adaptability aspect, on the pharma side, if you have to spend 10-15 years to develop a new drug and spend billions of dollars, then at the end the economics may not make sense. The margins are low, and this makes it less attractive to develop something adaptable to a bacteria that keeps changing. These factors all come together. We've known them for ages, but not much is being done in that space.
David Brühlmann [00:11:39]:
So how are we going to solve that problem as an industry? And more specifically, how are you solving that in your company?
José Luis Bila [00:11:46]:
So in our company, we are focusing on bacteriophages. Bacteriophages are viruses that are present where bacteria are present, and they usually exist to either control the growth or eliminate pathogenic bacteria. They infect and kill specific bacteria. They are very specific and natural. You can find them from many different sources. In our case, we isolate them from wastewater, agricultural products, or samples from people. They are very good because they are specific to the strain level or a group of bacterial strains. They are harmless to human cells, or at least that has been indicated so far. So they are quite safe compared to chemical molecules like antibiotics, which could be toxic in certain instances. These bacteriophages can be used as a weapon against pathogenic bacteria resistant to antibiotics. That’s what we focus on in our startup.
David Brühlmann [00:12:58]:
And I imagine these bacteriophages are very specific. So you have to do some analysis to know which one to pick. How do you find the needle in the haystack?
José Luis Bila [00:13:08]:
Exactly. Bacteriophages have been with us for over a hundred years and have been used in many locations. Specifically, ex-USSR geographies like Georgia have been using phages for quite some time. But the issue is that they are very specific. Antibiotics, being chemical molecules, can be given to a bunch of people, and they kill the infections in general, even if they are not perfect. Phages are too specific. Being too specific means you have to personalize to the individual, which makes the economics more difficult.
To find the right phage today, you need to take the bacterial isolate and loop within a phage library, testing manually one by one to find the right one. This uses plaque assays or liquid assays. It is laborious, which is why many doctors don’t use it. We asked doctors: bacteriophages are amazing — they can even penetrate biofilms, have less toxicity than antibiotics — so why aren't they using them? Even when you have a patient that is dying, why not resort to bacteriophages? The key issue is that finding the bacteriophage manually can take two to six months. This is insane for a patient with an acute or subacute infection, which is a good portion of the infections that we have today.
You don't have time. You need to react fast. Normally, if you isolate the bacteria and then you have to ship it to a phage bank that is somewhere, maybe you're in Switzerland and this phage bank is in Georgia and then another one in Portugal, you have to accept the fact that, even if you ship it, they might not have what you're looking for. So the entire manual process and then the production, it’s a whole nightmare.
This is where machine learning can help. Using ML, we can combine traits of bacteria and phages, learn what works and why, and train a model to identify the right phage for a bacterial strain without the manual hassle. At Precise Health, our startup, we are developing a platform interconnecting multiple phage banks. The doctor only needs DNA information and metadata of the bacteria, upload it to the platform, and within minutes we can scan multiple phage banks to find which phage is ready for use under local regulations.
David Brühlmann [00:16:58]:
So the traditional approach takes weeks or months, by which time the patient might be dead or in very bad condition. Now, with your machine learning approach, how fast can it identify and produce that bacteriophage?
José Luis Bila [00:17:23]:
The big dream for us is to have multiple phage banks holding already produced and qualified phage, let's say batches, to speak. And in that big dream, the idea would be that when doctor already decides that, Oh, my antibiogram shows that nothing on the shelf that I have is going to work, then can I use bacteriophages. And usually these can be done anywhere between 48 to 72 hours, of course, depending on the hospital capability. It really depends on which country you are. But in Switzerland we know that this can be done by hour to 72 for sure. And with that in mind, can I just search within minutes and get my phage? I don't know, even if it's somewhere in Finland, can I just, in two clicks, request the phage and that the Finnish biobank which has the ready-to-use phage already produced, send that to me next-day shipment, depending on the severity of the case that I have.
Our target actually is by day five, you get your phages in-house to be used under whatever regulations you're following in your specific country. But there is another issue. The production itself is an issue because isolating phages is not the issue. We have more than 200 phages in our laboratory, so we can go and produce all of them. But then it comes back to my very first comment regarding the bioprocessing. If I produce all the 200 phages in whatever batches, how likely am I to use a certain percentage that is going to make the economics actually, right? That is the key question. Again, coming back to our machine learning algorithm. What we can do is, by looking at specific regional fingerprints, we can already anticipate which phages within certain phage libraries or phage banks can maximize the chance of being utilized.
Just a simple example. If I go to a hospital in Switzerland, and let's say in Zurich, we often have kind of the same bacteria patterns that are running around because the population is the same, they are eating the same thing, they are breathing the same air. The travel patterns are quite relevant. By looking historically at the different bacteria that have been deposited and isolated in the ranks of the hospitals, can we use that information to predict or develop a fingerprint for the region of Zurich in this case? And with that fingerprint, can we look into the phage libraries that we have incorporated in our machine learning platform to scan for the phages that are likely to cover 80 to 90% of the fingerprint for Zurich? And can you do this for multiple segments, cities? Maybe it's not Zurich, maybe it's entire Switzerland or the entire Europe? We don't know for sure for now. Right? But that's what we are working towards to understand. By doing so, let's say for Zurich, you need X amount of phages that are likely to cover 80 to 90%.
Can you already take those and get them pre-produced and stock? And when the doctor needs them, they are already produced because you already know that it's very likely that it's going to be one of those. And then in case you have cases where the 10% is not covered, you can come back and search again within the libraries. And if the patient still has time to cope with the infection, this would have to be taken into production again and be taken into the library.
Using the exact same system, what if you were to get a continuous bacterial surveillance program from this hospital that I'm talking about in Zurich, every, I don't know, three to six months, we collect new bacteria and we check again, are we still covering 80 to 90% or are we down to 70 or 60%? And then can we scan again and say, how do we get back to the 90% and do the production? Then you have a continuous surveillance program and a continuous update of the bacteriophages which are produced. This is the way we believe is to go, and you reduce time, you make it fast and more effective, and more targeted, and then the economics will be correct.
David Brühlmann [00:21:37]:
That's a wrap on part one. Jose's story shows how personal tragedy can fuel scientific innovation. In part two, we'll dive deeper into the economics and regulatory challenges of AI-guided phage therapy. If you have process development or manufacturing questions, book a free call at www.bruehlmann-consulting.com/call and I’m happy to help you get started. And also, please leave us a review on Apple Podcasts or whatever platform you found us on. It helps other biotech scientists discover actionable insights like these. Thank you so much for tuning in, and I'll see you next time. All right, smart scientists, that's all for today on the Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then, let's continue to smarten up Biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
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About José Luis Bila
Dr. José Luis Bila is the Co-founder and CEO of Precise Health, a company dedicated to making phage therapy faster, smarter, and more accessible through AI-driven innovation. He earned his PhD in Chemistry from EPFL and began his career in life sciences consulting, advising global biotech and pharmaceutical firms on strategy and innovation. He later joined a MedTech startup developing rapid STI diagnostics.
Blending scientific rigor with entrepreneurial vision, José leads Precise Health’s strategy, product development, and partnerships. His personal experience—losing both parents to antibiotic-resistant infections—fuels his mission to bring effective, precision therapies to patients where traditional antibiotics no longer work.
Connect with José Luis Bila 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
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Do you wish to simplify your biologics drug development project? Contact Us
What happens when the charging elephant of biotech—antibody-drug conjugates (ADCs)—meets the organizational complexity of a 300-strong analytical powerhouse? The answer isn’t just in the science; it’s in how you marshal people, tech, and trust to outpace the clock and deliver therapies that matter.
This episode features Amanda Hoertz, VP of Analytical Development & Testing at KBI Biopharma. Amanda’s spent her career navigating the thicket of biologics—from biosimilars to the most challenging ADCs—while shepherding one of the largest analytical teams in the industry. Her perspective isn’t just tactical: it’s rooted in tenacity, team stewardship, and a practical playbook for bringing next-generation modalities to market. Check the first part of our conversation.
We definitely look at each molecule individually, but with the experience behind us. So as I mentioned, our tenure before - that's a huge advantage. We remember the molecules that are similar. There are a number of biosimilars where it's not the first time they've come to KBI. So we kind of have an idea of where to go down that path, and that really helps us accelerate for later clients.
The same thing with ADCs: if we know what molecule or method is going to be the trouble method—so I've already said that's the charge heterogeneity—we know to start with that method first in our method development.
David Brühlmann [00:00:36]:
Welcome back to Part Two of our conversation with Amanda Hoertz from KBI Biopharma. I'm your host, David Brühlmann, and in part one we explored Amanda's journey and the fundamentals of ADC development.
Now we are diving into the nitty-gritty: the analytical puzzles, the formulation challenges, and the scaling strategies that make ADCs so complex. Amanda will share KBI’s innovative approaches, leadership insights from managing 300 scientists, and the future trends shaping biologics development.
Let's shift our conversation towards the more, I'd say, project management or organizational aspects. What are some key best practices you follow at KBI to accelerate the path of ADC development?
Amanda Hoertz [00:02:46]:
So the approach is the same whether it's ADC or non-ADC. What we've found at KBI works best is we have a program manager, and that stays the same for the client. But then we also have dedicated teams in each space.
Once you have a dedicated director and group leader—for example, in the analytical space—that will be the same person you work with for the life of the project. It helps because we learn what this customer wants versus that customer. Right?
This customer may want all the raw data exported and sent to them on a secure shared drive so they can make overlays. Another client may not need that. One client may want everything in a PowerPoint presentation, one may not. One may have a critical filing deadline, one may not.
So by having a consistent point of contact—and that contact will stay the same if you have another molecule at KBI—you don’t have to relearn that relationship. Everything is consistent. You know how to get what you need, and you also know when they need something for, let's say, an IND update. You understand who to talk to, and you know what they need. It's a good relationship. If the client invests in it and KBI invests in it, it makes everything a lot faster.
What we've seen at some CDMOs that we outsource services to—for example, sterility testing and CCIT (Container Closure Integrity Testing)—it’s very confusing as to who to talk to. So when we have investigations at those other partner sites, and we are responsible for the relationship, I sometimes struggle to get answers out of a director or somebody at a different company. Not going to name names because I don't want to badger anybody in particular, but it's confusing.
And I can only imagine as a client, when you have a critical filing for your IND and you need this file now—if you don’t know who to contact or can’t get a clear tree of responsibility—how frustrating that would be. So we try to make sure that it’s seamless.
I'm not pretending that KBI is perfect. There are definitely times when a client needs something and the timeline is insane. We try to meet it and do everything we can.
That’s one of the strengths of the analytical portion of KBI. So, a typical CDMO has a QC team of about 20 to 30 people that execute testing—usually release and some limited stability. KBI, at the Hamlin site alone, has 200 analysts, and in the network it’s 400—300 of which are under my supervision.
It means that if you need something as a client, I can throw basically a flood of people and a flood of instruments on something to make sure you get what you need. It's an army. We can absolutely destroy any task by rearranging priorities.
And that’s one of the benefits of having the mammalian network for analytical testing under me, and the process development mammalian network under Leslie Wolfe at KBI, is that we can adjust priorities in real time. There’s no in-depth conversation or negotiation.
And even with our microbial partners, we leverage each other’s teams to make sure that we get what the client needs. We have excellent relationships across our entire network.
David Brühlmann [00:05:40]:
Having that many people is impressive, and it can definitely be an advantage if you manage your network well—or your people well.
So my question, Amanda: how do you ensure consistency and knowledge transfer among this huge army? And what is your framework for building analytical methods that are robust and scalable?
Amanda Hoertz [00:06:01]:
I can’t take credit for this because I’m benefiting from my parentage at KBI—they’ve set this up in a very scalable way.
So, at the Hamlin site specifically, there are about 200 people. We have eight directors, and then below the directors are group leaders. Below the group leaders are project leaders. So it’s a very structured tree, and that structure drives knowledge transfer as well.
Basically, we’ve all been “raised” in this environment. The average tenure at the North Carolina Analytical and Formulation Sciences (AFS) group is almost six years. And the average tenure for managers is close to 10 years. That is atypical for the industry, and it’s really the key to our success.
We very much advocate for our people. That’s my job—making sure I’m doing everything I can for the analysts all the way up to the managers. And those managers do the same. That inspires a lot of loyalty and consistency.
If we’re not constantly turning over analysts, we can benefit from their knowledge base and also have the bandwidth to do improvement projects. Analysts are, of course, the most common turnover point, but even there the average tenure is almost four years—compared to the industry average of around two years. That’s a big difference, and we benefit from it.
I think once you have a good culture, it’s actually easier to maintain it. But when you allow that to degrade, that’s when you start to face bigger problems. So I can’t take credit for creating it—I’m just trying to maintain what was entrusted to me when I started leading the department. That’s how so many people can work together effectively.
We also all have the same mentality: we know we need to deliver for the customer. There’s no confusion about priorities, and we have a very clear decision tree.
If I talk to one of my directors who’s working on a project and I say, “This other project has reached a critical status and we need to rearrange priorities,” first of all, we have the depth to move things around. Second, everyone understands how that priority was determined—and then it’s done.
And it works the same way for me. If my boss, Sigma Mostafa, our Chief Scientific Officer, tells me, “I need you to make sure this client or this activity is completed,” we just do it. Not in a military way, but in the sense that it’s clear: the decision has been made, now it’s time to execute. That clarity really helps.
David Brühlmann [00:08:08]:
That’s fantastic. I think clarity is key, and it’s great that you can leverage this huge network. So I’m curious, Amanda—you mentioned the culture is great and you put a lot of work into it. But on the technical side, how has digital transformation changed the way you operate? And how are you leveraging new technologies?
Amanda Hoertz [00:08:31]:
That’s a great question. Starting with things like a LIMS (Laboratory Information Management System) and an ELN (Electronic Laboratory Notebook), we’ve been rolling those out.
Hamlin is our largest site, so it’s always going to be the slowest to adopt new technologies. Right now, we have LIMS operating at our non-GMP sites and at two smaller GMP sites—our Boulder site (microbial) and our Geneva site (mammalian).
We are implementing LIMS across the network. As you’d imagine, we have over 650 active stability studies. Bringing those in from a paper-based system to electronic will take time, so we’re triaging and doing it incrementally. Our target is to be fully transitioned by the end of 2025.
That shift allows us to instantly understand statuses, compile data for clients, and track things in a much more robust way. We’ve had a very successful paper system, but paper can be damaged or lost. Electronic systems, when backed up properly, cannot. Same story for the ELN system—it’s already in place at our GMP and non-GMP site in Boulder, and at our non-GMP mammalian sites, and now we’re bringing it into our GMP mammalian sites.
It will allow us to link everything faster. Right now we already provide all raw data to clients, but it’s literally scanned pieces of paper. Soon, we’ll be able to deliver it in fully electronic format, searchable and linked—much more efficient.
Finally, at our non-GMP site we’ve implemented a lot of automation. Tecan systems are huge. For example, Derek Ryan, our Senior Director of Analytical Development in the mammalian network, and his team rely on them to process the hundreds and thousands of samples needed for PD on a regular basis.
We tried implementing a Tecan at our GMP site, but the complication was that the audit trail is essentially code—not straightforward for compliance. So Derek’s team tested alternatives in the non-GMP space. They pulled in the Waters Andrew+ pipetting systems. While they’re not using them with GMP audit functionality in development, they do have validated GMP audit trails.
So we purchased those for our GMP sites. The Waters Andrew+ systems take away all the repetitive pipetting from analysts. That improves consistency, reduces deviations and investigations, and allows analysts to set up ELISAs, dilution series, everything, and then just pop plates into the reader. Crucially, it has a clear GMP audit trail that reviewers can audit and understand. That’s a huge advancement.
On top of that, at KBI we have a very large amount of formulation development data. We’ve started to put that into a database and are using machine learning to see how we can predict formulations. For example, if a client needs a quick formulation—not fully optimized because of limited time or budget—we want to be able to set them up with the best possible chance of success.
All of these are works in progress, but it’s an exciting time. We’re adding efficiency and reducing some of the manual workload our analysts face.
David Brühlmann [00:11:57]:
Speaking of success, Amanda, what would you say are some best practices you follow to accelerate the path of ADC development? And what is core to your company culture that enables that?
Amanda Hoertz [00:12:11]:
We definitely look at each molecule individually, but with the experience behind us. As I mentioned, our tenure is a huge advantage. We remember the molecules that are similar. There are a number of biosimilars where it’s not the first time they’ve come to KBI, so we know where to start, and that helps us accelerate for later clients.
Same thing with ADCs—if we know what molecule or method is going to be the trouble spot (for example, charge heterogeneity), we start there in method development.
We also have a team, where after method development, they immediately start with method qualification. We have a number of platform approaches where we begin, and then we follow the molecule because it can always be different. But we understand where to start and what levers to pull—whether that’s improving resolution, improving a separation, or refining the method.
We make sure the method fully develops—whether that means resolving charged species, separating HMWs (high molecular weight species), or ensuring the peptide map is truly resolved.
We once had a client come to us with a charge method that showed only a single peak. They loved it, but we had to explain: if you’re not separating anything, the method has no value. We then showed them what could actually be separated with that method. They didn’t love seeing more than one peak, but for a method to be meaningful, it has to actually resolve species.
David Brühlmann [00:13:33]:
That’s a great example.
Amanda Hoertz [00:13:37]:
They didn’t like that very much, but we got them to where they needed to be.
David Brühlmann [00:13:40]:
I know, I know. It reminds me of some conversations I had in my career. I’d say technological advancements are not always welcomed, let’s put it like that. I’m not going to name names.
Amanda Hoertz [00:13:51]:
No, exactly. We want to understand—because that’s how the differences between batch one and batch two may be explained. If you see a difference in performance, you want to understand why. And as the industry continues to evolve, we may see different things. We may achieve better separation, we may now understand what the differences are. And we have to accept that.
David Brühlmann [00:14:13]:
Yeah, definitely. And there’s a lot going on, the industry is evolving. So what is your vision for the future of ADCs and biologics development in general? What trends do you see?
Amanda Hoertz [00:14:25]:
It’s really exciting—and I love it for patients. Having more targeted therapies and more options is huge.
We saw this in the early days with biologics—early IgG1s and IgG2s required much more customization to understand what you were doing. Now, many of those approaches are platforms. As we continue to see more ADCs produced, we’re going to get more comfortable with how to manufacture them efficiently, cost-effectively, and how to identify winners and losers faster. That’s huge.
KBI right now has the ability to test non-cytotoxic ADCs at all of our sites, and cytotoxic ADCs analytically at two of our sites. What we’re investing in is another U.S.-based lab where we can test GMP cytotoxic ADCs safely within the mammalian network—covering both process development and analytical development.
That’s going to require significant capital investment, since handling large amounts of toxic payload requires specialized facilities. We’re still determining whether to partner with clients for GMP production through a vendor or to build those capabilities fully in-house. But we see this as the next frontier, and we’re making those investments to grow with the industry.
Given our experience with niche molecules and with ADCs, I think we’re in the right position to maximize the benefit for patients.
David Brühlmann [00:15:47]:
So, you’ve built a successful career leading a team of 300 people. What advice would you give biotech scientists looking to excel in complex modalities like ADCs—and also in managing large, cross-functional organizations that aren’t all at the same site?
Amanda Hoertz [00:16:07]:
For managing a site this large, it all comes down to having the right people working for you and with you. There’s no way I could directly handle all the issues that can come up with 300 people. My direct reports need to be empowered to handle things, and then we triage as issues move up and down. That’s critical.
For building a successful career: people don’t love change, but change is part of what we do. My job has changed over the years, the industry has changed over the years. Absorbing information and being willing to move with what’s hot and what’s emerging in the market is important.
ADCs can be intimidating. There are plenty of drugs that don’t have toxic payloads, that are easier and lower risk. But this is where the industry is going. We need to find ways to develop them safely and successfully—for both patients and employees. So being nimble and willing to move with the market is critical.
When I came to KBI, I only intended to stay for two years. But I was in the right place at the right time, opportunities came up, and I took them. I don’t think there was a master plan. And I think many of my colleagues at KBI have had similar experiences—as the company grew, opportunities appeared, and it’s been a great place to be. I don’t know if that kind of growth can be planned or replicated—it just comes down to being willing to do what’s needed to make the company successful.
David Brühlmann [00:17:41]:
Well, this has been great, Amanda. Thank you so much for sharing your passion and insights. What would you say is the most important takeaway from our conversation?
Amanda Hoertz [00:17:51]:
I think the most important takeaway is that ADCs are an exciting new therapy, and they represent a significant improvement on existing therapies—providing patients better outcomes with fewer side effects.
There’s such a rich pipeline in the industry right now, and being able to adapt and characterize these biologics is the next challenge. We’re looking forward to being part of that solution for the industry. Thanks so much for taking the time to talk with me today—I really appreciate it.
David Brühlmann [00:18:12]:
That’s fantastic. Amanda, where can people get a hold of you?
Amanda Hoertz [00:18:16]:
The KBI website is a good place. There’s also our dedicated portal, https://standalone.kbi.bio/, which allows you to get quick quotes, ask questions—it’s very interactive and easy to use. We jokingly call it the “Domino’s Pizza Tracker” for biotech, because you can submit a request and then see exactly where it is in the process.
David Brühlmann [00:18:33]:
Excellent. I’ll leave all the links in the show notes so listeners can find them easily. And once again, Amanda, thank you so much for being on the show today. It was a huge pleasure.
Amanda Hoertz [00:18:43]:
Thanks, David. I appreciate it. Bye.
David Brühlmann [00:18:46]:
What a fascinating conversation with Amanda Hoertz. Her insights on ADC development, analytical strategies, and leadership are invaluable for any biotech scientist.
Please leave us a review on Apple Podcasts or whatever platform you found us on. It helps other biotech scientists like you discover the show. I thank you so much already - and I love hearing from you. So thank you very much for tuning in today. Stay tuned for part two where we'll dive into the analytical complexities of ADCs.
For additional bioprocessing tips, visit us at Smart Biotech Scientist Podcast - Master Bioprocess Development. 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
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About Amanda Hoertz
Amanda Hoertz is the Vice President of Analytical and Formulation Sciences (Mammalian Network) at KBI Biopharma, where she leads a team of more than 300 analytical scientists. Her organization supports method development, verification, qualification, validation, formulation design, forced degradation studies, characterization, and stability testing across preclinical to commercial-stage biopharmaceutical products.
With over 14 years at KBI, Amanda brings deep expertise in analytical strategy and biopharmaceutical development. She earned her Ph.D. in Chemistry from Duke University and holds additional academic experience from Johns Hopkins University and the University of Pennsylvania.
Connect with Amanda Hoertz on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
Antibody-drug conjugates (ADCs) are generating serious buzz in biotech corridors, offering a precision-guided missile against cancer while minimizing collateral damage. But the path from concept to clinic is far from straightforward—especially when the molecule itself bends the rules of traditional bioprocessing.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann welcomes Amanda Hoertz, Vice President of the Analytical and Formulation Sciences department for the Mammalian network at KBI Biopharma. With oversight of 300 scientists and deep expertise in taking the world's most complicated biologics to market, Amanda has spent her career at the intersection of scientific rigor and patient impact. Her approach goes far beyond cookie-cutter platforms, focusing instead on ground-up problem solving for complex modalities—including some of the trickiest ADCs in the pipeline.
A lot of clients, whether it's ADC or not, they're in the FIH stage. They need to understand whether or not they have a product where they're going to invest tens of millions of dollars into it. So they need to know: is it efficacious, is it manufacturable, is it stable, what can we do with it? And that way they can get through their tox studies and their phase one and then understand if they have the money to invest to go further.
So, information that can be calculated theoretically - pI, molecular weight - those can be done just based on the sequence. But any hands-on data you have - because as you know, proteins fold differently - a theoretical pI and an observed pI can be different.
David Brühlmann [00:00:40]:
Welcome to The Smart Biotech Scientist. I'm David Brühlmann, your host, and today we're diving deep into the fascinating world of antibody-drug conjugates with Amanda Hoertz, who's a Vice President of Analytical and Formulation Sciences at KBI Biopharma. Amanda leads 300 analytical scientists across KBI's mammalian network, tackling some of the most complex challenges in biologics development.
We'll explore her journey from chemistry PhD to industry leader and discover what makes ADCs both incredibly powerful and uniquely challenging to develop. Let's dive in.
David Brühlmann [00:02:35]:
Welcome, Amanda, to The Smart Biotech Scientist. It’s good to have you on today.
Amanda Hoertz [00:02:38]:
Hi David. Thank you for having me. I appreciate it.
David Brühlmann [00:02:43]:
It's a pleasure, Amanda. Share something that you believe about bioprocess development that most people disagree with.
Amanda Hoertz [00:02:51]:
This question makes me nervous, but I'll shoot for it because I don’t pretend to know what everyone else thinks. I think one thing that a lot of people believe is that you can adapt almost any biologic to a single platform. And I think the different classes of molecules and formats need to be considered and need special considerations.
I think we can benefit from a lot of the historical data and the potential power of machine learning to reduce de novo data generation. But a lot of these determinations are empirical and have to be done for that specific molecule.
David Brühlmann [00:03:21]:
That's an excellent point. There's definitely a lot of specificity for each molecule. Before we talk about ADCs, Amanda, let's talk about yourself because you've built an impressive career leading 300 scientists across KBI's network. So tell us how you got started in chemistry, in the pharmaceutical/biotech industry, and what were some interesting pit stops along the way.
Amanda Hoertz [00:03:48]:
Sure. I love science. I love the logical portion of it and how you can understand something and do an investigation and rule out factors. And, funnily enough for my poor family, it’s become part of my actual personal life. I think about things in a very analytical way, similar to an investigation we do at work.
My initial love of chemistry actually came from when I was in high school. My chemistry teacher was a PhD from Berkeley, which was obviously atypical for a high school chemistry teacher. And she had very, very high standards. She’d put us all at the board and we’d be so afraid of being wrong in front of the whole class that it was my major focus to make sure I understood those concepts the best - because that was my nightmare.
In seventh grade, I also became a type 1 diabetic. And it was so impressive to me that this little bottle of clear liquid - when you had such a small injection of insulin - could make you feel dramatically better or worse. So those things together really pushed me in the science direction.
When I was in undergraduate and graduate school, I worked on antibiotic resistance and understanding how we could modify those genes to try to make novel analogs of currently approved drugs and counteract antibiotic resistance, and to make those in a scalable, economic way. That pushed me towards industry.
What I loved about industry was that the work I did in academia was very conceptual and I didn’t really see it being applied in the short term, whereas the work I do at KBI is going into clinics, it’s being used now. Some of them fail, but I see it being realized. I see people getting the benefit, especially as KBI has matured. We’ve had a number of products that started out preclinical and now are commercial. We see the benefits, we see people taking these drugs. And it’s not just a concept that maybe 20–30 years from now it might happen — it’s actually happening. We know people are getting benefits. It’s a great feeling.
David Brühlmann [00:05:47]:
Now, specifically, you're now working in Analytical and Formulation Sciences. What excites you most about working in this very part of biotech today?
Amanda Hoertz [00:06:00]:
There are just so many different options coming forward for patients. We're on the cutting edge. We get to see all the different treatments, and as I said before, some of them fail. And that's a natural part of the industry. But we see how people are innovating their thought process, what different drugs can possibly be offered to patients.
And as we see them getting more and more specific, especially with something like ADCs, we see less side effects, we see patients getting improved options. And it's very exciting to be part of that and to see it being realized.
David Brühlmann [00:06:32]:
I'm very excited to talk about ADCs, because it's a huge trend in our industry - no doubt about that. A lot of people are developing ADCs, but tell us why they are such powerful therapies before we dive into the nitty-gritty of ADC development.
Amanda Hoertz [00:06:49]:
Sure. I mean, there is a payload attached to the antibody, and that can be either non-cytotoxic or cytotoxic. Obviously, the more potent ones are harder to handle. But they are allowing us to take cytotoxic drugs that in general couldn't be administered to a patient, and target them specifically for the tumor.
So by using this basically directing antibody, you're able to get those cytotoxic drugs directly to the tumor. And because we have less of the bystander effect - which is where cells that are nearby are also impacted in the cell death - it's more targeted.
General chemotherapy that started out a long time ago would only target rapidly dividing cells. So that would target other things that aren't part of the target and would result in things like hair loss and nerve damage. By being able to target these with ADCs, we're able to really just target the tumor and we're able to have these ADCs and immunotherapies really be a very specific treatment for the cancer drug.
David Brühlmann [00:07:46]:
And what makes ADC development very challenging compared to traditional monoclonals?
Amanda Hoertz [00:07:54]:
The same thing that’s a benefit is the same thing that makes it very challenging. So, at KBI we’ve worked on and manufactured many non-cytotoxic ADCs. Where it becomes really challenging is when you start working with the cytotoxic payloads.
Those, in very small amounts, are designed to kill cells. Therefore, there's a safety risk for the employees handling them for testing, and handling them for manufacturing. The biggest risk is obviously with the free payload.
Analytically, we handle that very carefully. We only need that for really one assay - the free drug - where we have to run a standard curve of the free drug and then run the actual sample to see how much has been released. But handling that free drug to be able to run that standard curve is the risk to our employees, and to any employee that would then service that instrument.
Conversely, in the process development and manufacturing space, they would need to handle an even larger amount of that payload to be able to conjugate it to the actual product. And so those are the highest risks. Making sure that we have the right safety protocols so that we are not exposing our employees to any of these deleterious effects - which are by design for the cancer cells, but not by design for the general population - is critical.
David Brühlmann [00:09:03]:
Can you give us just a high-level view of how ADCs are actually produced? I mean, you have quite a puzzle: you have the antibody, you have the linker, you have the payload. What’s the general sequence, especially for people who might not be familiar with ADC development?
Amanda Hoertz [00:09:19]:
Many ADCs are developed before they come to KBI, but for some we’re involved from the beginning. There’s an antibody - typically one that’s already well understood - and companies will modify the amino acid sequence to target the specific locations where the linkers can bind to then get that payload attached.
By modifying how the molecule is formed, you're able to target specific numbers of attachment sites and specific amounts of that cytotoxic payload. It is very important that we have the analytical tools to look at both the antibody, the free linker, and the free payload. The more stable that is, the better.
Obviously, we want it to conjugate, and then we want none of the free linker to be showing up. The free linker tends not to be a problem - it's just a sign that we're actually losing some of the payload. The payload obviously is a problem if we have high amounts of free payload.
So we develop analytical tools to monitor that over the course of stability under many stresses, and understand whether or not agitation stress, freeze–thaw stress, temperature stress, duration of stress, or chemical stresses - including acidic, basic, and oxidative - are influential in either damaging the linker or releasing that payload.
That’s the function of our stability studies: to quantify the amount of free payload that gets released. It's also impactful if we don't have payload attached, because then the drug is not efficacious.
So usually it’s the free payload that ends up being the problem, because in small amounts it's impactful. But we also want to make sure that the drug we're delivering still has that payload attached.
David Brühlmann [00:10:53]:
Let's make this practical. Let's assume I'm a CEO of a biotech company and we are developing an ADC and we're coming to you, to KBI. What are the first critical questions your team would ask my company to set up our analytical strategy for success?
Amanda Hoertz [00:11:09]:
One of the things that's great about KBI is that we're bespoke services: we offer what you need. A lot of companies have a platform approach: you give them this amount of material, or they produce this amount of material, and then they do these fixed experiments.
What we do is take in the information you have. So if you were coming to me with an ADC that you want to develop, I would want to understand what information you already have. What do you understand about the molecule? What can we leverage from historical data? And then we would set up, based on your timeline and your budget, what we can further investigate to get you to the next stage.
A lot of clients, whether it's ADC or not, are in the FIH stage. They need to understand whether or not they have a product where they're going to invest tens of millions of dollars into it. So they need to know: is it efficacious, is it manufacturable, is it stable, what can we do with it?
And that way they can get through their tox studies and their phase one, and then understand if they have the money to invest to go further.
Information that can be calculated theoretically - pI, molecular weight - those can be done just based on the sequence. But any hands-on data you have, because as you know, proteins fold differently, a theoretical pI and an observed pI can be different. That data helps us maximize your timeline and your budget.
David Brühlmann [00:12:23]:
You said that one thing that sets you apart is offering bespoke services. Because choosing a development partner or a CDMO can be quite overwhelming. So why should I, as this fictional CEO, choose KBI? Or why should I go to another CDMO? What are some decision parameters I should consider?
Amanda Hoertz [00:12:45]:
If you're looking for an IgG1 - a very “vanilla” antibody - you probably don't need the services of KBI.
KBI has a very high advanced-degree rate. We are very good at complex molecules. Our experience includes bispecifics, conjugated molecules - the list goes on. But we are going to be more expensive than the Lonzases or Thermos of the world.
So if you just need an antibody produced, that’s going to be your better path. But if you need something that has nuance, that requires more thought and more experimentation - that’s where KBI fits into that niche market with bespoke services.
We’re not going to compete with the huge WuXis of the world. But what we do bring is the experience you need to make your complicated molecule successful.
We’ve had a number of clients that request RFPs from KBI and then come back to us saying our proposals are more expensive. They go to a cheaper CDMO, and then later we get them back. By then they’ve sacrificed part of their budget and had a failure at a more platform-based CDMO. They’ve also sacrificed six or seven months of their timeline to figure that out.
So, for that theoretical CEO, the key is knowing how complicated your molecule is and whether you need the nuance we offer.
Our batch success rate for fiscal year 2024 was 93%, and we don’t do engineering runs. We put in place upfront the cell line, the analytical, and the process development engine, so that by the time we’re making it at the scale of 2,000 liters, you actually get a product successfully.
David Brühlmann [00:14:21]:
Let’s talk about the analytical puzzle. Because there’s a lot going on: we’ve got the antibody, the linker, the payload. We need all kinds of different analytical methods. So tell us your approach - how do you develop methods to characterize each component? Because at the end of the day, you need a sophisticated process control to ensure you’re actually getting the very ADC you want to develop.
Amanda Hoertz [00:14:46]:
Yes, there are a number of compendial methods that are standard for any antibody. We also have product quality methods — your classic SEC, where we need to understand the HMW. You want to keep aggregates low. That’s not specific to ADCs versus non-ADCs.
Where it gets specific for ADCs is in the characterization of the free payload, the free linker, and the charge heterogeneity. That’s usually where we see the most nuance required, both with cytotoxic and non-cytotoxic ADCs.
There seems to be more of a development challenge for the iCE or IEX methods we run. We see shifting profiles, we see inconsistency in achieving good separation. For example, in iCIEF, having the right blend of pharmalytes and the right handling conditions is critical to get us from the development stage, to the qualification stage where we’re generating GMP data, and then to the validation stage where we can execute this across multiple sites and multiple analysts. It has to be robust.
That’s the biggest challenge I’ve seen. The free payload tends to be a pretty straightforward reverse-phase method where we’re just titrating against a standard curve.
It’s the charge heterogeneity that keeps me up at night.
David Brühlmann [00:15:59]:
And what are the things you can do even upfront, before we would even come to KBI or another development partner? Are there some strategic choices companies should make to avoid, for instance, these headaches? I mean, I know there's a lot of complexity, but are there maybe some early decisions that simplify or reduce the complexity?
Amanda Hoertz [00:16:21]:
I think a lot of it is understanding your linker attachment sites. The most successful conjugated materials we’ve made are those where both the conjugation sites and the post-translational modifications that may influence them are very well understood.
We have a dedicated mass spec team. For example, we can produce forcibly degraded material so they can understand how it’s going to degrade, and at the molecular level identify which sites are prone to modification. Then we can actually monitor that.
For example, oxidation or other modifications can be identified through detailed peptide mapping. That allows us to pinpoint individual peaks, and when we see a peak grow, we understand what’s happening.
Peptide mapping requires some upfront development - for instance, recognizing that when this site gets oxidized, a shoulder or a peak appears. But once we’ve identified what each peak represents, we can monitor them in a much more streamlined and cost-effective way over the product’s lifetime.
David Brühlmann [00:17:20]:
You mentioned that the linker is very important, and I also imagine the conjugation method. So can you tell us what is the state of the art today? Because I mean, a lot has happened in the last few decades in ADC.
Amanda Hoertz [00:17:35]:
There are a number of ways to conjugate. Most commonly, thioethers, disulfide bonds, and peptides are used and they’ve been very successful.
Having specific attachment sites, like I said before, is also critical. You don’t want random attachment. That was the approach for the early ADCs, where we were hoping for the best and aiming for a certain amount of payload attached.
The actual conjugation decision-making is a little bit out of my expertise. I usually deal with the molecule after the payload is attached, and how we can understand its properties. But of course, how those decisions are made also has a direct impact on efficacy. You want to make sure none of the sites critical for binding are blocked.
We continue to refine as we learn. The ADC market is going exponential. There are 12 approved ADCs, but we’re aware - and the market is aware - of hundreds more in development. A lot of those won’t make it, but once a platform is successful, it can often be applied multiple times. So when a client has one success, that client usually has a higher rate of success in follow-on programs.
David Brühlmann [00:18:39]:
Now let’s talk about formulation, because that’s also quite a challenge. An antibody by itself is quite well managed, but probably more complex modalities pose extra issues. How does that play out for an ADC? What are the challenges, and what approaches have you developed as a team to tackle formulation challenges?
Amanda Hoertz [00:18:58]:
Absolutely - that is definitely a component. We need to develop a formulation for the drug substance intermediate (DSI), which is the pre-conjugation material. That formulation can’t inhibit the conjugation reaction. So we need to be careful: no surfactants, no small molecules that could interfere.
Our classic approach at KBI is to design a very simple formulation that is stable to freeze–thaw. Buffers and excipients can shift during freeze–thaw, and we don’t want to damage the biologic during that process. That’s critical for the DSI as it moves into DS production.
Once the product is conjugated, it’s more typical for us to include a small amount of surfactant to reduce stress from agitation and freeze–thaw, and to add excipients that increase stability.
The gold standard for biologics is 24 months of stability at 2–8 °C. So our goal is to design formulations - with appropriate excipients - to achieve that. But at the DSI stage we must be very cautious: no surfactants, minimal excipients, so that nothing interferes with conjugation.
David Brühlmann [00:20:03]:
And for those who are not familiar with ADCs - the DS formulation is important because certain companies are not able to produce the antibody and then do the conjugation at the same site. So there’s some shipment involved, right?
Amanda Hoertz [00:20:19]:
Exactly. For non-cytotoxic drugs, at KBI we are able to produce the DSI and then conjugate it to generate the DS. That is then typically shipped to a fill–finish facility for DP (drug product) manufacturing, which usually involves filtration and filling operations.
For cytotoxic ADCs, KBI currently does not offer the conjugation. So we generate the DSI, then ship it to another site for conjugation, and then onward to a different site for DP production, depending on the vendor.
The less handling required once it’s conjugated, the better. Every operation increases the risk of damaging the payload, damaging the product, and also increasing risk to employees.
So, our approach is to maintain a pH that is acceptable, with minimal excipients and no surfactants at the DSI step, so that stabilizers can be added later once it’s conjugated. Then the DP step is usually just filter, fill, and finish.
David Brühlmann [00:21:20]:
I got you. So you have a formulation for your DSI, you do the conjugation, and then you add whatever is needed to stabilize your molecule, right?
Amanda Hoertz [00:21:30]:
Exactly.
David Brühlmann [00:21:31]:
It makes sense.
Amanda Hoertz [00:21:32]:
The DSI and the DS are typically frozen formulations. That needs to be taken into consideration. The DP is typically stored at 2–8 °C. That’s the most financially successful option for companies when shipping to clinics - everyone has a refrigerator.
Once you start requiring storage at –20, –50, or –75 °C, you face challenges: do clinical sites have those freezers, and do you need to supply them to each site?
David Brühlmann [00:21:59]:
And to what extent are the DS and DP formulations for an ADC different from a standard mAb, or are they quite similar?
Amanda Hoertz [00:22:07]:
They are quite similar. It’s difficult for us to include a surfactant, although as I said, it reduces agitation as well as freeze–thaw impact. Depending on the molecule, we might need to add other stabilizers, like arginine, to reduce aggregation.
The pH is obviously critical. Different buffers can be more or less successful, and that’s generally determined empirically. Of course, we have targets based on the pH values we’re looking at, and that’s largely a function of the molecule’s isoelectric point (pI). So it’s also important to assess whether the linker or the payload impacts the overall pI.
David Brühlmann [00:22:42]:
That wraps up part one of our conversation with Amanda Hoertz. We’ve explored her remarkable journey and the exciting world of ADC development.
Please leave us a review on Apple Podcasts or whatever platform you found us on. It helps other biotech scientists like you discover the show. I thank you so much already - and I love hearing from you. So thank you very much for tuning in today. Stay tuned for part two where we'll dive into the analytical complexities of ADCs.
For additional bioprocessing tips, visit us at Smart Biotech Scientist Podcast - Master Bioprocess Development. 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
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About Amanda Hoertz
Amanda Hoertz is the Vice President of the Analytical and Formulation Sciences department for the Mammalian network at KBI Biopharma. She oversees a network of 300 analytical scientists who execute method development, verification, qualification, validation, formulation development, forced degradation, characterization, and stability testing for preclinical to commercial products at the CDMO.
She has been at KBI for 14.5 years and prior completed her Ph.D. in Chemistry at Duke University. Her undergraduate and other academic experiences include Johns Hopkins University and the University of Pennsylvania.
Connect with Amanda Hoertz on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
For too long, biotech innovators have viewed biological systems as inherently messy, unpredictable, and full of “black box” mysteries. But what if, armed with the latest digital tools, AI, and cross-disciplinary thinking, you could transform bioprocessing from a series of trial-and-error experiments to a streamlined, proactive design process?
This is the second part of a conversation between David Brühlmann and Carmen Jungo Rhême, a professor at the University of Applied Sciences in Fribourg, Switzerland, and director of the Biofactory Competence Center (BCC). They discuss how digital tools, real-time data modeling, and artificial intelligence are transforming bioprocess development—and shifting our perspective from viewing biology as unpredictable to seeing it as something designable and controllable.
With the tools we have today—particularly digital tools, real-time data modeling, and AI—we can understand and control biology in ways that were unimaginable just a few years ago. If we shift our mindset from “biology is messy” to “biology is designable,” it changes everything. I think it opens the door to more robust and faster process development, leading to quicker innovation and truly sustainable solutions. This mindset shift—from reactive to proactive, or from trial-and-error to design-and-control—is what will define the future of the biotech industry.
David Brühlmann [00:00:51]:
Welcome back to part two with Carmen Jungo Rhême, who is a professor at the University of Applied Sciences in Fribourg, Switzerland, and director of the Biofactory Competence Center.
If part one inspired you about fighting superbugs and sustainable food production, you’ll love this deep dive into the Biofactory Competence Center’s revolutionary approach.
We’re exploring how their non-classified cleanrooms seamlessly transfer to GMP facilities, their practical idea-to-scalable-process methodology, and the critical skill gaps Carmen has discovered while training both newcomers and industry veterans.
Ready to discover what makes bioprocess development truly scalable? Let’s dive in.
Now, let’s talk more specifically about what you’re doing at the Biofactory Competence Center. You’re a professor at the University of Applied Sciences in Fribourg and also leading the Biofactory Competence Center. We’ve mentioned that term several times already. Tell us a bit more — what is this center all about? What’s your mission, and what specific challenges and needs are you addressing?
Carmen Jungo Rhême [00:03:13]:
At the Biofactory Competence Center, our main goal is to work in collaboration with industrial and academic partners on applied research projects.
Universities typically focus on fundamental research, while universities of applied sciences, like ours, focus more on applied research. We collaborate with startups as well as larger companies — mainly in biotechnology, including the pharma and food industries.
On top of that, we also offer training for our students — practical training and theory in bioprocess engineering — and for professionals from industry, typically in upstream and downstream processing, as well as other skills such as aseptic techniques.
David Brühlmann [00:04:15]:
Let’s assume I’m the CEO of a startup company with a new technology or idea, and I need a development partner like your center. How does this process work? We come to you and say, “Hey, we have this amazing idea to solve antimicrobial resistance.” What happens next?
Carmen Jungo Rhême [00:04:40]:
Each project is different, but we always follow a structured approach. When a biotech company — large or small — comes to us with an idea, we start by listening and understanding their concept, goals, and challenges.
From there, we typically guide them through four key phases:
This is the general framework we follow.
David Brühlmann [00:07:00]:
Speaking of scale-up — when transferring to another partner, CDMO, or in-house facility — what are the typical scales you work at in your facility, and at what point do you transfer?
Carmen Jungo Rhême [00:07:20]:
We typically work with 5-liter bioreactors, which can mimic very well a 15,000-liter system. Most of our development studies are performed at 5-liter scale. We also have 50-liter bioreactors, but we often focus on the 5-liter scale because scale-up can be done directly to much larger systems — 1,000 or even 15,000 liters. This is quite common in the industry. You often lose too much time moving through intermediate pilot scales before final manufacturing.
David Brühlmann [00:08:00]:
Exactly — especially with digital tools and modeling, if your small-scale system is well-characterized, you should have everything you need to scale directly and succeed.
Carmen Jungo Rhême [00:08:14]:
Yes, definitely.
David Brühlmann [00:08:16]:
At the Biofactory Competence Center, you both co-develop with companies and train people for GMP or lab work. Tell us more about your training programs and the skill gaps you’re addressing.
Carmen Jungo Rhême [00:08:42]:
We offer operator training for the pharmaceutical industry. Over the last ten years, many facilities were built in Switzerland, and the industry had to hire a large number of operators. That’s why this training was created — to prepare competent people for GMP operations. It’s a five-week program covering GMP documentation, lab practices, pH and conductivity measurement, gowning, and more. For more advanced professionals, we also offer three-day trainings — one in upstream processing and one in downstream processing.
David Brühlmann [00:09:47]:
And those advanced trainings — are they GMP-focused or more technical?
Carmen Jungo Rhême [00:09:58]:
They’re more technical and scientific, not focused on GMP. For example, in the upstream course, we cover mass balance, calculating specific growth rates and doubling times, and determining feed rates for fed-batch cultures. So yes, more technical than regulatory.
David Brühlmann [00:10:25]:
You train university students and industry professionals, while the industry itself is changing rapidly — robotics, AI, digitalization. What do you think will be the top three skills that matter most in five years?
Carmen Jungo Rhême [00:10:54]:
That’s a good question. Many skills are important, but I’d highlight three:
This broader view is essential.
David Brühlmann [00:12:28]:
Absolutely. Systems thinking is often overlooked, yet it’s fundamental. In a connected world, being able to “connect the dots,” as Steve Jobs would say, helps professionals stand out and even turn their data into valuable assets.
Carmen Jungo Rhême [00:13:11]:
Exactly. Data is key — and systems thinking helps you use not only process data but environmental data, too.
David Brühlmann [00:13:25]:
You’ve been leading the Biofactory Competence Center for about a year and a half. What’s your vision for the next few years?
Carmen Jungo Rhême [00:13:56]:
In the coming years, I want to continue working on antimicrobial resistance — it’s a major global challenge. We’re collaborating with a Swiss startup, Micreos, based in Baden, where we express and purify endolysins to fight Staphylococcus aureus. I’m also passionate about sustainable food production, especially in Switzerland. Using waste products efficiently and combining them with digital tools to maximize data and outcomes is one of our goals.
And, of course, continuing to deliver impactful training for students and professionals — and learning from them in return — helps us keep improving.
David Brühlmann [00:15:30]:
Switzerland has always adapted quickly — to environmental, industrial, and now technological changes. What kind of mindset shift do you think we need as scientists moving forward?
Carmen Jungo Rhême [00:16:08]:
We need to stop seeing biology as unpredictable. For too long, biological systems have been treated as black boxes — mysterious and variable. But with digital tools, real-time data modeling, and AI, we can now understand and control biology in unprecedented ways. If we shift our mindset from “biology is messy” to “biology is designable,” it changes everything — enabling more robust, faster, and more sustainable processes.
This shift, from reactive to proactive and from trial and error to design and control, will define the future of the biotech industry.
David Brühlmann [00:17:21]:
Before we wrap up, what important topic haven’t I asked about that you’d like to share with our biotech community?
Carmen Jungo Rhême [00:17:30]:
We also have projects in digital transformation.
For example, we’re collaborating with DataHow and Beckman Coulter on a project funded by Innosuisse’s Innovation Booster. The goal is to demonstrate how digital technology and AI are transforming biotech.
The project focuses on optimizing green fluorescent protein (GFP) production in E. coli fed-batch cultures. We run design-of-experiment studies using Beckman Coulter’s BioLector XT micro-bioreactor system — a high-throughput platform that allows many experiments in parallel.
All process data is collected and used by DataHow to build a digital twin — a hybrid model to identify optimal process conditions quickly. It’s both a research and educational project: our students learn to use digital twin tools so they can apply them in industry.
David Brühlmann [00:19:40]:
Fantastic — I love this project. With everything we’ve discussed today, what’s your 22nd takeaway?
Carmen Jungo Rhême [00:19:54]:
Bioprocessing today isn’t just about managing complexity — it’s about mastering it. With the right mindset, tools, and training, we can turn biological variability into predictable, scalable, and sustainable solutions. Whether it’s fighting antimicrobial resistance, transforming food systems, or embracing digitalization — the future of biotech is about integrating the right tools for smarter bioprocessing.
David Brühlmann [00:20:38]:
Thank you so much, Carmen, for sharing your passion and expertise. Where can people connect with you?
Carmen Jungo Rhême [00:20:49]:
People can connect with me on LinkedIn or through the Biofactory Competence Center website — there’s an email address for inquiries or collaborations.
David Brühlmann [00:21:08]:
That’s great. I’ll leave all the information in the show notes. Thank you once again, Carmen, for being on the show today.
Carmen Jungo Rhême [00:21:31]:
Thank you very much, David. It was a pleasure.
David Brühlmann [00:21:34]:
Fantastic insights from Carmen Jungo Rhême on the future of bioprocess development and training — from seamless GMP transfers to essential skills for the next five years.
Remember, scaling doesn’t have to be overwhelming. If you need guidance on your process or technology development journey, schedule a free consultation at Brühlmann Consulting.
Thank you for tuning in — please leave a review and I’ll see you next time. All right, Smart Scientists — that’s all for today on the Smart Biotech Scientist Podcast. Thank you for joining us on your journey to bioprocess mastery. For more tips, visit smartbiotechscientist.com and stay tuned for more inspiring biotech insights. Until next time — 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
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About Carmen Jungo Rhême
Carmen Jungo Rhême is full professor at the Haute Ecole d’Ingénierie et d’Architecture de Fribourg (HEIA-FR) and Director of the Biofactory Competence Center (BCC). She has extensive experience (17 years) in the pharmaceutical industry with a proven track record in several biopharmaceutical companies manufacturing therapeutic recombinant proteins (Lonza, Merck Serono, UCB Farchim and CSL Behring).
She specialized in bioprocess development, both in cell culture and in purification of proteins, scale-up, and technology transfer of marked products. Since her start at HEIA-FR in November 2023, C. Jungo Rhême has initiated several research projects in the field of antimicrobial resistance, one of them in the field of antimicrobial resistance, sustainable food production, and digitalization of bioprocesses.
Connect with Carmen Jungo Rhême 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
Almost every corner of modern medicine and sustainable food production today is facing a massive challenge: how do we outpace drug-resistant “superbugs” and create food for a growing population using fewer resources? The answer, it turns out, may come down to how well we understand and control the biomanufacturing processes underpinning these biomaterials and biomolecules.
In this episode of Smart Biotech Scientist Podcast, David Brühlmann speaks with Carmen Jungo Rhême, Full Professor at the University of Applied Sciences in Fribourg, Switzerland and Director of the Biofactory Competence Center.
With years in the pharmaceutical industry at Lonza, Merck Serono, UCB Farchim, and CSL Behring, she now tackles global challenges like antimicrobial resistance, sustainable food, and digitalization. From her beginnings in chemical engineering at EPFL to leading at the nexus of academia and industry, Carmen is helping shape the future of smarter, more robust biotech.
The emergence and spread of drug-resistant pathogens challenge our ability to treat common infections with existing antimicrobials such as antibiotics. The World Health Organization has identified antimicrobial resistance as one of the top global challenges for humanity in the coming decade. Currently, several approaches are being explored by the scientific community, including the development of new vaccines, monoclonal antibodies, phage therapy, and recombinant proteins like endolysins to combat difficult-to-treat bacteria. At the Biofactory Competence Center, we are collaborating with the University Hospital in Lausanne in the field of phage therapy.
David Brühlmann [00:00:56]:
Welcome back to the Smart Biotech Scientist. I’m your host, David Brühlmann, and today I’m thrilled to have Carmen Jungo Rhême with us. Carmen is a Full Professor at the University of Applied Sciences in Fribourg, Switzerland, and Director of the Biofactory Competence Center.
With years of experience mastering bioprocess challenges at Lonza, Merck Serono, UCB Farchim, and CSL Behring, she now tackles global challenges including antimicrobial resistance, sustainable food production, and digitalization. From fighting superbugs to revolutionizing scalable process development, Carmen’s insights will change the way you think about the future of biotech.
Welcome, Carmen, to the Smart Biotech Scientist.
Carmen Jungo Rhême [00:03:08]:
Hello David, it’s great to see you and to be here talking with you.
David Brühlmann [00:03:12]:
It’s a pleasure, Carmen. We’ve been planning this interview for a while, and now it’s finally happening. To start, could you share something you believe about bioprocess development that most people might disagree with?
Carmen Jungo Rhême [00:03:28]:
Many people perceive bioprocessing as highly uncertain and lacking robustness. However, when bioprocesses are thoroughly characterized—meaning they are well understood and effectively controlled—they can be extremely robust. A typical bioprocess includes both upstream and downstream parameters, sometimes managing over 200 variables. This complexity highlights the need for comprehensive process characterization, which I believe is essential for enhancing robustness and minimizing variability.
David Brühlmann [00:04:16]:
Absolutely. I couldn’t agree more. That’s very well said, Carmen. We have several things in common and I actually I still remember that many, many years ago when we started studying chemical engineering at the EPFL in Lausanne, Switzerland, we met already at that time and then later on in our career our path have crossed several times again. But actually I'm getting ahead of the story. I would like you to draw us into the story. Tell us Carmen, how you first got started in the recombinant protein world and what were some interesting pit stops along the way that led you to now your role as a professor and leading the Biofactory Competence Center?
Carmen Jungo Rhême [00:05:01]:
Yes, I remember first of all when we were studying at EPFL, and I really remember the first day I saw you attending the classes. I would not have imagined at that time that we would also work in the same company, actually at Merck Serono, and especially that we would stay in contact. It’s really a pleasure.
So how did I arrive at the BCC? First of all, I would like to mention that during my studies at EPFL, I studied chemical engineering. I was inspired by the elegance of recombinant proteins—how you can take a gene, express it in a host cell, and produce something with therapeutic or industrial value.
Then my journey through companies like Lonza, Merck Serono, UCB Farchim, and CSL Behring was key before joining the Biofactory Competence Center. For example, at Lonza, I was immersed in the world of large-scale recombinant protein production, learning how to transfer and scale up from lab scale to manufacturing scale. At Merck Serono and UCB Farchim, I continued to strengthen my know-how in technology transfer, scale-up, and starting up new facilities. Finally, at CSL Behring, I expanded my experience in R&D.
What is also important is that I was able to develop a network in the pharma industry before joining academia. I think this is key—to have industry contacts to initiate projects and to stay aware of what’s happening in the field. At the BCC, I can continue to innovate in collaboration with industrial partners, and I can also share my experience with students, who represent the next generation of pharmaceutical scientists.
David Brühlmann [00:06:59]:
You make an excellent point, Carmen. Obviously, along our careers we learn a lot about science, but it’s not only about science—it’s really about developing a network. And I think especially at the stage we are in our respective careers, the network becomes increasingly important for collaborations, research projects, and so on. So if you’re listening and taking notes, Smart Biotech Scientist, put this at the top of your list: develop a network. That’s very important.
Carmen Jungo Rhême [00:07:32]:
Yes, I completely agree with you. That’s really key for your career—to develop a strong network. And often, when you have a question or a bigger challenge, you will go back to your network, and maybe someone will help you. You can do a lot of things with your network.
David Brühlmann [00:07:49]:
And I’ve heard some people say that your network is your net worth.
Carmen Jungo Rhême [00:07:54]:
Yes, I think that’s true.
David Brühlmann [00:07:58]:
Carmen, you are now tackling three massive challenges. Tell us what they are and what the common denominator between these is.
Carmen Jungo Rhême [00:08:07]:
I would say the three massive challenges at BCC are, first of all, antimicrobial resistance, sustainable food production, and digitalization. At first glance, these three topics might seem like separate challenges, but they are connected by a common factor: the need for smarter bioprocesses.
Antimicrobial resistance pushes us to rethink how we manage microbes—not just in medicine, but also in agriculture and food production. Secondly, sustainable food production demands that we use fewer resources, reduce waste, and maintain safety, all of which benefit from precise biological control. And third, digitalization is the enabler that ties it all together. By collecting and analyzing data from bioprocesses, we can better understand complex systems, predict outcomes, and make faster, more informed decisions. The common thread connecting these three topics is innovation at the intersection of biology and technology to develop smarter bioprocesses.
David Brühlmann [00:09:31]:
So let’s unpack this. How do we make bioprocessing smarter? I love that phrase because that’s the title of the podcast—Smart Biotech Scientist. So that’s excellent. Let’s start with antimicrobial resistance, because I think many of the listeners are not familiar with the challenges. And actually, how does a bioprocess look like to fight these “superbugs”? How does that work?
Carmen Jungo Rhême [00:09:58]:
First of all, it’s important to mention that the emergence and spread of drug-resistant pathogens challenges our ability to treat common infections with existing antimicrobials such as antibiotics. The World Health Organization lists antimicrobial resistance as one of the top challenges for humanity in the next decade.
Currently, several approaches are being explored by the scientific community. For example, the development of new vaccines, monoclonal antibodies, phage therapy, and also the use of recombinant proteins like endolysins to fight difficult-to-treat bacteria.
At the Biofactory Competence Center, we are working in collaboration with CHUV, the University Hospital in Lausanne, in the field of phage therapy. More precisely, we are collaborating with Dr. Grégory Rech from CHUV, who has been working in this area for more than 20 years, and with Dr. Jean-François Brunet from the Centre de Production Cellulaire in Épalinges, also part of CHUV.
They have the first GMP manufacturing line in Switzerland for the production and purification of phages, and they have already started to test it on patients. We are working in collaboration with CHUV, have transferred their process to BCC, and are now performing a full characterization using a Quality by Design approach. This is very important when we talk about smarter bioprocesses—it’s critical to have a complete understanding of the process. Of course, you know all the process steps, but it’s very important to list all the process variables, including process parameters and material attributes, such as raw materials, chemicals, and filters.
We conducted a full risk assessment on each process variable to categorize them as critical or non-critical—for example, regarding product quality or yield. After this assessment, we are now characterizing the key and critical process parameters, collecting data to achieve full process characterization. Of course, this is already standard practice in industry for recombinant protein production, and we are now applying it to a phage production process.
Maybe I can also say a few words about bacteriophages for people who are not familiar. Bacteriophages are viruses that specifically infect and kill bacteria. For each bacterium, there is a specific bacteriophage in nature. They exist naturally all over the planet—they can be found in water, soil, and other environments—and were actually used to treat bacterial infections at the beginning of the 20th century. They were discovered in 1917, while antibiotics were discovered in 1928.
However, their use declined in favor of antibiotics after World War II, because antibiotics were easier to use. Some countries, like Georgia, Poland, and others in the former Soviet Union, continued to use phages because they didn’t always have access to antibiotics. In the last two decades, there has been renewed interest in phage therapy in Europe, the US, and other countries.
We are very proud to develop an innovative process and contribute to its characterization in collaboration with CHUV. Recently, we also started addressing phage formulation through lyophilization, because currently most formulations are stored in liquid form. Lyophilized formulations offer advantages for stability and storage.
David Brühlmann [00:14:15]:
That’s excellent. You’re making a big difference—solving a major problem for humanity. Looking at the production process itself, how does it differ from more established processes, such as mammalian cell culture or E. coli fermentation? Is it similar or very different?
Carmen Jungo Rhême [00:14:35]:
It’s actually very similar. In a process for recombinant protein expression, you have an upstream phase for expression, followed by purification and final formulation. The production and purification of bacteriophages is very similar. The same technologies are used: first, you grow the host bacteria, then add the corresponding phage to amplify it. After harvest, you have a clarification step, followed by purification steps, which are very similar to recombinant protein purification. So the expertise and technologies are very similar.
David Brühlmann [00:15:28]:
And now moving on to your second area, sustainable food production, which has some similarities but also differences. Tell us a bit about what you’re doing there, and how it ties back to digital transformation.
Carmen Jungo Rhême [00:15:42]:
Yes, as you said, we are also working on sustainable food production in the food sector. For example, we work on the valorization of whey permeate by cultivating microalgae on a whey-permeate-based medium. It’s important to mention that there is a lot of whey permeate in Switzerland, thanks to the cheese industry.
We also have experience producing recombinant proteins through precision fermentation using Pichia pastoris. Returning to microalgae, there is huge potential in cultivating microalgae for the sustainable production of proteins and lipids for the food industry. We can grow microalgae to produce specific proteins or lipids, then extract and purify them. I think we are only at the beginning of a major change in the food industry. More and more, food waste will be transformed into high-value nutrients using microalgae, bacteria, yeasts, or even fungi.
David Brühlmann [00:16:59]:
That’s excellent. Now, in academia, I imagine you have a bit more freedom than we would in the corporate world or even in the startup world. Well, in startups, you have freedom, but sometimes you don’t have the resources to fully explore. So what myth or challenge would you like to tackle in your current role?
Carmen Jungo Rhême [00:17:20]:
In bioprocessing, we have more freedom than in industry, so we can be more creative and sometimes explore ideas that we don’t know will work. I think that’s the main difference. We also have projects with our students where there isn’t necessarily a client behind them. When students work on a project, we have complete freedom, and sometimes we take the time to assess more possibilities and just explore.
I think that’s the main difference with startups or industry, where you have to be fast and results are expected in a short time. In academia, with bachelor or master projects that aren’t linked to an industry client, we have the freedom to test new technologies. Sometimes companies also come to us needing data to test prototype equipment, and this is something we can do more freely than in industry.
David Brühlmann [00:18:28]:
And that concludes part one with Carmen Jungo Rhême—from her industry journey to tackling antimicrobial resistance and sustainable food production. In part two, we’ll dive into the Biofactory Competence Center’s game-changing approach to process development and training.
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
🧬 De-risk CMC development and get decision-making guidance with a new AI platform that transforms CMC overwhelm into predictable development success (launching early 2026). Join the waitlist here: https://david-jkhjdoje.scoreapp.com
About Carmen Jungo Rhême
Carmen Jungo Rhême is full professor at the Haute Ecole d’Ingénierie et d’Architecture de Fribourg (HEIA-FR) and Director of the Biofactory Competence Center (BCC). She has extensive experience (17 years) in the pharmaceutical industry with a proven track record in several biopharmaceutical companies manufacturing therapeutic recombinant proteins (Lonza, Merck Serono, UCB Farchim and CSL Behring).
She specialized in bioprocess development, both in cell culture and in purification of proteins, scale-up, and technology transfer of marked products. Since her start at HEIA-FR in November 2023, C. Jungo Rhême has initiated several research projects in the field of antimicrobial resistance, one of them in the field of antimicrobial resistance, sustainable food production, and digitalization of bioprocesses.
Connect with Carmen Jungo Rhême 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
