The biotech community is in continuous transformation. The landscape we navigate today will be dramatically different a decade from now, just as it was a decade ago. At the forefront of this evolution are advances in continuous manufacturing, artificial intelligence (AI), and the drive toward personalized medicine.
The recent episode of the Smart Biotech Scientist Podcast with David Brühlmann and Irina Ramos dives deep into these game-changing trends, offering a blueprint for scientists and leaders aiming to thrive in this high-stakes, high-impact field.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann interviews Irina Ramos, a chemical engineer who has worked across the spectrum of biopharma—from early lab research to global regulatory submissions—and contributed significantly to AstraZeneca’s global COVID-19 vaccine deployment.
I think we need to understand that the biotech community of today will not look the same in the next decade. Just like 10 years ago, this biotech community did not look like it does today.
I would emphasize that we are always learning, always studying, and always seeking solutions that meet the needs of these extraordinary products — products that are bringing us closer to personalized medicine.
We now have cell and gene therapies that can be customized for you, for your genome, for your specific conditions. So how can we, as manufacturers, envision that complexity and utilize innovation today that will solve the problems of tomorrow?
David Brühlmann [00:00:50]:
Welcome back to part two with Irina Ramos. We're continuing our conversation on continuous manufacturing implementation and moving into the broader topic of bioprocessing strategy. How do you build a CMC roadmap that won’t come back to bite you later?
And here’s the big one: How should teams prepare for AI integration without losing the human expertise that makes great process development possible? We’ll cover all that and more. Let’s jump back in.
So, Irina, in the spirit of keeping things simple — what phase-appropriate approach would you suggest? What should absolutely be done in Phase I? What can wait until later? And what are the critical decisions, for instance, that a startup founder has to make very early on to remain compliant as they grow — especially if they’re considering a hybrid or intensified bioprocessing approach?
Irina Ramos [00:02:58]:
Here’s an interesting exercise from the Nimble Consortium looking at different scenarios. One scenario is: what if you take an already approved product and convert that fed-batch process into a continuous one? Another scenario is: let’s use new, early-stage programs to feed into a continuous platform — and if they succeed, then you have a commercial need to scale up.
Both approaches are valid. What I hear from the community, though, is that it’s much harder to change an already approved program. The drivers are different. Perhaps the cost of goods (COGs) or manufacturing efficiency becomes a critical factor — and if you can transition to continuous processing, you might have an opportunity to lower costs.
But what I observe is that if your portfolio is large and you start implementing at the early stage, you typically need to demonstrate fed-batch versus intensified perfusion or steady-state perfusion. Perfusion almost always wins. If you can achieve that higher productivity, it feeds directly into your upstream cost of goods, and then downstream follows like a domino effect.
If you integrate capture next — which I think is the smart thing to do because you’re decreasing volume and concentrating your feed — you might then connect the low pH viral inactivation step afterward. Maybe you use a detergent ahead of capture; there are many possible configurations. You start realizing, “Oh, we already have this part — why not add a little piece here?”
Then you need to decide how you’ll control the end concentration and the diafiltration. Because our drug product colleagues — and if it’s not obvious, I’m more on the drug substance side — already have end-to-end solutions in place for fill-finish, dilution, and compounding.
So when we talk about end-to-end biomanufacturing, we should truly think end-to-end: from cell line development all the way to the drug product vial or even the combination product device.
David Brühlmann [00:05:03]:
Yeah, that’s a great point — thinking truly end-to-end. And what I also hear in what you’re saying is that there are different approaches: you can start small and expand. It’s not an all-or-nothing or one-time approach — it’s a process.
Irina Ramos [00:05:18]:
Yep, that’s exactly right.
David Brühlmann [00:05:20]:
Let’s circle back to your story — specifically your time at AstraZeneca. You led the technology transfer of the AstraZeneca COVID-19 vaccine. I’m curious — what did you learn in that high-pressure, fast-paced environment? What were your biggest experiences and takeaways?
Irina Ramos [00:05:39]:
We worked in a wonderful community of experts who truly wore the mission on their sleeves. We didn’t care how many hours we worked — we cared that we were helping save lives.
At the end of the day, the technical part — while complex and guided by a sort of “playbook” — was only one piece. What truly made it possible were the relationships, the conversations, and the willingness to go the extra mile.
It allowed us to envision a world that was fully connected across continents, where we could manage raw material suppliers and maintain as much order as possible — even with all the constraints we faced at that time: single-use components, supply shortages, and logistical hurdles. We worked together to accelerate wherever we could.
That’s the overarching view, but when you look deeper into the incredibly complex network that AstraZeneca built — and there were some extraordinary colleagues who made it happen — you start to see the cultural and operational constraints that had to be overcome. We really had to appeal to the mission to unite everyone’s efforts.
Regulatory agencies were incredibly responsive — much more than in normal circumstances. Normally, communication takes weeks or even months. But during this time, it was almost like having them on speed dial. We worked together because we shared the same goal: to save lives.
This wasn’t about filing a product for a niche indication — this was about everyone.
We also learned that many technical activities that would normally be done sequentially could, in fact, be run in parallel — at risk. Normally, you would complete one unit operation, validate it, and then start the next. Instead, we ran multiple streams simultaneously — filtration development, unit operations, validation studies — and then consolidated the data later.
Of course, we can’t expect to do this in normal circumstances — it requires immense resources, time, and global alignment — and the world won’t stop again to serve us in the same way. But it showed what’s possible when the mission is clear and everyone is aligned.
David Brühlmann [00:08:05]:
Now that the pandemic is behind us, we’ve all learned a lot — and the industry has changed. There are new ways of seeing and doing things. From your perspective, what are the lessons we should keep today? And what are the things we still need to implement to make drug development faster, better, and more reliable?
Irina Ramos [00:08:24]:
One of the main lessons is about productivity — and it’s not a question of if, but when we’ll face another pandemic. I hope it’s not during my lifetime, but there will be one. And during COVID, we learned that manufacturing was the bottleneck to getting products out to patients.
We also learned how critical product stability and distribution logistics were — remember the extreme temperature requirements for some vaccines? That’s why they couldn’t reach certain parts of the world where cold chain infrastructure was limited.
So how do we ensure that next time we can do better? There are already some smart, scalable solutions emerging — for example, modular or portable manufacturing units, where you can produce a vaccine in a single-use system or closed device, purify it, and administer it safely, all in one contained setup.
We also learned that we can take calculated risks without compromising product quality or patient safety — provided those risks are well understood and mitigated using tools we already have.
Another big takeaway was that by moving fast, we created templates and new ways of working. Now we know what we can continue doing efficiently — and where we need to scale back, because we no longer have the same level of funding or resources. It’s like we stretched the balloon during the pandemic, and now that balloon gives us more space — more knowledge, more flexibility, more understanding.
We also learned from what didn’t work — but we learned fast. And just as importantly, we saw a real cultural shift with regulators. Many of us no longer view them as “the police.” We now recognize that they play a crucial role and share our desire to see new technologies implemented. They want fewer manufacturing bottlenecks and better process control and capability.
So today, we have a much better two-way communication with regulatory agencies. We leverage industry consortiums and regulatory innovation programs, such as the Emerging Technology Program (ETP) at the FDA. And in Europe and other regions, there are similar initiatives — programs where regulators actively want to learn about your technology and provide feedback early on, even before it’s linked to a specific product. That’s a huge shift in how we collaborate.
David Brühlmann [00:10:40]:
Speaking of learning and adapting fast — we can’t ignore AI. It’s no longer the future; it’s already here. And AI in manufacturing is becoming a central part of bioprocessing conversations. How should teams prepare for AI integration, and what’s the biggest mindset shift they need to make?
Irina Ramos [00:11:04]:
We cannot simply trust the computer. Let me emphasize that again — AI is not about pressing a button and accepting whatever answer it gives.
AI is not entirely new — we’ve been using it for years under different names. Think about machine learning, predictive modeling, or computational process simulation — all of that is AI. It’s just that the buzzword has caught up with the tools.
Now, we need to demystify AI in bioprocessing. Is it a process development tool, or is it something that can take us all the way into GMP manufacturing? That’s a crucial distinction. Is it meant to predict outcomes, or to provide real-time insights? Is it product-specific, or more of a platform-level tool?
At the end of the day, we still need human critical thinking — someone who can connect the math to the physics, the model to the process. Without that, we risk running faster than we can control.
There are already some incredibly advanced labs, both in academia and industry, using a mix of existing, new, and modified AI tools — often connected directly to continuous manufacturing systems.
Imagine this: you’re running a chromatography column for 200 cycles. You already know what those chromatograms should look like from your historical data. When your operator is monitoring cycle 80, the AI model predicts that its profile resembles what cycle 150 used to look like — indicating resin degradation. So the system recommends planning a column replacement in 48 hours. Instead of reacting to a failure, you act proactively.
That’s a simple example, but the same applies to bioreactors, contamination detection, or filtration system monitoring.
Ultimately, the value of AI is defined by the problem you’re trying to solve — or prevent. If it’s a predictive tool, it’s only as good as the people interpreting it. That’s why we need to ensure new scientists coming from universities still understand the fundamentals — why we run things the way we do. That foundational knowledge becomes the input that trains AI tools, and it’s what enables teams to interpret the output in a meaningful, safe, and effective way.
David Brühlmann [00:13:51]:
What are the most important skills a scientist listening today needs to develop?
Irina Ramos [00:13:56]:
They need to truly understand the fundamentals — the scientific and engineering principles behind what we do. We’re still teaching those in schools, and for good reason.
I also teach at the university level, and we’re now dealing with students using tools like ChatGPT, Copilot, and other AI assistants to complete assignments. I strongly recommend: don’t use these tools to pass a class — use them to learn faster and understand better.
Back in my day, we had to spend half an hour in the library just to find a single book. Today, you can use AI to contextualize information and find patterns much faster. That’s the right way to use it — as an accelerator for learning, not a shortcut.
Professors are now reshaping how they teach, so students can use these tools responsibly and enter the workforce at a higher level of understanding.
Remember: you’re building your personal brand from the very beginning. Earlier, I mentioned the importance of trust, competence, and commitment — those apply here too. If your understanding is shallow, even if your grades look great, it won’t take you far. You’ll end up disappointed and frustrated, because your foundation will be weak — and once that’s set, it’s very hard to rebuild.
So, focus on your fundamentals. Find good mentors. Surround yourself with colleagues who are curious and willing to go deep into the principles. This isn’t an easy field — biochemical engineering can be tough, and sometimes it feels overwhelming. But keep at it. Work hard, stay curious, and one day you’ll look back and realize you’re helping change the world — one step at a time.
David Brühlmann [00:15:38]:
Before we wrap up, Irina — what burning question haven’t I asked that you’d like to share with our biotech community?
Irina Ramos [00:15:45]:
That’s a great question. I think we need to understand that the biotech community of today will look very different in the next decade — just as it looks completely different from ten years ago.
We’re always learning, studying, and searching for new solutions to meet the needs of increasingly complex and personalized therapies — like cell and gene therapies tailored to an individual’s DNA or specific condition.
The question I would pose is this: How can we, in manufacturing, anticipate that complexity and use innovation today to solve the problems of tomorrow? And beyond that — how can we bring together the new workforce and the experienced workforce in a truly collaborative way?
We need a “happy marriage” between creativity and experience. Younger scientists should never feel there’s a ceiling limiting their innovation, while more seasoned experts shouldn’t feel threatened by new ideas or technologies. We’re a highly regulated industry, and yes, many of our systems already “work.” But that doesn’t mean we can’t evolve. It’s a continuous effort built on goodwill, mutual respect, and always keeping the patient at the center of what we do.
David Brühlmann [00:17:09]:
This has been great, Irina. What’s the single most important takeaway from our conversation?
Irina Ramos [00:17:15]:
That in continuous manufacturing, one size does not fit all.
If you think it’s too complex, too expensive, or too big of a transformation — start small. Take incremental steps. Get together with your colleagues, run the numbers, and envision the possibilities.
Also, recognize that the workforce of the future will look very different because of AI. Let’s focus on how to get the best out of these tools — using them to enhance, not replace, human expertise.
And above all, stay passionate. This work has to make sense to you personally. You need to be driven by something bigger than yourself. I often remind my teams — when we’re deep in the details of a process or experiment — to zoom out. Ask yourself, Why are we here?
We’re not just in a gray lab doing routine work. We’re part of something transformative. And every so often, someone needs to step back and make sure we’re still focused on what truly matters. I hope that message came through in our conversation today.
David Brühlmann [00:18:19]:
Excellent. Thank you so much, Irina, for joining us and sharing your insights and passion. Where can people find you?
Irina Ramos [00:18:28]:
The easiest way is on LinkedIn — feel free to reach out there.
David Brühlmann [00:18:31]:
Perfect. Smart Biotech Scientists, I’ll include Irina’s link in the show notes. Please do connect with her. And once again, Irina, thank you so much — it’s been a real pleasure.
Irina Ramos [00:18:41]:
Thank you so much, David, for the opportunity.
David Brühlmann [00:18:45]:
There you have it — from continuous processing to AI readiness, Irina Ramos just gave us a masterclass in forward-thinking CMC leadership.
If this episode helped you see your challenges differently, please leave a review on Apple Podcasts or wherever you listen. It really helps other biotech professionals find us. Thank you so much for tuning in today. And remember: science may give you headaches, but biologic drug development shouldn’t. See you next time — and let’s keep smartening up biotech together.
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 Irina Ramos
Irina Ramos is a downstream bioprocessing expert with more than 15 years of experience advancing biologics from early development through regulatory milestones. She has led teams across process development, scalability, technology transfer, and validation, and has been a key contributor to innovations in platform technologies—especially in continuous manufacturing.
Irina also led the technology transfer of AstraZeneca’s COVID-19 vaccine process to an international manufacturing partner.
For over a decade, she has taught graduate-level biotechnology courses at UMBC. She holds a B.S. in Chemical Engineering from the University of Porto and a Ph.D. in Chemical & Biochemical Engineering from UMBC. She is deeply committed to mentorship and to creating tools that help scientists communicate more effectively.
Connect with Irina Ramos on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
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What does it take to build an innovation culture in one of the world’s most careful—and consequential—industries? Biotech is often seen as conservative by necessity, with every process and product touching real lives.
This episode dives straight into the balancing act of pushing boundaries with new technologies while rigorously protecting quality and patient safety—a challenge every scientist, engineer, and leader knows firsthand.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Irina Ramos, a chemical engineer by training who’s navigated everything from bench research to global regulatory filings, who played a key role in the worldwide rollout of AstraZeneca’s COVID vaccine.
The conservative aspect of our industry is necessary. We shouldn't fight it; we should embrace it. We work with products that have a direct impact on people’s lives, so there’s no way around it. We shouldn’t just fight for a less conservative aspect of the discussions. We should, however, leverage new ways of working and new technologies — maybe automation and maybe digitalization — with the appropriate checks and balances, and following ICH Q guidance, but adapted to the new solutions that will solve these new problems.
David Brühlmann [00:00:39]:
Have you ever wondered how innovation leaders balance risk-taking with regulatory compliance? Or how to navigate the transition from batch to continuous processing without disrupting your existing operations? Welcome to the Smart Biotech Scientist Podcast. Today we are diving deep with Irina Ramos, who is a downstream processing powerhouse and has led CMC programs from bench to regulatory filing. And yes, she helped transfer the AstraZeneca COVID-19 vaccine globally. Irina’s bringing the unfiltered truth about building an innovation culture in conservative environments. So grab your coffee — this episode is packed with plenty of insights.
Welcome, Irina. It’s great to have you on today.
Irina Ramos [00:02:40]:
Thank you, David. Happy to be here.
David Brühlmann [00:02:42]:
It’s a pleasure. Irina, share something that you believe about bioprocess development that most people disagree with.
Irina Ramos [00:02:50]:
I think it’s the vision for the future. Some of us might disagree on when we’ll be ready to accomplish that vision. Some people might disagree that, in the next 10 to 20 years, we will have lights-out manufacturing — which means we wouldn’t need people to walk in, gown up, and perform the tasks we do today. I do believe we can achieve that. I think we can envision a manufacturing facility that is smaller and doesn’t need people inside except to solve problems. And if everything runs at steady state, we don’t need the lights on.
David Brühlmann [00:03:31]:
I love this vision. It’s a great vision — I like it. Before we dive a bit further into today’s topic — we’re going to cover continuous processing, obviously, and a lot more — I’d love to go into your story. Can you please draw us into your journey and share what got you started in biotech, what were some interesting pit stops along the way, and what you’re doing today?
Irina Ramos [00:03:53]:
It’s a series of happy accidents. And I hope students and junior scientists are listening to this because sometimes, when they happen, we wonder if it’s a good thing or a bad thing — but it’s about how we embrace them.
My background is in Chemical Engineering. I’m originally from Portugal, and at the time, the biotech industry was not big there. There was no focus at the university to lead us into that industry. So the happy accident was an exchange student program that opened the door to the United States and exposed me to smaller-scale systems. I could look at cells, I could look at proteins, and I could interact with a microscope. I learned that I could apply chemical engineering principles to very small, microscopic things.
The interesting series of events is that when we enjoy working with the people we encounter in life, that becomes the driver for our choices. For me, it’s really about the people. I absolutely loved working with the professor I met in the U.S., Dr. Theresa Good, who invited me to apply for the PhD program — and 22 years later, I’m still here.
After the program, I got a job in industry at AstraZeneca — at the time, it was MedImmune — and we learned to work in a small-company environment with the resources of a large company. For over a decade, we grew with the AstraZeneca portfolio, and I learned what a chemical engineer could do from bench scale all the way to large scale. It was a great demonstration of how we can apply our background in the biotech industry.
David Brühlmann [00:05:42]:
Yeah, that’s great. I love the way you see it — it’s about people, and I totally agree. Obviously, we’re passionate about science, but ultimately, we work with people. That’s also part of my story — I love working with different, interesting people and learning from them.
So, Irina, you’ve obviously seen a lot of different companies and settings. You’ve led teams in early-stage development all the way through regulatory filings. I’d like to start by looking at that part of your work — what are the biggest mindset shifts you’ve seen successful CMC leaders make when they transition from scientist to innovation leader?
Irina Ramos [00:06:29]:
You are working with individuals who come from very different functions. Each function has its own role, responsibility, and of course, accountability. So in order to bring them all together, we all need to be aware of the goals for that project, for that program, and for the team.
There are two things. First, the goal setting has to be very clear. We all need to agree on the timeline, we all need to understand where the resources come from, and we need to give and take.
The second one is the risks — from the unknowns to the absolutely known and high-risk items. How do we all embrace the same risks? Traditionally, we call it a risk register. A risk register should not be divided into slices per function; it’s really to compile the risks so that the team understands we all embrace the same risks.
So in many ways, the secret — that is not so secret anymore — is: how do we build a team where we all understand, through good communication tools, what it will take to meet that timeline?
So often, you find that the stronger teams are the ones that share resources and knowledge — from leadership approval at the functional level to the overarching governance of the portfolio. So I think the key question is: how do you then build that trust, that we are all together for the same purpose?
David Brühlmann [00:07:52]:
We work in a conservative industry. Obviously, there is a lot of innovation going on, but still, biotech is quite conservative. So how do you build an innovation culture despite these constraints we have in our industry?
Irina Ramos [00:08:07]:
Everything starts with a problem — with a need. If that problem and need are a common denominator, then it’s much easier to convince others. You need a solution; you need to change; you need to seek that solution.
Often, you need to collaborate with vendors or other partners. If you don’t have that common understanding that the problem is real — and it’s no longer just a functional interest to improve — it’s harder to convince other stakeholders, because they don’t want to change. Things are working — why would we change?
It’s easier to innovate when you’re working on novel modalities, because the current platform doesn’t fit anymore. It’s also easier to innovate when you explain to leadership that you need to invest — and that such investment brings resources. That could be budget, people, or space. Maybe you even need a different facility to get that product out the door.
Because let’s be honest — in our industry, it’s the product and the discovery of that product that drive everything else. Manufacturing is a very important piece of the puzzle, but we need to make it work for that product to help patients.
So, new modalities, new constructs — I like to call them these “Frankenstein maps,” right? We no longer have the simple maps. The traditional 20-year-old platform doesn’t always work. So how do you translate that into innovation?
Innovation comes in different shapes and forms. Often, people like to think it’s a shiny piece of equipment that makes things work. Sometimes, innovation is in how we work — maybe we need to change processes to make things faster. We might need to automate documentation and template writing. We might need to innovate in the way we interact with regulatory agencies.
The vision has to be: what is the next platform? What does that look like? And does it apply across the portfolio, or do you have a dedicated platform per modality?
Once you have people on board with your problem, you can bring those stakeholders together to discuss a technology roadmap — and how that plays a role in your product launch. It’s no longer just early-stage work that matters; you have to think all the way to the filing — for a BLA, for instance.
It doesn’t mean you have to have all the answers at the beginning, though. People are often skeptical about what that strategy looks like — there’s so much we don’t know. And that’s okay. What’s not okay is to not think about or not address what we don’t know.
So we need to build that technology roadmap — identifying the milestones when we absolutely need to know the answer, and which functions need to be involved by that time.
At the end of the day, it’s really about human psychology. We want to be heard. We want our function to be represented when it’s supposed to be. So how do we build that alignment and reporting across those milestones for each function?
David Brühlmann [00:11:05]:
You’re making an important point — it’s about the way we think. And this leads me to the next question. What mindsets would you suggest the scientists listening should adopt to thrive in this environment and also to drive innovation?
Irina Ramos [00:11:22]:
We always want to work with competent people. We want to trust them because we believe they know what they’re talking about — from their specific function or area.
We also want to work with positive people — but people who live in reality. So, I’m talking about being good at what you do. And to get there, it’s not only through school — you have to be smart in the way you interact with people, the way you listen to them, and really do so with the intention of the common good — not a personal or individual agenda.
Just because I saw or heard something at a conference or read a really nice paper — how do I translate that idea into solving the problem my organization actually needs solved? Because if they don’t see that need, they won’t support you.
So we need competent people; you build trust, and then you have conversations that build strategy.
The conservative aspect of our industry is necessary. We shouldn’t fight it — we should embrace it. We work with products that have a direct impact on people’s lives, so there’s no way around it. We shouldn’t just fight for a less conservative aspect of the discussions. We should, however, leverage new ways of working and new technologies — maybe automation, maybe digitalization — with the appropriate checks and balances, and following ICH Q guidance, but adapted to the new solutions that will solve these new problems.
I want to emphasize that — and we’ll talk more about AI, I’m sure. We don’t have computers to replace us. We still need competent, valuable scientists and engineers to contextualize how we are going to apply those solutions.
David Brühlmann [00:13:05]:
Whether you’re leading innovation projects, working in CMC development, or preparing an IND or BLA filing, you have to interact with all kinds of stakeholders — especially in bigger companies.
One of our listeners mentioned that coordinating between various stakeholders across different departments and locations is their biggest challenge when implementing innovative solutions. But I think that applies to all kinds of settings.
I’d love to get your perspective, Irina. From your experience, what strategies have you found most effective for communication, collaboration, and ultimately achieving the results you’re aiming for?
Irina Ramos [00:13:52]:
I’ve had experiences like that too — absolutely. From time zones to geography to culture — even the way people think methodically about something, or how they go about giving updates and explaining what they’re doing.
I found different ways, depending on my colleagues. Sometimes, you might want to leverage a one-on-one. Maybe you have a one-on-one every other week with someone who has very limited English — think about that. In that more focused environment, you’re supporting that colleague.
And if you’re leading a team, you’re not alone anymore — that colleague is not alone anymore. You’re representing and filling the gaps as that colleague communicates to the rest of the team, if necessary.
You need strong project management skills in a team. That means when you present an agenda, sometimes it’s not enough — especially for teams from different cultures — to just have a bullet list of what we’re going to discuss. Maybe you need to be clear about expectations for those topics.
So I actually did that — I wrote not only what updates we needed from each function, but also the expectation: not only the timing, but the linkage of that topic to other functions. So now they come more prepared. The action items are very clear, and so are the timelines.
So: clear expectations, organized agendas, and meeting minutes that truly reflect what was discussed.
But we also need to be prepared for ad hoc discussions. In a recent example, I found that if we’re a bit more senior in the organization and have seen good ways of doing things, we are responsible for coaching. You might say, “Have you thought about this?” — and you need to build trust for your ideas to be accepted. Maybe you just ask them to trust you: “Let’s try it this way; let’s see if it works.”
It’s complex — and it applies to project teams, and to innovation as well. Sometimes innovation brings an extra layer of complexity: do they have the capability at that site? Do they have what it takes — from space, to bandwidth, to people, to know-how?
And we shouldn’t assume. Don’t assume anything. You should ask. Be curious about what those individuals or other teams do in their business-as-usual environment. So when you need to push them — to accelerate them, to think outside the box — you already understand what kind of tools you need to use to stretch a little bit more.
David Brühlmann [00:16:34]:
What I’m hearing comes down to clear communication, number two — trying to understand what their needs are, who they are — and then also having clear expectations, and making sure that things happen according to the agreed timeline.
Now, let’s shift our conversation to a topic that’s dear to your heart. Let’s talk about continuous manufacturing. For those who are still in the fed-batch world, let’s start out in a slightly controversial way: what is one misconception about continuous manufacturing that you hear most often — and how do you address it?
Irina Ramos [00:17:09]:
Too complex. Too expensive to implement. It’s going to take longer timelines to develop a continuous process — even before you scale it up. A lot of uncertainty, right? A lot of ambiguity.
It’s all valid. All of these concerns are valid — and they actually matter. And why do I call them misconceptions? Because it’s really company by company, facility by facility, and situation by situation.
Continuous manufacturing is not the solution for all products. Nor is there a single, one-size-fits-all solution for continuous processing. That’s why the community is growing — vendors are providing different solutions. And we don’t always call it “continuous.” Sometimes we call it intensified, or even automated manufacturing.
So how do we make sure that, for our portfolio and our existing facilities — both internal and external — we’re making the right choices? Because if you have to work with CMOs (Contract Manufacturing Organizations), you need to understand their ability to adapt to your products.
Then you need to make a decision: is it worth it? Can we have a forecast that we can actually trust? Often, these forecasts — for example, blockbuster forecasts — are not really met. They just give us an indication. But can we expand and apply that to the capacity we will need for that product?
So it all comes down to productivity for us in the bioprocess world. My background is downstream, but if my upstream colleagues don’t develop a highly productive upstream process in the bioreactor, downstream intensification doesn’t make sense.
You can think about traditional perfusion, steady-state perfusion, or dynamic perfusion. You can think about a couple of weeks’ duration or longer — duration doesn’t really matter. Productivity is the measure — how much mass per unit time per unit volume you’re able to produce. How much do you actually need?
Only after that does downstream make sense to intensify — to make it continuous. Even though some intensification tools in downstream could also be applied to fed-batch.
If we find a way to have a flexible facility — more like pieces of a puzzle — we can identify a stage-wise approach to implementation that makes sense for the product. For example, if you’ve aligned your upstream feeding strategy to achieve really high productivity — 5x, 10x improvement — then it makes sense to start thinking about downstream integration.
Where I’d like to highlight is the PAT part — Process Analytical Technology — all the analytical tools, sensors, and real-time or near real-time tools. If they’re being developed to facilitate or even enable continuous processing, why wouldn’t we use them in a fed-batch process?
So I actually think that, from an analytical perspective, we might have an even stronger push — from our customers — to get these technologies out the door. And I know the regulators want that, because at the end of the day, they want us to control our processes better.
So how can we entertain this vision — of what’s out there — and then make our own internal strategy that fits our portfolio needs for the next five to ten years?
The worst thing that can happen is if we set a strategy and keep it at steady state — “This works for now” — and no one is really thinking about the next three, five, or ten years in terms of portfolio evolution.
And then leadership comes with these very intriguing molecules — that may not be very productive, or that degrade very fast. And that’s where continuous can actually be your solution.
So I talked about productivity, and now I’m talking about product quality. Product quality could be another driver to implement continuous manufacturing — because then your intermediate is not sitting around for too long.
David Brühlmann [00:21:00]:
You’ve just come back from the Integrated Continuous Biomanufacturing (ICB) conference in Dubrovnik — I saw it on LinkedIn. What is the state of continuous manufacturing today like? What’s hot in this area?
Irina Ramos [00:21:12]:
The most interesting thing about the conference was that we focused on having more continents represented — leveraging the location in Europe. And we did that.
We also wanted to bring new people and institutions on board, so we offered pre-conference tutorials with three big names in this field who decided not to retire — and we’re very thankful for that. They’re still around, and they’re still incredibly valuable in providing context and mentorship.
So now, David, you can imagine — you have this wave of new people coming in, interested and curious. You have more organizations represented, presenting posters and oral talks. And at the end of the day, what you get is more diverse solutions to intensify and make the bioprocess continuous. That, in turn, enables many more “what if” conversations.
So, what’s hot right now? Well, we’re being bombarded — in a positive way — with new modalities and constructs that no longer fit the traditional platform. You need to find automated and smart ways to move the needle — to get closer to utilizing predictive models that reflect what’s happening in the process.
These models are predictive not only of real-time activities, but also of scalability. There are many technologies used in other industries that vendors are now bringing into bioprocessing — a field that’s admittedly late to the game in some respects — but we also bring unique challenges: closed systems, single-use components that need to last, and stringent GMP expectations.
A very important topic is how to leverage expectations around documentation, data gathering, and monitoring — and do more real or near real-time analysis. Some people are skeptical, others are encouraged by the idea of real-time release.
So how do we bring all these ideas together? Eventually, they converge into what we’re all striving for: faster processing, higher productivity, smaller facilities, single-use or hybrid setups (stainless steel combined with single-use). Ultimately, your PAT framework supports better process control and drives digitalization across the entire manufacturing process.
David Brühlmann [00:23:28]:
How do you decide whether to go for a hybrid approach or a fully end-to-end continuous process? Because as you said, there are so many options now — and it can be quite overwhelming. Are there some simple guiding principles to help scientists choose the best approach?
Irina Ramos [00:23:46]:
I think there are. A stage-wise approach is always better — even if your final goal is fully end-to-end continuous.
Unless you’re building from scratch — a greenfield or even a brownfield facility — it’s important to leverage what you already have. But you also need a portfolio that can feed into that long-term plan, including scalability considerations.
Maybe you start with clinical manufacturing, and then, if those products are successful, they become the ones feeding your commercial facility. So you need that vision — that roadmap.
A hybrid approach, especially while vendors are still maturing their solutions, is extremely important. And we shouldn’t only rely on the big vendors — we should also engage with smaller companies that bring innovative solutions and prevent the industry from becoming monopolized. Competition is healthy in this field.
In the end, it’s really about what fits your organization — if you have a clear vision of the kind of portfolio you’re going to focusing on.
David Brühlmann [00:24:49]:
To what extent do scientists need to prioritize the control strategy or real-time monitoring? That seems crucial for continuous manufacturing. Are there some easy solutions to start with?
Irina Ramos [00:25:01]:
Think about what you already do in fed-batch — how do you control the process? Look for parallels.
Maybe you don’t need everything under the sun — and that helps demystify the perceived complexity of these implementations. Just because something would be nice to have doesn’t mean it’s a must-have to control the process effectively today.
So, leave the nice-to-haves for later. Focus first on the must-haves, which are linked to regulatory expectations.
Look at what you already do in fed-batch — identify what you absolutely need to continue doing for continuous — and then maybe add sensors that provide feedback loop control.
For example, you might monitor concentrations or pH. If you’re titrating inline, you’ll want that sensor connected to a simple feedback loop. In fed-batch, you might not need that as much.
So, you’re not reinventing the wheel — these tools already exist. You’re just transferring that knowledge to equipment that’s now closed, enabling closed-system operations. There are many examples like that — where you can leverage existing sensors and technologies to provide the necessary control and insight.
David Brühlmann [00:26:11]:
That’s it for part one. We’ve explored leadership mindset shifts, innovation, culture building, and the advantages of continuous manufacturing.
If these insights sparked something for you, please leave a review on Apple Podcasts or your favorite platform — it helps scientists like you discover these conversations.
Stay tuned for part two, where Irina reveals what the COVID vaccine taught us about what’s truly essential in process development — plus her take on AI in biomanufacturing.
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, you’ll help empower more scientists like you.
For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech insights in our next episode. Until then — let’s continue to smarten up biotech!
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Irina Ramos
Irina Ramos has developed a career in downstream process development of Biologics, from the bench to team's leadership. Her work was applied directly in portfolio projects, leading CMC projects to regulatory filing milestones, and in innovating and implementing platform technologies, in particular related to continuous manufacturing.
In 15+ years of experience in downstream process development, Irina has worked in Early and Late stage process development, process scalability, CMC leadership, technology transfer and process validation. She led the technology transfer of the AstraZeneca COVID-19 vaccine process to an international partner.
Connect with Irina Ramos on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
Imagine unlocking a biological sample and, instead of peering under the usual “streetlight” of targeted analysis, being able to illuminate the entire landscape of metabolites, glycans, and unknown impurities.
This episode is all about breaking the limits of current bioprocess analytics, featuring a practical journey into cryogenic infrared ion spectroscopy—technology designed to reveal what’s been invisible until now.
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Tom Rizzo, Professor Emeritus at EPFL and Co-Founder and CSO at ISOSPEC Analytics, a life science company that aims at simplifying molecular identification.
The streetlight effect goes as follows: you lose your keys in the dark, and the only place you look for them is under the streetlamp, because that’s the only place you can see. Now, you don’t know where you lost your keys, but you only look under the streetlamp because it’s dark everywhere else.
Well, you know, in the field of metabolomics, for example, one can identify only a small fraction of the total available metabolites. So what do you do? You do a targeted analysis — you look for certain metabolites, the ones that you can see. But there’s this whole 90% of them, perhaps, that you can’t see.
And so, by using this technology — where we can identify the large majority of metabolites, for example, and we believe that we can — it opens up a whole new world of investigation. Because you can look anywhere for your solution, and not only under the streetlamp.
David Brühlmann [00:00:53]:
Welcome to The Smart Biotech Scientist. I’m David Brühlmann, and this is part two of our conversation with Tom Rizzo, who is a Professor Emeritus at EPFL and now Chief Scientific Officer at ISOSPEC Analytics.
Last time, we covered the science behind cryogenic infrared ion spectroscopy, and today we’re getting practical — how this technology helps you discover biomarkers, identify mysterious degradation products, and accelerate process development.
We’ll also explore Tom’s transition from academia to entrepreneurship and what it takes to commercialize breakthrough technology. If you’re dealing with complex mixtures and unknown impurities — stay tuned. This is for you.
This new dimension — what will it enable scientists, companies, pharmaceutical organizations, or even biomarker researchers to do? Can we detect new molecules we weren’t able to detect before? Or will the workflow be faster?
I’ve done a lot of omics work in my career, which can be very complex. I imagine your technology could simplify that too.
Tom Rizzo [00:03:17]:
Okay, well, there are several parts to my answer on this one.
In the area of glycans, for example, there’s a real difficulty in analyzing different isomers. And we know — and there’s evidence in the scientific literature — that, for example, the glycosylation of a monoclonal antibody affects its efficacy, safety, and lifetime.
But if you can’t resolve the different isomers, you don’t know what’s responsible for that difference in efficacy. So being able to do isomer-specific glycan analysis sheds new light on the mechanisms or effects of glycosylation on efficacy.
That’s one side of things — being able to determine, down to the isomeric level, what species are adorning your protein is an important part of understanding its function and why it functions so well.
But there’s also a broader issue, particularly when you talk about different omics. Metabolomics, for example. And I like to use the analogy of the streetlight effect. Do you know what the streetlight effect is? Have you heard of this before?
David Brühlmann [00:04:18]:
No, I don’t know.
Tom Rizzo [00:04:19]:
Okay, so the streetlight effect goes as follows: you lose your keys in the dark, right? And the only place you look for them is under the streetlamp, because that’s the only place you can see. Now, you don’t know where you lost your keys, but you only look under the streetlamp because it’s dark everywhere else.
Well, you know, in the field of metabolomics, for example, one can identify only a small fraction of the total available metabolites. So what do you do? You do a targeted analysis — you look for certain metabolites, the ones that you can see. But there’s this whole 90% of them, perhaps, that you can’t see.
And so, by using this technology — where we can identify the large majority of metabolites, for example, and we believe that we can — it opens up a whole new world of investigation. Because you can look anywhere for your solution, and not only under the streetlamp.
So I think that’s an apt analogy to the situation, because current techniques can only identify such a small fraction of biological molecules — metabolites in particular.
David Brühlmann [00:05:21]:
Yeah, and I think it will open a lot of avenues in disease detection, because you’re making such a good point. If you go to the doctor, they have their program, and we’ve been measuring the same biomarkers for many years.
But now, I think with this expanded capability, we’ll be able to pick up a lot more metabolites. And on top of that, with AI, I imagine the possibilities will be endless.
So I just have a follow-up question about where you think AI will take this. And also, I think another very practical aspect is that with omics or with mass spectrometry, you usually need huge libraries to correctly identify your molecule. How does that work with your technology?
Tom Rizzo [00:06:05]:
You’re right. If you want to identify a molecule from its infrared spectrum, you need to have a library of infrared spectra — and, in fact, we have mechanisms for that.
Normally, you would think that you need a standard, and getting standards for different isomeric molecules can be extremely difficult, if not impossible. So this could potentially be a problem.
But we’ve developed techniques to interpret infrared spectra of molecules without having a standard. Here’s how it works: let me take the example of glycans, because that’s one we’ve done a lot of work on. You measure the infrared spectrum of an apparent molecule, and you find that it’s not in your database of spectra. Well, what do you do?
What we do then is fragment the molecule. We can measure the infrared spectra of the different fragments. Usually, you can find fragments that are characteristic of one isomer or another. So if you’re looking for a substitution on one branch or another branch, you break it off and then you see just that branch — not the rest of the molecule — and you can determine which species is on there.
Then, for that smaller molecule, you ask: is its infrared spectrum in our database? If it is, then you’ve identified it. And from that, you can say, “Okay, now we’ve identified the parent molecule, because we know that substitution was on this branch.”
If, after the first fragmentation, you still can’t identify the molecule, you can fragment again and measure the infrared spectrum of that smaller piece. You can continue doing that until you reach small enough species that are in our database.
Once you’ve identified the infrared spectrum of the parent molecule, you don’t have to repeat this fragmentation process — you just do it once. It’s now in your database. If, later, you have a larger molecule that fragments back to that known one, you only need to go as far as finding that fragment in the database.
So, we have a mechanism by which we grow the database from smaller species to larger species — by taking large molecules, breaking them down until we reach fragments for which we already have data. That’s the general procedure for analyzing large, more complex molecules.
Now, in the case of smaller molecules, like metabolites, we rely to some degree on computed spectra — quantum chemistry calculations. For smaller species, these calculations are actually quite accurate. That’s what helps us on the smaller end of the spectrum. And there are now AI-based techniques that enhance quantum calculations by matching them with measured infrared spectra.
So that’s one way we’ll be using AI — in combination with quantum chemistry calculations to improve the matching of computed and experimental infrared spectra.
But we also use AI in other ways. As you asked — once we get all this information about metabolites, what do we do with it? How do we use those measurements to increase our understanding of disease mechanisms?
There, we can use AI together with all this analytical information — alongside more traditional omics data — to integrate everything and help medical researchers better understand mechanisms of disease. We really think AI will help bridge the gap between analytical measurements and the understanding of disease mechanisms.
David Brühlmann [00:09:23]:
Yeah, that’s where I see both the opportunity and the challenge. I’ve had several conversations with my former boss about omics, and the question was always the same: It’s great — we’re able to generate tons of data — but it’s very difficult to draw meaningful conclusions from it.
What does it actually mean? For instance, at that time we were optimizing bioprocesses — we could see that there were changes, but what did those changes really mean? What should we change to actually make the process better?
So I think AI will definitely help us make much better decisions. My question now is: what is your vision for the technology? And as you’re combining this with AI and other technologies, where do you see this going in the next few years?
Tom Rizzo [00:10:09]:
We see it going in a couple of different directions.
One is that we’re establishing a platform that we can provide as a service — a platform to help medical researchers and pharmaceutical companies analyze biological samples with the goal of learning more about disease and understanding disease mechanisms.
So on one hand, we provide a service: people can send us samples, and we can not only give them data — not just an Excel sheet with “what’s in their sample” — but, using AI, we can also help them interpret those results. We can give them the tools to start analyzing and uncovering disease mechanisms. In other words, we would provide not just data, but a data and analytics platform equipped with AI tools to help interpret and visualize the findings.
On the other hand, we also want to put these tools directly into the hands of researchers. As I mentioned before, we’re working together with Agilent to produce a commercial instrument, allowing people to access this technology themselves and apply it in ways we might not even imagine. So we see it going in that direction.
Additionally, we have the ability to produce databases of infrared spectra using our current instruments. That means we can also offer a specialized service — developing custom spectral databases in different domains, because we have the expertise and infrastructure to do so.
David Brühlmann [00:11:28]:
When do you think this device will be available? Are we talking months, years — what’s your current timeline?
Tom Rizzo [00:11:38]:
So, we currently have a service that—
David Brühlmann [00:11:40]:
—you already offer as a service, yes.
Tom Rizzo [00:11:42]:
Exactly. We’re already working with hospitals, and we have collaborations here in Switzerland, Germany, and Belgium. But for people to actually get their hands on the instruments, it usually takes a couple of years to go from a prototype to a commercial product. An add-on module to existing instruments might become available relatively soon, but for a fully integrated instrument, I’d say we’re still a couple of years away.
David Brühlmann [00:12:05]:
Now I’d like to bridge your academic career with your current entrepreneurial career. A lot of our listeners are either in academia or they’re startup founders, so I’d love to get your take on this. You’ve seen both worlds — and the transition between them — and you’ve worked with many scientists throughout your career at EPFL.
What advice would you give to a scientist, a PhD student, or a postdoc who would now like to start his or her own company?
Tom Rizzo [00:12:37]:
As I said before, it’s a completely different world, right? And there’s a lot to learn — I’m still learning quite a bit. You really have to be passionate about what you’re doing. It’s not for the faint-hearted. Many startups just don’t make it, and one has to go in realizing that.
But from a very practical standpoint — something we’ve experienced personally — we only had about one year of overlap between my academic laboratory and the company, because of my retirement. Once you’re out on your own and no longer have that academic lab, you lose the ability to do R&D in the same way you did before.
That’s a real drawback. R&D in a startup is tough, because investors want to see how you can make money. Just doing R&D to improve your technique doesn’t generate income. If a professor spins off a startup while continuing to run an academic lab, and can do more fundamental R&D there while the company focuses on commercialization — that’s a huge advantage. Because the weight of doing R&D within a startup can be heavy.
There are also competing interests — someone might want to use the machine to understand the technology better, but at the same time, you have to deliver results for your customers. So that’s one very practical piece of advice I can offer.
David Brühlmann [00:13:52]:
Excellent. Tom, at the beginning of our conversation, you talked about legacy — about the purpose behind this new season as an entrepreneur. I’m really curious about that. You decided to start this new adventure with the company — what is giving you your sense of purpose now? And what legacy do you hope to build through this entrepreneurial chapter?
Tom Rizzo [00:14:11]:
As I look at things now, what drives me to some degree is life experience. You know, my father died of colon cancer. I’ve had friends with prostate cancer. My wife had leukemia. As you go through your career and get older, you see people — friends, colleagues — who suffer from these diseases, many of which, if diagnosed early enough, could have been treated effectively.
Fortunately, my wife’s leukemia was treated with a stem cell transplant, which was completely successful. She’s 100% in remission, and we’re deeply grateful for that. So the whole idea of early diagnostics is something that drives me. Can we find biomarkers that allow us to detect disease years before it develops?
If our technology helps in the early diagnosis of even one of these diseases, I would consider that a tremendous success. That would be a legacy worth leaving.
David Brühlmann [00:15:09]:
Wow. I love that. Very powerful. This has been great, Tom. Before we wrap up, what burning question haven’t I asked — something you’re eager to share with our biotech community?
Tom Rizzo [00:15:21]:
I think you’ve covered it pretty well, David. Offhand, I can’t think of any burning question you haven’t asked.
Actually, maybe not a burning question — but a technical one that you didn’t ask: how do we actually measure the infrared spectrum?
David Brühlmann [00:15:38]:
Oh yes, good point — tell me!
Tom Rizzo [00:15:41]:
Now, if you think about it — how can you make a technique that sensitive? Let’s consider the physics for a moment. You have a light source — maybe it emits 10¹⁷ photons per second. Let’s say you have a thousand molecules in your sample. If you try to detect absorption directly, you’re comparing 10¹⁷ photons to a change of only a thousand. That difference — 10¹⁷ minus 10³ — is effectively still 10¹⁷. So, you can’t detect the absorption of just a few molecules that way.
So how do we measure the infrared spectrum of samples inside a mass spectrometer when there are so few ions — sometimes as few as a thousand? The answer is: we use a special technique called messenger tagging. We attach a weakly bound tag — typically nitrogen (N₂) — to the molecule, because nitrogen binds weakly to ions. We then look at the mass of the molecule plus this tag.
When the molecule absorbs infrared light (at a specific vibrational frequency), that energy redistributes and causes the messenger tag to detach — or “pop off” — the molecule. We can detect that change in mass: the molecule without the nitrogen tag is lighter by 28 daltons. By measuring the ratio of tagged to untagged ions as a function of laser frequency, we can build an infrared spectrum. That’s how we achieve such high sensitivity — we’re not directly measuring photon absorption, but rather a consequence of it, which we can detect with exquisite precision. So, maybe not a burning question — but definitely one worth asking!
David Brühlmann [00:17:42]:
Wow, that’s actually really important — and fascinating! I’m glad you explained that. So, as we wrap up, Tom — what’s the most important takeaway you want our listeners to walk away with?
Tom Rizzo [00:17:55]:
I think the most important takeaway is that soon, you will no longer be restricted to analyzing only a small fraction of molecules in a biological sample.
By adding this new data dimension, we’ll be able to gain a much more comprehensive view of all the chemical changes occurring — for example, when a drug is metabolized, we’ll be able to observe all the metabolites, not just a selected few. This extra data dimension will fundamentally change how we do drug development and disease diagnostics.
David Brühlmann [00:18:33]:
Fantastic. Tom, where can people connect with you, learn more about your company, or explore your services — and maybe even get their hands on your instrument once it’s available?
Tom Rizzo [00:18:46]:
We have a website: www.isospecanalytics.com, or you can send me an email at tom@isospec.ch — that’s the simplest way to reach me.
We’re also on LinkedIn — just search for ISOSPEC Analytics. Through our website or LinkedIn, you’ll find updates and announcements — for example, when new instruments become available. We already announced our collaboration with Agilent, and we hope to share more about our next steps soon.
David Brühlmann [00:19:14]:
Well, thank you so much, Tom, for sharing your passion and purpose — and for explaining how you’re transforming the analytical space. It’s been a huge pleasure talking to you today and having you on the show.
Tom Rizzo [00:19:29]:
Well, it’s been my pleasure, David. I look forward to listening to more of your podcasts as well.
David Brühlmann [00:19:34]:
Thank you for joining me for this two-part conversation with Tom Rizzo. I hope you’re walking away with fresh insights on how advanced analytical techniques can solve real problems in bioprocess development and therapeutic characterization.
If this episode sparked ideas or answered questions you’ve been wrestling with, I’d love to hear about it. Please leave us a review on Apple Podcasts or your favorite platform.
Until next time — keep doing biotech the smart way.
Alright, smart scientists — that’s all for today on The Smart Biotech Scientist Podcast. Thank you for tuning in and joining us on your journey to bioprocess mastery. If you enjoyed this episode, please leave a review on your favorite podcast platform — it helps us reach and empower more scientists like you. For additional bioprocessing insights, visit www.bruehlmann-consulting.com. Stay tuned for more inspiring biotech discussions 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 Tom Rizzo
Tom Rizzo received his PhD in Physical Chemistry from the University of Wisconsin–Madison in 1983, following his undergraduate studies at Rensselaer Polytechnic Institute. After postdoctoral research at the University of Chicago, he joined the University of Rochester before moving to the École Polytechnique Fédérale de Lausanne (EPFL), where he became Professor of Chemistry and later served as Dean of the School of Basic Sciences.
His work focuses on integrating laser spectroscopy, ion mobility, and mass spectrometry to advance biomolecular analysis. Upon retiring from EPFL in 2023, he assumed the role of Chief Scientific Officer at ISOSPEC Analytics, a company applying his research to biomarker discovery and molecular diagnostics. His achievements have been recognized with the Bourke Award, the Ron Hites Award, and an ERC Advanced Grant, and he is a Fellow of both the American Physical Society (APS) and the American Association for the Advancement of Science (AAAS).
Connect with Tom Rizzo on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
Do you wish to simplify your biologics drug development project? Contact Us
The identification and characterization of biological molecules is central to the biotech industry, particularly in drug development, bioprocess optimization, and advanced analytics. Yet, despite revolutionary advances, many scientists still struggle with the limitations of traditional tools like mass spectrometry (MS) and nuclear magnetic resonance (NMR).
In this episode from the Smart Biotech Scientist Podcast, David Brühlmann meets Tom Rizzo, Professor Emeritus at EPFL and Co-Founder and CSO at ISOSPEC Analytics, a start-up that spun off from his laboratory, where they aim at simplifying molecular identification.
Mass spectrometry is an incredibly sensitive tool. NMR is a fantastic method for analyzing and identifying molecules, but you need a lot of material to acquire an NMR spectrum. A mass spectrometer, on the other hand, detects ions—and because you can detect ions, it’s incredibly sensitive.
You can essentially detect individual ions by doing spectroscopy inside a mass spectrometer. We were able to measure well-resolved infrared spectra with the sensitivity of mass spectrometry. And that’s what makes it really unique.
David Brühlmann [00:00:34]:
Welcome to The Smart Biotech Scientist. I’m your host, David Brühlmann. Today’s guest is Professor Tom Rizzo, former Dean of the School of Basic Sciences at EPFL in Lausanne, Switzerland, and now Chief Scientific Officer at ISOSPEC Analytics.
We’re diving into cryogenic infrared ion spectroscopy—a game-changing analytical technique that could revolutionize how you identify unknowns, characterize glycans, and solve those frustrating structural puzzles in your bioprocess development.
If you’ve ever struggled with molecular identification using traditional mass spec alone, this conversation will open your eyes to what’s possible today. Let’s get started.
Welcome, Tom—it’s great to have you on today.
Tom Rizzo [00:02:35]:
Thanks, David, for having me. I’ve listened to a few of your podcasts and really enjoyed them, so it’s an honor to be here.
David Brühlmann [00:02:42]:
The pleasure’s mine, Tom. To start, share something you believe about biomolecular analysis that most people would disagree with.
Tom Rizzo [00:02:51]:
I don’t like to be too controversial, but I’d say there hasn’t been a fundamentally new commercially available technology for the analysis of biological molecules in the past 10 years—probably not since the introduction of ion mobility into mass spectrometry.
David Brühlmann [00:03:08]:
All right—now we’re getting into the nitty-gritty of analytics! I’m excited to unpack that. But first, I’d love to hear your story, Tom—what got you started in science, what sparked your passion for chemistry, and what were some pivotal moments throughout your long and distinguished academic career?
Tom Rizzo [00:03:31]:
This will date me a little bit, David, but I grew up in the ’60s, and I grew up in New York. In 1964–65, there was a New York World’s Fair not too far from where we lived. My parents would take us almost every other weekend to this World’s Fair. All of the big technology companies had buildings with presentations and demonstrations—there was IBM, there was Bell Labs, there was DuPont—and I was just fascinated by it all.
I remember very, very distinctly the DuPont exhibit, where they did chemistry demonstrations. I was fascinated by this. In fact, many years later, I performed those same demonstrations in front of my chemistry class at university, and even in my kids’ elementary school classes. That really sparked my interest in science.
I knew I wanted to be a scientist, but I wasn’t sure whether it would be chemistry or not. In fact, I thought I would be a physicist—I thought my mathematics background was pretty good—so I was planning to go into physics. But I took an Advanced Placement course in chemistry in my last year of high school. Part of the course involved being given small vials with unknown samples, and we had to determine what was in them. This was really the start of my interest in analytical chemistry, if you will.
The way we were graded was that the teacher would give you a piece of paper with tabs on it, and each tab had a different answer. If you pulled the first tab and got it right, you received the highest grade. If it took two pulls, your grade went successively lower. I set up a little laboratory in my room at home to run these chemical tests. That really grabbed and sparked my interest in chemistry.
So I decided to study chemistry at university, but I was oriented more toward the physical side of chemistry. When I went on for a PhD, I pursued physical chemistry, but I also did a minor in physics. I’ve always kept that interest in physics. But deep down, my interest in analytical chemistry was there from the early days.
When I started my PhD, I was doing something very “physics-y,” in a sense. I was really looking at molecules by how they interacted with light. The whole field of spectroscopy fascinated me. I really liked the tools of physics. I thought lasers were cool—that’s what I pursued.
By the time I finished my PhD, I asked myself: how large a molecule could we get interesting information from using spectroscopy? This led me to a postdoc at the University of Chicago with Don Levy, a distinguished professor who developed techniques to put large molecules into the gas phase and measure their spectra. We measured the first spectrum of an isolated amino acid in the gas phase at low temperatures, and that was kind of my start.
When I took my first faculty position at the University of Rochester, my goal was to combine mass spectrometry and laser spectroscopy as a new way to study biological molecules. But I had no reputation in mass spectrometry at all. I was trying to get my grants funded, and no one wanted to fund this kind of work. I got close at the National Institutes of Health and close at the National Science Foundation, but no one would fund it.
I had to step back, since I needed to get tenure as an assistant professor. I returned to studying the physics of smaller molecules and managed to do well enough to earn tenure. Shortly afterward, I was approached about taking a position at the École Polytechnique Fédérale de Lausanne (EPFL). I applied, got the position, and we packed up our family and moved to Switzerland. There, I continued my work looking at the chemical physics of laser-excited molecules.
But I always had in the back of my mind the experiments I wanted to do as an assistant professor—combining laser spectroscopy and mass spectrometry—I just never had the funding.
An opportunity came at EPFL, where I had spent two years as a department head. I said I would never do that job again—it was really uninteresting. But the president of the institution changed and asked me to take over as department head in chemistry again. I agreed, on one condition: that I would finally be able to do the experiment using laser spectroscopy and mass spectrometry, which I had never been able to fund.
This was in the year 2000. I told him, if you give me the money to build this machine, I’ll serve another term as department head. He agreed. I wrote up a project and obtained 400,000 Swiss francs to build a new machine.
That was the beginning of the end for me, in the sense that ever since, I’ve been involved in the spectroscopy of biological molecules in combination with mass spectrometry.
David Brühlmann [00:08:26]:
Wow, what a fascinating story—and I love your persistence throughout your journey. And just a little fun fact: Tom was actually my professor at EPFL! I’m really happy to reconnect after all these years.
I still remember your physical chemistry class, which I really enjoyed. There’s one thing that has stayed with me to this day—the “umbrella flip” of the ammonia molecule in quantum mechanics. That was such a great class! Hard to believe it’s been about 20 years—I still remember that example vividly.
Now, throughout your career—you recently wrapped up your time at EPFL. What led you to become the Chief Scientific Officer of ISOSPEC Analytics instead of enjoying a well-deserved retirement? How did this transition come about?
Tom Rizzo [00:09:17]:
Good question. As you approach retirement, you start looking back on your career and asking, What has it all been good for?
Most of my work over the years has been curiosity-driven research. I was fascinated by what we could learn about large molecules through spectroscopy at low temperatures. Early on, I wasn’t really thinking about practical applications or commercial products. But toward the end of my academic career, I began reflecting on my scientific legacy.
I developed a real passion for seeing the techniques we’d developed in the lab make their way into practical use—to become part of a commercial instrument that could help people analyze biomolecules or even diagnose disease. That became very motivating for me.
I had a couple of postdocs in my group who shared that same passion. So, about a year before my formal retirement from EPFL, we founded ISOSPEC Analytics, and I took on the role of Chief Scientific Officer. When I officially retired a year later, I maintained my affiliation with ISOSPEC and left EPFL.
Honestly, it turned out to be a great way to retire. When you leave an institution after 30 years, from one day to the next you go from being “somebody” to being “nobody,” right? But at the company, I felt my expertise and experience were still highly valued. It was a wonderful transition from academia to industry.
And the world of startups is completely different from academia—there’s so much new to learn, and I found that very stimulating.
David Brühlmann [00:10:48]:
That’s inspiring, Tom. So let’s unpack the technology itself and dive into the details of what you’re developing. You’re working on a novel infrared spectroscopy method—but infrared spectroscopy has been a cornerstone analytical technique for decades.
Can you explain the fundamental breakthroughs you and your team have achieved? How is your approach different from traditional IR methods?
Tom Rizzo [00:11:14]:
There are several key differences. You’re right—infrared spectroscopy has been around for decades and has been a cornerstone of analytical chemistry. But traditional infrared spectroscopy, especially in the condensed phase, has some major limitations.
In the gas phase, infrared spectroscopy can be extremely detailed and precise because molecules are isolated. But for biological molecules—typically studied in solution or in complex matrices—the spectra are often broadened and distorted by environmental interactions. The vibrational modes are influenced by their surroundings, so the spectral bands become broad and less uniquely characteristic of a specific molecule.
If you can instead bring large molecules—like peptides or sugars—into the gas phase and remove them from that solution-phase environment, the spectra become much simpler. The lines sharpen significantly.
Then, if you cool those gas-phase molecules to cryogenic temperatures, the spectra become even simpler and sharper, because thermal motion is minimized.
Cryogenic spectroscopy can also be done in rare-gas matrices, where you can achieve very sharp spectra—but that’s a labor-intensive process and not practical for analytical applications. So our goal was to make this simplification accessible in a practical, analytical way: by cooling isolated ions to cryogenic temperatures in the gas phase.
And perhaps the most important innovation is that we can measure infrared spectra with the sensitivity of mass spectrometry.
Mass spectrometry is incredibly sensitive. NMR is also a fantastic tool for analyzing and identifying molecules, but it requires much larger sample amounts. In contrast, mass spectrometry can detect individual ions.
By doing spectroscopy inside a mass spectrometer, we can essentially perform spectroscopy on single ions—and obtain well-resolved infrared spectra with the unmatched sensitivity of mass spectrometry. That’s what makes our approach truly unique.
David Brühlmann [00:13:33]:
Just to make sure I fully understand—your method—is it using mass spectrometry, or is it something separate?
Tom Rizzo [00:13:41]:
It’s actually performed inside a mass spectrometer. We have a cryogenic cell integrated into the instrument. You can separate ions using liquid chromatography, ion mobility, or any technique that couples to mass spectrometry. Once the ions are separated, we send them into the cryogenic cell and measure their infrared spectra. The molecules are mass-selected—they could also be mobility-selected or LC-selected—but it’s all done inside the mass spectrometer.
David Brühlmann [00:14:12]:
Ah, I see. Could you recap the main advantages over traditional MS-based methods? Are the differences at the sample, matrix, or quantity level?
MS/MS is used very widely for the identification of molecules, and it’s a very effective technique. However, it has some drawbacks. For example, when you have different isomers—molecules that have the same mass but differ only very slightly—MS/MS can struggle. The position of a hydroxyl group on a molecule, for instance, or in the case of sugars, which have enormous isomeric complexity, distinguishing just the linkage between two sugars can be extremely difficult. Molecules that are so similar can fragment in similar ways, making it hard to distinguish isomers by their MS/MS spectra.
There was an interesting paper within the last year by a group in Nijmegen. Often, to interpret MS/MS spectra, you use calculations to predict what the fragmentation products would be. They compared these predictions with actual measurements of fragments made using spectroscopy, and found that, in a large majority of cases, the calculations were simply wrong. It’s just too difficult to calculate fragmentation patterns accurately enough to be certain about assignments using MS/MS techniques.
By contrast, the infrared spectrum of a molecule cooled to very low temperatures provides an absolute, distinguishing fingerprint. No two molecules will have exactly the same spectrum if measured at high enough resolution. A cryogenic infrared spectrum, using the technique we call CIRIS—cryogenic infrared ion spectroscopy—can even distinguish subtle differences, like whether a hydroxyl group is pointing up or down. That small difference is enough to shift the vibrational frequencies of a molecule and differentiate one isomer from another.
This makes the technique extremely sensitive, and it also gives a high degree of certainty in identification. It introduces a completely new data dimension. Earlier, you asked me about my controversial statement regarding biomolecular analysis, when I said there are no new technologies. What I meant is that there hasn’t been a new data dimension for analyzing molecules—until now.
With a cryogenic infrared spectrum, you add that new dimension. In addition to retention time, mass, and fragmentation patterns, you now gain an independent, orthogonal measurement. For every mass, you get a cryogenic infrared spectrum—a barcode or fingerprint of that molecule. It’s truly a new dimension in biomolecular analysis.
David Brühlmann [00:17:01]:
So, in layman’s terms, it’s almost an orthogonal method—another dimension of data that complements traditional mass spec.
Tom Rizzo [00:17:12]:
Exactly. You get all the information from traditional methods, plus this new dimension. And we’ve optimized the technique so that it doesn’t significantly increase measurement time—you get this extra layer of data essentially for free.
David Brühlmann [00:17:29]:
How well does this integrate into traditional workflows? Many biotech companies use LC-MS setups—would this method be compatible or disrupt their processes?
Tom Rizzo [00:17:43]:
If I can digress for a moment: one lesson I’ve learned moving into industry is that having the best technique isn’t enough. People have established workflows, and changing those is very difficult. Going into a pharmaceutical company and telling scientists to do things differently can be a tough sell.
So our goal is to integrate as seamlessly as possible. We don’t yet have a commercial instrument, but we’re collaborating with Agilent to bring our technology to the market. In the meantime, we have a purpose-built instrument in our lab.
The idea is that you can continue your existing workflow—run LC-MS as usual—but inside the mass spectrometer, a laser scans as the peaks elute. You analyze the mass and measure the infrared spectrum simultaneously. The perturbation to the workflow is almost invisible. On your screen, you’ll see the new infrared spectrum plotted alongside your regular data.
Modern lasers make this feasible. When I was a PhD student, lasers were huge, complicated, and required a physics degree to operate. Now, the laser is fully integrated inside the instrument—there’s nothing to adjust. It’s completely transparent and doesn’t impede the workflow. It simply adds this new dimension of data.
David Brühlmann [00:19:31]:
That wraps up part one with Tom Rizzo. We explored the fundamental science behind cryogenic infrared ion spectroscopy and how it addresses real analytical challenges in the lab.
In part two, we’ll dive into practical applications for biomarker discovery, therapeutic development, and how this technology is becoming accessible for biotech companies like yours.
If you found this episode valuable, please leave us a review on Apple Podcasts or wherever you listen. Thank you for joining us on your journey to bioprocess mastery. For additional bioprocessing tips, visit us at www.bruehlmann-consulting.com.
Stay tuned for more inspiring biotech insights in our next episode. Until then, let’s continue to smarten up biotech.
Disclaimer: This transcript was generated with the assistance of artificial intelligence. While efforts have been made to ensure accuracy, it may contain errors, omissions, or misinterpretations. The text has been lightly edited and optimized for readability and flow. Please do not rely on it as a verbatim record.
Book a free consultation to help you get started on any questions you may have about bioprocess development: https://bruehlmann-consulting.com/call
About Tom Rizzo
Tom Rizzo earned his PhD in Physical Chemistry from the University of Wisconsin–Madison in 1983 after completing his undergraduate studies at Rensselaer Polytechnic Institute. Following postdoctoral work at the University of Chicago, he joined the University of Rochester and later became a Professor of Chemistry at the École Polytechnique Fédérale de Lausanne (EPFL), where he also served as Dean of the School of Basic Sciences.
His research combines laser spectroscopy, ion mobility, and mass spectrometry for biomolecular analysis. After retiring from EPFL in 2023, he became Chief Scientific Officer at ISOSPEC Analytics, a start-up focused on biomarker discovery. His honors include the Bourke Award, Ron Hites Award, and an ERC Advanced Grant, and he is a Fellow of the APS and AAAS.
Connect with Tom Rizzo 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
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.
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. 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.
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
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.
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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.
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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.
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
